factor_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "#!/usr/bin/env python\n",
    "\"\"\"\n",
    "Colored by Group Example\n",
    "========================\n",
    "Generating a word cloud that assigns colors to words based on\n",
    "a predefined mapping from colors to words\n",
    "\"\"\"\n",
    "\n",
    "from wordcloud import (WordCloud, get_single_color_func)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "class SimpleGroupedColorFunc(object):\n",
    "    \"\"\"Create a color function object which assigns EXACT colors\n",
    "       to certain words based on the color to words mapping\n",
    "       Parameters\n",
    "       ----------\n",
    "       color_to_words : dict(str -> list(str))\n",
    "         A dictionary that maps a color to the list of words.\n",
    "       default_color : str\n",
    "         Color that will be assigned to a word that's not a member\n",
    "         of any value from color_to_words.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, color_to_words, default_color):\n",
    "        self.word_to_color = {word: color\n",
    "                              for (color, words) in color_to_words.items()\n",
    "                              for word in words}\n",
    "\n",
    "        self.default_color = default_color\n",
    "\n",
    "    def __call__(self, word, **kwargs):\n",
    "        return self.word_to_color.get(word, self.default_color)\n",
    "\n",
    "\n",
    "class GroupedColorFunc(object):\n",
    "    \"\"\"Create a color function object which assigns DIFFERENT SHADES of\n",
    "       specified colors to certain words based on the color to words mapping.\n",
    "       Uses wordcloud.get_single_color_func\n",
    "       Parameters\n",
    "       ----------\n",
    "       color_to_words : dict(str -> list(str))\n",
    "         A dictionary that maps a color to the list of words.\n",
    "       default_color : str\n",
    "         Color that will be assigned to a word that's not a member\n",
    "         of any value from color_to_words.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, color_to_words, default_color):\n",
    "        self.color_func_to_words = [\n",
    "            (get_single_color_func(color), set(words))\n",
    "            for (color, words) in color_to_words.items()]\n",
    "\n",
    "        self.default_color_func = get_single_color_func(default_color)\n",
    "\n",
    "    def get_color_func(self, word):\n",
    "        \"\"\"Returns a single_color_func associated with the word\"\"\"\n",
    "        try:\n",
    "            color_func = next(\n",
    "                color_func for (color_func, words) in self.color_func_to_words\n",
    "                if word in words)\n",
    "        except StopIteration:\n",
    "            color_func = self.default_color_func\n",
    "\n",
    "        return color_func\n",
    "\n",
    "    def __call__(self, word, **kwargs):\n",
    "        return self.get_color_func(word)(word, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Change To Liabilities</th>\n",
       "      <th>Total Cash From Operating Activities</th>\n",
       "      <th>Net Borrowings</th>\n",
       "      <th>Total Cashflows From Investing Activities</th>\n",
       "      <th>Investments</th>\n",
       "      <th>Net Income</th>\n",
       "      <th>Total Liab</th>\n",
       "      <th>Total Assets</th>\n",
       "      <th>Retained Earnings</th>\n",
       "      <th>Cash</th>\n",
       "      <th>Net Receivables</th>\n",
       "      <th>Long Term Debt</th>\n",
       "      <th>Short Long Term Debt</th>\n",
       "      <th>Total Current Assets</th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.027500e+10</td>\n",
       "      <td>4.696600e+10</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1.610600e+10</td>\n",
       "      <td>-1.292900e+10</td>\n",
       "      <td>3.463000e+10</td>\n",
       "      <td>3.092590e+11</td>\n",
       "      <td>3.811910e+11</td>\n",
       "      <td>1.443500e+10</td>\n",
       "      <td>3.711900e+10</td>\n",
       "      <td>6.525300e+10</td>\n",
       "      <td>1.066290e+11</td>\n",
       "      <td>1.116900e+10</td>\n",
