07_linear_models/02_fama_macbeth.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to build a linear factor model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algorithmic trading strategies use linear factor models to quantify the relationship between the return of an asset and the sources of risk that represent the main drivers of these returns. Each factor risk carries a premium, and the total asset return can be expected to correspond to a weighted average of these risk premia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several practical applications of factor models across the portfolio management process from construction and asset selection to risk management and performance evaluation. The importance of factor models continues to grow as common risk factors are now tradeable:\n",
    "\n",
    "- A summary of the returns of many assets by a much smaller number of factors reduces the amount of data required to estimate the covariance matrix when optimizing a portfolio\n",
    "- An estimate of the exposure of an asset or a portfolio to these factors allows for the management of the resultant risk, for instance by entering suitable hedges when risk factors are themselves traded\n",
    "- A factor model also permits the assessment of the incremental signal content of new alpha factors\n",
    "- A factor model can also help assess whether a manager's performance relative to a benchmark is indeed due to skill in selecting assets and timing the market, or if instead, the performance can be explained by portfolio tilts towards known return drivers that can today be replicated as low-cost, passively managed funds without incurring active management fees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:07.984117Z",
     "start_time": "2021-04-15T19:55:07.982285Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:09.073976Z",
     "start_time": "2021-04-15T19:55:08.102748Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from statsmodels.api import OLS, add_constant\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "from linearmodels.asset_pricing import LinearFactorModel\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:09.077317Z",
     "start_time": "2021-04-15T19:55:09.075045Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fama and French make updated risk factor and research portfolio data available through their [website](http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html), and you can use the `pandas_datareader` package to obtain the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk Factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In particular, we will be using the five Fama—French factors that result from sorting stocks first into three size groups and then into two for each of the remaining three firm-specific factors. \n",
    "\n",
    "Hence, the factors involve three sets of value-weighted portfolios formed as 3 x 2 sorts on size and book-to-market, size and operating profitability, and size and investment. The risk factor values computed as the average returns of the portfolios (PF) as outlined in the following table:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Label | Name                          | Description                                                                                                                                                                               |\n",
    "|-------|-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| SMB   | Small Minus Big               | Average return on the nine small stock portfolios minus the average return on the nine big stock portfolios                                                                               |\n",
    "| HML   | High Minus Low                | Average return on the two value portfolios minus the average return on the two growth portfolios                                                                                          |\n",
    "| RMW   | Robust minus Weak             | Average return on the two robust operating profitability portfolios minus the average return on the two weak operating profitability portfolios                                           |\n",
    "| CMA   | Conservative Minus Aggressive | Average return on the two conservative investment portfolios minus the average return on the two aggressive investment portfolios                                                         |\n",
    "| Rm-Rf | Excess return on the market   | Value-weight return of all firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ at the beginning of month t with 'good' data for t minus the one-month Treasury bill rate |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Fama-French 5 factors are based on the 6 value-weight portfolios formed on size and book-to-market, the 6 value-weight portfolios formed on size and operating profitability, and the 6 value-weight portfolios formed on size and investment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use returns at a monthly frequency that we obtain for the period 2010 – 2017 as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:13.367412Z",
     "start_time": "2021-04-15T19:55:13.065455Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 96 entries, 2010-01 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Mkt-RF  96 non-null     float64\n",
      " 1   SMB     96 non-null     float64\n",
      " 2   HML     96 non-null     float64\n",
      " 3   RMW     96 non-null     float64\n",
      " 4   CMA     96 non-null     float64\n",
      " 5   RF      96 non-null     float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 5.2 KB\n"
     ]
    }
   ],
   "source": [
    "ff_factor = 'F-F_Research_Data_5_Factors_2x3'\n",
