estimating_risk_factor_model_for_stock.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "483b68b9",
   "metadata": {},
   "source": [
    "# Quantitative Finance Project: Estimating a Risk Factor Model for a Stock with Live Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f738d6f",
   "metadata": {},
   "source": [
    "YouTube Tutorial: https://www.youtube.com/watch?v=gN7JOFOO-eM&ab_channel=TechFin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f6ea05",
   "metadata": {},
   "source": [
    "### 1. Factor Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb9c3206",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19275ae5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F-F_Research_Data_Factors',\n",
       " 'F-F_Research_Data_Factors_weekly',\n",
       " 'F-F_Research_Data_Factors_daily',\n",
       " 'F-F_Research_Data_5_Factors_2x3',\n",
       " 'F-F_Research_Data_5_Factors_2x3_daily',\n",
       " 'Portfolios_Formed_on_ME',\n",
       " 'Portfolios_Formed_on_ME_Wout_Div',\n",
       " 'Portfolios_Formed_on_ME_Daily',\n",
       " 'Portfolios_Formed_on_BE-ME',\n",
       " 'Portfolios_Formed_on_BE-ME_Wout_Div',\n",
       " 'Portfolios_Formed_on_BE-ME_Daily',\n",
       " 'Portfolios_Formed_on_OP',\n",
       " 'Portfolios_Formed_on_OP_Wout_Div',\n",
       " 'Portfolios_Formed_on_OP_Daily',\n",
       " 'Portfolios_Formed_on_INV',\n",
       " 'Portfolios_Formed_on_INV_Wout_Div',\n",
       " 'Portfolios_Formed_on_INV_Daily',\n",
       " '6_Portfolios_2x3',\n",
       " '6_Portfolios_2x3_Wout_Div',\n",
       " '6_Portfolios_2x3_weekly',\n",
       " '6_Portfolios_2x3_daily',\n",
       " '25_Portfolios_5x5',\n",
       " '25_Portfolios_5x5_Wout_Div',\n",
       " '25_Portfolios_5x5_Daily',\n",
       " '100_Portfolios_10x10',\n",
       " '100_Portfolios_10x10_Wout_Div',\n",
       " '100_Portfolios_10x10_Daily',\n",
       " '6_Portfolios_ME_OP_2x3',\n",
       " '6_Portfolios_ME_OP_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_OP_2x3_daily',\n",
       " '25_Portfolios_ME_OP_5x5',\n",
       " '25_Portfolios_ME_OP_5x5_Wout_Div',\n",
       " '25_Portfolios_ME_OP_5x5_daily',\n",
       " '100_Portfolios_ME_OP_10x10',\n",
       " '100_Portfolios_10x10_ME_OP_Wout_Div',\n",
       " '100_Portfolios_ME_OP_10x10_daily',\n",
       " '6_Portfolios_ME_INV_2x3',\n",
       " '6_Portfolios_ME_INV_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_INV_2x3_daily',\n",
       " '25_Portfolios_ME_INV_5x5',\n",
       " '25_Portfolios_ME_INV_5x5_Wout_Div',\n",
       " '25_Portfolios_ME_INV_5x5_daily',\n",
       " '100_Portfolios_ME_INV_10x10',\n",
       " '100_Portfolios_10x10_ME_INV_Wout_Div',\n",
       " '100_Portfolios_ME_INV_10x10_daily',\n",
       " '25_Portfolios_BEME_OP_5x5',\n",
       " '25_Portfolios_BEME_OP_5x5_Wout_Div',\n",
       " '25_Portfolios_BEME_OP_5x5_daily',\n",
       " '25_Portfolios_BEME_INV_5x5',\n",
       " '25_Portfolios_BEME_INV_5x5_Wout_Div',\n",
       " '25_Portfolios_BEME_INV_5x5_daily',\n",
       " '25_Portfolios_OP_INV_5x5',\n",
       " '25_Portfolios_OP_INV_5x5_Wout_Div',\n",
       " '25_Portfolios_OP_INV_5x5_daily',\n",
       " '32_Portfolios_ME_BEME_OP_2x4x4',\n",
       " '32_Portfolios_ME_BEME_OP_2x4x4_Wout_Div',\n",
       " '32_Portfolios_ME_BEME_INV_2x4x4',\n",
       " '32_Portfolios_ME_BEME_INV_2x4x4_Wout_Div',\n",
       " '32_Portfolios_ME_OP_INV_2x4x4',\n",
       " '32_Portfolios_ME_OP_INV_2x4x4_Wout_Div',\n",
       " 'Portfolios_Formed_on_E-P',\n",
       " 'Portfolios_Formed_on_E-P_Wout_Div',\n",
       " 'Portfolios_Formed_on_CF-P',\n",
       " 'Portfolios_Formed_on_CF-P_Wout_Div',\n",
       " 'Portfolios_Formed_on_D-P',\n",
       " 'Portfolios_Formed_on_D-P_Wout_Div',\n",
       " '6_Portfolios_ME_EP_2x3',\n",
       " '6_Portfolios_ME_EP_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_CFP_2x3',\n",
       " '6_Portfolios_ME_CFP_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_DP_2x3',\n",
       " '6_Portfolios_ME_DP_2x3_Wout_Div',\n",
       " 'F-F_Momentum_Factor',\n",
       " 'F-F_Momentum_Factor_daily',\n",
       " '6_Portfolios_ME_Prior_12_2',\n",
       " '6_Portfolios_ME_Prior_12_2_Daily',\n",
       " '25_Portfolios_ME_Prior_12_2',\n",
       " '25_Portfolios_ME_Prior_12_2_Daily',\n",
       " '10_Portfolios_Prior_12_2',\n",
       " '10_Portfolios_Prior_12_2_Daily',\n",
       " 'F-F_ST_Reversal_Factor',\n",
       " 'F-F_ST_Reversal_Factor_daily',\n",
       " '6_Portfolios_ME_Prior_1_0',\n",
       " '6_Portfolios_ME_Prior_1_0_Daily',\n",
       " '25_Portfolios_ME_Prior_1_0',\n",
       " '25_Portfolios_ME_Prior_1_0_Daily',\n",
       " '10_Portfolios_Prior_1_0',\n",
       " '10_Portfolios_Prior_1_0_Daily',\n",
       " 'F-F_LT_Reversal_Factor',\n",
       " 'F-F_LT_Reversal_Factor_daily',\n",
       " '6_Portfolios_ME_Prior_60_13',\n",
       " '6_Portfolios_ME_Prior_60_13_Daily',\n",
       " '25_Portfolios_ME_Prior_60_13',\n",
       " '25_Portfolios_ME_Prior_60_13_Daily',\n",
       " '10_Portfolios_Prior_60_13',\n",
       " '10_Portfolios_Prior_60_13_Daily',\n",
       " 'Portfolios_Formed_on_AC',\n",
       " '25_Portfolios_ME_AC_5x5',\n",
       " 'Portfolios_Formed_on_BETA',\n",
       " '25_Portfolios_ME_BETA_5x5',\n",
       " 'Portfolios_Formed_on_NI',\n",
       " '25_Portfolios_ME_NI_5x5',\n",
       " 'Portfolios_Formed_on_VAR',\n",
       " '25_Portfolios_ME_VAR_5x5',\n",
       " 'Portfolios_Formed_on_RESVAR',\n",
       " '25_Portfolios_ME_RESVAR_5x5',\n",
       " '5_Industry_Portfolios',\n",
       " '5_Industry_Portfolios_Wout_Div',\n",
       " '5_Industry_Portfolios_daily',\n",
       " '10_Industry_Portfolios',\n",
       " '10_Industry_Portfolios_Wout_Div',\n",
       " '10_Industry_Portfolios_daily',\n",
       " '12_Industry_Portfolios',\n",
       " '12_Industry_Portfolios_Wout_Div',\n",
       " '12_Industry_Portfolios_daily',\n",
       " '17_Industry_Portfolios',\n",
       " '17_Industry_Portfolios_Wout_Div',\n",
       " '17_Industry_Portfolios_daily',\n",
       " '30_Industry_Portfolios',\n",
       " '30_Industry_Portfolios_Wout_Div',\n",
       " '30_Industry_Portfolios_daily',\n",
       " '38_Industry_Portfolios',\n",
       " '38_Industry_Portfolios_Wout_Div',\n",
       " '38_Industry_Portfolios_daily',\n",
       " '48_Industry_Portfolios',\n",
       " '48_Industry_Portfolios_Wout_Div',\n",
       " '48_Industry_Portfolios_daily',\n",
       " '49_Industry_Portfolios',\n",
       " '49_Industry_Portfolios_Wout_Div',\n",
       " '49_Industry_Portfolios_daily',\n",
       " 'ME_Breakpoints',\n",
       " 'BE-ME_Breakpoints',\n",
       " 'OP_Breakpoints',\n",
       " 'INV_Breakpoints',\n",
       " 'E-P_Breakpoints',\n",
       " 'CF-P_Breakpoints',\n",
       " 'D-P_Breakpoints',\n",
       " 'Prior_2-12_Breakpoints',\n",
       " 'Developed_3_Factors',\n",
       " 'Developed_3_Factors_Daily',\n",
       " 'Developed_ex_US_3_Factors',\n",