       "      <td>1.531540e+11</td>\n",
       "      <td>0.213565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.405000e+10</td>\n",
       "      <td>2.020000e+10</td>\n",
       "      <td>3.220000e+09</td>\n",
       "      <td>8.350000e+08</td>\n",
       "      <td>4.608000e+09</td>\n",
       "      <td>2.055100e+10</td>\n",
       "      <td>2.879120e+11</td>\n",
       "      <td>3.510020e+11</td>\n",
       "      <td>5.562000e+09</td>\n",
       "      <td>3.494000e+10</td>\n",
       "      <td>5.150600e+10</td>\n",
       "      <td>1.091060e+11</td>\n",
       "      <td>9.613000e+09</td>\n",
       "      <td>1.348360e+11</td>\n",
       "      <td>0.102917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.070000e+08</td>\n",
       "      <td>2.109400e+10</td>\n",
       "      <td>3.220000e+09</td>\n",
       "      <td>3.572000e+09</td>\n",
       "      <td>5.747000e+09</td>\n",
       "      <td>2.174400e+10</td>\n",
       "      <td>2.655600e+11</td>\n",
       "      <td>3.298400e+11</td>\n",
       "      <td>9.233000e+09</td>\n",
       "      <td>3.405000e+10</td>\n",
       "      <td>3.390800e+10</td>\n",
       "      <td>1.057520e+11</td>\n",
       "      <td>8.039000e+09</td>\n",
       "      <td>1.144230e+11</td>\n",
       "      <td>0.096063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.336600e+10</td>\n",
       "      <td>2.398100e+10</td>\n",
       "      <td>1.042300e+10</td>\n",
       "      <td>-1.036800e+10</td>\n",
       "      <td>-7.895000e+09</td>\n",
       "      <td>2.363000e+10</td>\n",
       "      <td>2.679800e+11</td>\n",
       "      <td>3.371580e+11</td>\n",
       "      <td>1.526100e+10</td>\n",
       "      <td>3.846600e+10</td>\n",
       "      <td>3.303600e+10</td>\n",
       "      <td>1.086420e+11</td>\n",
       "      <td>8.003000e+09</td>\n",
       "      <td>1.214650e+11</td>\n",
       "      <td>-0.094029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.909000e+09</td>\n",
       "      <td>2.208600e+10</td>\n",
       "      <td>-3.100000e+09</td>\n",
       "      <td>-1.258000e+10</td>\n",
       "      <td>4.271000e+09</td>\n",
       "      <td>1.432300e+10</td>\n",
       "      <td>2.823040e+11</td>\n",
       "      <td>4.205490e+11</td>\n",
       "      <td>8.591500e+10</td>\n",
       "      <td>3.622000e+10</td>\n",
       "      <td>3.264900e+10</td>\n",
       "      <td>5.494400e+10</td>\n",
       "      <td>1.687000e+09</td>\n",
       "      <td>1.615800e+11</td>\n",
       "      <td>0.025503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>-1.901744e+09</td>\n",
       "      <td>2.682230e+10</td>\n",
       "      <td>4.167232e+09</td>\n",
       "      <td>-1.333849e+10</td>\n",
       "      <td>1.799423e+09</td>\n",
       "      <td>1.594900e+10</td>\n",
       "      <td>1.832240e+11</td>\n",
       "      <td>4.644270e+11</td>\n",
       "      <td>1.082500e+11</td>\n",
       "      <td>1.326220e+11</td>\n",
       "      <td>3.519000e+10</td>\n",
       "      <td>3.983000e+10</td>\n",
       "      <td>3.733200e+10</td>\n",
       "      <td>2.332740e+11</td>\n",
       "      <td>0.029178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>3.073530e+08</td>\n",
       "      <td>2.212551e+10</td>\n",
       "      <td>6.475969e+09</td>\n",
       "      <td>-2.322245e+10</td>\n",
       "      <td>-6.018215e+09</td>\n",
       "      <td>1.746024e+10</td>\n",
       "      <td>1.711889e+11</td>\n",
       "      <td>4.287926e+11</td>\n",
       "      <td>1.002505e+11</td>\n",
       "      <td>1.131049e+11</td>\n",
       "      <td>3.437743e+10</td>\n",
       "      <td>5.108895e+10</td>\n",
       "      <td>2.212810e+10</td>\n",
       "      <td>2.116034e+11</td>\n",
       "      <td>0.246093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>3.097020e+08</td>\n",
       "      <td>2.312352e+10</td>\n",
       "      <td>6.037583e+09</td>\n",
       "      <td>-1.848232e+10</td>\n",
       "      <td>-1.060335e+10</td>\n",
       "      <td>1.194308e+10</td>\n",
       "      <td>1.814853e+11</td>\n",
       "      <td>4.216774e+11</td>\n",
       "      <td>8.236386e+10</td>\n",