    "ff_factor_data = web.DataReader(ff_factor, 'famafrench', start='2010', end='2017-12')[0]\n",
    "ff_factor_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:13.386698Z",
     "start_time": "2021-04-15T19:55:13.368623Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<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>RMW</th>\n",
       "      <th>CMA</th>\n",
       "      <th>RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.158750</td>\n",
       "      <td>0.054063</td>\n",
       "      <td>-0.051771</td>\n",
       "      <td>0.126042</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.012604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.580187</td>\n",
       "      <td>2.290739</td>\n",
       "      <td>2.191621</td>\n",
       "      <td>1.591052</td>\n",
       "      <td>1.409858</td>\n",
       "      <td>0.022583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-7.890000</td>\n",
       "      <td>-4.510000</td>\n",
       "      <td>-4.520000</td>\n",
       "      <td>-3.930000</td>\n",
       "      <td>-3.350000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.917500</td>\n",
       "      <td>-1.660000</td>\n",
       "      <td>-1.627500</td>\n",
       "      <td>-1.160000</td>\n",
       "      <td>-0.965000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.235000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>-0.305000</td>\n",
       "      <td>0.135000</td>\n",
       "      <td>-0.015000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.197500</td>\n",
       "      <td>1.517500</td>\n",
       "      <td>1.142500</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>0.927500</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.350000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>8.220000</td>\n",
       "      <td>3.530000</td>\n",
       "      <td>3.780000</td>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Mkt-RF        SMB        HML        RMW        CMA         RF\n",
       "count  96.000000  96.000000  96.000000  96.000000  96.000000  96.000000\n",
       "mean    1.158750   0.054063  -0.051771   0.126042   0.052813   0.012604\n",
       "std     3.580187   2.290739   2.191621   1.591052   1.409858   0.022583\n",
       "min    -7.890000  -4.510000  -4.520000  -3.930000  -3.350000   0.000000\n",
       "25%    -0.917500  -1.660000  -1.627500  -1.160000  -0.965000   0.000000\n",
       "50%     1.235000   0.190000  -0.305000   0.135000  -0.015000   0.000000\n",
       "75%     3.197500   1.517500   1.142500   1.140000   0.927500   0.010000\n",
       "max    11.350000   6.800000   8.220000   3.530000   3.780000   0.090000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff_factor_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Portfolios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fama and French also make available numerous portfolios that we can illustrate the estimation of the factor exposures, as well as the value of the risk premia available in the market for a given time period. We will use a panel of the 17 industry portfolios at a monthly frequency. \n",
    "\n",
    "We will subtract the risk-free rate from the returns because the factor model works with excess returns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:14.483710Z",
     "start_time": "2021-04-15T19:55:13.388307Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 96 entries, 2010-01 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Food    96 non-null     float64\n",
      " 1   Mines   96 non-null     float64\n",
      " 2   Oil     96 non-null     float64\n",
      " 3   Clths   96 non-null     float64\n",
      " 4   Durbl   96 non-null     float64\n",
      " 5   Chems   96 non-null     float64\n",
      " 6   Cnsum   96 non-null     float64\n",
      " 7   Cnstr   96 non-null     float64\n",
      " 8   Steel   96 non-null     float64\n",
      " 9   FabPr   96 non-null     float64\n",
      " 10  Machn   96 non-null     float64\n",
      " 11  Cars    96 non-null     float64\n",
      " 12  Trans   96 non-null     float64\n",
      " 13  Utils   96 non-null     float64\n",
      " 14  Rtail   96 non-null     float64\n",
      " 15  Finan   96 non-null     float64\n",
      " 16  Other   96 non-null     float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 13.5 KB\n"
     ]
    }
   ],
   "source": [
    "ff_portfolio = '17_Industry_Portfolios'\n",
    "ff_portfolio_data = web.DataReader(ff_portfolio, 'famafrench', start='2010', end='2017-12')[0]\n",
    "ff_portfolio_data = ff_portfolio_data.sub(ff_factor_data.RF, axis=0)\n",
    "ff_portfolio_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:14.519355Z",
     "start_time": "2021-04-15T19:55:14.484749Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food</th>\n",
       "      <th>Mines</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Clths</th>\n",
       "      <th>Durbl</th>\n",
       "      <th>Chems</th>\n",
       "      <th>Cnsum</th>\n",
       "      <th>Cnstr</th>\n",
       "      <th>Steel</th>\n",
       "      <th>FabPr</th>\n",
       "      <th>Machn</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Utils</th>\n",
       "      <th>Rtail</th>\n",
       "      <th>Finan</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.045625</td>\n",
       "      <td>0.197083</td>\n",
       "      <td>0.547917</td>\n",
       "      <td>1.396979</td>\n",
       "      <td>1.155208</td>\n",
       "      <td>1.303229</td>\n",
       "      <td>1.136250</td>\n",
       "      <td>1.731354</td>\n",
       "      <td>0.555625</td>\n",
       "      <td>1.350521</td>\n",
       "      <td>1.227604</td>\n",
       "      <td>1.278854</td>\n",
       "      <td>1.465000</td>\n",
