NOPAT_ROIC_ROIIC_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OPERATIONAL EFFICIENCY OF CAPITAL - ROIC & ROIIC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Positive earnings do not always equate to value creation. For a company to create value, it needs to earn a higher return on its invested capital than its cost of capital. ROIC and ROIIC are financial metrics that can be used to assess whether a company is creating value. The metrics tell us about the operational efficiency of the business from a capital perspective: \"Is the company generating a return on the capital it has utilized to run the business?\". However, ROIC is not always comparable across companies/industries. Treatment of intangible assets can create issues. For more on the topic of intangibles, read \"ROIC and Intangible Assets\" by Michael Mauboussin. This notebook will calculate ROIC and ROIIC within one sector (Consumer Staples via Vanguard Consumer Staples ETF \"VDC\"). This will make the results more comparable across companies."
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.display.float_format = '{:20,.4f}'.format\n",
    "\n",
    "import os\n",
    "os.chdir(\"E:\\\\SEGResearch\\\\Research\\\\Equity Research\\\\\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IMPORT DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = \"2023_ROIC_Data.xlsx\"\n",
    "\n",
    "# FROM THE INCOME STATEMENT\n",
    "AMORT_EXP = pd.read_excel(file_name, sheet_name=\"AMORT_EXP\", index_col=0)\n",
    "EBIT_OPER = pd.read_excel(file_name, sheet_name=\"EBIT_OPER\", index_col=0)\n",
    "INT_EXP_DEBT = pd.read_excel(file_name, sheet_name=\"INT_EXP_DEBT\", index_col=0)\n",
    "INC_TAX = pd.read_excel(file_name, sheet_name=\"INC_TAX\", index_col=0)\n",
    "NET_INC = pd.read_excel(file_name, sheet_name=\"NET_INC\", index_col=0)\n",
    "\n",
    "# FROM THE BALANCE SHEET\n",
    "\n",
    "# LIABILITIES\n",
    "LIABS = pd.read_excel(file_name, sheet_name=\"LIABS\", index_col=0)\n",
    "LIABS_CURR = pd.read_excel(file_name, sheet_name=\"LIABS_CURR\", index_col=0)\n",
    "DEBT_ST = pd.read_excel(file_name, sheet_name=\"DEBT_ST\", index_col=0)\n",
    "DEBT_LT_NET = pd.read_excel(file_name, sheet_name=\"DEBT_LT_NET\", index_col=0)\n",
    "DFD_TAX = pd.read_excel(file_name, sheet_name=\"DFD_TAX\", index_col=0)\n",
    "OPER_LEASE_COMMIT = pd.read_excel(file_name, sheet_name=\"OPER_LEASE_COMMIT\", index_col=0)\n",
    "\n",
    "# SHAREHOLDER EQUITY\n",
    "PFD_STK = pd.read_excel(file_name, sheet_name=\"PFD_STK\", index_col=0)\n",
    "SHLDRS_EQ = pd.read_excel(file_name, sheet_name=\"SHLDRS_EQ\", index_col=0)\n",
    "\n",
    "# COST OF DEBT & EQUITY\n",
    "WAVG_COST_DEBT = pd.read_excel(file_name, sheet_name=\"WAVG_COST_DEBT\", index_col=0)\n",
    "ERP = pd.read_excel(file_name, sheet_name=\"ERP\", index_col=0, parse_dates = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LT_LIABS = LIABS- LIABS_CURR\n",
    "OTHER_LT_LIABS = LT_LIABS - DEBT_LT_NET - DFD_TAX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WEIGHTED AVERAGE COST OF DEBT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LANC</th>\n",
       "      <th>USNA</th>\n",
       "      <th>WBA</th>\n",
       "      <th>IMKTA</th>\n",
       "      <th>KO</th>\n",
       "      <th>IPAR</th>\n",
       "      <th>MKC</th>\n",
       "      <th>HAIN</th>\n",
       "      <th>CLX</th>\n",
       "      <th>MNST</th>\n",
       "      <th>...</th>\n",
       "      <th>AVO</th>\n",
       "      <th>SKIN</th>\n",
       "      <th>BHIL</th>\n",
       "      <th>NAPA</th>\n",
       "      <th>WEST</th>\n",
       "      <th>SOVO</th>\n",
       "      <th>OLPX</th>\n",
       "      <th>COCO</th>\n",
       "      <th>KVUE</th>\n",
       "      <th>US92202X209</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6991</td>\n",
       "      <td>0.8616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.4975</td>\n",
       "      <td>5.9800</td>\n",
       "      <td>1.6369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7500</td>\n",
       "      <td>2.2302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5438</td>\n",
       "      <td>5.9800</td>\n",
       "      <td>2.4284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.6159</td>\n",
       "      <td>1.0140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0283</td>\n",
       "      <td>2.7166</td>\n",
       "      <td>1.8276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6315</td>\n",
       "      <td>5.3090</td>\n",
       "      <td>2.1367</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>2.5976</td>\n",
       "      <td>2.4842</td>\n",
       "      <td>2.6238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5912</td>\n",
       "      <td>5.0397</td>\n",
       "      <td>2.3187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6279</td>\n",
       "      <td>1.8100</td>\n",
       "      <td>2.0436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.1918</td>\n",
       "      <td>6.2214</td>\n",
       "      <td>1.9853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.1274</td>\n",
       "      <td>2.9300</td>\n",
       "      <td>2.4641</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.8985</td>\n",
       "      <td>5.4430</td>\n",
       "      <td>2.8699</td>\n",
       "      <td>1.2000</td>\n",
       "      <td>4.0972</td>\n",
       "      <td>3.5100</td>\n",
       "      <td>3.6343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.4776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.8457</td>\n",
       "      <td>5.1122</td>\n",
       "      <td>2.0142</td>\n",
       "      <td>1.9174</td>\n",
       "      <td>2.5355</td>\n",
       "      <td>4.2000</td>\n",
       "      <td>2.5798</td>\n",
       "      <td>5.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.7853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2673</td>\n",
       "      <td>5.3359</td>\n",
       "      <td>1.2621</td>\n",
       "      <td>2.6847</td>\n",
       "      <td>1.2755</td>\n",
       "      <td>1.8900</td>\n",
       "      <td>1.2281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7000</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8036</td>\n",
       "      <td>3.6759</td>\n",
       "      <td>2.1337</td>\n",
       "      <td>2.3244</td>\n",
       "      <td>1.7875</td>\n",
       "      <td>1.0900</td>\n",
       "      <td>1.3020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7728</td>\n",
       "      <td>6.3502</td>\n",
       "      <td>4.5827</td>\n",
       "      <td>2.9082</td>\n",
       "      <td>5.1392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.3554</td>\n",
       "      <td>5.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5597</td>\n",
       "      <td>3.1763</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9100</td>\n",
       "      <td>7.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0932</td>\n",
       "      <td>7.0396</td>\n",
       "      <td>4.5305</td>\n",
       "      <td>2.8944</td>\n",
       "      <td>5.4393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.9740</td>\n",
       "      <td>5.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.4440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.3000</td>\n",
       "      <td>5.7000</td>\n",
       "      <td>9.1400</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7712</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            LANC  USNA                  WBA                IMKTA  \\\n",
       "2012-12-31   NaN   NaN                  NaN               6.6991   \n",
       "2013-12-31   NaN   NaN                  NaN               5.7500   \n",
       "2014-12-31   NaN   NaN                  NaN               5.6159   \n",
       "2015-12-31   NaN   NaN               3.6315               5.3090   \n",
       "2016-12-31   NaN   NaN               2.5912               5.0397   \n",
       "2017-12-31   NaN   NaN               3.1918               6.2214   \n",
       "2018-12-31   NaN   NaN               3.8985               5.4430   \n",
       "2019-12-31   NaN   NaN               2.8457               5.1122   \n",
       "2020-12-31   NaN   NaN               2.2673               5.3359   \n",
       "2021-12-31   NaN   NaN               1.8036               3.6759   \n",
       "2022-12-31   NaN   NaN               4.7728               6.3502   \n",
       "2023-12-31   NaN   NaN               6.0932               7.0396   \n",
       "\n",
       "                             KO                 IPAR                  MKC  \\\n",
       "2012-12-31               0.8616                  NaN               1.4975   \n",
       "2013-12-31               2.2302                  NaN               2.5438   \n",
       "2014-12-31               1.0140                  NaN               2.0283   \n",
       "2015-12-31               2.1367               0.8000               2.5976   \n",
       "2016-12-31               2.3187                  NaN               2.6279   \n",
       "2017-12-31               1.9853                  NaN               3.1274   \n",
       "2018-12-31               2.8699               1.2000               4.0972   \n",
       "2019-12-31               2.0142               1.9174               2.5355   \n",
       "2020-12-31               1.2621               2.6847               1.2755   \n",
       "2021-12-31               2.1337               2.3244               1.7875   \n",
       "2022-12-31               4.5827               2.9082               5.1392   \n",
       "2023-12-31               4.5305               2.8944               5.4393   \n",
       "\n",
       "                           HAIN                  CLX                 MNST  \\\n",
       "2012-12-31               5.9800               1.6369                  NaN   \n",
       "2013-12-31               5.9800               2.4284                  NaN   \n",
       "2014-12-31               2.7166               1.8276                  NaN   \n",
       "2015-12-31               2.4842               2.6238                  NaN   \n",
       "2016-12-31               1.8100               2.0436                  NaN   \n",
       "2017-12-31               2.9300               2.4641                  NaN   \n",
       "2018-12-31               3.5100               3.6343                  NaN   \n",
       "2019-12-31               4.2000               2.5798               5.5000   \n",
       "2020-12-31               1.8900               1.2281                  NaN   \n",
       "2021-12-31               1.0900               1.3020                  NaN   \n",
       "2022-12-31                  NaN               4.3554               5.5000   \n",
       "2023-12-31                  NaN               4.9740               5.5000   \n",
       "\n",
       "            ...                  AVO  SKIN                 BHIL  \\\n",
       "2012-12-31  ...                  NaN   NaN                  NaN   \n",
       "2013-12-31  ...                  NaN   NaN                  NaN   \n",
       "2014-12-31  ...                  NaN   NaN                  NaN   \n",
       "2015-12-31  ...                  NaN   NaN                  NaN   \n",
       "2016-12-31  ...                  NaN   NaN                  NaN   \n",
       "2017-12-31  ...                  NaN   NaN                  NaN   \n",
       "2018-12-31  ...               4.4776   NaN                  NaN   \n",
       "2019-12-31  ...               3.7853   NaN                  NaN   \n",
       "2020-12-31  ...               2.2033   NaN                  NaN   \n",
       "2021-12-31  ...               2.0850   NaN                  NaN   \n",
       "2022-12-31  ...               5.5891   NaN               3.5597   \n",
       "2023-12-31  ...               7.4440   NaN                  NaN   \n",
       "\n",
       "                           NAPA                 WEST                 SOVO  \\\n",
       "2012-12-31                  NaN                  NaN                  NaN   \n",
       "2013-12-31                  NaN                  NaN                  NaN   \n",
       "2014-12-31                  NaN                  NaN                  NaN   \n",
       "2015-12-31                  NaN                  NaN                  NaN   \n",
       "2016-12-31                  NaN                  NaN                  NaN   \n",
       "2017-12-31                  NaN                  NaN                  NaN   \n",
       "2018-12-31                  NaN                  NaN                  NaN   \n",
       "2019-12-31                  NaN                  NaN               7.5000   \n",
       "2020-12-31               1.8000                  NaN               5.7000   \n",
       "2021-12-31               1.9067                  NaN               4.5000   \n",
       "2022-12-31               3.1763                  NaN               7.9100   \n",
       "2023-12-31               6.3000               5.7000               9.1400   \n",
       "\n",
       "                           OLPX  COCO                 KVUE  US92202X209  \n",
       "2012-12-31                  NaN   NaN                  NaN          NaN  \n",
       "2013-12-31                  NaN   NaN                  NaN          NaN  \n",