       " 'Developed_ex_US_3_Factors_Daily',\n",
       " 'Europe_3_Factors',\n",
       " 'Europe_3_Factors_Daily',\n",
       " 'Japan_3_Factors',\n",
       " 'Japan_3_Factors_Daily',\n",
       " 'Asia_Pacific_ex_Japan_3_Factors',\n",
       " 'Asia_Pacific_ex_Japan_3_Factors_Daily',\n",
       " 'North_America_3_Factors',\n",
       " 'North_America_3_Factors_Daily',\n",
       " 'Developed_5_Factors',\n",
       " 'Developed_5_Factors_Daily',\n",
       " 'Developed_ex_US_5_Factors',\n",
       " 'Developed_ex_US_5_Factors_Daily',\n",
       " 'Europe_5_Factors',\n",
       " 'Europe_5_Factors_Daily',\n",
       " 'Japan_5_Factors',\n",
       " 'Japan_5_Factors_Daily',\n",
       " 'Asia_Pacific_ex_Japan_5_Factors',\n",
       " 'Asia_Pacific_ex_Japan_5_Factors_Daily',\n",
       " 'North_America_5_Factors',\n",
       " 'North_America_5_Factors_Daily',\n",
       " 'Developed_Mom_Factor',\n",
       " 'Developed_Mom_Factor_Daily',\n",
       " 'Developed_ex_US_Mom_Factor',\n",
       " 'Developed_ex_US_Mom_Factor_Daily',\n",
       " 'Europe_Mom_Factor',\n",
       " 'Europe_Mom_Factor_Daily',\n",
       " 'Japan_Mom_Factor',\n",
       " 'Japan_Mom_Factor_Daily',\n",
       " 'Asia_Pacific_ex_Japan_MOM_Factor',\n",
       " 'Asia_Pacific_ex_Japan_MOM_Factor_Daily',\n",
       " 'North_America_Mom_Factor',\n",
       " 'North_America_Mom_Factor_Daily',\n",
       " 'Developed_6_Portfolios_ME_BE-ME',\n",
       " 'Developed_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_BE-ME',\n",
       " 'Developed_ex_US_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Europe_6_Portfolios_ME_BE-ME',\n",
       " 'Europe_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Japan_6_Portfolios_ME_BE-ME',\n",
       " 'Japan_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_BE-ME',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_BE-ME_daily',\n",
       " 'North_America_6_Portfolios_ME_BE-ME',\n",
       " 'North_America_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_25_Portfolios_ME_BE-ME',\n",
       " 'Developed_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_BE-ME',\n",
       " 'Developed_ex_US_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Europe_25_Portfolios_ME_BE-ME',\n",
       " 'Europe_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Japan_25_Portfolios_ME_BE-ME',\n",
       " 'Japan_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_BE-ME',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_BE-ME_daily',\n",
       " 'North_America_25_Portfolios_ME_BE-ME',\n",
       " 'North_America_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_6_Portfolios_ME_OP',\n",
       " 'Developed_6_Portfolios_ME_OP_Daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_OP',\n",
       " 'Developed_ex_US_6_Portfolios_ME_OP_Daily',\n",
       " 'Europe_6_Portfolios_ME_OP',\n",
       " 'Europe_6_Portfolios_ME_OP_Daily',\n",
       " 'Japan_6_Portfolios_ME_OP',\n",
       " 'Japan_6_Portfolios_ME_OP_Daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_OP',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_OP_Daily',\n",
       " 'North_America_6_Portfolios_ME_OP',\n",
       " 'North_America_6_Portfolios_ME_OP_Daily',\n",
       " 'Developed_25_Portfolios_ME_OP',\n",
       " 'Developed_25_Portfolios_ME_OP_Daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_OP',\n",
       " 'Developed_ex_US_25_Portfolios_ME_OP_Daily',\n",
       " 'Europe_25_Portfolios_ME_OP',\n",
       " 'Europe_25_Portfolios_ME_OP_Daily',\n",
       " 'Japan_25_Portfolios_ME_OP',\n",
       " 'Japan_25_Portfolios_ME_OP_Daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_OP',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_OP_Daily',\n",
       " 'North_America_25_Portfolios_ME_OP',\n",
       " 'North_America_25_Portfolios_ME_OP_Daily',\n",
       " 'Developed_6_Portfolios_ME_INV',\n",
       " 'Developed_6_Portfolios_ME_INV_Daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_INV',\n",
       " 'Developed_ex_US_6_Portfolios_ME_INV_Daily',\n",
       " 'Europe_6_Portfolios_ME_INV',\n",
       " 'Europe_6_Portfolios_ME_INV_Daily',\n",
       " 'Japan_6_Portfolios_ME_INV',\n",
       " 'Japan_6_Portfolios_ME_INV_Daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_INV',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_INV_Daily',\n",
       " 'North_America_6_Portfolios_ME_INV',\n",
       " 'North_America_6_Portfolios_ME_INV_Daily',\n",
       " 'Developed_25_Portfolios_ME_INV',\n",
       " 'Developed_25_Portfolios_ME_INV_Daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_INV',\n",
       " 'Developed_ex_US_25_Portfolios_ME_INV_Daily',\n",
       " 'Europe_25_Portfolios_ME_INV',\n",
       " 'Europe_25_Portfolios_ME_INV_Daily',\n",
       " 'Japan_25_Portfolios_ME_INV',\n",
       " 'Japan_25_Portfolios_ME_INV_Daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_INV',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_INV_Daily',\n",
       " 'North_America_25_Portfolios_ME_INV',\n",
       " 'North_America_25_Portfolios_ME_INV_Daily',\n",
       " 'Developed_6_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_ex_US_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Europe_6_Portfolios_ME_Prior_12_2',\n",
       " 'Europe_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Japan_6_Portfolios_ME_Prior_12_2',\n",
       " 'Japan_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_Prior_12_2',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'North_America_6_Portfolios_ME_Prior_12_2',\n",
       " 'North_America_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_25_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_ex_US_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Europe_25_Portfolios_ME_Prior_12_2',\n",
       " 'Europe_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Japan_25_Portfolios_ME_Prior_12_2',\n",
       " 'Japan_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_Prior_12_2',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'North_America_25_Portfolios_ME_Prior_12_2',\n",
       " 'North_America_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Developed_ex_US_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Europe_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Japan_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'North_America_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Developed_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Developed_ex_US_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Europe_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'North_America_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Developed_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Developed_ex_US_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Europe_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'North_America_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Emerging_5_Factors',\n",
