ffm_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "from pathlib import Path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_csv_path = Path('monthly_data.csv')\n",
    "monthly_df = pd.read_csv(stock_csv_path, index_col = 'Date')\n",
    "monthly_df_daily_returns = monthly_df.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DXC</th>\n",
       "      <th>BBY</th>\n",
       "      <th>AMG</th>\n",
       "      <th>CNP</th>\n",
       "      <th>WMT</th>\n",
       "      <th>MRK</th>\n",
       "      <th>NEM</th>\n",
       "      <th>VFC</th>\n",
       "      <th>MDT</th>\n",
       "      <th>HST</th>\n",
       "      <th>...</th>\n",
       "      <th>UNH</th>\n",
       "      <th>HSY</th>\n",
       "      <th>FB</th>\n",
       "      <th>EIX</th>\n",
       "      <th>SBUX</th>\n",
       "      <th>MCO</th>\n",
       "      <th>HII</th>\n",
       "      <th>RL</th>\n",
       "      <th>LNT</th>\n",
       "      <th>AXP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2/27/2015</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>3/31/2015</th>\n",
       "      <td>-0.072857</td>\n",
       "      <td>-0.002622</td>\n",
       "      <td>-0.007578</td>\n",
       "      <td>-0.018278</td>\n",
       "      <td>-0.014131</td>\n",
       "      <td>-0.010162</td>\n",
       "      <td>-0.174545</td>\n",
       "      <td>-0.013202</td>\n",
       "      <td>0.009122</td>\n",
       "      <td>-0.029476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044493</td>\n",
       "      <td>-0.027654</td>\n",
       "      <td>0.041155</td>\n",
       "      <td>-0.021137</td>\n",
       "      <td>0.012942</td>\n",
       "      <td>0.070765</td>\n",
       "      <td>-0.005552</td>\n",
       "      <td>-0.039418</td>\n",
       "      <td>-0.009434</td>\n",
       "      <td>-0.039329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4/30/2015</th>\n",
       "      <td>-0.012714</td>\n",
       "      <td>-0.083091</td>\n",
       "      <td>0.052845</td>\n",
       "      <td>0.027437</td>\n",
       "      <td>-0.051064</td>\n",
       "      <td>0.036186</td>\n",
       "      <td>0.220175</td>\n",
       "      <td>-0.038242</td>\n",
       "      <td>-0.045391</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058247</td>\n",
       "      <td>-0.089090</td>\n",
       "      <td>-0.041961</td>\n",
       "      <td>-0.024492</td>\n",
       "      <td>0.047096</td>\n",
       "      <td>0.035838</td>\n",
       "      <td>-0.061077</td>\n",
       "      <td>0.014524</td>\n",
       "      <td>-0.031611</td>\n",
       "      <td>-0.008577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5/31/2015</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/30/2015</th>\n",
       "      <td>0.025611</td>\n",
       "      <td>-0.052519</td>\n",
       "      <td>-0.033299</td>\n",
       "      <td>-0.081165</td>\n",
       "      <td>-0.085489</td>\n",
       "      <td>-0.036887</td>\n",
       "      <td>-0.117304</td>\n",
       "      <td>-0.032477</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>-0.005595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.099791</td>\n",
       "      <td>-0.028147</td>\n",
       "      <td>0.088866</td>\n",
       "      <td>-0.081105</td>\n",
       "      <td>0.084925</td>\n",
       "      <td>0.007206</td>\n",
       "      <td>-0.141581</td>\n",
       "      <td>-0.004303</td>\n",
       "      <td>-0.045477</td>\n",
       "      <td>0.007241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                DXC       BBY       AMG       CNP       WMT       MRK  \\\n",
       "Date                                                                    \n",
       "2/27/2015       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3/31/2015 -0.072857 -0.002622 -0.007578 -0.018278 -0.014131 -0.010162   \n",
       "4/30/2015 -0.012714 -0.083091  0.052845  0.027437 -0.051064  0.036186   \n",
       "5/31/2015  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "6/30/2015  0.025611 -0.052519 -0.033299 -0.081165 -0.085489 -0.036887   \n",