       "2014-12-31                  NaN   NaN                  NaN          NaN  \n",
       "2015-12-31                  NaN   NaN                  NaN          NaN  \n",
       "2016-12-31                  NaN   NaN                  NaN          NaN  \n",
       "2017-12-31                  NaN   NaN                  NaN          NaN  \n",
       "2018-12-31                  NaN   NaN                  NaN          NaN  \n",
       "2019-12-31                  NaN   NaN                  NaN          NaN  \n",
       "2020-12-31               7.5000   NaN                  NaN          NaN  \n",
       "2021-12-31               7.5000   NaN                  NaN          NaN  \n",
       "2022-12-31               7.9000   NaN                  NaN          NaN  \n",
       "2023-12-31               9.0000   NaN               4.7712          NaN  \n",
       "\n",
       "[12 rows x 106 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WAVG_COST_DEBT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some companies are missing values (typically they don't have debt on the balance sheet). We still have to estimate their cost of debt. Since these companies are all within one sector, we will fill in the missing values with the row average. Of course, this is not ideal, as we assume that the missing cost of debt will equal the sector average for the period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LANC</th>\n",
       "      <th>USNA</th>\n",
       "      <th>WBA</th>\n",
       "      <th>IMKTA</th>\n",
       "      <th>KO</th>\n",
       "      <th>IPAR</th>\n",
       "      <th>MKC</th>\n",
       "      <th>HAIN</th>\n",
       "      <th>CLX</th>\n",
       "      <th>MNST</th>\n",
       "      <th>...</th>\n",
       "      <th>AVO</th>\n",
       "      <th>SKIN</th>\n",
       "      <th>BHIL</th>\n",
       "      <th>NAPA</th>\n",
       "      <th>WEST</th>\n",
       "      <th>SOVO</th>\n",
       "      <th>OLPX</th>\n",
       "      <th>COCO</th>\n",
       "      <th>KVUE</th>\n",
       "      <th>US92202X209</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0670</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0575</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0254</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0562</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>0.0272</td>\n",
       "      <td>0.0183</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0531</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0080</td>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0248</td>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0259</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0263</td>\n",
       "      <td>0.0181</td>\n",
       "      <td>0.0204</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0319</td>\n",
       "      <td>0.0622</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0313</td>\n",
       "      <td>0.0293</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>0.0544</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0410</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0448</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0285</td>\n",
       "      <td>0.0511</td>\n",
       "      <td>0.0201</td>\n",
       "      <td>0.0192</td>\n",
       "      <td>0.0254</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0227</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.0189</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0220</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0209</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0477</td>\n",
       "      <td>0.0635</td>\n",
       "      <td>0.0458</td>\n",
       "      <td>0.0291</td>\n",
       "      <td>0.0514</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0436</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0559</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0356</td>\n",
       "      <td>0.0318</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0791</td>\n",
       "      <td>0.0790</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>0.0595</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>0.0704</td>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0289</td>\n",
       "      <td>0.0544</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>0.0497</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0744</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>0.0630</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.0914</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>0.0477</td>\n",
       "      <td>0.0595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           LANC                 USNA                  WBA  \\\n",
       "2012-12-31               0.0353               0.0353               0.0353   \n",
       "2013-12-31               0.0382               0.0382               0.0382   \n",
       "2014-12-31               0.0358               0.0358               0.0358   \n",
       "2015-12-31               0.0357               0.0357               0.0363   \n",
       "2016-12-31               0.0367               0.0367               0.0259   \n",
       "2017-12-31               0.0378               0.0378               0.0319   \n",
       "2018-12-31               0.0445               0.0445               0.0390   \n",
       "2019-12-31               0.0398               0.0398               0.0285   \n",
       "2020-12-31               0.0307               0.0307               0.0227   \n",
       "2021-12-31               0.0300               0.0300               0.0180   \n",
       "2022-12-31               0.0517               0.0517               0.0477   \n",
       "2023-12-31               0.0595               0.0595               0.0609   \n",
       "\n",
       "                          IMKTA                   KO                 IPAR  \\\n",
       "2012-12-31               0.0670               0.0086               0.0353   \n",
       "2013-12-31               0.0575               0.0223               0.0382   \n",
       "2014-12-31               0.0562               0.0101               0.0358   \n",
       "2015-12-31               0.0531               0.0214               0.0080   \n",
       "2016-12-31               0.0504               0.0232               0.0367   \n",
       "2017-12-31               0.0622               0.0199               0.0378   \n",
       "2018-12-31               0.0544               0.0287               0.0120   \n",
       "2019-12-31               0.0511               0.0201               0.0192   \n",
       "2020-12-31               0.0534               0.0126               0.0268   \n",
       "2021-12-31               0.0368               0.0213               0.0232   \n",
       "2022-12-31               0.0635               0.0458               0.0291   \n",
       "2023-12-31               0.0704               0.0453               0.0289   \n",
       "\n",
       "                            MKC                 HAIN                  CLX  \\\n",
       "2012-12-31               0.0150               0.0598               0.0164   \n",
       "2013-12-31               0.0254               0.0598               0.0243   \n",
       "2014-12-31               0.0203               0.0272               0.0183   \n",
       "2015-12-31               0.0260               0.0248               0.0262   \n",
       "2016-12-31               0.0263               0.0181               0.0204   \n",
       "2017-12-31               0.0313               0.0293               0.0246   \n",
       "2018-12-31               0.0410               0.0351               0.0363   \n",
       "2019-12-31               0.0254               0.0420               0.0258   \n",
       "2020-12-31               0.0128               0.0189               0.0123   \n",
       "2021-12-31               0.0179               0.0109               0.0130   \n",
       "2022-12-31               0.0514               0.0517               0.0436   \n",
       "2023-12-31               0.0544               0.0595               0.0497   \n",
       "\n",
       "                           MNST  ...                  AVO  \\\n",
       "2012-12-31               0.0353  ...               0.0353   \n",
       "2013-12-31               0.0382  ...               0.0382   \n",
       "2014-12-31               0.0358  ...               0.0358   \n",
       "2015-12-31               0.0357  ...               0.0357   \n",
       "2016-12-31               0.0367  ...               0.0367   \n",
       "2017-12-31               0.0378  ...               0.0378   \n",
       "2018-12-31               0.0445  ...               0.0448   \n",
       "2019-12-31               0.0550  ...               0.0379   \n",
       "2020-12-31               0.0307  ...               0.0220   \n",
       "2021-12-31               0.0300  ...               0.0209   \n",
       "2022-12-31               0.0550  ...               0.0559   \n",
       "2023-12-31               0.0550  ...               0.0744   \n",
       "\n",
       "                           SKIN                 BHIL                 NAPA  \\\n",
       "2012-12-31               0.0353               0.0353               0.0353   \n",
       "2013-12-31               0.0382               0.0382               0.0382   \n",
       "2014-12-31               0.0358               0.0358               0.0358   \n",
       "2015-12-31               0.0357               0.0357               0.0357   \n",
       "2016-12-31               0.0367               0.0367               0.0367   \n",
       "2017-12-31               0.0378               0.0378               0.0378   \n",
       "2018-12-31               0.0445               0.0445               0.0445   \n",
       "2019-12-31               0.0398               0.0398               0.0398   \n",
       "2020-12-31               0.0307               0.0307               0.0180   \n",
       "2021-12-31               0.0300               0.0300               0.0191   \n",
       "2022-12-31               0.0517               0.0356               0.0318   \n",
       "2023-12-31               0.0595               0.0595               0.0630   \n",
       "\n",
       "                           WEST                 SOVO                 OLPX  \\\n",
       "2012-12-31               0.0353               0.0353               0.0353   \n",
       "2013-12-31               0.0382               0.0382               0.0382   \n",
       "2014-12-31               0.0358               0.0358               0.0358   \n",
       "2015-12-31               0.0357               0.0357               0.0357   \n",
       "2016-12-31               0.0367               0.0367               0.0367   \n",
       "2017-12-31               0.0378               0.0378               0.0378   \n",
       "2018-12-31               0.0445               0.0445               0.0445   \n",
       "2019-12-31               0.0398               0.0750               0.0398   \n",
       "2020-12-31               0.0307               0.0570               0.0750   \n",
       "2021-12-31               0.0300               0.0450               0.0750   \n",
       "2022-12-31               0.0517               0.0791               0.0790   \n",
       "2023-12-31               0.0570               0.0914               0.0900   \n",
       "\n",
       "                           COCO                 KVUE          US92202X209  \n",
       "2012-12-31               0.0353               0.0353               0.0353  \n",
       "2013-12-31               0.0382               0.0382               0.0382  \n",
       "2014-12-31               0.0358               0.0358               0.0358  \n",
       "2015-12-31               0.0357               0.0357               0.0357  \n",
       "2016-12-31               0.0367               0.0367               0.0367  \n",
       "2017-12-31               0.0378               0.0378               0.0378  \n",
       "2018-12-31               0.0445               0.0445               0.0445  \n",
       "2019-12-31               0.0398               0.0398               0.0398  \n",
       "2020-12-31               0.0307               0.0307               0.0307  \n",
       "2021-12-31               0.0300               0.0300               0.0300  \n",
       "2022-12-31               0.0517               0.0517               0.0517  \n",
       "2023-12-31               0.0595               0.0477               0.0595  \n",
       "\n",
       "[12 rows x 106 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COST_DEBT = WAVG_COST_DEBT.apply(lambda row: row.fillna(row.mean()), axis = 1) # Fill missing values with rows average\n",
    "COST_DEBT = COST_DEBT/100\n",
    "COST_DEBT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NET OPERATING PROFIT BEFORE TAX (NOPBT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Earnings Before Interest & Taxes\n",
    "\n",
    " $+$ Amortization of Acquired Intangibles\n",
    " \n",
    " $+$ Implicit Operating Lease Interest Expense\n",
    "\n",
    "\n",