       " 'Emerging_MOM_Factor',\n",
       " 'Emerging_Markets_6_Portfolios_ME_BE-ME',\n",
       " 'Emerging_Markets_6_Portfolios_ME_OP',\n",
       " 'Emerging_Markets_6_Portfolios_ME_INV',\n",
       " 'Emerging_Markets_6_Portfolios_ME_Prior_12_2',\n",
       " 'Emerging_Markets_4_Portfolios_BE-ME_OP',\n",
       " 'Emerging_Markets_4_Portfolios_OP_INV',\n",
       " 'Emerging_Markets_4_Portfolios_BE-ME_INV']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get different risk factor portfolios\n",
    "pandas_datareader.famafrench.get_available_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3be004b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To get factors, we need to determine a stock date (go as far as we can)\n",
    "start = '1926-01-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b861ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To get the Fama-French factors since the date, monthly\n",
    "ff = pandas_datareader.famafrench.FamaFrenchReader('F-F_Research_Data_Factors',freq='M', start=start).read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60e4ea7f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0:          Mkt-RF   SMB   HML    RF\n",
       " Date                             \n",
       " 1926-07    2.96 -2.56 -2.43  0.22\n",
       " 1926-08    2.64 -1.17  3.82  0.25\n",
       " 1926-09    0.36 -1.40  0.13  0.23\n",
       " 1926-10   -3.24 -0.09  0.70  0.32\n",
       " 1926-11    2.53 -0.10 -0.51  0.31\n",
       " ...         ...   ...   ...   ...\n",
       " 2023-01    6.65  5.02 -4.05  0.35\n",
       " 2023-02   -2.58  1.21 -0.78  0.34\n",
       " 2023-03    2.51 -5.59 -9.01  0.36\n",
       " 2023-04    0.61 -3.34 -0.03  0.35\n",
       " 2023-05    0.35  1.53 -7.80  0.36\n",
       " \n",
       " [1163 rows x 4 columns],\n",
       " 1:       Mkt-RF    SMB    HML    RF\n",
       " Date                            \n",
       " 1927   29.47  -2.04  -4.54  3.12\n",
       " 1928   35.39   4.51  -6.17  3.56\n",
       " 1929  -19.54 -30.70  11.67  4.75\n",
       " 1930  -31.23  -5.17 -11.54  2.41\n",
       " 1931  -45.11   3.70 -13.95  1.07\n",
       " ...      ...    ...    ...   ...\n",
       " 2018   -6.95  -3.21  -9.73  1.83\n",
       " 2019   28.28  -6.11 -10.34  2.15\n",
       " 2020   23.66  13.18 -46.56  0.45\n",
       " 2021   23.56  -3.89  25.53  0.04\n",
       " 2022  -21.60  -6.82  25.81  1.43\n",
       " \n",
       " [96 rows x 4 columns],\n",
       " 'DESCR': 'F-F Research Data Factors\\n-------------------------\\n\\nThis file was created by CMPT_ME_BEME_RETS using the 202305 CRSP database. The 1-month TBill return is from Ibbotson and Associates, Inc. Copyright 2023 Kenneth R. French\\n\\n  0 : (1163 rows x 4 cols)\\n  1 : Annual Factors: January-December (96 rows x 4 cols)'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To display the dataframe\n",
    "ff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b4331b95",
   "metadata": {
    "scrolled": true
   },
   "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>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1926-07</th>\n",
       "      <td>2.96</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-08</th>\n",
       "      <td>2.64</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-09</th>\n",
       "      <td>0.36</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-10</th>\n",
       "      <td>-3.24</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-11</th>\n",
       "      <td>2.53</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01</th>\n",
       "      <td>6.65</td>\n",
       "      <td>5.02</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02</th>\n",
       "      <td>-2.58</td>\n",
       "      <td>1.21</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03</th>\n",
       "      <td>2.51</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>-9.01</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04</th>\n",
       "      <td>0.61</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05</th>\n",
       "      <td>0.35</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-7.80</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1163 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mkt-RF   SMB   HML    RF\n",
       "Date                             \n",
       "1926-07    2.96 -2.56 -2.43  0.22\n",
       "1926-08    2.64 -1.17  3.82  0.25\n",
       "1926-09    0.36 -1.40  0.13  0.23\n",
       "1926-10   -3.24 -0.09  0.70  0.32\n",
       "1926-11    2.53 -0.10 -0.51  0.31\n",
       "...         ...   ...   ...   ...\n",
       "2023-01    6.65  5.02 -4.05  0.35\n",
       "2023-02   -2.58  1.21 -0.78  0.34\n",
       "2023-03    2.51 -5.59 -9.01  0.36\n",
       "2023-04    0.61 -3.34 -0.03  0.35\n",
       "2023-05    0.35  1.53 -7.80  0.36\n",
       "\n",
       "[1163 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get the dataframe of monthly returns for the 3 factors\n",
    "ff_df = ff[0]\n",
    "ff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfc73891",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='Date'>, <Axes: xlabel='Date'>,\n",
       "       <Axes: xlabel='Date'>, <Axes: xlabel='Date'>], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To plot the actual returns for each factor\n",
    "ff_df.plot(subplots=True,figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a441f0ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='Date'>, <Axes: xlabel='Date'>,\n",
       "       <Axes: xlabel='Date'>, <Axes: xlabel='Date'>], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To plot smooth rolling averages\n",
    "ff_df.rolling(72).mean().plot(subplots=True,figsize=(12,4))\n",
    "\n",
    "# The market factor and the size factor took a dip in the late 1970s, but have been moving inversely to each other.\n",
    "# Size and value have somewhat been moving together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf93f141",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To add the momentum factor\n",
    "ff_mom_df = pandas_datareader.famafrench.FamaFrenchReader('F-F_Momentum_Factor', freq='M',start=start).read()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da11dd74",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='Date'>], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To plot the momentum factor dataframe\n",
    "ff_mom_df.rolling(72).mean().plot(subplots=True,figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3ac8a9e",
   "metadata": {
    "scrolled": true
   },
   "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>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RF</th>\n",
       "      <th>Mom</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1927-01</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>4.54</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927-02</th>\n",