       "\n",
       "                NEM       VFC       MDT       HST  ...       UNH       HSY  \\\n",
       "Date                                               ...                       \n",
       "2/27/2015       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "3/31/2015 -0.174545 -0.013202  0.009122 -0.029476  ...  0.044493 -0.027654   \n",
       "4/30/2015  0.220175 -0.038242 -0.045391 -0.001982  ... -0.058247 -0.089090   \n",
       "5/31/2015  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "6/30/2015 -0.117304 -0.032477  0.000349 -0.005595  ...  0.099791 -0.028147   \n",
       "\n",
       "                 FB       EIX      SBUX       MCO       HII        RL  \\\n",
       "Date                                                                    \n",
       "2/27/2015       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3/31/2015  0.041155 -0.021137  0.012942  0.070765 -0.005552 -0.039418   \n",
       "4/30/2015 -0.041961 -0.024492  0.047096  0.035838 -0.061077  0.014524   \n",
       "5/31/2015  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "6/30/2015  0.088866 -0.081105  0.084925  0.007206 -0.141581 -0.004303   \n",
       "\n",
       "                LNT       AXP  \n",
       "Date                           \n",
       "2/27/2015       NaN       NaN  \n",
       "3/31/2015 -0.009434 -0.039329  \n",
       "4/30/2015 -0.031611 -0.008577  \n",
       "5/31/2015  0.000000  0.000000  \n",
       "6/30/2015 -0.045477  0.007241  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_df_daily_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59, 100)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_df_daily_returns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58, 100)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_df_daily_returns.dropna(inplace = True)\n",
    "monthly_df_daily_returns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_df_daily_returns.index = pd.DatetimeIndex(monthly_df_daily_returns.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-03-31', '2015-04-30', '2015-05-31', '2015-06-30',\n",
       "               '2015-07-31', '2015-08-31', '2015-09-30', '2015-10-31',\n",
       "               '2015-11-30', '2015-12-31', '2016-01-31', '2016-02-29',\n",
       "               '2016-03-31', '2016-04-30', '2016-05-31', '2016-06-30',\n",
       "               '2016-07-31', '2016-08-31', '2016-09-30', '2016-10-31',\n",
       "               '2016-11-30', '2016-12-31', '2017-01-31', '2017-02-28',\n",
       "               '2017-03-31', '2017-04-30', '2017-05-31', '2017-06-30',\n",
       "               '2017-07-31', '2017-08-31', '2017-09-30', '2017-10-31',\n",
       "               '2017-11-30', '2017-12-31', '2018-01-31', '2018-02-28',\n",
       "               '2018-03-31', '2018-04-30', '2018-05-31', '2018-06-30',\n",
       "               '2018-07-31', '2018-08-31', '2018-09-30', '2018-10-31',\n",
       "               '2018-11-30', '2018-12-31', '2019-01-31', '2019-02-28',\n",
       "               '2019-03-31', '2019-04-30', '2019-05-31', '2019-06-30',\n",
       "               '2019-07-31', '2019-08-31', '2019-09-30', '2019-10-31',\n",
       "               '2019-11-30', '2019-12-31'],\n",
       "              dtype='datetime64[ns]', name='Date', freq=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_df_daily_returns.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58, 4)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ffm_csv_path = Path('F-F_Research_Data_Factors.csv')\n",
    "ffm_data = pd.read_csv(ffm_csv_path, index_col='Date')\n",
    "ffm_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['3/31/15', '4/30/15', '5/31/15', '6/30/15', '7/31/15', '8/31/15',\n",
       "       '9/30/15', '10/31/15', '11/30/15', '12/31/15', '1/31/16', '2/29/16',\n",
       "       '3/31/16', '4/30/16', '5/31/16', '6/30/16', '7/31/16', '8/31/16',\n",
       "       '9/30/16', '10/31/16', '11/30/16', '12/31/16', '1/31/17', '2/28/17',\n",
       "       '3/31/17', '4/30/17', '5/31/17', '6/30/17', '7/31/17', '8/31/17',\n",