    "Net Operating Profit Before Taxes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add back the Amortization of Acquired Intangible Assets. The company expenses its investment to replenish acquired intangible assets in the income statement. Since the replenishment is already expensed, we do not want to penalize the income twice, first from the expense to replenish and second from the amortization of the initial purchase price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we do not have the duration of the lease, we estimated the implicit lease interest crudely (not with the present value). We know that the Operating Lease Expense contains both \"Rent Payment\" and an implicit \"Interest Expense.\" First, we will assume that the interest rate on the lease will be equivalent to the company's cost of debt. Second, we will assume that rent and interest are proportional to the operating lease payment. I believe this is reasonable since the company will likely have to continue renewing the leases.  \n",
    "\n",
    "Estimated Operating Lease Interest Expense = Operating Leases $*$ Cost of Debt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "AMORT_EXP = AMORT_EXP.ffill(limit=1).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LANC</th>\n",
       "      <th>USNA</th>\n",
       "      <th>WBA</th>\n",
       "      <th>IMKTA</th>\n",
       "      <th>KO</th>\n",
       "      <th>IPAR</th>\n",
       "      <th>MKC</th>\n",
       "      <th>HAIN</th>\n",
       "      <th>CLX</th>\n",
       "      <th>MNST</th>\n",
       "      <th>...</th>\n",
       "      <th>AVO</th>\n",
       "      <th>SKIN</th>\n",
       "      <th>BHIL</th>\n",
       "      <th>NAPA</th>\n",
       "      <th>WEST</th>\n",
       "      <th>SOVO</th>\n",
       "      <th>OLPX</th>\n",
       "      <th>COCO</th>\n",
       "      <th>KVUE</th>\n",
       "      <th>US92202X209</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>0.4072</td>\n",
       "      <td>0.6442</td>\n",
       "      <td>1,265.7740</td>\n",
       "      <td>7.4369</td>\n",
       "      <td>8.0219</td>\n",
       "      <td>1.0623</td>\n",
       "      <td>1.2743</td>\n",
       "      <td>5.8179</td>\n",
       "      <td>4.1087</td>\n",
       "      <td>0.5502</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>0.5043</td>\n",
       "      <td>0.6862</td>\n",
       "      <td>1,362.3827</td>\n",
       "      <td>5.6770</td>\n",
       "      <td>23.3052</td>\n",
       "      <td>1.2780</td>\n",
       "      <td>2.1928</td>\n",
       "      <td>5.6921</td>\n",
       "      <td>7.2366</td>\n",
       "      <td>0.6630</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>0.4112</td>\n",
       "      <td>0.8195</td>\n",
       "      <td>1,235.3624</td>\n",
       "      <td>4.9764</td>\n",
       "      <td>9.7850</td>\n",
       "      <td>1.2510</td>\n",
       "      <td>1.8721</td>\n",
       "      <td>2.6947</td>\n",
       "      <td>6.0127</td>\n",
       "      <td>0.4650</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>0.5545</td>\n",
       "      <td>1.0687</td>\n",
       "      <td>1,378.8989</td>\n",
       "      <td>4.1837</td>\n",
       "      <td>15.2987</td>\n",
       "      <td>0.2615</td>\n",
       "      <td>2.4496</td>\n",
       "      <td>2.4557</td>\n",
       "      <td>7.8978</td>\n",
       "      <td>0.3825</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>0.8546</td>\n",
       "      <td>0.7330</td>\n",
       "      <td>883.3063</td>\n",
       "      <td>1.3633</td>\n",
       "      <td>15.8370</td>\n",
       "      <td>1.0135</td>\n",
       "      <td>2.8565</td>\n",
       "      <td>1.9401</td>\n",
       "      <td>6.7029</td>\n",
       "      <td>0.6888</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>1.0275</td>\n",
       "      <td>0.8918</td>\n",
       "      <td>1,047.2610</td>\n",
       "      <td>1.8206</td>\n",
       "      <td>10.7604</td>\n",
       "      <td>0.9723</td>\n",
       "      <td>5.3791</td>\n",
       "      <td>2.9838</td>\n",
       "      <td>8.5010</td>\n",
       "      <td>0.6312</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>1.0301</td>\n",
       "      <td>1.1153</td>\n",
       "      <td>1,276.2160</td>\n",
       "      <td>2.2745</td>\n",
       "      <td>14.3493</td>\n",
       "      <td>0.5281</td>\n",
       "      <td>6.3015</td>\n",
       "      <td>3.6382</td>\n",
       "      <td>11.9932</td>\n",
       "      <td>1.2444</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>1.5227</td>\n",
       "      <td>1.0969</td>\n",
       "      <td>918.2464</td>\n",
       "      <td>2.6491</td>\n",
       "      <td>31.1188</td>\n",
       "      <td>0.6345</td>\n",
       "      <td>4.1126</td>\n",
       "      <td>4.9956</td>\n",
       "      <td>10.0354</td>\n",
       "      <td>1.5382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.6945</td>\n",
       "      <td>716.2217</td>\n",
       "      <td>3.3455</td>\n",
       "      <td>23.1337</td>\n",
       "      <td>0.7768</td>\n",
       "      <td>1.8827</td>\n",
       "      <td>2.1436</td>\n",
       "      <td>4.6053</td>\n",
       "      <td>0.7487</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1788</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.4953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>0.7707</td>\n",
       "      <td>0.5404</td>\n",
       "      <td>564.4982</td>\n",
       "      <td>2.0002</td>\n",
       "      <td>35.1422</td>\n",
       "      <td>0.8812</td>\n",
       "      <td>2.7564</td>\n",
       "      <td>1.2622</td>\n",
       "      <td>5.3511</td>\n",
       "      <td>0.7403</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4609</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0454</td>\n",
       "      <td>4.1635</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>1.6042</td>\n",
       "      <td>0.7791</td>\n",
       "      <td>1,447.6717</td>\n",
       "      <td>3.2943</td>\n",
       "      <td>73.9640</td>\n",
       "      <td>0.9179</td>\n",
       "      <td>13.2283</td>\n",
       "      <td>7.5952</td>\n",
       "      <td>18.5541</td>\n",
       "      <td>2.3106</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1593</td>\n",
       "      <td>0.8495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0784</td>\n",
       "      <td>7.9644</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>1.6259</td>\n",
       "      <td>0.7680</td>\n",
       "      <td>1,947.4448</td>\n",
       "      <td>6.2264</td>\n",
       "      <td>68.9540</td>\n",
       "      <td>0.9252</td>\n",
       "      <td>14.2347</td>\n",
       "      <td>7.7524</td>\n",
       "      <td>21.4878</td>\n",
       "      <td>3.8121</td>\n",
       "      <td>...</td>\n",
       "      <td>8.9105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1381</td>\n",
       "      <td>1.4539</td>\n",
       "      <td>0.7790</td>\n",
       "      <td>1.5567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0994</td>\n",
       "      <td>7.3476</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           LANC                 USNA                  WBA  \\\n",
       "2012-12-31               0.4072               0.6442           1,265.7740   \n",
       "2013-12-31               0.5043               0.6862           1,362.3827   \n",
       "2014-12-31               0.4112               0.8195           1,235.3624   \n",
       "2015-12-31               0.5545               1.0687           1,378.8989   \n",
       "2016-12-31               0.8546               0.7330             883.3063   \n",
       "2017-12-31               1.0275               0.8918           1,047.2610   \n",
       "2018-12-31               1.0301               1.1153           1,276.2160   \n",
       "2019-12-31               1.5227               1.0969             918.2464   \n",
       "2020-12-31               0.8125               0.6945             716.2217   \n",
       "2021-12-31               0.7707               0.5404             564.4982   \n",
       "2022-12-31               1.6042               0.7791           1,447.6717   \n",
       "2023-12-31               1.6259               0.7680           1,947.4448   \n",
       "\n",
       "                          IMKTA                   KO                 IPAR  \\\n",
       "2012-12-31               7.4369               8.0219               1.0623   \n",
       "2013-12-31               5.6770              23.3052               1.2780   \n",
       "2014-12-31               4.9764               9.7850               1.2510   \n",
       "2015-12-31               4.1837              15.2987               0.2615   \n",
       "2016-12-31               1.3633              15.8370               1.0135   \n",
       "2017-12-31               1.8206              10.7604               0.9723   \n",
       "2018-12-31               2.2745              14.3493               0.5281   \n",
       "2019-12-31               2.6491              31.1188               0.6345   \n",
       "2020-12-31               3.3455              23.1337               0.7768   \n",
       "2021-12-31               2.0002              35.1422               0.8812   \n",
       "2022-12-31               3.2943              73.9640               0.9179   \n",
       "2023-12-31               6.2264              68.9540               0.9252   \n",
       "\n",
       "                            MKC                 HAIN                  CLX  \\\n",
       "2012-12-31               1.2743               5.8179               4.1087   \n",
       "2013-12-31               2.1928               5.6921               7.2366   \n",
       "2014-12-31               1.8721               2.6947               6.0127   \n",
       "2015-12-31               2.4496               2.4557               7.8978   \n",
       "2016-12-31               2.8565               1.9401               6.7029   \n",
       "2017-12-31               5.3791               2.9838               8.5010   \n",
       "2018-12-31               6.3015               3.6382              11.9932   \n",
       "2019-12-31               4.1126               4.9956              10.0354   \n",
       "2020-12-31               1.8827               2.1436               4.6053   \n",
       "2021-12-31               2.7564               1.2622               5.3511   \n",
       "2022-12-31              13.2283               7.5952              18.5541   \n",
       "2023-12-31              14.2347               7.7524              21.4878   \n",
       "\n",
       "                           MNST  ...                  AVO  SKIN  \\\n",
       "2012-12-31               0.5502  ...                  NaN   NaN   \n",
       "2013-12-31               0.6630  ...                  NaN   NaN   \n",
       "2014-12-31               0.4650  ...                  NaN   NaN   \n",
       "2015-12-31               0.3825  ...                  NaN   NaN   \n",
       "2016-12-31               0.6888  ...                  NaN   NaN   \n",
       "2017-12-31               0.6312  ...                  NaN   NaN   \n",
       "2018-12-31               1.2444  ...                  NaN   NaN   \n",
       "2019-12-31               1.5382  ...               0.7945   NaN   \n",
       "2020-12-31               0.7487  ...               1.1788   NaN   \n",
       "2021-12-31               0.7403  ...               1.5617   NaN   \n",
       "2022-12-31               2.3106  ...               5.8853   NaN   \n",
       "2023-12-31               3.8121  ...               8.9105   NaN   \n",
       "\n",
       "                           BHIL                 NAPA                 WEST  \\\n",
       "2012-12-31                  NaN                  NaN                  NaN   \n",
       "2013-12-31                  NaN                  NaN                  NaN   \n",
       "2014-12-31                  NaN                  NaN                  NaN   \n",
       "2015-12-31                  NaN                  NaN                  NaN   \n",
       "2016-12-31                  NaN                  NaN                  NaN   \n",
       "2017-12-31                  NaN                  NaN                  NaN   \n",
       "2018-12-31                  NaN                  NaN                  NaN   \n",
       "2019-12-31                  NaN                  NaN                  NaN   \n",
       "2020-12-31                  NaN               0.5131                  NaN   \n",
       "2021-12-31                  NaN               0.4609                  NaN   \n",
       "2022-12-31               0.1593               0.8495                  NaN   \n",
       "2023-12-31               0.1381               1.4539               0.7790   \n",
       "\n",
       "                           SOVO  OLPX                 COCO  \\\n",
       "2012-12-31                  NaN   NaN                  NaN   \n",
       "2013-12-31                  NaN   NaN                  NaN   \n",
       "2014-12-31                  NaN   NaN                  NaN   \n",
       "2015-12-31                  NaN   NaN                  NaN   \n",
       "2016-12-31                  NaN   NaN                  NaN   \n",
       "2017-12-31                  NaN   NaN                  NaN   \n",
       "2018-12-31                  NaN   NaN                  NaN   \n",