       "      <td>4.18</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927-03</th>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927-04</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.25</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927-05</th>\n",
       "      <td>5.44</td>\n",
       "      <td>1.53</td>\n",
       "      <td>4.73</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01</th>\n",
       "      <td>6.65</td>\n",
       "      <td>5.02</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-15.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02</th>\n",
       "      <td>-2.58</td>\n",
       "      <td>1.21</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03</th>\n",
       "      <td>2.51</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>-9.01</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04</th>\n",
       "      <td>0.61</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05</th>\n",
       "      <td>0.35</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-7.80</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1157 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mkt-RF   SMB   HML    RF  Mom   \n",
       "Date                                     \n",
       "1927-01   -0.06 -0.37  4.54  0.25    0.36\n",
       "1927-02    4.18  0.04  2.94  0.26   -2.14\n",
       "1927-03    0.13 -1.65 -2.61  0.30    3.61\n",
       "1927-04    0.46  0.30  0.81  0.25    4.30\n",
       "1927-05    5.44  1.53  4.73  0.30    3.00\n",
       "...         ...   ...   ...   ...     ...\n",
       "2023-01    6.65  5.02 -4.05  0.35  -15.98\n",
       "2023-02   -2.58  1.21 -0.78  0.34    0.21\n",
       "2023-03    2.51 -5.59 -9.01  0.36   -2.50\n",
       "2023-04    0.61 -3.34 -0.03  0.35    1.65\n",
       "2023-05    0.35  1.53 -7.80  0.36   -0.63\n",
       "\n",
       "[1157 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To combine both data frames to make a dataframe with 4 factors (merge by date)\n",
    "ff_df\n",
    "ff_mom_df\n",
    "\n",
    "# Merging them\n",
    "import pandas as pd\n",
    "ffac_merged_df = pd.merge(ff_df,ff_mom_df,on='Date',how='inner',sort=True,copy=True,indicator=False, validate='one_to_one')\n",
    "ffac_merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9dbc5c8",
   "metadata": {},
   "source": [
    "### 2. Risk Asset Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2af9ec7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yfinance in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (0.2.18)\n",
      "Requirement already satisfied: pandas>=1.3.0 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (1.5.3)\n",
      "Requirement already satisfied: cryptography>=3.3.2 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (39.0.1)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (1.23.5)\n",
      "Requirement already satisfied: lxml>=4.9.1 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (4.9.1)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: pytz>=2022.5 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (2022.7)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (2.3.8)\n",
      "Requirement already satisfied: html5lib>=1.1 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (1.1)\n",
      "Requirement already satisfied: requests>=2.26 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (2.28.1)\n",
      "Requirement already satisfied: appdirs>=1.4.4 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (1.4.4)\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from yfinance) (4.11.1)\n",
      "Requirement already satisfied: soupsieve>1.2 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.3.2.post1)\n",
      "Requirement already satisfied: cffi>=1.12 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from cryptography>=3.3.2->yfinance) (1.15.1)\n",
      "Requirement already satisfied: webencodings in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
      "Requirement already satisfied: six>=1.9 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from requests>=2.26->yfinance) (2022.12.7)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from requests>=2.26->yfinance) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from requests>=2.26->yfinance) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from requests>=2.26->yfinance) (1.26.14)\n",
      "Requirement already satisfied: pycparser in /Users/cara-lifarrell/anaconda3/lib/python3.10/site-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance) (2.21)\n"
     ]
    }
   ],
   "source": [
    "# Install Yahoo Finance package to get stock data\n",
    "!pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "791a81c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "yf.pdr_override()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46ba0d2c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "# Getting data for Apple to compare returns on AAPL VS the risk factors\n",
    "AAPL_data = pandas_datareader.data.get_data_yahoo('AAPL',start=start)['Adj Close'].resample('M').ffill().pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03868884",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1980-12-31         NaN\n",
       "1981-01-31   -0.172163\n",
       "1981-02-28   -0.061943\n",
       "1981-03-31   -0.075475\n",
       "1981-04-30    0.158163\n",
       "                ...   \n",
       "2023-03-31    0.118649\n",
       "2023-04-30    0.028987\n",
       "2023-05-31    0.046058\n",
       "2023-06-30    0.094330\n",
       "2023-07-31   -0.004331\n",
       "Freq: M, Name: Adj Close, Length: 512, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AAPL_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e671bf7",
   "metadata": {
    "scrolled": false
   },
   "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>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-31</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-31</th>\n",
       "      <td>-0.172163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-28</th>\n",
       "      <td>-0.061943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-03-31</th>\n",
       "      <td>-0.075475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-04-30</th>\n",
       "      <td>0.158163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31</th>\n",
       "      <td>0.118649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-30</th>\n",
       "      <td>0.028987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-31</th>\n",
       "      <td>0.046058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>0.094330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-31</th>\n",
       "      <td>-0.004331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close\n",
       "Date                 \n",
       "1980-12-31        NaN\n",
       "1981-01-31  -0.172163\n",
       "1981-02-28  -0.061943\n",
       "1981-03-31  -0.075475\n",