       "      <td>1.240005e+11</td>\n",
       "      <td>3.162152e+10</td>\n",
       "      <td>5.096679e+10</td>\n",
       "      <td>1.426338e+10</td>\n",
       "      <td>2.078333e+11</td>\n",
       "      <td>0.065629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1.284689e+09</td>\n",
       "      <td>1.828067e+10</td>\n",
       "      <td>2.195216e+09</td>\n",
       "      <td>-7.119745e+09</td>\n",
       "      <td>-1.668570e+08</td>\n",
       "      <td>1.042792e+10</td>\n",
       "      <td>1.445894e+11</td>\n",
       "      <td>3.942622e+11</td>\n",
       "      <td>9.034396e+10</td>\n",
       "      <td>1.072852e+11</td>\n",
       "      <td>3.101319e+10</td>\n",
       "      <td>2.770260e+10</td>\n",
       "      <td>3.212053e+10</td>\n",
       "      <td>1.790831e+11</td>\n",
       "      <td>0.081508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.830338e+09</td>\n",
       "      <td>-1.701114e+09</td>\n",
       "      <td>-6.440656e+09</td>\n",
       "      <td>-8.117702e+09</td>\n",
       "      <td>8.268477e+09</td>\n",
       "      <td>1.603868e+11</td>\n",
       "      <td>2.964236e+11</td>\n",
       "      <td>6.696965e+10</td>\n",
       "      <td>3.188104e+10</td>\n",
       "      <td>4.557126e+10</td>\n",
       "      <td>1.326215e+10</td>\n",
       "      <td>6.051582e+09</td>\n",
       "      <td>2.003298e+11</td>\n",
       "      <td>0.092308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Change To Liabilities  Total Cash From Operating Activities  \\\n",
       "0             2.027500e+10                          4.696600e+10   \n",
       "1             1.405000e+10                          2.020000e+10   \n",
       "2             3.070000e+08                          2.109400e+10   \n",
       "3            -2.336600e+10                          2.398100e+10   \n",
       "12            8.909000e+09                          2.208600e+10   \n",
       "..                     ...                                   ...   \n",
       "367          -1.901744e+09                          2.682230e+10   \n",
       "368           3.073530e+08                          2.212551e+10   \n",
       "369           3.097020e+08                          2.312352e+10   \n",
       "370           1.284689e+09                          1.828067e+10   \n",
       "379           0.000000e+00                          6.830338e+09   \n",
       "\n",
       "     Net Borrowings  Total Cashflows From Investing Activities   Investments  \\\n",
       "0     -1.000000e+09                              -1.610600e+10 -1.292900e+10   \n",
       "1      3.220000e+09                               8.350000e+08  4.608000e+09   \n",
       "2      3.220000e+09                               3.572000e+09  5.747000e+09   \n",
       "3      1.042300e+10                              -1.036800e+10 -7.895000e+09   \n",
       "12    -3.100000e+09                              -1.258000e+10  4.271000e+09   \n",
       "..              ...                                        ...           ...   \n",
       "367    4.167232e+09                              -1.333849e+10  1.799423e+09   \n",
       "368    6.475969e+09                              -2.322245e+10 -6.018215e+09   \n",
       "369    6.037583e+09                              -1.848232e+10 -1.060335e+10   \n",
       "370    2.195216e+09                              -7.119745e+09 -1.668570e+08   \n",
       "379   -1.701114e+09                              -6.440656e+09 -8.117702e+09   \n",
       "\n",
       "       Net Income    Total Liab  Total Assets  Retained Earnings  \\\n",
       "0    3.463000e+10  3.092590e+11  3.811910e+11       1.443500e+10   \n",
       "1    2.055100e+10  2.879120e+11  3.510020e+11       5.562000e+09   \n",
       "2    2.174400e+10  2.655600e+11  3.298400e+11       9.233000e+09   \n",
       "3    2.363000e+10  2.679800e+11  3.371580e+11       1.526100e+10   \n",
       "12   1.432300e+10  2.823040e+11  4.205490e+11       8.591500e+10   \n",
       "..            ...           ...           ...                ...   \n",
       "367  1.594900e+10  1.832240e+11  4.644270e+11       1.082500e+11   \n",