       "2019-12-31                  NaN   NaN                  NaN   \n",
       "2020-12-31               1.4953   NaN               0.0802   \n",
       "2021-12-31               1.1023   NaN               0.0454   \n",
       "2022-12-31               1.6185   NaN               0.0784   \n",
       "2023-12-31               1.5567   NaN               0.0994   \n",
       "\n",
       "                           KVUE  US92202X209  \n",
       "2012-12-31                  NaN          NaN  \n",
       "2013-12-31                  NaN          NaN  \n",
       "2014-12-31                  NaN          NaN  \n",
       "2015-12-31                  NaN          NaN  \n",
       "2016-12-31                  NaN          NaN  \n",
       "2017-12-31                  NaN          NaN  \n",
       "2018-12-31                  NaN          NaN  \n",
       "2019-12-31                  NaN          NaN  \n",
       "2020-12-31                  NaN          NaN  \n",
       "2021-12-31               4.1635          NaN  \n",
       "2022-12-31               7.9644          NaN  \n",
       "2023-12-31               7.3476          NaN  \n",
       "\n",
       "[12 rows x 106 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OPER_LEASE_COMMIT = OPER_LEASE_COMMIT.ffill() # We forward fill the data since there are missing values between periods\n",
    "OPER_LEASE_INT = OPER_LEASE_COMMIT * COST_DEBT\n",
    "OPER_LEASE_INT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put it all together to calculate Net Operating Income Before Taxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LANC</th>\n",
       "      <th>USNA</th>\n",
       "      <th>WBA</th>\n",
       "      <th>IMKTA</th>\n",
       "      <th>KO</th>\n",
       "      <th>IPAR</th>\n",
       "      <th>MKC</th>\n",
       "      <th>HAIN</th>\n",
       "      <th>CLX</th>\n",
       "      <th>MNST</th>\n",
       "      <th>...</th>\n",
       "      <th>AVO</th>\n",
       "      <th>SKIN</th>\n",
       "      <th>BHIL</th>\n",
       "      <th>NAPA</th>\n",
       "      <th>WEST</th>\n",
       "      <th>SOVO</th>\n",
       "      <th>OLPX</th>\n",
       "      <th>COCO</th>\n",
       "      <th>KVUE</th>\n",
       "      <th>US92202X209</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>144.7772</td>\n",
       "      <td>99.9322</td>\n",
       "      <td>5,068.7740</td>\n",
       "      <td>129.1694</td>\n",
       "      <td>11,645.0219</td>\n",
       "      <td>89.4493</td>\n",
       "      <td>584.9743</td>\n",
       "      <td>159.8429</td>\n",
       "      <td>901.1087</td>\n",
       "      <td>551.2222</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>163.3553</td>\n",
       "      <td>118.2902</td>\n",
       "      <td>5,314.3827</td>\n",
       "      <td>126.9895</td>\n",
       "      <td>11,252.3052</td>\n",
       "      <td>86.2320</td>\n",
       "      <td>602.1928</td>\n",
       "      <td>209.0361</td>\n",
       "      <td>982.2366</td>\n",
       "      <td>600.3280</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>155.1242</td>\n",
       "      <td>117.3175</td>\n",
       "      <td>5,303.3624</td>\n",
       "      <td>127.4957</td>\n",
       "      <td>11,353.7850</td>\n",
       "      <td>59.9200</td>\n",
       "      <td>616.7721</td>\n",
       "      <td>258.8837</td>\n",
       "      <td>968.0127</td>\n",
       "      <td>752.9700</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>157.0205</td>\n",
       "      <td>143.6747</td>\n",
       "      <td>6,779.8989</td>\n",
       "      <td>141.1745</td>\n",
       "      <td>10,043.2987</td>\n",
       "      <td>72.6425</td>\n",
       "      <td>620.1496</td>\n",
       "      <td>273.1937</td>\n",
       "      <td>1,021.8978</td>\n",
       "      <td>936.2005</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>188.3296</td>\n",
       "      <td>140.9590</td>\n",
       "      <td>7,648.3063</td>\n",
       "      <td>131.1739</td>\n",
       "      <td>9,563.8370</td>\n",
       "      <td>74.6805</td>\n",
       "      <td>672.9565</td>\n",
       "      <td>305.5201</td>\n",
       "      <td>1,082.7029</td>\n",
       "      <td>1,175.1478</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>198.0195</td>\n",
       "      <td>134.8758</td>\n",
       "      <td>7,692.2610</td>\n",
       "      <td>128.2687</td>\n",
       "      <td>8,970.9604</td>\n",
       "      <td>87.7093</td>\n",
       "      <td>808.0791</td>\n",
       "      <td>181.7978</td>\n",
       "      <td>1,140.5010</td>\n",
       "      <td>1,246.1432</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>179.1751</td>\n",
       "      <td>190.9733</td>\n",
       "      <td>8,075.2160</td>\n",
       "      <td>126.0698</td>\n",
       "      <td>9,439.3493</td>\n",
       "      <td>102.1901</td>\n",
       "      <td>972.3015</td>\n",
       "      <td>170.2562</td>\n",
       "      <td>1,147.9932</td>\n",
       "      <td>1,323.3634</td>\n",
       "      <td>...</td>\n",
       "      <td>18.7210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>182.7087</td>\n",
       "      <td>148.7289</td>\n",
       "      <td>6,597.2464</td>\n",
       "      <td>150.4899</td>\n",
       "      <td>10,655.1188</td>\n",
       "      <td>110.3905</td>\n",
       "      <td>1,001.3126</td>\n",
       "      <td>74.7376</td>\n",
       "      <td>1,134.0354</td>\n",
       "      <td>1,427.3772</td>\n",
       "      <td>...</td>\n",
       "      <td>107.3015</td>\n",
       "      <td>25.8250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.7270</td>\n",
       "      <td>60.3370</td>\n",
       "      <td>15.3070</td>\n",
       "      <td>2,885.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>182.9645</td>\n",
       "      <td>178.5115</td>\n",
       "      <td>4,924.2217</td>\n",
       "      <td>279.5482</td>\n",
       "      <td>9,562.1337</td>\n",
       "      <td>76.1268</td>\n",
       "      <td>1,039.2827</td>\n",
       "      <td>146.6666</td>\n",
       "      <td>1,282.6053</td>\n",
       "      <td>1,641.8017</td>\n",
       "      <td>...</td>\n",
       "      <td>69.5788</td>\n",
       "      <td>-1.2095</td>\n",
       "      <td>-0.0056</td>\n",
       "      <td>93.0421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.7893</td>\n",
       "      <td>148.4690</td>\n",
       "      <td>31.7572</td>\n",
       "      <td>3,394.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>187.3857</td>\n",
       "      <td>171.9144</td>\n",
       "      <td>5,105.4982</td>\n",
       "      <td>341.8019</td>\n",
       "      <td>10,897.1422</td>\n",
       "      <td>157.2292</td>\n",
       "      <td>1,140.5564</td>\n",
       "      <td>195.3772</td>\n",
       "      <td>1,274.3511</td>\n",
       "      <td>1,807.9073</td>\n",
       "      <td>...</td>\n",
       "      <td>63.2617</td>\n",
       "      <td>15.5880</td>\n",
       "      <td>-110.1640</td>\n",
       "      <td>87.3659</td>\n",
       "      <td>-0.8854</td>\n",
       "      <td>89.4023</td>\n",
       "      <td>405.6710</td>\n",
       "      <td>25.8544</td>\n",
       "      <td>3,481.1635</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>149.6622</td>\n",
       "      <td>110.1161</td>\n",
       "      <td>4,857.6717</td>\n",
       "      <td>378.5688</td>\n",
       "      <td>12,146.9640</td>\n",
       "      <td>210.2599</td>\n",
       "      <td>964.1283</td>\n",
       "      <td>157.1302</td>\n",
       "      <td>745.5541</td>\n",
       "      <td>1,594.6316</td>\n",
       "      <td>...</td>\n",
       "      <td>24.1853</td>\n",
       "      <td>11.7970</td>\n",
       "      <td>-124.0657</td>\n",
       "      <td>96.3135</td>\n",
       "      <td>51.5020</td>\n",
       "      <td>136.2945</td>\n",
       "      <td>413.1760</td>\n",
       "      <td>11.0674</td>\n",
       "      <td>3,074.9644</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>170.6169</td>\n",
       "      <td>95.6740</td>\n",
       "      <td>5,749.4448</td>\n",
       "      <td>295.4607</td>\n",
       "      <td>11,616.9540</td>\n",
       "      <td>259.8382</td>\n",
       "      <td>1,073.1347</td>\n",
       "      <td>107.0824</td>\n",
       "      <td>878.4878</td>\n",
       "      <td>1,979.3671</td>\n",
       "      <td>...</td>\n",
       "      <td>18.9105</td>\n",
       "      <td>-102.8100</td>\n",
       "      <td>-84.7869</td>\n",
       "      <td>137.7009</td>\n",
       "      <td>2.8600</td>\n",
       "      <td>119.0727</td>\n",
       "      <td>158.0350</td>\n",
       "      <td>56.5924</td>\n",
       "      <td>3,268.3476</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           LANC                 USNA                  WBA  \\\n",
       "2012-12-31             144.7772              99.9322           5,068.7740   \n",
       "2013-12-31             163.3553             118.2902           5,314.3827   \n",
       "2014-12-31             155.1242             117.3175           5,303.3624   \n",
       "2015-12-31             157.0205             143.6747           6,779.8989   \n",
       "2016-12-31             188.3296             140.9590           7,648.3063   \n",
       "2017-12-31             198.0195             134.8758           7,692.2610   \n",
       "2018-12-31             179.1751             190.9733           8,075.2160   \n",
       "2019-12-31             182.7087             148.7289           6,597.2464   \n",
       "2020-12-31             182.9645             178.5115           4,924.2217   \n",
       "2021-12-31             187.3857             171.9144           5,105.4982   \n",
       "2022-12-31             149.6622             110.1161           4,857.6717   \n",
       "2023-12-31             170.6169              95.6740           5,749.4448   \n",
       "\n",
       "                          IMKTA                   KO                 IPAR  \\\n",
       "2012-12-31             129.1694          11,645.0219              89.4493   \n",
       "2013-12-31             126.9895          11,252.3052              86.2320   \n",
       "2014-12-31             127.4957          11,353.7850              59.9200   \n",
       "2015-12-31             141.1745          10,043.2987              72.6425   \n",
       "2016-12-31             131.1739           9,563.8370              74.6805   \n",
       "2017-12-31             128.2687           8,970.9604              87.7093   \n",
       "2018-12-31             126.0698           9,439.3493             102.1901   \n",
       "2019-12-31             150.4899          10,655.1188             110.3905   \n",
       "2020-12-31             279.5482           9,562.1337              76.1268   \n",
       "2021-12-31             341.8019          10,897.1422             157.2292   \n",
       "2022-12-31             378.5688          12,146.9640             210.2599   \n",
       "2023-12-31             295.4607          11,616.9540             259.8382   \n",
       "\n",
       "                            MKC                 HAIN                  CLX  \\\n",
       "2012-12-31             584.9743             159.8429             901.1087   \n",
       "2013-12-31             602.1928             209.0361             982.2366   \n",
       "2014-12-31             616.7721             258.8837             968.0127   \n",
       "2015-12-31             620.1496             273.1937           1,021.8978   \n",
       "2016-12-31             672.9565             305.5201           1,082.7029   \n",
       "2017-12-31             808.0791             181.7978           1,140.5010   \n",
       "2018-12-31             972.3015             170.2562           1,147.9932   \n",
       "2019-12-31           1,001.3126              74.7376           1,134.0354   \n",
       "2020-12-31           1,039.2827             146.6666           1,282.6053   \n",
       "2021-12-31           1,140.5564             195.3772           1,274.3511   \n",
       "2022-12-31             964.1283             157.1302             745.5541   \n",
       "2023-12-31           1,073.1347             107.0824             878.4878   \n",
       "\n",
       "                           MNST  ...                  AVO  \\\n",
       "2012-12-31             551.2222  ...                  NaN   \n",
       "2013-12-31             600.3280  ...                  NaN   \n",
       "2014-12-31             752.9700  ...                  NaN   \n",
       "2015-12-31             936.2005  ...                  NaN   \n",
       "2016-12-31           1,175.1478  ...                  NaN   \n",
       "2017-12-31           1,246.1432  ...                  NaN   \n",
       "2018-12-31           1,323.3634  ...              18.7210   \n",
       "2019-12-31           1,427.3772  ...             107.3015   \n",
       "2020-12-31           1,641.8017  ...              69.5788   \n",
       "2021-12-31           1,807.9073  ...              63.2617   \n",
       "2022-12-31           1,594.6316  ...              24.1853   \n",
       "2023-12-31           1,979.3671  ...              18.9105   \n",
       "\n",
       "                           SKIN                 BHIL                 NAPA  \\\n",