       "1981-04-30   0.158163\n",
       "...               ...\n",
       "2023-03-31   0.118649\n",
       "2023-04-30   0.028987\n",
       "2023-05-31   0.046058\n",
       "2023-06-30   0.094330\n",
       "2023-07-31  -0.004331\n",
       "\n",
       "[512 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get it as a dataframe\n",
    "AAPL_df = AAPL_data.to_frame()\n",
    "AAPL_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c7ab6d",
   "metadata": {},
   "source": [
    "### 3. Cleaning the Data to Compare Risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f14e8e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check index data type for index (time stamp) VS for risk factors\n",
    "AAPL_df.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97973c88",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "period[M]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data type is a period, which is different from the other database. Cannot merge...\n",
    "ffac_merged_df.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3ce4a014",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the index in both dataframe\n",
    "# Change it into a string to then modify it to date form with year and month\n",
    "AAPL_df['str_date'] = AAPL_df.index.astype(str)\n",
    "AAPL_df['dt_date'] = pd.to_datetime(AAPL_df['str_date']).dt.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9c646ace",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To verify changes have been made (it's an object)\n",
    "AAPL_df.dt_date.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8d88a9fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing the index for the risk factor database\n",
    "ffac_merged_df['str_date'] = ffac_merged_df.index.astype(str)\n",
    "ffac_merged_df['dt_date'] = pd.to_datetime(ffac_merged_df['str_date']).dt.strftime('%Y-%m')\n",
    "\n",
    "# It is also now an object\n",
    "ffac_merged_df.dt_date.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "adf599ab",
   "metadata": {
    "scrolled": true
   },
   "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>Adj Close</th>\n",
       "      <th>str_date_x</th>\n",
       "      <th>dt_date</th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RF</th>\n",
       "      <th>Mom</th>\n",
       "      <th>str_date_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1980-12-31</td>\n",
       "      <td>1980-12</td>\n",
       "      <td>-4.52</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>2.79</td>\n",
       "      <td>1.31</td>\n",
       "      <td>-6.68</td>\n",
       "      <td>1980-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.172163</td>\n",
       "      <td>1981-01-31</td>\n",
       "      <td>1981-01</td>\n",
       "      <td>-5.04</td>\n",
       "      <td>2.92</td>\n",
       "      <td>6.72</td>\n",
       "      <td>1.04</td>\n",
       "      <td>-7.95</td>\n",
       "      <td>1981-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.061943</td>\n",
       "      <td>1981-02-28</td>\n",
       "      <td>1981-02</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.07</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>1981-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.075475</td>\n",
       "      <td>1981-03-31</td>\n",
       "      <td>1981-03</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.54</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1981-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.158163</td>\n",
       "      <td>1981-04-30</td>\n",
       "      <td>1981-04</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>4.40</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>1981-04</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.110521</td>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>6.65</td>\n",
       "      <td>5.02</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-15.98</td>\n",
       "      <td>2023-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.023183</td>\n",
       "      <td>2023-02-28</td>\n",
       "      <td>2023-02</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>1.21</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2023-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.118649</td>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2023-03</td>\n",
       "      <td>2.51</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>-9.01</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>2023-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.028987</td>\n",
       "      <td>2023-04-30</td>\n",
       "      <td>2023-04</td>\n",
       "      <td>0.61</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2023-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.046058</td>\n",
       "      <td>2023-05-31</td>\n",
       "      <td>2023-05</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-7.80</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>2023-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Adj Close  str_date_x  dt_date  Mkt-RF   SMB   HML    RF  Mom     \\\n",
       "0          NaN  1980-12-31  1980-12   -4.52 -0.26  2.79  1.31   -6.68   \n",
       "1    -0.172163  1981-01-31  1981-01   -5.04  2.92  6.72  1.04   -7.95   \n",
       "2    -0.061943  1981-02-28  1981-02    0.57 -0.34  1.02  1.07   -1.35   \n",
       "3    -0.075475  1981-03-31  1981-03    3.56  3.54  0.64  1.21    0.74   \n",
       "4     0.158163  1981-04-30  1981-04   -2.11  4.40  2.28  1.08   -0.91   \n",
       "..         ...         ...      ...     ...   ...   ...   ...     ...   \n",
       "505   0.110521  2023-01-31  2023-01    6.65  5.02 -4.05  0.35  -15.98   \n",
       "506   0.023183  2023-02-28  2023-02   -2.58  1.21 -0.78  0.34    0.21   \n",
       "507   0.118649  2023-03-31  2023-03    2.51 -5.59 -9.01  0.36   -2.50   \n",
       "508   0.028987  2023-04-30  2023-04    0.61 -3.34 -0.03  0.35    1.65   \n",
       "509   0.046058  2023-05-31  2023-05    0.35  1.53 -7.80  0.36   -0.63   \n",
       "\n",
       "    str_date_y  \n",
       "0      1980-12  \n",
       "1      1981-01  \n",
       "2      1981-02  \n",
       "3      1981-03  \n",
       "4      1981-04  \n",
       "..         ...  \n",
       "505    2023-01  \n",
       "506    2023-02  \n",
       "507    2023-03  \n",
       "508    2023-04  \n",
       "509    2023-05  \n",
       "\n",
       "[510 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the dataframes\n",
    "AAPL_ffac_merge_df = pd.merge(AAPL_df,ffac_merged_df,how='inner',on='dt_date',sort=True,copy=True,indicator=False,validate='one_to_one')\n",
    "AAPL_ffac_merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ac34c0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning the table\n",
    "# Dropping str_date_x and str_date_y\n",
    "AAPL_ffac_merge_df.drop(columns=['str_date_x','str_date_y'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91274429",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename Adj Close\n",