       "368  1.746024e+10  1.711889e+11  4.287926e+11       1.002505e+11   \n",
       "369  1.194308e+10  1.814853e+11  4.216774e+11       8.236386e+10   \n",
       "370  1.042792e+10  1.445894e+11  3.942622e+11       9.034396e+10   \n",
       "379  8.268477e+09  1.603868e+11  2.964236e+11       6.696965e+10   \n",
       "\n",
       "             Cash  Net Receivables  Long Term Debt  Short Long Term Debt  \\\n",
       "0    3.711900e+10     6.525300e+10    1.066290e+11          1.116900e+10   \n",
       "1    3.494000e+10     5.150600e+10    1.091060e+11          9.613000e+09   \n",
       "2    3.405000e+10     3.390800e+10    1.057520e+11          8.039000e+09   \n",
       "3    3.846600e+10     3.303600e+10    1.086420e+11          8.003000e+09   \n",
       "12   3.622000e+10     3.264900e+10    5.494400e+10          1.687000e+09   \n",
       "..            ...              ...             ...                   ...   \n",
       "367  1.326220e+11     3.519000e+10    3.983000e+10          3.733200e+10   \n",
       "368  1.131049e+11     3.437743e+10    5.108895e+10          2.212810e+10   \n",
       "369  1.240005e+11     3.162152e+10    5.096679e+10          1.426338e+10   \n",
       "370  1.072852e+11     3.101319e+10    2.770260e+10          3.212053e+10   \n",
       "379  3.188104e+10     4.557126e+10    1.326215e+10          6.051582e+09   \n",
       "\n",
       "     Total Current Assets   Returns  \n",
       "0            1.531540e+11  0.213565  \n",
       "1            1.348360e+11  0.102917  \n",
       "2            1.144230e+11  0.096063  \n",
       "3            1.214650e+11 -0.094029  \n",
       "12           1.615800e+11  0.025503  \n",
       "..                    ...       ...  \n",
       "367          2.332740e+11  0.029178  \n",
       "368          2.116034e+11  0.246093  \n",
       "369          2.078333e+11  0.065629  \n",
       "370          1.790831e+11  0.081508  \n",
       "379          2.003298e+11  0.092308  \n",
       "\n",
       "[84 rows x 15 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/Processed/linear_reg_data.csv\")\n",
    "df = df.dropna().drop(columns=[\"Unnamed: 0\"]).reset_index(drop=True)\n",
    "df = df[df[\"Net Income\"] > 1000000000]\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const                                        5.825244e+10\n",
       "Change To Liabilities                        1.000000e+00\n",
       "Total Cash From Operating Activities         3.473057e-01\n",
       "Net Borrowings                              -1.577814e-02\n",
       "Total Cashflows From Investing Activities   -1.270778e+00\n",
       "Investments                                  1.479920e-02\n",
       "Net Income                                   8.131597e-01\n",
       "Total Liab                                  -7.365760e-02\n",
       "Total Assets                                 4.976205e-02\n",
       "Retained Earnings                           -2.056288e-01\n",
       "Cash                                        -7.511013e-02\n",
       "Net Receivables                              7.291306e-02\n",
       "Long Term Debt                              -3.087043e-01\n",
       "Short Long Term Debt                         1.137247e+00\n",
       "Total Current Assets                         6.000365e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns.values.tolist()\n",
    "columns.remove(\"Returns\")\n",
    "X = df[columns]\n",
    "y = df[\"Returns\"]\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "results = sm.OLS(y, X).fit()\n",
    "output = results.params/abs(results.params.iloc[1])\n",
    "output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps this tells us that for large cap tech companies fundemental factors are not such a good indicator of performance, lets see if anything changes if we normalize the values before pluggin them into the regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Change To Liabilities</th>\n",
       "      <th>Net Borrowings</th>\n",
       "      <th>Investments</th>\n",
       "      <th>Net Income</th>\n",
       "      <th>Total Liab</th>\n",