       "2012-12-31                  NaN                  NaN                  NaN   \n",
       "2013-12-31                  NaN                  NaN                  NaN   \n",
       "2014-12-31                  NaN                  NaN                  NaN   \n",
       "2015-12-31                  NaN                  NaN                  NaN   \n",
       "2016-12-31                  NaN                  NaN                  NaN   \n",
       "2017-12-31                  NaN                  NaN                  NaN   \n",
       "2018-12-31                  NaN                  NaN                  NaN   \n",
       "2019-12-31              25.8250                  NaN              71.0320   \n",
       "2020-12-31              -1.2095              -0.0056              93.0421   \n",
       "2021-12-31              15.5880            -110.1640              87.3659   \n",
       "2022-12-31              11.7970            -124.0657              96.3135   \n",
       "2023-12-31            -102.8100             -84.7869             137.7009   \n",
       "\n",
       "                           WEST                 SOVO                 OLPX  \\\n",
       "2012-12-31                  NaN                  NaN                  NaN   \n",
       "2013-12-31                  NaN                  NaN                  NaN   \n",
       "2014-12-31                  NaN                  NaN                  NaN   \n",
       "2015-12-31                  NaN                  NaN                  NaN   \n",
       "2016-12-31                  NaN                  NaN                  NaN   \n",
       "2017-12-31                  NaN                  NaN                  NaN   \n",
       "2018-12-31                  NaN                  NaN                  NaN   \n",
       "2019-12-31                  NaN              17.7270              60.3370   \n",
       "2020-12-31                  NaN              70.7893             148.4690   \n",
       "2021-12-31              -0.8854              89.4023             405.6710   \n",
       "2022-12-31              51.5020             136.2945             413.1760   \n",
       "2023-12-31               2.8600             119.0727             158.0350   \n",
       "\n",
       "                           COCO                 KVUE  US92202X209  \n",
       "2012-12-31                  NaN                  NaN          NaN  \n",
       "2013-12-31                  NaN                  NaN          NaN  \n",
       "2014-12-31                  NaN                  NaN          NaN  \n",
       "2015-12-31                  NaN                  NaN          NaN  \n",
       "2016-12-31                  NaN                  NaN          NaN  \n",
       "2017-12-31                  NaN                  NaN          NaN  \n",
       "2018-12-31                  NaN                  NaN          NaN  \n",
       "2019-12-31              15.3070           2,885.0000          NaN  \n",
       "2020-12-31              31.7572           3,394.0000          NaN  \n",
       "2021-12-31              25.8544           3,481.1635          NaN  \n",
       "2022-12-31              11.0674           3,074.9644          NaN  \n",
       "2023-12-31              56.5924           3,268.3476          NaN  \n",
       "\n",
       "[12 rows x 106 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOPBT = EBIT_OPER + AMORT_EXP.fillna(0) + OPER_LEASE_INT.fillna(0)\n",
    "NOPBT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot for a sanity check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20ddfe0d650>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NOPBT.sum(axis = 1).plot()\n",
    "EBIT_OPER.sum(axis=1).plot(title = 'NOPBT and EBIT for Consumer Staples Stocks')\n",
    "plt.legend([\"NOPBT\", \"EBIT\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks as expected. NOBPT should be higher than EBIT since we are adding back amortization from acquired intangibles and implicit interest expense from operating leases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CORPORATE TAX CONSIDERATIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The typical way to calculate NOPAT: \n",
    "NOPAT = EBITA - cash taxes \n",
    "cash taxes = income taxes (income statement) $+$ net deferred tax (balance sheet change from prior year) $+$ interest expense tax shield\n",
    "\n",
    "A few notes on corporate taxes:\n",
    "1. Corporate tax rates and laws change. This means that past years might not always be comparable to current and future years. \n",
    "2. Companies with negative operating earnings don't pay taxes and carry the loss forward to offset future earnings. However, this is only an asset if the business can generate income in the future.\n",
    "3. Many companies operate globally, and using the US tax regulation, we ignore favorable tax treatment from the global geographical revenue mix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "INT_EXP_DEBT = INT_EXP_DEBT.ffill(limit=1).fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of 2022, interest expense deduction is limited to 30% of EBIT. We will limit interest deductibility to 30% of EBIT for all past periods. I understand that the deductible limit was different in the past, but we will keep it simple and assume that it was always the current 30% of the EBIT limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Equities with Interest Expense exceeding 30% of EBIT (Most Recent)'}>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "INT_TO_EBIT = (INT_EXP_DEBT/EBIT_OPER)\n",
    "INT_ABOVE30_EBIT = (INT_TO_EBIT.iloc[-1] > 0.3)\n",
    "INT_TO_EBIT.loc[:,INT_ABOVE30_EBIT].iloc[-1].sort_values(ascending = False).plot(kind = \"bar\", title = \"Equities with Interest Expense exceeding 30% of EBIT (Most Recent)\", figsize = (12,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LANC</th>\n",
       "      <th>USNA</th>\n",
       "      <th>WBA</th>\n",
       "      <th>IMKTA</th>\n",
       "      <th>KO</th>\n",
       "      <th>IPAR</th>\n",
       "      <th>MKC</th>\n",
       "      <th>HAIN</th>\n",
       "      <th>CLX</th>\n",
       "      <th>MNST</th>\n",
       "      <th>...</th>\n",
       "      <th>AVO</th>\n",
       "      <th>SKIN</th>\n",
       "      <th>BHIL</th>\n",
       "      <th>NAPA</th>\n",
       "      <th>WEST</th>\n",
       "      <th>SOVO</th>\n",
       "      <th>OLPX</th>\n",
       "      <th>COCO</th>\n",
       "      <th>KVUE</th>\n",
       "      <th>US92202X209</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>78.0000</td>\n",
       "      <td>36.5197</td>\n",
       "      <td>433.0000</td>\n",
       "      <td>1.7220</td>\n",
       "      <td>57.9000</td>\n",
       "      <td>15.8000</td>\n",
       "      <td>123.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>168.0000</td>\n",
       "      <td>36.3937</td>\n",
       "      <td>440.0000</td>\n",
       "      <td>1.3800</td>\n",
       "      <td>57.0000</td>\n",
       "      <td>19.4000</td>\n",
       "      <td>119.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>162.0000</td>\n",
       "      <td>36.7558</td>\n",
       "      <td>513.0000</td>\n",
       "      <td>1.4780</td>\n",
       "      <td>54.5000</td>\n",
       "      <td>23.4430</td>\n",
       "      <td>99.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>609.0000</td>\n",
       "      <td>41.0972</td>\n",
       "      <td>843.0000</td>\n",
       "      <td>1.8260</td>\n",
       "      <td>58.2000</td>\n",
       "      <td>24.8550</td>\n",
       "      <td>95.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4440</td>\n",
       "      <td>596.0000</td>\n",
       "      <td>38.9432</td>\n",
       "      <td>687.0000</td>\n",
       "      <td>2.4400</td>\n",
       "      <td>57.3000</td>\n",
       "      <td>25.1610</td>\n",
       "      <td>82.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0460</td>\n",
       "      <td>693.0000</td>\n",
       "      <td>37.9344</td>\n",
       "      <td>758.0000</td>\n",
       "      <td>2.4920</td>\n",
       "      <td>96.2000</td>\n",
       "      <td>21.2740</td>\n",
       "      <td>82.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0360</td>\n",
       "      <td>616.0000</td>\n",
       "      <td>37.1386</td>\n",
       "      <td>884.0000</td>\n",
       "      <td>2.8780</td>\n",
       "      <td>175.0000</td>\n",
       "      <td>26.9250</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.3960</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0660</td>\n",
       "      <td>704.0000</td>\n",
       "      <td>44.3522</td>\n",
       "      <td>894.0000</td>\n",
       "      <td>2.3460</td>\n",
       "      <td>165.7000</td>\n",
       "      <td>16.3344</td>\n",
       "      <td>91.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3200</td>\n",
       "      <td>4.0386</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14.1786</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.1630</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5070</td>\n",
       "      <td>639.0000</td>\n",
       "      <td>42.2047</td>\n",
       "      <td>1,374.0000</td>\n",
       "      <td>2.1700</td>\n",
       "      <td>144.4000</td>\n",
       "      <td>17.4550</td>\n",
       "      <td>93.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.7000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>17.9240</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>13.8291</td>\n",
       "      <td>30.7776</td>\n",
       "      <td>0.7910</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>491.0000</td>\n",
       "      <td>25.4631</td>\n",
       "      <td>747.0000</td>\n",
       "      <td>3.0250</td>\n",
       "      <td>146.8000</td>\n",
       "      <td>8.0290</td>\n",
       "      <td>93.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.7000</td>\n",
       "      <td>0.6873</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>13.6180</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>18.3264</td>\n",
       "      <td>61.1480</td>\n",
       "      <td>0.3600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1920</td>\n",
       "      <td>396.0000</td>\n",
       "      <td>21.8489</td>\n",
       "      <td>861.0000</td>\n",
       "      <td>9.8990</td>\n",
       "      <td>179.6000</td>\n",
       "      <td>13.5220</td>\n",
       "      <td>97.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.2500</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>6.7770</td>\n",
       "      <td>13.4106</td>\n",
       "      <td>27.7390</td>\n",
       "      <td>44.5780</td>\n",
       "      <td>0.2580</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2620</td>\n",
       "      <td>580.0000</td>\n",
       "      <td>23.0564</td>\n",
       "      <td>1,541.0000</td>\n",
       "      <td>14.0530</td>\n",
       "      <td>201.8000</td>\n",
       "      <td>26.7942</td>\n",
       "      <td>103.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5500</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>11.7210</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>28.5195</td>\n",
       "      <td>32.4666</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>358.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           LANC                 USNA                  WBA  \\\n",
       "2012-12-31               0.0000               0.0200              78.0000   \n",
       "2013-12-31               0.0000               0.0010             168.0000   \n",
       "2014-12-31               0.0000               0.1290             162.0000   \n",
       "2015-12-31               0.0000               0.0150             609.0000   \n",
       "2016-12-31               0.0000               0.4440             596.0000   \n",
       "2017-12-31               0.0000               0.0460             693.0000   \n",
       "2018-12-31               0.0000               0.0360             616.0000   \n",
       "2019-12-31               0.0000               0.0660             704.0000   \n",
       "2020-12-31               0.0000               0.5070             639.0000   \n",
       "2021-12-31               0.0000               0.0570             491.0000   \n",
       "2022-12-31               0.0000               0.1920             396.0000   \n",
       "2023-12-31               0.0000               0.2620             580.0000   \n",
       "\n",
       "                          IMKTA                   KO                 IPAR  \\\n",
       "2012-12-31              36.5197             433.0000               1.7220   \n",
       "2013-12-31              36.3937             440.0000               1.3800   \n",
       "2014-12-31              36.7558             513.0000               1.4780   \n",
       "2015-12-31              41.0972             843.0000               1.8260   \n",
       "2016-12-31              38.9432             687.0000               2.4400   \n",
       "2017-12-31              37.9344             758.0000               2.4920   \n",
       "2018-12-31              37.1386             884.0000               2.8780   \n",
       "2019-12-31              44.3522             894.0000               2.3460   \n",