    "AAPL_ffac_merge_df.rename(columns={'Adj Close':'AAPL'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d389e778",
   "metadata": {
    "scrolled": true
   },
   "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>AAPL</th>\n",
       "      <th>dt_date</th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RF</th>\n",
       "      <th>Mom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1980-12</td>\n",
       "      <td>-4.52</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>2.79</td>\n",
       "      <td>1.31</td>\n",
       "      <td>-6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.172163</td>\n",
       "      <td>1981-01</td>\n",
       "      <td>-5.04</td>\n",
       "      <td>2.92</td>\n",
       "      <td>6.72</td>\n",
       "      <td>1.04</td>\n",
       "      <td>-7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.061943</td>\n",
       "      <td>1981-02</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.07</td>\n",
       "      <td>-1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.075475</td>\n",
       "      <td>1981-03</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.54</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.158163</td>\n",
       "      <td>1981-04</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>4.40</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-0.91</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.110521</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>6.65</td>\n",
       "      <td>5.02</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-15.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.023183</td>\n",
       "      <td>2023-02</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>1.21</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.118649</td>\n",
       "      <td>2023-03</td>\n",
       "      <td>2.51</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>-9.01</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.028987</td>\n",
       "      <td>2023-04</td>\n",
       "      <td>0.61</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.046058</td>\n",
       "      <td>2023-05</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-7.80</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AAPL  dt_date  Mkt-RF   SMB   HML    RF  Mom   \n",
       "0         NaN  1980-12   -4.52 -0.26  2.79  1.31   -6.68\n",
       "1   -0.172163  1981-01   -5.04  2.92  6.72  1.04   -7.95\n",
       "2   -0.061943  1981-02    0.57 -0.34  1.02  1.07   -1.35\n",
       "3   -0.075475  1981-03    3.56  3.54  0.64  1.21    0.74\n",
       "4    0.158163  1981-04   -2.11  4.40  2.28  1.08   -0.91\n",
       "..        ...      ...     ...   ...   ...   ...     ...\n",
       "505  0.110521  2023-01    6.65  5.02 -4.05  0.35  -15.98\n",
       "506  0.023183  2023-02   -2.58  1.21 -0.78  0.34    0.21\n",
       "507  0.118649  2023-03    2.51 -5.59 -9.01  0.36   -2.50\n",
       "508  0.028987  2023-04    0.61 -3.34 -0.03  0.35    1.65\n",
       "509  0.046058  2023-05    0.35  1.53 -7.80  0.36   -0.63\n",
       "\n",
       "[510 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Better, but AAPL's returns are in % whereas the factors are in % (need to have same units)\n",
    "AAPL_ffac_merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "65d27d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subtracting the risk-free rate from AAPL returns to get the risk premia\n",
    "AAPL_ffac_merge_df['AAPL_RF'] = AAPL_ffac_merge_df['AAPL']*100-AAPL_ffac_merge_df['RF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cdc2b63d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>AAPL</th>\n",
       "      <th>dt_date</th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RF</th>\n",
       "      <th>Mom</th>\n",
       "      <th>AAPL_RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1980-12</td>\n",
       "      <td>-4.52</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>2.79</td>\n",
       "      <td>1.31</td>\n",
       "      <td>-6.68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.172163</td>\n",
       "      <td>1981-01</td>\n",
       "      <td>-5.04</td>\n",
       "      <td>2.92</td>\n",
       "      <td>6.72</td>\n",
       "      <td>1.04</td>\n",
       "      <td>-7.95</td>\n",
       "      <td>-18.256256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.061943</td>\n",
       "      <td>1981-02</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.07</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>-7.264308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.075475</td>\n",
       "      <td>1981-03</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.54</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-8.757507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.158163</td>\n",
       "      <td>1981-04</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>4.40</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>14.736261</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.110521</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>6.65</td>\n",
       "      <td>5.02</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-15.98</td>\n",
       "      <td>10.702102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.023183</td>\n",
       "      <td>2023-02</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>1.21</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.978308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.118649</td>\n",
       "      <td>2023-03</td>\n",
       "      <td>2.51</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>-9.01</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>11.504862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.028987</td>\n",
       "      <td>2023-04</td>\n",
       "      <td>0.61</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.548723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.046058</td>\n",
       "      <td>2023-05</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-7.80</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>4.245833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AAPL  dt_date  Mkt-RF   SMB   HML    RF  Mom       AAPL_RF\n",
       "0         NaN  1980-12   -4.52 -0.26  2.79  1.31   -6.68        NaN\n",
       "1   -0.172163  1981-01   -5.04  2.92  6.72  1.04   -7.95 -18.256256\n",
       "2   -0.061943  1981-02    0.57 -0.34  1.02  1.07   -1.35  -7.264308\n",
       "3   -0.075475  1981-03    3.56  3.54  0.64  1.21    0.74  -8.757507\n",
       "4    0.158163  1981-04   -2.11  4.40  2.28  1.08   -0.91  14.736261\n",
       "..        ...      ...     ...   ...   ...   ...     ...        ...\n",
       "505  0.110521  2023-01    6.65  5.02 -4.05  0.35  -15.98  10.702102\n",