       "      <td>0.890313</td>\n",
       "      <td>1.234375</td>\n",
       "      <td>1.241562</td>\n",
       "      <td>1.282396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.795857</td>\n",
       "      <td>7.902185</td>\n",
       "      <td>5.577552</td>\n",
       "      <td>5.025167</td>\n",
       "      <td>5.137482</td>\n",
       "      <td>5.594216</td>\n",
       "      <td>3.174283</td>\n",
       "      <td>5.246518</td>\n",
       "      <td>7.389824</td>\n",
       "      <td>4.694408</td>\n",
       "      <td>4.811242</td>\n",
       "      <td>5.718887</td>\n",
       "      <td>4.150833</td>\n",
       "      <td>3.235140</td>\n",
       "      <td>3.508655</td>\n",
       "      <td>4.809791</td>\n",
       "      <td>3.708972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.170000</td>\n",
       "      <td>-24.380000</td>\n",
       "      <td>-12.010000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-13.210000</td>\n",
       "      <td>-17.390000</td>\n",
       "      <td>-7.300000</td>\n",
       "      <td>-13.960000</td>\n",
       "      <td>-20.490000</td>\n",
       "      <td>-11.960000</td>\n",
       "      <td>-9.080000</td>\n",
       "      <td>-11.650000</td>\n",
       "      <td>-8.560000</td>\n",
       "      <td>-6.990000</td>\n",
       "      <td>-9.180000</td>\n",
       "      <td>-11.040000</td>\n",
       "      <td>-7.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.785000</td>\n",
       "      <td>-5.847500</td>\n",
       "      <td>-3.167500</td>\n",
       "      <td>-1.865000</td>\n",
       "      <td>-2.017500</td>\n",
       "      <td>-1.445000</td>\n",
       "      <td>-0.920000</td>\n",
       "      <td>-2.462500</td>\n",
       "      <td>-4.410000</td>\n",
       "      <td>-1.447500</td>\n",
       "      <td>-2.047500</td>\n",
       "      <td>-1.245000</td>\n",
       "      <td>-0.880000</td>\n",
       "      <td>-0.745000</td>\n",
       "      <td>-0.962500</td>\n",
       "      <td>-1.467500</td>\n",
       "      <td>-1.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.930000</td>\n",
       "      <td>-0.460000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>1.435000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>2.190000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>1.485000</td>\n",
       "      <td>1.545000</td>\n",
       "      <td>0.645000</td>\n",
       "      <td>1.505000</td>\n",
       "      <td>1.215000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>1.955000</td>\n",
       "      <td>1.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.187500</td>\n",
       "      <td>5.715000</td>\n",
       "      <td>3.915000</td>\n",
       "      <td>3.857500</td>\n",
       "      <td>4.322500</td>\n",
       "      <td>4.442500</td>\n",
       "      <td>3.317500</td>\n",
       "      <td>5.390000</td>\n",
       "      <td>4.220000</td>\n",
       "      <td>3.837500</td>\n",
       "      <td>4.657500</td>\n",
       "      <td>4.802500</td>\n",
       "      <td>4.235000</td>\n",
       "      <td>2.952500</td>\n",
       "      <td>3.355000</td>\n",
       "      <td>4.092500</td>\n",
       "      <td>3.517500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.670000</td>\n",
       "      <td>21.920000</td>\n",
       "      <td>16.300000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>16.580000</td>\n",
       "      <td>18.370000</td>\n",
       "      <td>8.290000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>21.350000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>14.650000</td>\n",
       "      <td>20.860000</td>\n",
       "      <td>13.160000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>12.360000</td>\n",
       "      <td>13.480000</td>\n",
       "      <td>10.790000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Food       Mines      Oil        Clths      Durbl      Chems  \\\n",
       "count  96.000000  96.000000  96.000000  96.000000  96.000000  96.000000   \n",
       "mean    1.045625   0.197083   0.547917   1.396979   1.155208   1.303229   \n",
       "std     2.795857   7.902185   5.577552   5.025167   5.137482   5.594216   \n",
       "min    -5.170000 -24.380000 -12.010000 -10.000000 -13.210000 -17.390000   \n",
       "25%    -0.785000  -5.847500  -3.167500  -1.865000  -2.017500  -1.445000   \n",
       "50%     0.930000  -0.460000   1.040000   1.160000   1.205000   1.435000   \n",
       "75%     3.187500   5.715000   3.915000   3.857500   4.322500   4.442500   \n",
       "max     6.670000  21.920000  16.300000  17.200000  16.580000  18.370000   \n",
       "\n",
       "           Cnsum      Cnstr      Steel      FabPr      Machn      Cars   \\\n",
       "count  96.000000  96.000000  96.000000  96.000000  96.000000  96.000000   \n",
       "mean    1.136250   1.731354   0.555625   1.350521   1.227604   1.278854   \n",
       "std     3.174283   5.246518   7.389824   4.694408   4.811242   5.718887   \n",
       "min    -7.300000 -13.960000 -20.490000 -11.960000  -9.080000 -11.650000   \n",
       "25%    -0.920000  -2.462500  -4.410000  -1.447500  -2.047500  -1.245000   \n",
       "50%     1.470000   2.190000   0.660000   1.485000   1.545000   0.645000   \n",
       "75%     3.317500   5.390000   4.220000   3.837500   4.657500   4.802500   \n",
       "max     8.290000  15.550000  21.350000  17.660000  14.650000  20.860000   \n",
       "\n",
       "           Trans      Utils      Rtail      Finan      Other  \n",
       "count  96.000000  96.000000  96.000000  96.000000  96.000000  \n",
       "mean    1.465000   0.890313   1.234375   1.241562   1.282396  \n",
       "std     4.150833   3.235140   3.508655   4.809791   3.708972  \n",