       "       '9/30/17', '10/31/17', '11/30/17', '12/31/17', '1/31/18', '2/28/18',\n",
       "       '3/31/18', '4/30/18', '5/31/18', '6/30/18', '7/31/18', '8/31/18',\n",
       "       '9/30/18', '10/31/18', '11/30/18', '12/31/18', '1/31/19', '2/28/19',\n",
       "       '3/31/19', '4/30/19', '5/31/19', '6/30/19', '7/31/19', '8/31/19',\n",
       "       '9/30/19', '10/31/19', '11/30/19', '12/31/19'],\n",
       "      dtype='object', name='Date')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ffm_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ffm_data.index = pd.DatetimeIndex(ffm_data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "fit = reg.fit(X=ffm_data[['SMB','HML','RF']],\n",
    "              y=monthly_df_daily_returns.AAPL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00493159, -0.00134568,  0.15507276])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.regression import linear_model as lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_var = results_dict['AAPL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict = {}\n",
    "params = {}\n",
    "\n",
    "for ticker in monthly_df_daily_returns.columns:\n",
    "    results_dict[ticker] = lm.OLS(endog=monthly_df_daily_returns.loc[:,ticker],\n",
    "                  exog=sm.add_constant(ffm_data[['SMB','HML','RF']])).fit()\n",
    "    \n",
    "    params[ticker] = results_dict[ticker].params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DXC': const    0.054602\n",
       " SMB      0.006667\n",
       " HML     -0.010830\n",
       " RF      -0.471852\n",
       " dtype: float64, 'BBY': const    0.019541\n",
       " SMB      0.002211\n",
       " HML      0.004370\n",
       " RF       0.037560\n",
       " dtype: float64, 'AMG': const    0.000376\n",
       " SMB      0.007329\n",
       " HML      0.002759\n",
       " RF      -0.129089\n",
       " dtype: float64, 'CNP': const    0.012997\n",
       " SMB     -0.001113\n",
       " HML     -0.000859\n",
       " RF      -0.048640\n",
       " dtype: float64, 'WMT': const   -0.000909\n",
       " SMB     -0.005975\n",
       " HML      0.000550\n",
       " RF       0.115350\n",
       " dtype: float64, 'MRK': const    0.001373\n",
       " SMB      0.000667\n",
       " HML      0.000679\n",
       " RF       0.121664\n",
       " dtype: float64, 'NEM': const    0.016994\n",
       " SMB     -0.002308\n",
       " HML     -0.000540\n",
       " RF      -0.029715\n",
       " dtype: float64, 'VFC': const   -0.006049\n",
       " SMB      0.001397\n",
       " HML      0.002010\n",
       " RF       0.197302\n",
       " dtype: float64, 'MDT': const    0.003467\n",
       " SMB      0.002080\n",
       " HML     -0.006028\n",
       " RF       0.054285\n",
       " dtype: float64, 'HST': const    0.001035\n",
       " SMB      0.010113\n",
       " HML      0.003301\n",
       " RF       0.057921\n",
       " dtype: float64, 'MTD': const    0.019620\n",
       " SMB      0.003991\n",
       " HML     -0.003864\n",
       " RF      -0.018828\n",
       " dtype: float64, 'CCI': const    0.007325\n",
       " SMB     -0.000576\n",
       " HML     -0.004602\n",
       " RF       0.048203\n",
       " dtype: float64, 'KMI': const   -0.027913\n",
       " SMB      0.011821\n",
       " HML      0.005913\n",
       " RF       0.321152\n",
       " dtype: float64, 'RMD': const    0.009099\n",
       " SMB      0.003927\n",
       " HML     -0.003076\n",
       " RF       0.111120\n",
       " dtype: float64, 'LUV': const    0.012251\n",
       " SMB      0.005401\n",
       " HML      0.000884\n",
       " RF      -0.036382\n",
       " dtype: float64, 'NWS': const   -0.002270\n",
       " SMB      0.002310\n",
       " HML      0.003681\n",
       " RF       0.058211\n",
       " dtype: float64, 'AMGN': const    0.009037\n",
       " SMB      0.003206\n",
       " HML     -0.002195\n",
       " RF       0.030524\n",
       " dtype: float64, 'APD': const   -0.001083\n",
       " SMB      0.002608\n",
       " HML      0.002296\n",