       "      <th>Total Assets</th>\n",
       "      <th>Retained Earnings</th>\n",
       "      <th>Cash</th>\n",
       "      <th>Net Receivables</th>\n",
       "      <th>Total Current Liabilities</th>\n",
       "      <th>Total Current Assets</th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.027500e+10</td>\n",
       "      <td>-1.000000e+09</td>\n",
       "      <td>-1.292900e+10</td>\n",
       "      <td>3.463000e+10</td>\n",
       "      <td>3.092590e+11</td>\n",
       "      <td>3.811910e+11</td>\n",
       "      <td>1.443500e+10</td>\n",
       "      <td>3.711900e+10</td>\n",
       "      <td>6.525300e+10</td>\n",
       "      <td>1.475740e+11</td>\n",
       "      <td>1.531540e+11</td>\n",
       "      <td>-0.027782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.405000e+10</td>\n",
       "      <td>3.220000e+09</td>\n",
       "      <td>4.608000e+09</td>\n",
       "      <td>2.055100e+10</td>\n",
       "      <td>2.879120e+11</td>\n",
       "      <td>3.510020e+11</td>\n",
       "      <td>5.562000e+09</td>\n",
       "      <td>3.494000e+10</td>\n",
       "      <td>5.150600e+10</td>\n",
       "      <td>1.254810e+11</td>\n",
       "      <td>1.348360e+11</td>\n",
       "      <td>-0.017667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.070000e+08</td>\n",
       "      <td>3.220000e+09</td>\n",
       "      <td>5.747000e+09</td>\n",
       "      <td>2.174400e+10</td>\n",
       "      <td>2.655600e+11</td>\n",
       "      <td>3.298400e+11</td>\n",
       "      <td>9.233000e+09</td>\n",
       "      <td>3.405000e+10</td>\n",
       "      <td>3.390800e+10</td>\n",
       "      <td>1.077540e+11</td>\n",
       "      <td>1.144230e+11</td>\n",
       "      <td>0.087700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.336600e+10</td>\n",
       "      <td>1.042300e+10</td>\n",
       "      <td>-7.895000e+09</td>\n",
       "      <td>2.363000e+10</td>\n",
       "      <td>2.679800e+11</td>\n",
       "      <td>3.371580e+11</td>\n",
       "      <td>1.526100e+10</td>\n",
       "      <td>3.846600e+10</td>\n",
       "      <td>3.303600e+10</td>\n",
       "      <td>1.063850e+11</td>\n",
       "      <td>1.214650e+11</td>\n",
       "      <td>0.093300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.756580e+08</td>\n",
       "      <td>-1.583400e+07</td>\n",
       "      <td>-1.164580e+09</td>\n",
       "      <td>-1.172211e+09</td>\n",
       "      <td>9.179654e+09</td>\n",
       "      <td>1.233908e+10</td>\n",
       "      <td>-7.177918e+09</td>\n",
       "      <td>4.482641e+09</td>\n",
       "      <td>9.593400e+07</td>\n",
       "      <td>6.573192e+09</td>\n",
       "      <td>1.092664e+10</td>\n",
       "      <td>-0.075584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>9.141800e+07</td>\n",
       "      <td>8.567000e+08</td>\n",
       "      <td>-1.031670e+08</td>\n",
       "      <td>-5.296900e+07</td>\n",
       "      <td>3.265000e+09</td>\n",
       "      <td>6.206500e+09</td>\n",
       "      <td>-2.284300e+09</td>\n",
       "      <td>1.364400e+09</td>\n",
       "      <td>2.175000e+08</td>\n",
       "      <td>1.325400e+09</td>\n",
       "      <td>1.859800e+09</td>\n",
       "      <td>-0.003049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>1.003800e+07</td>\n",
       "      <td>-1.111000e+06</td>\n",
       "      <td>9.364000e+06</td>\n",
       "      <td>-2.288900e+07</td>\n",
       "      <td>2.494960e+08</td>\n",
       "      <td>4.292190e+08</td>\n",
       "      <td>-5.282650e+08</td>\n",
       "      <td>1.165650e+08</td>\n",
       "      <td>7.359000e+07</td>\n",
       "      <td>1.952220e+08</td>\n",
       "      <td>3.053730e+08</td>\n",
       "      <td>0.033318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>-5.239000e+06</td>\n",
       "      <td>-1.111000e+06</td>\n",
       "      <td>-1.630000e+05</td>\n",
       "      <td>-2.369300e+07</td>\n",
       "      <td>2.362830e+08</td>\n",
       "      <td>4.136250e+08</td>\n",
       "      <td>-5.053760e+08</td>\n",
       "      <td>1.045890e+08</td>\n",
       "      <td>5.878200e+07</td>\n",
       "      <td>1.795360e+08</td>\n",