       "2020-12-31              42.2047           1,374.0000               2.1700   \n",
       "2021-12-31              25.4631             747.0000               3.0250   \n",
       "2022-12-31              21.8489             861.0000               9.8990   \n",
       "2023-12-31              23.0564           1,541.0000              14.0530   \n",
       "\n",
       "                            MKC                 HAIN                  CLX  \\\n",
       "2012-12-31              57.9000              15.8000             123.0000   \n",
       "2013-12-31              57.0000              19.4000             119.0000   \n",
       "2014-12-31              54.5000              23.4430              99.0000   \n",
       "2015-12-31              58.2000              24.8550              95.0000   \n",
       "2016-12-31              57.3000              25.1610              82.0000   \n",
       "2017-12-31              96.2000              21.2740              82.0000   \n",
       "2018-12-31             175.0000              26.9250              79.0000   \n",
       "2019-12-31             165.7000              16.3344              91.0000   \n",
       "2020-12-31             144.4000              17.4550              93.0000   \n",
       "2021-12-31             146.8000               8.0290              93.0000   \n",
       "2022-12-31             179.6000              13.5220              97.0000   \n",
       "2023-12-31             201.8000              26.7942             103.0000   \n",
       "\n",
       "                           MNST  ...                  AVO  \\\n",
       "2012-12-31               0.0000  ...               0.0000   \n",
       "2013-12-31               0.0000  ...               0.0000   \n",
       "2014-12-31               0.0000  ...               0.0000   \n",
       "2015-12-31               0.0000  ...               0.0000   \n",
       "2016-12-31               0.0000  ...               0.0000   \n",
       "2017-12-31               0.0000  ...               0.0000   \n",
       "2018-12-31               0.0000  ...               5.3960   \n",
       "2019-12-31               0.0000  ...              10.3200   \n",
       "2020-12-31               0.0000  ...               6.7000   \n",
       "2021-12-31               0.0000  ...               3.7000   \n",
       "2022-12-31               0.0000  ...               5.2500   \n",
       "2023-12-31               0.0000  ...               2.5500   \n",
       "\n",
       "                           SKIN                 BHIL                 NAPA  \\\n",
       "2012-12-31               0.0000               0.0000               0.0000   \n",
       "2013-12-31               0.0000               0.0000               0.0000   \n",
       "2014-12-31               0.0000               0.0000               0.0000   \n",
       "2015-12-31               0.0000               0.0000               0.0000   \n",
       "2016-12-31               0.0000               0.0000               0.0000   \n",
       "2017-12-31               0.0000               0.0000               0.0000   \n",
       "2018-12-31               0.0000               0.0000               0.0000   \n",
       "2019-12-31               4.0386               0.0000              14.1786   \n",
       "2020-12-31               0.0000               0.0000              17.9240   \n",
       "2021-12-31               0.6873              -0.0000              13.6180   \n",
       "2022-12-31              -0.0000              -0.0000               6.7770   \n",
       "2023-12-31              -0.0000              -0.0000              11.7210   \n",
       "\n",
       "                           WEST                 SOVO                 OLPX  \\\n",
       "2012-12-31               0.0000               0.0000               0.0000   \n",
       "2013-12-31               0.0000               0.0000               0.0000   \n",
       "2014-12-31               0.0000               0.0000               0.0000   \n",
       "2015-12-31               0.0000               0.0000               0.0000   \n",
       "2016-12-31               0.0000               0.0000               0.0000   \n",
       "2017-12-31               0.0000               0.0000               0.0000   \n",
       "2018-12-31               0.0000               0.0000               0.0000   \n",
       "2019-12-31               0.0000              -0.0000               0.0000   \n",
       "2020-12-31               0.0000              13.8291              30.7776   \n",
       "2021-12-31               0.0000              18.3264              61.1480   \n",
       "2022-12-31              13.4106              27.7390              44.5780   \n",
       "2023-12-31              -0.0000              28.5195              32.4666   \n",
       "\n",
       "                           COCO                 KVUE          US92202X209  \n",
       "2012-12-31               0.0000               0.0000               0.0000  \n",
       "2013-12-31               0.0000               0.0000               0.0000  \n",
       "2014-12-31               0.0000               0.0000               0.0000  \n",
       "2015-12-31               0.0000               0.0000               0.0000  \n",
       "2016-12-31               0.0000               0.0000               0.0000  \n",
       "2017-12-31               0.0000               0.0000               0.0000  \n",
       "2018-12-31               0.0000               0.0000               0.0000  \n",
       "2019-12-31               1.1630               0.0000               0.0000  \n",
       "2020-12-31               0.7910               0.0000               0.0000  \n",
       "2021-12-31               0.3600               0.0000               0.0000  \n",
       "2022-12-31               0.2580               0.0000               0.0000  \n",
       "2023-12-31               0.0310             358.0000               0.0000  \n",
       "\n",
       "[12 rows x 106 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INT_TO_EBIT[INT_TO_EBIT > 0.3] = 0.3 # Max deductiblee interest to EBIT is 30% of EBIT, so we put a ceiling at 0.3.\n",
    "INT_TO_EBIT[INT_TO_EBIT < 0] = 0 # We cannot deduct the interest if EBIT is negative, so we put a floor at 0. \n",
    "DEDUCTIBLE_INT = (INT_TO_EBIT * EBIT_OPER).fillna(0)\n",
    "DEDUCTIBLE_INT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to calculate the Effective Tax Rate. This will allow us to estimate the tax shield from interest expense. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LANC</th>\n",
       "      <th>USNA</th>\n",
       "      <th>WBA</th>\n",
       "      <th>IMKTA</th>\n",
       "      <th>KO</th>\n",
       "      <th>IPAR</th>\n",
       "      <th>MKC</th>\n",
       "      <th>HAIN</th>\n",
       "      <th>CLX</th>\n",
       "      <th>MNST</th>\n",
       "      <th>...</th>\n",
       "      <th>AVO</th>\n",
       "      <th>SKIN</th>\n",
       "      <th>BHIL</th>\n",
       "      <th>NAPA</th>\n",
       "      <th>WEST</th>\n",
       "      <th>SOVO</th>\n",
       "      <th>OLPX</th>\n",
       "      <th>COCO</th>\n",
       "      <th>KVUE</th>\n",
       "      <th>US92202X209</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>0.3505</td>\n",
       "      <td>0.3259</td>\n",
       "      <td>0.3520</td>\n",
       "      <td>0.1964</td>\n",
       "      <td>0.2393</td>\n",
       "      <td>1.2026</td>\n",
       "      <td>0.2413</td>\n",
       "      <td>0.2841</td>\n",
       "      <td>0.2793</td>\n",
       "      <td>0.3798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>0.3257</td>\n",
       "      <td>0.3218</td>\n",
       "      <td>0.3945</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>0.2593</td>\n",
       "      <td>0.3769</td>\n",
       "      <td>0.2246</td>\n",
       "      <td>0.1798</td>\n",
       "      <td>0.2888</td>\n",
       "      <td>0.3756</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>0.3401</td>\n",
       "      <td>0.3361</td>\n",
       "      <td>0.4031</td>\n",
       "      <td>0.2314</td>\n",
       "      <td>0.1982</td>\n",
       "      <td>0.3627</td>\n",
       "      <td>0.2395</td>\n",
       "      <td>0.2914</td>\n",
       "      <td>0.3134</td>\n",
       "      <td>0.3492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>0.3414</td>\n",
       "      <td>0.3382</td>\n",
       "      <td>0.2146</td>\n",
       "      <td>0.2563</td>\n",
       "      <td>0.2282</td>\n",
       "      <td>0.3232</td>\n",
       "      <td>0.2151</td>\n",
       "      <td>0.1894</td>\n",
       "      <td>0.3144</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>0.3406</td>\n",
       "      <td>0.2778</td>\n",
       "      <td>0.1565</td>\n",
       "      <td>0.2345</td>\n",
       "      <td>0.1694</td>\n",
       "      <td>0.3520</td>\n",
       "      <td>0.2322</td>\n",
       "      <td>0.2491</td>\n",
       "      <td>0.3137</td>\n",
       "      <td>0.3153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>0.3111</td>\n",
       "      <td>0.5442</td>\n",
       "      <td>0.1214</td>\n",
       "      <td>0.2403</td>\n",
       "      <td>0.6282</td>\n",
       "      <td>0.2825</td>\n",
       "      <td>0.1924</td>\n",
       "      <td>0.1362</td>\n",
       "      <td>0.2941</td>\n",
       "      <td>0.3088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>0.2233</td>\n",
       "      <td>0.3466</td>\n",
       "      <td>0.1583</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1731</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2053</td>\n",
       "      <td>0.2292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8677</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>0.2548</td>\n",
       "      <td>0.3418</td>\n",
       "      <td>0.1147</td>\n",
       "      <td>0.1692</td>\n",
       "      <td>0.1715</td>\n",
       "      <td>0.2776</td>\n",
       "      <td>0.1611</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1843</td>\n",
       "      <td>0.2179</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2281</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1659</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.8579</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1406</td>\n",
       "      <td>0.2696</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>0.2377</td>\n",
       "      <td>0.3019</td>\n",
       "      <td>0.0961</td>\n",
       "      <td>0.2044</td>\n",
       "      <td>0.2122</td>\n",
       "      <td>0.2765</td>\n",
       "      <td>0.1719</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>0.1326</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2193</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.1470</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1448</td>\n",
       "      <td>0.0778</td>\n",
       "      <td>0.3583</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>0.2394</td>\n",
       "      <td>0.3181</td>\n",
       "      <td>0.1660</td>\n",
       "      <td>0.2292</td>\n",
       "      <td>0.2450</td>\n",
       "      <td>0.2725</td>\n",
       "      <td>0.1748</td>\n",
       "      <td>0.2219</td>\n",
       "      <td>0.1462</td>\n",
       "      <td>0.2352</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3420</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2652</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0602</td>\n",
       "      <td>0.1536</td>\n",
       "      <td>0.2130</td>\n",
       "      <td>0.2919</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>0.1588</td>\n",
       "      <td>0.3649</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2359</td>\n",
       "      <td>0.1769</td>\n",
       "      <td>0.2137</td>\n",
       "      <td>0.1841</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.1954</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2114</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2562</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1679</td>\n",
       "      <td>0.3099</td>\n",
       "      <td>0.2023</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>0.1923</td>\n",
       "      <td>0.4154</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2340</td>\n",
       "      <td>0.1964</td>\n",
       "      <td>0.2459</td>\n",
       "      <td>0.1704</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0931</td>\n",
       "      <td>0.2221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2588</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.2282</td>\n",
       "      <td>1.3473</td>\n",
       "      <td>0.1716</td>\n",
       "      <td>0.1398</td>\n",
       "      <td>0.1999</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           LANC                 USNA                  WBA  \\\n",
       "2012-12-31               0.3505               0.3259               0.3520   \n",
       "2013-12-31               0.3257               0.3218               0.3945   \n",
       "2014-12-31               0.3401               0.3361               0.4031   \n",
       "2015-12-31               0.3414               0.3382               0.2146   \n",
       "2016-12-31               0.3406               0.2778               0.1565   \n",