       "min    -8.560000  -6.990000  -9.180000 -11.040000  -7.920000  \n",
       "25%    -0.880000  -0.745000  -0.962500  -1.467500  -1.075000  \n",
       "50%     1.505000   1.215000   0.880000   1.955000   1.575000  \n",
       "75%     4.235000   2.952500   3.355000   4.092500   3.517500  \n",
       "max    13.160000   7.900000  12.360000  13.480000  10.790000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff_portfolio_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equity Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.200618Z",
     "start_time": "2021-04-15T19:55:14.520935Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore('../data/assets.h5') as store:\n",
    "    prices = store['/quandl/wiki/prices'].adj_close.unstack().loc['2010':'2017']\n",
    "    equities = store['/us_equities/stocks'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.245270Z",
     "start_time": "2021-04-15T19:55:20.201602Z"
    }
   },
   "outputs": [],
   "source": [
    "sectors = equities.filter(prices.columns, axis=0).sector.to_dict()\n",
    "prices = prices.filter(sectors.keys()).dropna(how='all', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.330305Z",
     "start_time": "2021-04-15T19:55:20.246605Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Columns: 1986 entries, A to ZUMZ\n",
      "dtypes: float64(1986)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "returns = prices.resample('M').last().pct_change().mul(100).to_period('M')\n",
    "returns = returns.dropna(how='all').dropna(axis=1)\n",
    "returns.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Align data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.336370Z",
     "start_time": "2021-04-15T19:55:20.332866Z"
    }
   },
   "outputs": [],
   "source": [
    "ff_factor_data = ff_factor_data.loc[returns.index]\n",
    "ff_portfolio_data = ff_portfolio_data.loc[returns.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.370190Z",
     "start_time": "2021-04-15T19:55:20.338297Z"
    }
   },
   "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>RMW</th>\n",
       "      <th>CMA</th>\n",
       "      <th>RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.206316</td>\n",
       "      <td>0.051053</td>\n",
       "      <td>-0.055789</td>\n",
       "      <td>0.139789</td>\n",
       "      <td>0.048842</td>\n",
       "      <td>0.012737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.568555</td>\n",
       "      <td>2.302701</td>\n",
       "      <td>2.202892</td>\n",
       "      <td>1.593750</td>\n",
       "      <td>1.416798</td>\n",
       "      <td>0.022665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-7.890000</td>\n",
       "      <td>-4.510000</td>\n",
       "      <td>-4.520000</td>\n",
       "      <td>-3.930000</td>\n",
       "      <td>-3.350000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.565000</td>\n",
       "      <td>-1.670000</td>\n",
       "      <td>-1.655000</td>\n",
       "      <td>-0.965000</td>\n",
       "      <td>-0.990000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.290000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>-0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.265000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>1.165000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>0.935000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.350000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>8.220000</td>\n",
       "      <td>3.530000</td>\n",
       "      <td>3.780000</td>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Mkt-RF        SMB        HML        RMW        CMA         RF\n",
       "count  95.000000  95.000000  95.000000  95.000000  95.000000  95.000000\n",
       "mean    1.206316   0.051053  -0.055789   0.139789   0.048842   0.012737\n",
       "std     3.568555   2.302701   2.202892   1.593750   1.416798   0.022665\n",
       "min    -7.890000  -4.510000  -4.520000  -3.930000  -3.350000   0.000000\n",
       "25%    -0.565000  -1.670000  -1.655000  -0.965000  -0.990000   0.000000\n",
       "50%     1.290000   0.150000  -0.360000   0.140000  -0.020000   0.000000\n",
       "75%     3.265000   1.555000   1.165000   1.140000   0.935000   0.010000\n",
       "max    11.350000   6.800000   8.220000   3.530000   3.780000   0.090000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff_factor_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute excess Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.493870Z",
     "start_time": "2021-04-15T19:55:20.371460Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Columns: 1986 entries, A to ZUMZ\n",
      "dtypes: float64(1986)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "excess_returns = returns.sub(ff_factor_data.RF, axis=0)\n",
    "excess_returns.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.510527Z",
     "start_time": "2021-04-15T19:55:20.495111Z"
    }
   },
   "outputs": [],
   "source": [
    "excess_returns = excess_returns.clip(lower=np.percentile(excess_returns, 1),\n",
    "                                     upper=np.percentile(excess_returns, 99))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fama-Macbeth Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given data on risk factors and portfolio returns, it is useful to estimate the portfolio's exposure, that is, how much the risk factors drive portfolio returns, as well as how much the exposure to a given factor is worth, that is, the what market's risk factor premium is. The risk premium then permits to estimate the return for any portfolio provided the factor exposure is known or can be assumed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.520870Z",