       "      <td>2.890710e+08</td>\n",
       "      <td>-0.079054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>-2.451000e+06</td>\n",
       "      <td>-1.111000e+06</td>\n",
       "      <td>-3.995000e+06</td>\n",
       "      <td>-1.766100e+07</td>\n",
       "      <td>2.426200e+08</td>\n",
       "      <td>4.141270e+08</td>\n",
       "      <td>-4.816830e+08</td>\n",
       "      <td>1.011160e+08</td>\n",
       "      <td>6.131200e+07</td>\n",
       "      <td>1.824400e+08</td>\n",
       "      <td>2.866320e+08</td>\n",
       "      <td>-0.110018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>2.319500e+07</td>\n",
       "      <td>-1.110000e+06</td>\n",
       "      <td>-1.800500e+07</td>\n",
       "      <td>-1.879100e+07</td>\n",
       "      <td>2.495940e+08</td>\n",
       "      <td>4.215070e+08</td>\n",
       "      <td>-4.640220e+08</td>\n",
       "      <td>9.411000e+07</td>\n",
       "      <td>8.058500e+07</td>\n",
       "      <td>1.879970e+08</td>\n",
       "      <td>2.937400e+08</td>\n",
       "      <td>0.135903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>706 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Change To Liabilities  Net Borrowings   Investments    Net Income  \\\n",
       "0             2.027500e+10   -1.000000e+09 -1.292900e+10  3.463000e+10   \n",
       "1             1.405000e+10    3.220000e+09  4.608000e+09  2.055100e+10   \n",
       "2             3.070000e+08    3.220000e+09  5.747000e+09  2.174400e+10   \n",
       "3            -2.336600e+10    1.042300e+10 -7.895000e+09  2.363000e+10   \n",
       "4             5.756580e+08   -1.583400e+07 -1.164580e+09 -1.172211e+09   \n",
       "..                     ...             ...           ...           ...   \n",
       "701           9.141800e+07    8.567000e+08 -1.031670e+08 -5.296900e+07   \n",
       "702           1.003800e+07   -1.111000e+06  9.364000e+06 -2.288900e+07   \n",
       "703          -5.239000e+06   -1.111000e+06 -1.630000e+05 -2.369300e+07   \n",
       "704          -2.451000e+06   -1.111000e+06 -3.995000e+06 -1.766100e+07   \n",
       "705           2.319500e+07   -1.110000e+06 -1.800500e+07 -1.879100e+07   \n",
       "\n",
       "       Total Liab  Total Assets  Retained Earnings          Cash  \\\n",
       "0    3.092590e+11  3.811910e+11       1.443500e+10  3.711900e+10   \n",
       "1    2.879120e+11  3.510020e+11       5.562000e+09  3.494000e+10   \n",
       "2    2.655600e+11  3.298400e+11       9.233000e+09  3.405000e+10   \n",
       "3    2.679800e+11  3.371580e+11       1.526100e+10  3.846600e+10   \n",
       "4    9.179654e+09  1.233908e+10      -7.177918e+09  4.482641e+09   \n",
       "..            ...           ...                ...           ...   \n",
       "701  3.265000e+09  6.206500e+09      -2.284300e+09  1.364400e+09   \n",
       "702  2.494960e+08  4.292190e+08      -5.282650e+08  1.165650e+08   \n",
       "703  2.362830e+08  4.136250e+08      -5.053760e+08  1.045890e+08   \n",
       "704  2.426200e+08  4.141270e+08      -4.816830e+08  1.011160e+08   \n",
       "705  2.495940e+08  4.215070e+08      -4.640220e+08  9.411000e+07   \n",
       "\n",
       "     Net Receivables  Total Current Liabilities  Total Current Assets  \\\n",
       "0       6.525300e+10               1.475740e+11          1.531540e+11   \n",
       "1       5.150600e+10               1.254810e+11          1.348360e+11   \n",
       "2       3.390800e+10               1.077540e+11          1.144230e+11   \n",
       "3       3.303600e+10               1.063850e+11          1.214650e+11   \n",
       "4       9.593400e+07               6.573192e+09          1.092664e+10   \n",
       "..               ...                        ...                   ...   \n",
       "701     2.175000e+08               1.325400e+09          1.859800e+09   \n",
       "702     7.359000e+07               1.952220e+08          3.053730e+08   \n",
       "703     5.878200e+07               1.795360e+08          2.890710e+08   \n",
       "704     6.131200e+07               1.824400e+08          2.866320e+08   \n",
       "705     8.058500e+07               1.879970e+08          2.937400e+08   \n",