       "506  0.023183  2023-02   -2.58  1.21 -0.78  0.34    0.21   1.978308\n",
       "507  0.118649  2023-03    2.51 -5.59 -9.01  0.36   -2.50  11.504862\n",
       "508  0.028987  2023-04    0.61 -3.34 -0.03  0.35    1.65   2.548723\n",
       "509  0.046058  2023-05    0.35  1.53 -7.80  0.36   -0.63   4.245833\n",
       "\n",
       "[510 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AAPL_ffac_merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5042af51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing NaN\n",
    "AAPL_ffac_merge_df.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6595f14",
   "metadata": {},
   "source": [
    "### 4. Running a Multivariate Regression of Returns of an Asset on a 4-Factor Risk Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d4b0a77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AAPL', 'dt_date', 'Mkt-RF', 'SMB', 'HML', 'RF', 'MOM', 'AAPL_RF']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check columns in the dataframe\n",
    "list(AAPL_ffac_merge_df)\n",
    "# Mom factor has 3 spaces behind it, so rename it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ce2c9d59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming MOM factor\n",
    "AAPL_ffac_merge_df.rename(columns={'Mom   ':'MOM'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "83ec62e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the OLS regression model\n",
    "from statsmodels.api import OLS\n",
    "results = OLS(AAPL_ffac_merge_df['AAPL_RF'],AAPL_ffac_merge_df[['Mkt-RF','SMB','HML','MOM']],missing='drop').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "15717eb1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>AAPL_RF</td>     <th>  R-squared (uncentered):</th>      <td>   0.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.287</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   52.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 20 Jul 2023</td> <th>  Prob (F-statistic):</th>          <td>9.09e-37</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:58:34</td>     <th>  Log-Likelihood:    </th>          <td> -1940.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   509</td>      <th>  AIC:               </th>          <td>   3888.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   505</td>      <th>  BIC:               </th>          <td>   3905.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mkt-RF</th> <td>    1.2735</td> <td>    0.114</td> <td>   11.153</td> <td> 0.000</td> <td>    1.049</td> <td>    1.498</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SMB</th>    <td>    0.2242</td> <td>    0.170</td> <td>    1.317</td> <td> 0.189</td> <td>   -0.110</td> <td>    0.559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HML</th>    <td>   -0.8231</td> <td>    0.163</td> <td>   -5.046</td> <td> 0.000</td> <td>   -1.144</td> <td>   -0.503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MOM</th>    <td>   -0.0443</td> <td>    0.115</td> <td>   -0.385</td> <td> 0.700</td> <td>   -0.270</td> <td>    0.181</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>15.638</td> <th>  Durbin-Watson:     </th> <td>   1.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  33.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.054</td> <th>  Prob(JB):          </th> <td>6.37e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.245</td> <th>  Cond. No.          </th> <td>    1.86</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                AAPL_RF   R-squared (uncentered):                   0.292\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.287\n",
       "Method:                 Least Squares   F-statistic:                              52.15\n",
       "Date:                Thu, 20 Jul 2023   Prob (F-statistic):                    9.09e-37\n",
       "Time:                        23:58:34   Log-Likelihood:                         -1940.1\n",
       "No. Observations:                 509   AIC:                                      3888.\n",
       "Df Residuals:                     505   BIC:                                      3905.\n",
       "Df Model:                           4                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Mkt-RF         1.2735      0.114     11.153      0.000       1.049       1.498\n",
       "SMB            0.2242      0.170      1.317      0.189      -0.110       0.559\n",
       "HML           -0.8231      0.163     -5.046      0.000      -1.144      -0.503\n",
       "MOM           -0.0443      0.115     -0.385      0.700      -0.270       0.181\n",
       "==============================================================================\n",
       "Omnibus:                       15.638   Durbin-Watson:                   1.947\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               33.137\n",
       "Skew:                          -0.054   Prob(JB):                     6.37e-08\n",
       "Kurtosis:                       4.245   Cond. No.                         1.86\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()\n",
    "# Does not have an intercept (aka and alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "197d8715",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To add a constant (the alpha)\n",
    "import statsmodels.tools\n",
    "AAPL_ffac_merge_df_c = statsmodels.tools.add_constant(AAPL_ffac_merge_df,prepend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7bf388a5",
   "metadata": {
    "scrolled": true
   },
   "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>const</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>dt_date</th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RF</th>\n",
       "      <th>MOM</th>\n",
       "      <th>AAPL_RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.172163</td>\n",
       "      <td>1981-01</td>\n",
       "      <td>-5.04</td>\n",
       "      <td>2.92</td>\n",
       "      <td>6.72</td>\n",
       "      <td>1.04</td>\n",
       "      <td>-7.95</td>\n",
       "      <td>-18.256256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.061943</td>\n",
       "      <td>1981-02</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.07</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>-7.264308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.075475</td>\n",
       "      <td>1981-03</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.54</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-8.757507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.158163</td>\n",
       "      <td>1981-04</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>4.40</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>14.736261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167398</td>\n",
       "      <td>1981-05</td>\n",
       "      <td>0.11</td>\n",
       "      <td>2.00</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>1.15</td>\n",
       "      <td>3.70</td>\n",