       " RF       0.165393\n",
       " dtype: float64, 'TGT': const   -0.008147\n",
       " SMB      0.001429\n",
       " HML      0.009642\n",
       " RF       0.302853\n",
       " dtype: float64, 'MYL': const   -0.002842\n",
       " SMB      0.004527\n",
       " HML      0.001895\n",
       " RF      -0.090977\n",
       " dtype: float64, 'GPC': const    0.000800\n",
       " SMB      0.005121\n",
       " HML      0.000189\n",
       " RF       0.062764\n",
       " dtype: float64, 'FCX': const   -0.001891\n",
       " SMB      0.010998\n",
       " HML      0.012395\n",
       " RF       0.110709\n",
       " dtype: float64, 'KO': const    0.000684\n",
       " SMB     -0.005419\n",
       " HML      0.002014\n",
       " RF       0.081595\n",
       " dtype: float64, 'DISCK': const   -0.009428\n",
       " SMB      0.002983\n",
       " HML      0.003645\n",
       " RF       0.178962\n",
       " dtype: float64, 'FISV': const    0.014536\n",
       " SMB      0.002486\n",
       " HML     -0.001265\n",
       " RF       0.064057\n",
       " dtype: float64, 'ACN': const    0.016281\n",
       " SMB      0.002845\n",
       " HML     -0.000670\n",
       " RF       0.022446\n",
       " dtype: float64, 'SRCL': const   -0.022215\n",
       " SMB      0.001094\n",
       " HML      0.001069\n",
       " RF       0.172978\n",
       " dtype: float64, 'IT': const    0.013323\n",
       " SMB      0.006997\n",
       " HML      0.000762\n",
       " RF       0.009617\n",
       " dtype: float64, 'PHM': const    0.002120\n",
       " SMB      0.005431\n",
       " HML      0.002604\n",
       " RF       0.145191\n",
       " dtype: float64, 'ALB': const    0.033678\n",
       " SMB      0.001754\n",
       " HML     -0.001914\n",
       " RF      -0.280653\n",
       " dtype: float64, 'AAPL': const    0.005988\n",
       " SMB      0.004932\n",
       " HML     -0.001346\n",
       " RF       0.155073\n",
       " dtype: float64, 'EFX': const    0.011079\n",
       " SMB      0.005193\n",
       " HML     -0.005881\n",
       " RF      -0.013284\n",
       " dtype: float64, 'FE': const   -0.002451\n",
       " SMB     -0.001924\n",
       " HML      0.000739\n",
       " RF       0.145771\n",
       " dtype: float64, 'COST': const    0.005980\n",
       " SMB     -0.001803\n",
       " HML     -0.000682\n",
       " RF       0.102900\n",
       " dtype: float64, 'GWW': const    0.002411\n",
       " SMB      0.006431\n",
       " HML      0.003227\n",
       " RF       0.124559\n",
       " dtype: float64, 'M': const   -0.011542\n",
       " SMB      0.005896\n",
       " HML      0.004078\n",
       " RF       0.025431\n",
       " dtype: float64, 'CRM': const    0.014873\n",
       " SMB     -0.000074\n",
       " HML     -0.005548\n",
       " RF      -0.000396\n",
       " dtype: float64, 'LKQ': const    0.014538\n",
       " SMB      0.005347\n",
       " HML      0.001513\n",
       " RF      -0.047619\n",
       " dtype: float64, 'AON': const    0.006681\n",
       " SMB      0.001586\n",
       " HML      0.000944\n",
       " RF       0.097052\n",
       " dtype: float64, 'PEP': const    0.004087\n",
       " SMB     -0.002782\n",
       " HML      0.000271\n",
       " RF       0.053900\n",
       " dtype: float64, 'RJF': const    0.018682\n",
       " SMB      0.009820\n",
       " HML      0.004638\n",
       " RF      -0.045672\n",
       " dtype: float64, 'ORCL': const    0.001090\n",
       " SMB      0.002700\n",
       " HML      0.000762\n",
       " RF       0.060270\n",
       " dtype: float64, 'MHK': const    0.014373\n",
       " SMB      0.009916\n",
       " HML     -0.006904\n",
       " RF      -0.202153\n",
       " dtype: float64, 'PPL': const    0.006572\n",
       " SMB     -0.001814\n",
       " HML     -0.000341\n",
       " RF       0.004824\n",
       " dtype: float64, 'FMC': const    0.008033\n",
       " SMB      0.010667\n",
       " HML      0.000197\n",
       " RF       0.104410\n",
       " dtype: float64, 'OKE': const    0.013576\n",
       " SMB      0.008781\n",