       "\n",
       "      Returns  \n",
       "0   -0.027782  \n",
       "1   -0.017667  \n",
       "2    0.087700  \n",
       "3    0.093300  \n",
       "4   -0.075584  \n",
       "..        ...  \n",
       "701 -0.003049  \n",
       "702  0.033318  \n",
       "703 -0.079054  \n",
       "704 -0.110018  \n",
       "705  0.135903  \n",
       "\n",
       "[706 rows x 12 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/Processed/predict_linear_reg_data.csv\")\n",
    "df = df.dropna().drop(columns=[\"Unnamed: 0\"]).reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const                        8.163774e+09\n",
       "Change To Liabilities       -1.000000e+00\n",
       "Net Borrowings              -2.681154e-01\n",
       "Investments                 -1.197345e-01\n",
       "Net Income                   1.812647e-01\n",
       "Total Liab                  -3.747576e-02\n",
       "Total Assets                 6.354751e-03\n",
       "Retained Earnings           -1.192692e-02\n",
       "Cash                         2.896582e-02\n",
       "Net Receivables              1.567684e-02\n",
       "Total Current Liabilities    1.059928e-01\n",
       "Total Current Assets        -4.476471e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns.values.tolist()\n",
    "columns.remove(\"Returns\")\n",
    "X = df[columns]\n",
    "y = df[\"Returns\"]\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "results = sm.OLS(y, X).fit()\n",
    "output = results.params/abs(results.params.iloc[1])\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Change To Liabilities': 1573,\n",
       " 'Net Borrowings': 421,\n",
       " 'Investments': 188,\n",
       " 'Net Income': 285,\n",
       " 'Total Liab': 58,\n",
       " 'Total Assets': 10,\n",
       " 'Retained Earnings': 18,\n",
       " 'Cash': 45,\n",
       " 'Net Receivables': 24,\n",
       " 'Total Current Liabilities': 166,\n",
       " 'Total Current Assets': 70}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets generate a word cloud of these findings\n",
    "min_val = min(abs(results.params))\n",
    "word_cloud_output = results.params/min_val\n",
    "word_cloud_output\n",
    "\n",
    "word_could_dict= {}\n",
    "for i in range(1,len(word_cloud_output)):\n",
    "    word_could_dict[word_cloud_output.index[i]] = int(abs(word_cloud_output[i]) * 10)\n",
    "word_could_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Since the text is small collocations are turned off and text is lower-cased\n",
    "wc = WordCloud(width = 1800, height = 720,collocations=False).generate_from_frequencies(word_could_dict)\n",
    "\n",
    "color_to_words = {\n",
    "    # words below will be colored with a green single color function\n",
    "    '#00ff00': [word_cloud_output.index[i] for i in range(1,len(word_cloud_output)) if word_cloud_output[i] > 0],\n",
    "    # will be colored with a red single color function\n",
    "    'red': [word_cloud_output.index[i] for i in range(1,len(word_cloud_output)) if word_cloud_output[i] < 0]\n",
    "}\n",
    "\n",
    "# Words that are not in any of the color_to_words values\n",
    "# will be colored with a grey single color function\n",
    "default_color = 'grey'\n",
    "\n",
    "# Create a color function with single tone\n",
    "# grouped_color_func = SimpleGroupedColorFunc(color_to_words, default_color)\n",
    "\n",
    "# Create a color function with multiple tones\n",
    "grouped_color_func = GroupedColorFunc(color_to_words, default_color)\n",
    "\n",
    "# Apply our color function\n",
    "wc.recolor(color_func=grouped_color_func)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.savefig(\"3 month analysis factor_model.png\",  dpi=200,bbox_inches='tight', pad_inches=0)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d2feefea56b0213a6ec2bb54630eea72d1e2849cc5d96656889c1540b99c0617"
  },
  "kernelspec": {
   "display_name": "Python 3.9.8 ('venv_for_ml': venv)",
   "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": "3.9.8"
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  "orig_nbformat": 4
 },
 "nbformat": 4,
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}