     "start_time": "2021-04-15T19:55:20.511704Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Food    95 non-null     float64\n",
      " 1   Mines   95 non-null     float64\n",
      " 2   Oil     95 non-null     float64\n",
      " 3   Clths   95 non-null     float64\n",
      " 4   Durbl   95 non-null     float64\n",
      " 5   Chems   95 non-null     float64\n",
      " 6   Cnsum   95 non-null     float64\n",
      " 7   Cnstr   95 non-null     float64\n",
      " 8   Steel   95 non-null     float64\n",
      " 9   FabPr   95 non-null     float64\n",
      " 10  Machn   95 non-null     float64\n",
      " 11  Cars    95 non-null     float64\n",
      " 12  Trans   95 non-null     float64\n",
      " 13  Utils   95 non-null     float64\n",
      " 14  Rtail   95 non-null     float64\n",
      " 15  Finan   95 non-null     float64\n",
      " 16  Other   95 non-null     float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 13.4 KB\n"
     ]
    }
   ],
   "source": [
    "ff_portfolio_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.538124Z",
     "start_time": "2021-04-15T19:55:20.521998Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Mkt-RF  95 non-null     float64\n",
      " 1   SMB     95 non-null     float64\n",
      " 2   HML     95 non-null     float64\n",
      " 3   RMW     95 non-null     float64\n",
      " 4   CMA     95 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 4.5 KB\n"
     ]
    }
   ],
   "source": [
    "ff_factor_data = ff_factor_data.drop('RF', axis=1)\n",
    "ff_factor_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To address the inference problem caused by the correlation of the residuals, Fama and MacBeth proposed a two-step methodology for a cross-sectional regression of returns on factors. The two-stage Fama—Macbeth regression is designed to estimate the premium rewarded for the exposure to a particular risk factor by the market. The two stages consist of:\n",
    "\n",
    "- First stage: N time-series regression, one for each asset or portfolio, of its excess returns on the factors to estimate the factor loadings.\n",
    "\n",
    "- Second stage: T cross-sectional regression, one for each time period, to estimate the risk premium.\n",
    "\n",
    "See corresponding section in Chapter 7 of [Machine Learning for Trading](https://www.amazon.com/Hands-Machine-Learning-Algorithmic-Trading-ebook/dp/B07JLFH7C5/ref=sr_1_2?ie=UTF8&qid=1548455634&sr=8-2&keywords=machine+learning+algorithmic+trading) for details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute the factor risk premia as the time average and get t-statistic to assess their individual significance, using the assumption that the risk premia estimates are independent over time.\n",
    "\n",
    "If we had a very large and representative data sample on traded risk factors we could use the sample mean as a risk premium estimate. However, we typically do not have a sufficiently long history to and the margin of error around the sample mean could be quite large. \n",
    "\n",
    "The Fama—Macbeth methodology leverages the covariance of the factors with other assets to determine the factor premia. The second moment of asset returns is easier to estimate than the first moment, and obtaining more granular data improves estimation considerably, which is not true of mean estimation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Factor Exposures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can implement the first stage to obtain the 17 factor loading estimates as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.643488Z",
     "start_time": "2021-04-15T19:55:20.539304Z"
    }
   },
   "outputs": [],
   "source": [
    "betas = []\n",
    "for industry in ff_portfolio_data:\n",
    "    step1 = OLS(endog=ff_portfolio_data.loc[ff_factor_data.index, industry], \n",
    "                exog=add_constant(ff_factor_data)).fit()\n",
    "    betas.append(step1.params.drop('const'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.658189Z",
     "start_time": "2021-04-15T19:55:20.646568Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 17 entries, Food  to Other\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Mkt-RF  17 non-null     float64\n",
      " 1   SMB     17 non-null     float64\n",
      " 2   HML     17 non-null     float64\n",
      " 3   RMW     17 non-null     float64\n",
      " 4   CMA     17 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 1.3+ KB\n"
     ]
    }
   ],
   "source": [
    "betas = pd.DataFrame(betas, \n",
    "                     columns=ff_factor_data.columns, \n",
    "                     index=ff_portfolio_data.columns)\n",
    "betas.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Risk Premia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second stage, we run 96 regressions of the period returns for the cross section of portfolios on the factor loadings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.759883Z",
     "start_time": "2021-04-15T19:55:20.659165Z"
    }
   },
   "outputs": [],
   "source": [
    "lambdas = []\n",
    "for period in ff_portfolio_data.index:\n",
    "    step2 = OLS(endog=ff_portfolio_data.loc[period, betas.index], \n",
    "                exog=betas).fit()\n",
    "    lambdas.append(step2.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.780931Z",
     "start_time": "2021-04-15T19:55:20.760976Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Mkt-RF  95 non-null     float64\n",
      " 1   SMB     95 non-null     float64\n",
      " 2   HML     95 non-null     float64\n",
      " 3   RMW     95 non-null     float64\n",
      " 4   CMA     95 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 9.3 KB\n"
     ]
    }
   ],
   "source": [
    "lambdas = pd.DataFrame(lambdas, \n",