       "2017-12-31               0.3111               0.5442               0.1214   \n",
       "2018-12-31               0.2233               0.3466               0.1583   \n",
       "2019-12-31               0.2548               0.3418               0.1147   \n",
       "2020-12-31               0.2377               0.3019               0.0961   \n",
       "2021-12-31               0.2394               0.3181               0.1660   \n",
       "2022-12-31               0.1588               0.3649               0.0000   \n",
       "2023-12-31               0.1923               0.4154               0.0000   \n",
       "\n",
       "                          IMKTA                   KO                 IPAR  \\\n",
       "2012-12-31               0.1964               0.2393               1.2026   \n",
       "2013-12-31               0.0449               0.2593               0.3769   \n",
       "2014-12-31               0.2314               0.1982               0.3627   \n",
       "2015-12-31               0.2563               0.2282               0.3232   \n",
       "2016-12-31               0.2345               0.1694               0.3520   \n",
       "2017-12-31               0.2403               0.6282               0.2825   \n",
       "2018-12-31               0.0000               0.1731               0.2760   \n",
       "2019-12-31               0.1692               0.1715               0.2776   \n",
       "2020-12-31               0.2044               0.2122               0.2765   \n",
       "2021-12-31               0.2292               0.2450               0.2725   \n",
       "2022-12-31               0.2359               0.1769               0.2137   \n",
       "2023-12-31               0.2340               0.1964               0.2459   \n",
       "\n",
       "                            MKC                 HAIN                  CLX  \\\n",
       "2012-12-31               0.2413               0.2841               0.2793   \n",
       "2013-12-31               0.2246               0.1798               0.2888   \n",
       "2014-12-31               0.2395               0.2914               0.3134   \n",
       "2015-12-31               0.2151               0.1894               0.3144   \n",
       "2016-12-31               0.2322               0.2491               0.3137   \n",
       "2017-12-31               0.1924               0.1362               0.2941   \n",
       "2018-12-31               0.0000               0.0000               0.2053   \n",
       "2019-12-31               0.1611               0.0000               0.1843   \n",
       "2020-12-31               0.1719               0.0467               0.1945   \n",
       "2021-12-31               0.1748               0.2219               0.1462   \n",
       "2022-12-31               0.1841               0.1630               0.1954   \n",
       "2023-12-31               0.1704               0.0000               0.0931   \n",
       "\n",
       "                           MNST  ...                  AVO  \\\n",
       "2012-12-31               0.3798  ...               0.0000   \n",
       "2013-12-31               0.3756  ...               0.0000   \n",
       "2014-12-31               0.3492  ...               0.0000   \n",
       "2015-12-31               0.3700  ...               0.0000   \n",
       "2016-12-31               0.3153  ...               0.0000   \n",
       "2017-12-31               0.3088  ...               0.0000   \n",
       "2018-12-31               0.2292  ...               0.8677   \n",
       "2019-12-31               0.2179  ...               0.2281   \n",
       "2020-12-31               0.1326  ...               0.2193   \n",
       "2021-12-31               0.2352  ...               0.3420   \n",
       "2022-12-31               0.2400  ...               0.2114   \n",
       "2023-12-31               0.2221  ...               0.2588   \n",
       "\n",
       "                           SKIN                 BHIL                 NAPA  \\\n",
       "2012-12-31               0.0000               0.0000               0.0000   \n",
       "2013-12-31               0.0000               0.0000               0.0000   \n",
       "2014-12-31               0.0000               0.0000               0.0000   \n",
       "2015-12-31               0.0000               0.0000               0.0000   \n",
       "2016-12-31               0.0000               0.0000               0.0000   \n",
       "2017-12-31               0.0000               0.0000               0.0000   \n",
       "2018-12-31               0.0000               0.0000               0.0000   \n",
       "2019-12-31               0.0000               0.0000               0.1659   \n",
       "2020-12-31              -0.0000              -0.0000               0.1470   \n",
       "2021-12-31               0.0000               0.0000               0.2652   \n",
       "2022-12-31               0.0000               0.0000               0.2562   \n",
       "2023-12-31               0.0143               0.0022               0.2282   \n",
       "\n",
       "                           WEST                 SOVO                 OLPX  \\\n",
       "2012-12-31               0.0000               0.0000               0.0000   \n",
       "2013-12-31               0.0000               0.0000               0.0000   \n",
       "2014-12-31               0.0000               0.0000               0.0000   \n",
       "2015-12-31               0.0000               0.0000               0.0000   \n",
       "2016-12-31               0.0000               0.0000               0.0000   \n",
       "2017-12-31               0.0000               0.0000               0.0000   \n",
       "2018-12-31               0.0000               0.0000               0.0000   \n",
       "2019-12-31               0.0000               3.8579               0.0000   \n",
       "2020-12-31               0.0000               0.1448               0.0778   \n",
       "2021-12-31              -0.0000               0.0602               0.1536   \n",
       "2022-12-31               0.0025               0.0000               0.1679   \n",
       "2023-12-31               1.3473               0.1716               0.1398   \n",
       "\n",
       "                           COCO                 KVUE          US92202X209  \n",
       "2012-12-31               0.0000               0.0000               0.0000  \n",
       "2013-12-31               0.0000               0.0000               0.0000  \n",
       "2014-12-31               0.0000               0.0000               0.0000  \n",
       "2015-12-31               0.0000               0.0000               0.0000  \n",
       "2016-12-31               0.0000               0.0000               0.0000  \n",
       "2017-12-31               0.0000               0.0000               0.0000  \n",
       "2018-12-31               0.0000               0.0000               0.0000  \n",
       "2019-12-31               0.1406               0.2696               0.0000  \n",
       "2020-12-31               0.3583               0.0000               0.0000  \n",
       "2021-12-31               0.2130               0.2919               0.0000  \n",
       "2022-12-31               0.3099               0.2023               0.0000  \n",
       "2023-12-31               0.1999               0.1790               0.0000  \n",
       "\n",
       "[12 rows x 106 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EFF_TAX_RATE = INC_TAX/EBIT_OPER\n",
    "EFF_TAX_RATE[EFF_TAX_RATE < 0] = 0 # Set the effective tax rate to 0 for companies with negative EBIT.\n",
    "EFF_TAX_RATE = EFF_TAX_RATE.fillna(0)\n",
    "EFF_TAX_RATE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we will calculate the cash taxes and net operating profits after taxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "INT_TAX_SHIELD = DEDUCTIBLE_INT * EFF_TAX_RATE\n",
    "CHG_DFD_TAX = DFD_TAX.diff().fillna(0)*-1 # The increase/decrease in deferred taxes\n",
    "CASH_TAXES =  INC_TAX + CHG_DFD_TAX + INT_TAX_SHIELD\n",
    "\n",
    "CASH_TAXES[CASH_TAXES<0] = 0 # set all negative values to 0, since a company cannot have negative taxes\n",
    "\n",
    "NOPAT = NOPBT - CASH_TAXES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot before-tax and after-tax net operating profit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20ddfc6ab50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NOPBT.mean(axis=1).plot()\n",
    "NOPAT.mean(axis=1).plot(title = \"Total Net Operating Profit Before and After Taxes\")\n",
    "plt.legend([\"NOPBT\", \"NOPAT\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see an improvement in the after-tax income post-2018, consistent with the Tax Cuts and Jobs Act of 2017. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### INVESTED CAPITAL\n",
    "\n",
    "Invested capital is the total amount of money a company raises by issuing securities to equity shareholders and debt to bondholders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the Financial Approach to calculate invested capital:\n",
    "\n",
    "Short-Term Debt \\\n",
    "$+$ Long-Term Debt \\\n",
    "$+$ Deferred Taxes \\\n",
    "$+$ Other Long-term Liabilities \\\n",
    "$+$ Preferred Stock \\\n",
    "$+$ Common Equity \n",
    "\n",
    "=\n",
    "\n",
    "Invested Capital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We forward fill one time and set nans to 0. \n",
    "INVESTED_CAPITAL = DEBT_ST.ffill(limit=1).fillna(0) \\\n",
    "    + DEBT_LT_NET.ffill(limit=1).fillna(0) \\\n",
    "    + DFD_TAX.fillna(0).fillna(0) \\\n",
    "    + OTHER_LT_LIABS.ffill(limit=1).fillna(0) \\\n",
    "    + PFD_STK.ffill(limit=1).fillna(0) \\\n",
    "    + SHLDRS_EQ.ffill(limit=1).fillna(0)\n",
    "\n",
    "INVESTED_CAPITAL = INVESTED_CAPITAL.replace(0,np.nan) # To avoid division by zero, we replace zero values with nans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RETURN ON INVESTED CAPITAL (ROIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROIC = NOPAT/ INVESTED_CAPITAL\n",
    "ROIC = ROIC.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which companies had the highest ROIC as of the most recent period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MED                  0.4282\n",
       "CALM                 0.4164\n",
       "VGR                  0.3169\n",
       "MO                   0.3160\n",
       "PM                   0.2782\n",
       "CL                   0.2663\n",
       "FIZZ                 0.2608\n",
       "HSY                  0.2415\n",
       "KMB                  0.2139\n",
       "IPAR                 0.2083\n",
       "Name: 2023-12-31 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ROIC.iloc[-1].sort_values(ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at only one period of ROIC data is not enough since ROIC can move around quite aggressively. Let's plot historical ROIC for CL, KO, HSY, and COST:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Historical ROIC of Selected Stocks'}>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROIC.loc[:,[\"CL\", \"MED\", \"KO\", \"COST\"]].plot(title = \"Historical ROIC of Selected Stocks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Historical 3-Year Rolling Standard Deviation of ROIC'}>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROLL3_ROIC_STDEV = ROIC.rolling(3).std()\n",
    "ROLL3_ROIC_STDEV.loc[:,[\"CL\", \"MED\", \"KO\", \"COST\"]].plot(title = \"Historical 3-Year Rolling Standard Deviation of ROIC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also plot the historical average ROIC for consumer staples overall: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Average ROIC of Consumer Staples Equities'}>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROIC.dropna().mean(axis=1).plot(title= \"Average ROIC of Consumer Staples Equities\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RETURN ON INCREMENTAL INVESTED CAPITAL (ROIIC): \n",
    "\n",
    "ROIIC measures capital allocation efficiency by comparing the change in a company’s Net Operating Profit After Taxes (NOPAT) relative the change in invested capital.\n",
    "\n",
    "ROICC = Change in NOPAT $/$ Preceding Period Invested Capital\n",
    "\n",
    "ROIIC = (Year1 NOPAT - Year0 NOPAT) $/$ (Year0 Invested Capital - Year1 Invested Capital)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LANC</th>\n",
       "      <th>USNA</th>\n",
       "      <th>WBA</th>\n",
       "      <th>IMKTA</th>\n",
       "      <th>KO</th>\n",
       "      <th>IPAR</th>\n",
       "      <th>MKC</th>\n",
       "      <th>HAIN</th>\n",
       "      <th>CLX</th>\n",
       "      <th>MNST</th>\n",
       "      <th>...</th>\n",
       "      <th>AVO</th>\n",
       "      <th>SKIN</th>\n",
       "      <th>BHIL</th>\n",
       "      <th>NAPA</th>\n",
       "      <th>WEST</th>\n",
       "      <th>SOVO</th>\n",
       "      <th>OLPX</th>\n",
       "      <th>COCO</th>\n",
       "      <th>KVUE</th>\n",
       "      <th>US92202X209</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>-1.4572</td>\n",
       "      <td>-0.2624</td>\n",
       "      <td>1.0236</td>\n",
       "      <td>-0.2845</td>\n",
       "      <td>-0.1778</td>\n",