       " HML      0.004585\n",
       " RF       0.086364\n",
       " dtype: float64, 'EQR': const   -0.003910\n",
       " SMB      0.001709\n",
       " HML     -0.000817\n",
       " RF       0.124130\n",
       " dtype: float64, 'RCL': const    0.019752\n",
       " SMB      0.003941\n",
       " HML     -0.005637\n",
       " RF      -0.063044\n",
       " dtype: float64, 'IRM': const    0.004095\n",
       " SMB      0.003249\n",
       " HML     -0.001111\n",
       " RF       0.015229\n",
       " dtype: float64, 'BA': const    0.021117\n",
       " SMB     -0.000208\n",
       " HML      0.000754\n",
       " RF      -0.027618\n",
       " dtype: float64, 'ABC': const   -0.001830\n",
       " SMB      0.003802\n",
       " HML     -0.001026\n",
       " RF       0.032676\n",
       " dtype: float64, 'GIS': const    0.000944\n",
       " SMB      0.000799\n",
       " HML      0.001153\n",
       " RF       0.046156\n",
       " dtype: float64, 'SYF': const    0.001133\n",
       " SMB      0.010176\n",
       " HML      0.004036\n",
       " RF       0.097664\n",
       " dtype: float64, 'BMY': const    0.007596\n",
       " SMB      0.004047\n",
       " HML     -0.001775\n",
       " RF      -0.017003\n",
       " dtype: float64, 'ADBE': const    0.026194\n",
       " SMB      0.003425\n",
       " HML     -0.005826\n",
       " RF      -0.008753\n",
       " dtype: float64, 'SNPS': const    0.013601\n",
       " SMB      0.004408\n",
       " HML     -0.001289\n",
       " RF       0.087158\n",
       " dtype: float64, 'BWA': const   -0.007120\n",
       " SMB      0.003750\n",
       " HML      0.002314\n",
       " RF       0.079303\n",
       " dtype: float64, 'MSI': const    0.009732\n",
       " SMB      0.005207\n",
       " HML      0.002410\n",
       " RF       0.119599\n",
       " dtype: float64, 'COF': const    0.007187\n",
       " SMB      0.007294\n",
       " HML      0.006504\n",
       " RF       0.053618\n",
       " dtype: float64, 'LYB': const    0.010861\n",
       " SMB      0.007371\n",
       " HML      0.007171\n",
       " RF       0.000326\n",
       " dtype: float64, 'RTN': const    0.022820\n",
       " SMB      0.002863\n",
       " HML     -0.002937\n",
       " RF      -0.092745\n",
       " dtype: float64, 'FFIV': const    0.011258\n",
       " SMB      0.000107\n",
       " HML     -0.000844\n",
       " RF      -0.081548\n",
       " dtype: float64, 'HLT': const    0.003496\n",
       " SMB      0.003717\n",
       " HML     -0.000632\n",
       " RF       0.120956\n",
       " dtype: float64, 'CVS': const   -0.007681\n",
       " SMB     -0.001042\n",
       " HML      0.000158\n",
       " RF       0.069121\n",
       " dtype: float64, 'NEE': const    0.012106\n",
       " SMB     -0.003592\n",
       " HML     -0.002758\n",
       " RF       0.055872\n",
       " dtype: float64, 'KMX': const   -0.005108\n",
       " SMB      0.003210\n",
       " HML      0.003810\n",
       " RF       0.166041\n",
       " dtype: float64, 'INCY': const    0.011598\n",
       " SMB      0.013601\n",
       " HML     -0.007199\n",
       " RF      -0.072670\n",
       " dtype: float64, 'MGM': const    0.016231\n",
       " SMB      0.003770\n",
       " HML      0.000803\n",
       " RF      -0.048515\n",
       " dtype: float64, 'AES': const   -0.005298\n",
       " SMB      0.004959\n",
       " HML     -0.000526\n",
       " RF       0.218827\n",
       " dtype: float64, 'GM': const    0.003766\n",
       " SMB      0.008070\n",
       " HML      0.002941\n",
       " RF       0.052166\n",
       " dtype: float64, 'FDX': const    0.013414\n",
       " SMB      0.006983\n",
       " HML      0.000363\n",
       " RF      -0.131301\n",
       " dtype: float64, 'ADP': const    0.007503\n",
       " SMB      0.004284\n",
       " HML      0.000618\n",
       " RF       0.090903\n",
       " dtype: float64, 'HOLX': const    0.007978\n",
       " SMB      0.002835\n",
       " HML      0.001912\n",
       " RF       0.034877\n",
       " dtype: float64, 'NRG': const    0.004716\n",