    "                       index=ff_portfolio_data.index,\n",
    "                       columns=betas.columns.tolist())\n",
    "lambdas.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.902217Z",
     "start_time": "2021-04-15T19:55:20.781976Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambdas.mean().sort_values().plot.barh(figsize=(12, 4))\n",
    "sns.despine()\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:20.908357Z",
     "start_time": "2021-04-15T19:55:20.903193Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mkt-RF    0.340179\n",
       "SMB      -0.013216\n",
       "HML      -0.261636\n",
       "RMW      -0.035186\n",
       "CMA      -0.172461\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = lambdas.mean().div(lambdas.std())\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:21.278124Z",
     "start_time": "2021-04-15T19:55:20.909336Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = 24  # months\n",
    "ax1 = plt.subplot2grid((1, 3), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 3), (0, 1), colspan=2)\n",
    "lambdas.mean().sort_values().plot.barh(ax=ax1)\n",
    "lambdas.rolling(window).mean().dropna().plot(lw=1,\n",
    "                                             figsize=(14, 5),\n",
    "                                             sharey=True,\n",
    "                                             ax=ax2)\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:21.839217Z",
     "start_time": "2021-04-15T19:55:21.279210Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = 24  # months\n",
    "lambdas.rolling(window).mean().dropna().plot(lw=2,\n",
    "                                             figsize=(14, 7),\n",
    "                                             subplots=True,\n",
    "                                             sharey=True)\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fama-Macbeth with the LinearModels library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear_models library extends statsmodels with various models for panel data and also implements the two-stage Fama—MacBeth procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:21.859424Z",
     "start_time": "2021-04-15T19:55:21.840019Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      LinearFactorModel Estimation Summary                      \n",
      "================================================================================\n",
      "No. Test Portfolios:                 17   R-squared:                      0.6885\n",
      "No. Factors:                          5   J-statistic:                    17.038\n",
      "No. Observations:                    95   P-value                         0.1482\n",
      "Date:                  Thu, Apr 15 2021   Distribution:                 chi2(12)\n",
      "Time:                          14:55:21                                         \n",
      "Cov. Estimator:                  robust                                         \n",
      "                                                                                \n",
      "                            Risk Premia Estimates                             \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "Mkt-RF         1.2208     0.4076     2.9951     0.0027      0.4219      2.0197\n",
      "SMB           -0.0523     0.7979    -0.0656     0.9477     -1.6161      1.5115\n",
      "HML           -1.0316     0.6332    -1.6292     0.1033     -2.2726      0.2094\n",
      "RMW           -0.1044     0.7738    -0.1350     0.8926     -1.6210      1.4121\n",
      "CMA           -0.6252     0.5222    -1.1973     0.2312     -1.6488      0.3983\n",
      "==============================================================================\n",
      "\n",
      "Covariance estimator:\n",
      "HeteroskedasticCovariance\n",
      "See full_summary for complete results\n"
     ]
    }
   ],
   "source": [
    "mod = LinearFactorModel(portfolios=ff_portfolio_data, \n",
    "                        factors=ff_factor_data)\n",
    "res = mod.fit()\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:21.921756Z",
     "start_time": "2021-04-15T19:55:21.861590Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      LinearFactorModel Estimation Summary                      \n",
      "================================================================================\n",
      "No. Test Portfolios:                 17   R-squared:                      0.6885\n",
      "No. Factors:                          5   J-statistic:                    17.038\n",
      "No. Observations:                    95   P-value                         0.1482\n",
      "Date:                  Thu, Apr 15 2021   Distribution:                 chi2(12)\n",
      "Time:                          14:55:21                                         \n",
      "Cov. Estimator:                  robust                                         \n",
      "                                                                                \n",
      "                            Risk Premia Estimates                             \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "Mkt-RF         1.2208     0.4076     2.9951     0.0027      0.4219      2.0197\n",
      "SMB           -0.0523     0.7979    -0.0656     0.9477     -1.6161      1.5115\n",
      "HML           -1.0316     0.6332    -1.6292     0.1033     -2.2726      0.2094\n",
      "RMW           -0.1044     0.7738    -0.1350     0.8926     -1.6210      1.4121\n",
      "CMA           -0.6252     0.5222    -1.1973     0.2312     -1.6488      0.3983\n",
      "\n",
      "\n",
      "                              Food  Coefficients                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "alpha          0.1874     0.2393     0.7831     0.4336     -0.2816      0.6563\n",