       "      <td>8.8795</td>\n",
       "      <td>-0.4052</td>\n",
       "      <td>0.1257</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>0.2878</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>0.7981</td>\n",
       "      <td>-0.6774</td>\n",
       "      <td>1.3299</td>\n",
       "      <td>0.2899</td>\n",
       "      <td>-0.3859</td>\n",
       "      <td>0.6896</td>\n",
       "      <td>-0.0321</td>\n",
       "      <td>0.1406</td>\n",
       "      <td>-0.5114</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>0.7583</td>\n",
       "      <td>0.3213</td>\n",
       "      <td>-0.0485</td>\n",
       "      <td>-0.2073</td>\n",
       "      <td>0.1536</td>\n",
       "      <td>-0.0281</td>\n",
       "      <td>0.1207</td>\n",
       "      <td>0.1545</td>\n",
       "      <td>-0.1968</td>\n",
       "      <td>0.3642</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>-0.7098</td>\n",
       "      <td>0.3385</td>\n",
       "      <td>0.1972</td>\n",
       "      <td>-0.0698</td>\n",
       "      <td>-2.3050</td>\n",
       "      <td>-0.4451</td>\n",
       "      <td>1.2074</td>\n",
       "      <td>0.7160</td>\n",
       "      <td>0.2463</td>\n",
       "      <td>0.4819</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>-0.2406</td>\n",
       "      <td>1.7664</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.5380</td>\n",
       "      <td>1.2132</td>\n",
       "      <td>-0.7201</td>\n",
       "      <td>-0.0144</td>\n",
       "      <td>-0.1920</td>\n",
       "      <td>36.5971</td>\n",
       "      <td>-0.8295</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>2.0011</td>\n",
       "      <td>-4.0309</td>\n",
       "      <td>-0.4671</td>\n",
       "      <td>-0.1103</td>\n",
       "      <td>-0.8879</td>\n",
       "      <td>-0.2298</td>\n",
       "      <td>-0.0958</td>\n",
       "      <td>1.4204</td>\n",
       "      <td>-0.2663</td>\n",
       "      <td>-20.1783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>0.4729</td>\n",
       "      <td>0.6353</td>\n",
       "      <td>0.6127</td>\n",
       "      <td>1.7748</td>\n",
       "      <td>-2.2752</td>\n",
       "      <td>-0.4546</td>\n",
       "      <td>-0.3571</td>\n",
       "      <td>-3.0687</td>\n",
       "      <td>0.7957</td>\n",
       "      <td>0.7073</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5690</td>\n",
       "      <td>0.2863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>1.8108</td>\n",
       "      <td>0.4063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>-0.1109</td>\n",
       "      <td>-0.5257</td>\n",
       "      <td>0.0347</td>\n",
       "      <td>-0.3007</td>\n",
       "      <td>1.5651</td>\n",
       "      <td>1.4940</td>\n",
       "      <td>0.0919</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.4445</td>\n",
       "      <td>0.6222</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3035</td>\n",
       "      <td>-0.1943</td>\n",
       "      <td>2.2902</td>\n",
       "      <td>0.3788</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1810</td>\n",
       "      <td>0.2697</td>\n",
       "      <td>-28.9670</td>\n",
       "      <td>-0.0649</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>2.0740</td>\n",
       "      <td>-19.5143</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>1.5249</td>\n",
       "      <td>1.0554</td>\n",
       "      <td>0.3145</td>\n",
       "      <td>-0.3650</td>\n",
       "      <td>-0.3189</td>\n",
       "      <td>-1.4737</td>\n",
       "      <td>-0.2798</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4037</td>\n",
       "      <td>0.2515</td>\n",
       "      <td>-1.7459</td>\n",
       "      <td>-0.1002</td>\n",
       "      <td>0.1845</td>\n",
       "      <td>0.3482</td>\n",
       "      <td>0.0473</td>\n",
       "      <td>4.1523</td>\n",
       "      <td>0.0755</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>1.3739</td>\n",
       "      <td>3.8604</td>\n",
       "      <td>1.6279</td>\n",
       "      <td>-1.8195</td>\n",
       "      <td>0.3575</td>\n",
       "      <td>0.5968</td>\n",
       "      <td>0.3306</td>\n",
       "      <td>-0.0858</td>\n",
       "      <td>-0.4526</td>\n",
       "      <td>6.2227</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.6514</td>\n",
       "      <td>-0.1332</td>\n",
       "      <td>0.2114</td>\n",
       "      <td>1.2106</td>\n",
       "      <td>-1.2893</td>\n",
       "      <td>-21.8719</td>\n",
       "      <td>2.4826</td>\n",
       "      <td>1.4497</td>\n",
       "      <td>-1.2887</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           LANC                 USNA                  WBA  \\\n",
       "2012-12-31                  NaN                  NaN                  NaN   \n",
       "2013-12-31                  NaN                  NaN                  NaN   \n",
       "2014-12-31              -1.4572              -0.2624               1.0236   \n",
       "2015-12-31               0.7981              -0.6774               1.3299   \n",
       "2016-12-31               0.7583               0.3213              -0.0485   \n",
       "2017-12-31              -0.7098               0.3385               0.1972   \n",
       "2018-12-31              -0.2406               1.7664              -0.0043   \n",
       "2019-12-31               2.0011              -4.0309              -0.4671   \n",
       "2020-12-31               0.4729               0.6353               0.6127   \n",
       "2021-12-31              -0.1109              -0.5257               0.0347   \n",
       "2022-12-31               2.0740             -19.5143               0.0595   \n",
       "2023-12-31               1.3739               3.8604               1.6279   \n",
       "\n",
       "                          IMKTA                   KO                 IPAR  \\\n",
       "2012-12-31                  NaN                  NaN                  NaN   \n",
       "2013-12-31                  NaN                  NaN                  NaN   \n",
       "2014-12-31              -0.2845              -0.1778               8.8795   \n",
       "2015-12-31               0.2899              -0.3859               0.6896   \n",
       "2016-12-31              -0.2073               0.1536              -0.0281   \n",
       "2017-12-31              -0.0698              -2.3050              -0.4451   \n",
       "2018-12-31               0.5380               1.2132              -0.7201   \n",
       "2019-12-31              -0.1103              -0.8879              -0.2298   \n",
       "2020-12-31               1.7748              -2.2752              -0.4546   \n",
       "2021-12-31              -0.3007               1.5651               1.4940   \n",
       "2022-12-31               1.5249               1.0554               0.3145   \n",
       "2023-12-31              -1.8195               0.3575               0.5968   \n",
       "\n",
       "                            MKC                 HAIN                  CLX  \\\n",
       "2012-12-31                  NaN                  NaN                  NaN   \n",
       "2013-12-31                  NaN                  NaN                  NaN   \n",
       "2014-12-31              -0.4052               0.1257               0.0691   \n",
       "2015-12-31              -0.0321               0.1406              -0.5114   \n",
       "2016-12-31               0.1207               0.1545              -0.1968   \n",
       "2017-12-31               1.2074               0.7160               0.2463   \n",
       "2018-12-31              -0.0144              -0.1920              36.5971   \n",
       "2019-12-31              -0.0958               1.4204              -0.2663   \n",
       "2020-12-31              -0.3571              -3.0687               0.7957   \n",
       "2021-12-31               0.0919               0.0097               0.4445   \n",
       "2022-12-31              -0.3650              -0.3189              -1.4737   \n",
       "2023-12-31               0.3306              -0.0858              -0.4526   \n",
       "\n",
       "                           MNST  ...                  AVO  \\\n",
       "2012-12-31                  NaN  ...                  NaN   \n",
       "2013-12-31                  NaN  ...                  NaN   \n",
       "2014-12-31               0.2878  ...                  NaN   \n",
       "2015-12-31               0.0174  ...                  NaN   \n",
       "2016-12-31               0.3642  ...                  NaN   \n",
       "2017-12-31               0.4819  ...                  NaN   \n",
       "2018-12-31              -0.8295  ...                  NaN   \n",
       "2019-12-31             -20.1783  ...               0.4011   \n",
       "2020-12-31               0.7073  ...               1.5690   \n",
       "2021-12-31               0.6222  ...               2.3035   \n",
       "2022-12-31              -0.2798  ...              -0.4037   \n",
       "2023-12-31               6.2227  ...              -1.6514   \n",
       "\n",
       "                           SKIN                 BHIL                 NAPA  \\\n",
       "2012-12-31                  NaN                  NaN                  NaN   \n",
       "2013-12-31                  NaN                  NaN                  NaN   \n",
       "2014-12-31                  NaN                  NaN                  NaN   \n",
       "2015-12-31                  NaN                  NaN                  NaN   \n",
       "2016-12-31                  NaN                  NaN                  NaN   \n",
       "2017-12-31                  NaN                  NaN                  NaN   \n",
       "2018-12-31                  NaN                  NaN                  NaN   \n",
       "2019-12-31                  NaN                  NaN                  NaN   \n",
       "2020-12-31               0.2863                  NaN               0.0268   \n",
       "2021-12-31              -0.1943               2.2902               0.3788   \n",
       "2022-12-31               0.2515              -1.7459              -0.1002   \n",
       "2023-12-31              -0.1332               0.2114               1.2106   \n",
       "\n",
       "                           WEST                 SOVO                 OLPX  \\\n",
       "2012-12-31                  NaN                  NaN                  NaN   \n",
       "2013-12-31                  NaN                  NaN                  NaN   \n",
       "2014-12-31                  NaN                  NaN                  NaN   \n",
       "2015-12-31                  NaN                  NaN                  NaN   \n",
       "2016-12-31                  NaN                  NaN                  NaN   \n",
       "2017-12-31                  NaN                  NaN                  NaN   \n",
       "2018-12-31                  NaN                  NaN                  NaN   \n",
       "2019-12-31                  NaN                  NaN                  NaN   \n",
       "2020-12-31                  NaN               0.1413               1.8108   \n",
       "2021-12-31                  NaN               0.1810               0.2697   \n",
       "2022-12-31               0.1845               0.3482               0.0473   \n",
       "2023-12-31              -1.2893             -21.8719               2.4826   \n",
       "\n",
       "                           COCO                 KVUE  US92202X209  \n",
       "2012-12-31                  NaN                  NaN          NaN  \n",
       "2013-12-31                  NaN                  NaN          NaN  \n",
       "2014-12-31                  NaN                  NaN          NaN  \n",
       "2015-12-31                  NaN                  NaN          NaN  \n",
       "2016-12-31                  NaN                  NaN          NaN  \n",
       "2017-12-31                  NaN                  NaN          NaN  \n",
       "2018-12-31                  NaN                  NaN          NaN  \n",
       "2019-12-31                  NaN                  NaN          NaN  \n",
       "2020-12-31               0.4063                  NaN          NaN  \n",
       "2021-12-31             -28.9670              -0.0649          NaN  \n",
       "2022-12-31               4.1523               0.0755          NaN  \n",
       "2023-12-31               1.4497              -1.2887          NaN  \n",
       "\n",
       "[12 rows x 106 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ROIIC = NOPAT.diff() / INVESTED_CAPITAL.diff().shift(1)\n",
    "ROIIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROIIC.loc[:,[\"CL\", \"MED\", \"KO\", \"COST\"]].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROICC can be very volatile, let's plot the mean on a rolling 3-year basis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROIIC.rolling(3).mean().loc[:,[\"CL\", \"MED\", \"KO\", \"COST\"]].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite having the highest ROIC, Medifast Inc (MED) is experiencing a decline in its incremental return on capital (ROIIC). Of course, we cannot conclude anything from that without further research.  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}