       " SMB      0.003993\n",
       " HML     -0.001240\n",
       " RF       0.141942\n",
       " dtype: float64, 'NBL': const   -0.012247\n",
       " SMB      0.012066\n",
       " HML      0.010516\n",
       " RF       0.152819\n",
       " dtype: float64, 'MSFT': const    0.022565\n",
       " SMB     -0.002318\n",
       " HML      0.001699\n",
       " RF       0.042636\n",
       " dtype: float64, 'COG': const   -0.002901\n",
       " SMB      0.007995\n",
       " HML     -0.000315\n",
       " RF      -0.012510\n",
       " dtype: float64, 'OXY': const    0.005643\n",
       " SMB      0.007829\n",
       " HML     -0.000082\n",
       " RF      -0.105655\n",
       " dtype: float64, 'GOOGL': const    0.023360\n",
       " SMB     -0.001914\n",
       " HML     -0.004818\n",
       " RF      -0.094033\n",
       " dtype: float64, 'GS': const    0.009674\n",
       " SMB      0.003897\n",
       " HML      0.008605\n",
       " RF       0.009771\n",
       " dtype: float64, 'HRB': const   -0.002286\n",
       " SMB      0.000981\n",
       " HML     -0.005757\n",
       " RF       0.000230\n",
       " dtype: float64, 'AJG': const    0.009557\n",
       " SMB      0.000763\n",
       " HML      0.001040\n",
       " RF       0.074273\n",
       " dtype: float64, 'CINF': const    0.010693\n",
       " SMB      0.001783\n",
       " HML      0.002503\n",
       " RF       0.071726\n",
       " dtype: float64, 'TXN': const    0.015741\n",
       " SMB      0.004391\n",
       " HML      0.000626\n",
       " RF       0.034913\n",
       " dtype: float64, 'ETN': const    0.003890\n",
       " SMB      0.007716\n",
       " HML      0.002059\n",
       " RF       0.090844\n",
       " dtype: float64, 'EXPE': const    0.015295\n",
       " SMB     -0.000372\n",
       " HML     -0.002536\n",
       " RF      -0.114739\n",
       " dtype: float64, 'TMO': const    0.010901\n",
       " SMB      0.000543\n",
       " HML     -0.004054\n",
       " RF       0.068159\n",
       " dtype: float64, 'BSX': const    0.015858\n",
       " SMB      0.003105\n",
       " HML     -0.007019\n",
       " RF       0.014305\n",
       " dtype: float64, 'CTAS': const    0.016999\n",
       " SMB      0.006533\n",
       " HML      0.000280\n",
       " RF       0.082512\n",
       " dtype: float64, 'WBA': const    0.003260\n",
       " SMB     -0.002313\n",
       " HML      0.001379\n",
       " RF      -0.054945\n",
       " dtype: float64, 'UNH': const    0.023115\n",
       " SMB      0.004480\n",
       " HML     -0.002035\n",
       " RF      -0.043002\n",
       " dtype: float64, 'HSY': const   -0.002364\n",
       " SMB     -0.007383\n",
       " HML     -0.001692\n",
       " RF       0.122579\n",
       " dtype: float64, 'FB': const    0.024876\n",
       " SMB      0.005878\n",
       " HML     -0.007652\n",
       " RF      -0.084624\n",
       " dtype: float64, 'EIX': const    0.005676\n",
       " SMB     -0.006059\n",
       " HML      0.001479\n",
       " RF       0.030555\n",
       " dtype: float64, 'SBUX': const    0.004142\n",
       " SMB     -0.000443\n",
       " HML      0.001478\n",
       " RF       0.120032\n",
       " dtype: float64, 'MCO': const    0.010974\n",
       " SMB      0.006066\n",
       " HML     -0.003062\n",
       " RF       0.091236\n",
       " dtype: float64, 'HII': const    0.019722\n",
       " SMB      0.001667\n",
       " HML     -0.000449\n",
       " RF      -0.071924\n",
       " dtype: float64, 'RL': const   -0.008081\n",
       " SMB      0.009828\n",
       " HML      0.004103\n",
       " RF       0.161168\n",
       " dtype: float64, 'LNT': const    0.009055\n",
       " SMB     -0.001988\n",
       " HML     -0.001094\n",
       " RF       0.039521\n",
       " dtype: float64, 'AXP': const    0.001631\n",
       " SMB      0.002037\n",
       " HML      0.003925\n",
       " RF       0.117401\n",
       " dtype: float64}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
  }
 ],
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