      "Mkt-RF         0.6866     0.0465     14.773     0.0000      0.5955      0.7777\n",
      "SMB           -0.3100     0.1126    -2.7532     1.9941     -0.5307     -0.0893\n",
      "HML           -0.3493     0.1420    -2.4595     1.9861     -0.6277     -0.0710\n",
      "RMW            0.3075     0.1243     2.4747     0.0133      0.0640      0.5510\n",
      "CMA            0.4666     0.1636     2.8517     0.0043      0.1459      0.7873\n",
      "\n",
      "\n",
      "                              Mines Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.6490     0.5444    -1.1922     1.7668     -1.7159      0.4180\n",
      "Mkt-RF         1.2987     0.1950     6.6584     0.0000      0.9164      1.6810\n",
      "SMB            0.1805     0.3380     0.5341     0.5933     -0.4819      0.8429\n",
      "HML            0.1891     0.3305     0.5721     0.5673     -0.4587      0.8368\n",
      "RMW            0.1449     0.4295     0.3375     0.7358     -0.6968      0.9867\n",
      "CMA            0.6112     0.5507     1.1098     0.2671     -0.4682      1.6905\n",
      "\n",
      "\n",
      "                              Oil   Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.2044     0.3452     0.5922     0.5537     -0.4722      0.8811\n",
      "Mkt-RF         1.0553     0.1027     10.273     0.0000      0.8540      1.2567\n",
      "SMB            0.1554     0.1941     0.8005     0.4234     -0.2251      0.5359\n",
      "HML            0.6685     0.2048     3.2642     0.0011      0.2671      1.0698\n",
      "RMW           -0.0247     0.2326    -0.1064     1.0847     -0.4806      0.4311\n",
      "CMA            0.3117     0.2831     1.1010     0.2709     -0.2432      0.8666\n",
      "\n",
      "\n",
      "                              Clths Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.1726     0.3388     0.5093     0.6105     -0.4915      0.8367\n",
      "Mkt-RF         0.9685     0.1214     7.9776     0.0000      0.7306      1.2065\n",
      "SMB            0.3430     0.1966     1.7450     0.0810     -0.0422      0.7282\n",
      "HML           -0.1882     0.2098    -0.8969     1.6302     -0.5993      0.2230\n",
      "RMW            0.5649     0.2720     2.0767     0.0378      0.0318      1.0980\n",
      "CMA            0.0381     0.3163     0.1205     0.9040     -0.5818      0.6581\n",
      "\n",
      "\n",
      "                              Durbl Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.1564     0.3204    -0.4883     1.3747     -0.7843      0.4715\n",
      "Mkt-RF         1.1740     0.0834     14.072     0.0000      1.0105      1.3375\n",
      "SMB            0.5378     0.1194     4.5035     0.0000      0.3037      0.7719\n",
      "HML            0.0706     0.1480     0.4771     0.6333     -0.2195      0.3607\n",
      "RMW            0.5117     0.1940     2.6380     0.0083      0.1315      0.8919\n",
      "CMA           -0.1310     0.2655    -0.4936     1.3784     -0.6514      0.3893\n",
      "\n",
      "\n",
      "                              Chems Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.2048     0.3111    -0.6584     1.4897     -0.8145      0.4049\n",
      "Mkt-RF         1.3510     0.1063     12.704     0.0000      1.1426      1.5594\n",
      "SMB            0.1660     0.1489     1.1154     0.2647     -0.1257      0.4578\n",
      "HML            0.1952     0.1480     1.3189     0.1872     -0.0949      0.4852\n",
      "RMW            0.1410     0.1912     0.7374     0.4609     -0.2338      0.5158\n",
      "CMA           -0.2301     0.2633    -0.8738     1.6178     -0.7462      0.2860\n",
      "\n",
      "\n",
      "                              Cnsum Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.0380     0.3566    -0.1065     1.0848     -0.7368      0.6609\n",
      "Mkt-RF         0.7625     0.0591     12.897     0.0000      0.6466      0.8784\n",
      "SMB           -0.3327     0.1006    -3.3088     1.9991     -0.5298     -0.1356\n",
      "HML           -0.5773     0.1259    -4.5845     2.0000     -0.8241     -0.3305\n",
      "RMW           -0.0606     0.1316    -0.4603     1.3547     -0.3186      0.1974\n",
      "CMA            0.5748     0.2271     2.5306     0.0114      0.1296      1.0199\n",
      "\n",
      "\n",
      "                              Cnstr Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.6213     0.3917     1.5862     0.1127     -0.1464      1.3890\n",
      "Mkt-RF         1.1161     0.0828     13.478     0.0000      0.9538      1.2784\n",
      "SMB            0.4463     0.1337     3.3389     0.0008      0.1843      0.7083\n",
      "HML            0.0920     0.1892     0.4861     0.6269     -0.2789      0.4629\n",
      "RMW           -0.0107     0.2232    -0.0482     1.0384     -0.4482      0.4267\n",
      "CMA            0.1409     0.2425     0.5811     0.5612     -0.3344      0.6162\n",
      "\n",
      "\n",
      "                              Steel Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.3503     0.4030    -0.8692     1.6153     -1.1403      0.4396\n",
      "Mkt-RF         1.4647     0.1381     10.604     0.0000      1.1940      1.7355\n",
      "SMB            0.4104     0.2548     1.6103     0.1073     -0.0891      0.9098\n",
      "HML            0.4000     0.2653     1.5076     0.1317     -0.1200      0.9200\n",
      "RMW            0.1355     0.3342     0.4054     0.6852     -0.5196      0.7906\n",
      "CMA            0.4840     0.4192     1.1547     0.2482     -0.3376      1.3056\n",
      "\n",
      "\n",
      "                              FabPr Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.2168     0.2831     0.7659     0.4437     -0.3381      0.7718\n",