       "      <td>15.589751</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.110521</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>6.65</td>\n",
       "      <td>5.02</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-15.98</td>\n",
       "      <td>10.702102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023183</td>\n",
       "      <td>2023-02</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>1.21</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.978308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.118649</td>\n",
       "      <td>2023-03</td>\n",
       "      <td>2.51</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>-9.01</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>11.504862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.028987</td>\n",
       "      <td>2023-04</td>\n",
       "      <td>0.61</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.548723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.046058</td>\n",
       "      <td>2023-05</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-7.80</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>4.245833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>509 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     const      AAPL  dt_date  Mkt-RF   SMB   HML    RF    MOM    AAPL_RF\n",
       "1      1.0 -0.172163  1981-01   -5.04  2.92  6.72  1.04  -7.95 -18.256256\n",
       "2      1.0 -0.061943  1981-02    0.57 -0.34  1.02  1.07  -1.35  -7.264308\n",
       "3      1.0 -0.075475  1981-03    3.56  3.54  0.64  1.21   0.74  -8.757507\n",
       "4      1.0  0.158163  1981-04   -2.11  4.40  2.28  1.08  -0.91  14.736261\n",
       "5      1.0  0.167398  1981-05    0.11  2.00 -0.42  1.15   3.70  15.589751\n",
       "..     ...       ...      ...     ...   ...   ...   ...    ...        ...\n",
       "505    1.0  0.110521  2023-01    6.65  5.02 -4.05  0.35 -15.98  10.702102\n",
       "506    1.0  0.023183  2023-02   -2.58  1.21 -0.78  0.34   0.21   1.978308\n",
       "507    1.0  0.118649  2023-03    2.51 -5.59 -9.01  0.36  -2.50  11.504862\n",
       "508    1.0  0.028987  2023-04    0.61 -3.34 -0.03  0.35   1.65   2.548723\n",
       "509    1.0  0.046058  2023-05    0.35  1.53 -7.80  0.36  -0.63   4.245833\n",
       "\n",
       "[509 rows x 9 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AAPL_ffac_merge_df_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "df83c5fa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>0</td>        <th>  R-squared:         </th> <td>   0.287</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   50.76</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 21 Jul 2023</td> <th>  Prob (F-statistic):</th> <td>6.59e-36</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:02:20</td>     <th>  Log-Likelihood:    </th> <td> -1936.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   509</td>      <th>  AIC:               </th> <td>   3882.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   504</td>      <th>  BIC:               </th> <td>   3903.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    1.4253</td> <td>    0.499</td> <td>    2.856</td> <td> 0.004</td> <td>    0.445</td> <td>    2.406</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mkt-RF</th> <td>    1.2085</td> <td>    0.116</td> <td>   10.450</td> <td> 0.000</td> <td>    0.981</td> <td>    1.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SMB</th>    <td>    0.2199</td> <td>    0.169</td> <td>    1.301</td> <td> 0.194</td> <td>   -0.112</td> <td>    0.552</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HML</th>    <td>   -0.8911</td> <td>    0.164</td> <td>   -5.443</td> <td> 0.000</td> <td>   -1.213</td> <td>   -0.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MOM</th>    <td>   -0.1006</td> <td>    0.116</td> <td>   -0.869</td> <td> 0.385</td> <td>   -0.328</td> <td>    0.127</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.918</td> <th>  Durbin-Watson:     </th> <td>   1.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  30.824</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.052</td> <th>  Prob(JB):          </th> <td>2.03e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.201</td> <th>  Cond. No.          </th> <td>    5.14</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      0   R-squared:                       0.287\n",
       "Model:                            OLS   Adj. R-squared:                  0.282\n",
       "Method:                 Least Squares   F-statistic:                     50.76\n",
       "Date:                Fri, 21 Jul 2023   Prob (F-statistic):           6.59e-36\n",
       "Time:                        00:02:20   Log-Likelihood:                -1936.1\n",
       "No. Observations:                 509   AIC:                             3882.\n",
       "Df Residuals:                     504   BIC:                             3903.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          1.4253      0.499      2.856      0.004       0.445       2.406\n",
       "Mkt-RF         1.2085      0.116     10.450      0.000       0.981       1.436\n",
       "SMB            0.2199      0.169      1.301      0.194      -0.112       0.552\n",
       "HML           -0.8911      0.164     -5.443      0.000      -1.213      -0.569\n",
       "MOM           -0.1006      0.116     -0.869      0.385      -0.328       0.127\n",
       "==============================================================================\n",
       "Omnibus:                       14.918   Durbin-Watson:                   1.970\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               30.824\n",
       "Skew:                          -0.052   Prob(JB):                     2.03e-07\n",
       "Kurtosis:                       4.201   Cond. No.                         5.14\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = OLS(AAPL_ffac_merge_df_c['AAPL_RF'],AAPL_ffac_merge_df_c[['const','Mkt-RF','SMB','HML','MOM']],missing='drop').fit()\n",
    "results.summary(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e76febe",
   "metadata": {},
   "source": [
    "### 5. Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e22c6556",
   "metadata": {},
   "source": [
    "- The alpha is 1.4253% per month (highly positive and significant)\n",
    "- Has a significant beta on the market (1.2085 with P-value = 0.000)\n",
    "- It has an insignificant exposure to size (SMB) with its P-value of 0.112\n",
    "- It has a negative significant exposure to value (which makes sense since AAPL is a growth stock)\n",
    "- It has an insignificant exposure to MOM\n",
    "- Overall, approximately 28.7% of the variability of AAPL's returns is explained, suggesting that there is a rather big idiosyncratic component"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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