      "Mkt-RF         1.0695     0.0734     14.573     0.0000      0.9257      1.2133\n",
      "SMB            0.4602     0.0979     4.7024     0.0000      0.2684      0.6520\n",
      "HML           -0.0294     0.1111    -0.2646     1.2087     -0.2471      0.1883\n",
      "RMW            0.1531     0.1456     1.0510     0.2933     -0.1324      0.4385\n",
      "CMA            0.1865     0.1855     1.0055     0.3147     -0.1771      0.5502\n",
      "\n",
      "\n",
      "                              Machn Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.3139     0.2688    -1.1677     1.7571     -0.8409      0.2130\n",
      "Mkt-RF         1.1883     0.0582     20.424     0.0000      1.0742      1.3023\n",
      "SMB            0.1817     0.1074     1.6922     0.0906     -0.0287      0.3921\n",
      "HML            0.0384     0.1060     0.3621     0.7173     -0.1694      0.2462\n",
      "RMW            0.0540     0.1581     0.3416     0.7327     -0.2559      0.3639\n",
      "CMA           -0.3765     0.1786    -2.1079     1.9650     -0.7266     -0.0264\n",
      "\n",
      "\n",
      "                              Cars  Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.0952     0.3906    -0.2436     1.1925     -0.8607      0.6704\n",
      "Mkt-RF         1.1895     0.0996     11.949     0.0000      0.9944      1.3846\n",
      "SMB            0.5941     0.1290     4.6069     0.0000      0.3414      0.8469\n",
      "HML            0.0213     0.1757     0.1214     0.9034     -0.3231      0.3658\n",
      "RMW            0.0223     0.2201     0.1012     0.9194     -0.4091      0.4537\n",
      "CMA            0.0123     0.2993     0.0412     0.9671     -0.5743      0.5990\n",
      "\n",
      "\n",
      "                              Trans Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.4814     0.3269     1.4725     0.1409     -0.1594      1.1221\n",
      "Mkt-RF         1.0248     0.0508     20.161     0.0000      0.9251      1.1244\n",
      "SMB            0.2537     0.1030     2.4641     0.0137      0.0519      0.4556\n",
      "HML            0.0117     0.1222     0.0961     0.9235     -0.2278      0.2512\n",
      "RMW            0.3778     0.1606     2.3525     0.0186      0.0630      0.6926\n",
      "CMA            0.2634     0.2018     1.3056     0.1917     -0.1320      0.6589\n",
      "\n",
      "\n",
      "                              Utils Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.3695     0.3118     1.1849     0.2361     -0.2417      0.9807\n",
      "Mkt-RF         0.5022     0.0911     5.5126     0.0000      0.3237      0.6808\n",
      "SMB           -0.2454     0.1567    -1.5664     1.8827     -0.5525      0.0617\n",
      "HML           -0.2932     0.1772    -1.6549     1.9021     -0.6405      0.0540\n",
      "RMW            0.2424     0.1949     1.2435     0.2137     -0.1396      0.6244\n",
      "CMA            0.5207     0.2955     1.7621     0.0781     -0.0585      1.0998\n",
      "\n",
      "\n",
      "                              Rtail Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.0421     0.2681    -0.1570     1.1247     -0.5676      0.4834\n",
      "Mkt-RF         0.9087     0.0689     13.192     0.0000      0.7737      1.0437\n",
      "SMB            0.1316     0.0994     1.3235     0.1857     -0.0633      0.3265\n",
      "HML           -0.3830     0.1296    -2.9560     1.9969     -0.6370     -0.1291\n",
      "RMW            0.6884     0.1610     4.2748     0.0000      0.3728      1.0041\n",
      "CMA            0.1961     0.1741     1.1259     0.2602     -0.1452      0.5373\n",
      "\n",
      "\n",
      "                              Finan Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.3752     0.3715     1.0097     0.3126     -0.3530      1.1034\n",
      "Mkt-RF         1.0565     0.0426     24.782     0.0000      0.9730      1.1401\n",
      "SMB            0.0756     0.0856     0.8825     0.3775     -0.0923      0.2434\n",
      "HML            0.7333     0.0878     8.3550     0.0000      0.5613      0.9054\n",
      "RMW           -0.4296     0.1061    -4.0490     1.9999     -0.6376     -0.2216\n",
      "CMA           -0.5083     0.1124    -4.5216     2.0000     -0.7286     -0.2879\n",
      "\n",
      "\n",
      "                              Other Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.1368     0.2280    -0.5998     1.4513     -0.5837      0.3102\n",
      "Mkt-RF         1.0416     0.0244     42.676     0.0000      0.9937      1.0894\n",
      "SMB           -0.1150     0.0397    -2.8966     1.9962     -0.1927     -0.0372\n",
      "HML           -0.2042     0.0379    -5.3820     2.0000     -0.2786     -0.1299\n",
      "RMW           -0.0685     0.0626    -1.0934     1.7258     -0.1912      0.0543\n",
      "CMA            0.0194     0.0657     0.2945     0.7684     -0.1095      0.1482\n",
      "==============================================================================\n",
      "\n",
      "Covariance estimator:\n",
      "HeteroskedasticCovariance\n",
      "See full_summary for complete results\n"
     ]
    }
   ],
   "source": [
    "print(res.full_summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This provides us with the same result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T19:55:21.926652Z",
     "start_time": "2021-04-15T19:55:21.922840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mkt-RF    1.220797\n",
       "SMB      -0.052301\n",
       "HML      -1.031603\n",
       "RMW      -0.104427\n",
       "CMA      -0.625245\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambdas.mean()"
   ]
  }
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