07-linear-regression-answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "setup"
    ]
   },
   "source": [
    "(c) 2016 - present. Enplus Advisors, Inc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "setup"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "tags": [
     "setup"
    ]
   },
   "outputs": [],
   "source": [
    "dat = pd.read_csv('starmine.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Regress the Starmine indicator on 1-month returns. \n",
    "Get a summary of the model.\n",
    "\n",
    "* What are the coefficient estimates?\n",
    "* What is the R-squared?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>ret_0_1_m</td>    <th>  R-squared:         </th>  <td>   0.008</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.008</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   151.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 17 May 2016</td> <th>  Prob (F-statistic):</th>  <td>1.30e-34</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:52:39</td>     <th>  Log-Likelihood:    </th>  <td>  15597.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 19702</td>      <th>  AIC:               </th> <td>-3.119e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 19700</td>      <th>  BIC:               </th> <td>-3.117e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.0073</td> <td>    0.002</td> <td>    4.554</td> <td> 0.000</td> <td>    0.004     0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smi</th>       <td>    0.0003</td> <td> 2.75e-05</td> <td>   12.294</td> <td> 0.000</td> <td>    0.000     0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3354.662</td> <th>  Durbin-Watson:     </th> <td>   1.881</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>32693.833</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.529</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.222</td>  <th>  Cond. No.          </th> <td>    121.</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              ret_0_1_m   R-squared:                       0.008\n",
       "Model:                            OLS   Adj. R-squared:                  0.008\n",
       "Method:                 Least Squares   F-statistic:                     151.1\n",
       "Date:                Tue, 17 May 2016   Prob (F-statistic):           1.30e-34\n",
       "Time:                        19:52:39   Log-Likelihood:                 15597.\n",
       "No. Observations:               19702   AIC:                        -3.119e+04\n",
       "Df Residuals:                   19700   BIC:                        -3.117e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.0073      0.002      4.554      0.000         0.004     0.010\n",
       "smi            0.0003   2.75e-05     12.294      0.000         0.000     0.000\n",
       "==============================================================================\n",
       "Omnibus:                     3354.662   Durbin-Watson:                   1.881\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            32693.833\n",
       "Skew:                           0.529   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.222   Cond. No.                         121.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = smf.ols('ret_0_1_m ~ smi', data=dat).fit()\n",
    "m1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:** \n",
    "\n",
    "Examine the model results graphically.\n",
    "* Create a residual plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Ro0b1+V5Kya3eCSGEEEIyECrhBsIK+NfeeAM1AL72xhuuK+J1dXX43e9+hy984QsoLCzE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SwldJqOOlELn9be9yIDA/uJH+63MDCwBwE7f0HcBGMAnbeHviAabYZRKzQp/mdIpos7qR7vncg+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\n5bIul8rlIZqa0oyNaSFRTyOGTPU1h+NESKVaKBTC8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C5VvysS6WJuzhRbW0Q7eOVznzuf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H39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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d07d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "fig = sm.graphics.plot_fit(m1, \"smi\", ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "* Are there any problems with the plot? How might we resolve them?\n",
    "* How do we remove the intercept?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>ret_0_1_m</td>    <th>  R-squared:         </th>  <td>   0.054</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.054</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   1122.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 17 May 2016</td> <th>  Prob (F-statistic):</th>  <td>3.13e-239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:52:40</td>     <th>  Log-Likelihood:    </th>  <td>  15586.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 19702</td>      <th>  AIC:               </th> <td>-3.117e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 19701</td>      <th>  BIC:               </th> <td>-3.116e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smi</th> <td>    0.0004</td> <td> 1.33e-05</td> <td>   33.492</td> <td> 0.000</td> <td>    0.000     0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3351.036</td> <th>  Durbin-Watson:     </th> <td>   1.881</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>32561.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.529</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.209</td>  <th>  Cond. No.          </th> <td>    1.00</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              ret_0_1_m   R-squared:                       0.054\n",
       "Model:                            OLS   Adj. R-squared:                  0.054\n",
       "Method:                 Least Squares   F-statistic:                     1122.\n",
       "Date:                Tue, 17 May 2016   Prob (F-statistic):          3.13e-239\n",
       "Time:                        19:52:40   Log-Likelihood:                 15586.\n",
       "No. Observations:               19702   AIC:                        -3.117e+04\n",
       "Df Residuals:                   19701   BIC:                        -3.116e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "smi            0.0004   1.33e-05     33.492      0.000         0.000     0.000\n",
       "==============================================================================\n",
       "Omnibus:                     3351.036   Durbin-Watson:                   1.881\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            32561.085\n",
       "Skew:                           0.529   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.209   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = smf.ols('ret_0_1_m ~ smi - 1', data=dat).fit()\n",
    "m2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "How would we run a Fama-French style regression?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>ret_0_1_m</td>    <th>  R-squared:         </th>  <td>   0.008</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.008</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   49.73</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 17 May 2016</td> <th>  Prob (F-statistic):</th>  <td>5.21e-32</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:52:40</td>     <th>  Log-Likelihood:    </th>  <td>  15305.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 19237</td>      <th>  AIC:               </th> <td>-3.060e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 19233</td>      <th>  BIC:               </th> <td>-3.057e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>    0.0208</td> <td>    0.012</td> <td>    1.727</td> <td> 0.084</td> <td>   -0.003     0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smi</th>             <td>    0.0003</td> <td> 2.87e-05</td> <td>   11.329</td> <td> 0.000</td> <td>    0.000     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(cap_usd)</th> <td>   -0.0007</td> <td>    0.001</td> <td>   -1.214</td> <td> 0.225</td> <td>   -0.002     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(pb)</th>      <td>    0.0018</td> <td>    0.001</td> <td>    1.655</td> <td> 0.098</td> <td>   -0.000     0.004</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3249.117</td> <th>  Durbin-Watson:     </th> <td>   1.889</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>32631.960</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.511</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.298</td>  <th>  Cond. No.          </th> <td>    939.</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              ret_0_1_m   R-squared:                       0.008\n",
       "Model:                            OLS   Adj. R-squared:                  0.008\n",
       "Method:                 Least Squares   F-statistic:                     49.73\n",
       "Date:                Tue, 17 May 2016   Prob (F-statistic):           5.21e-32\n",
       "Time:                        19:52:40   Log-Likelihood:                 15305.\n",
       "No. Observations:               19237   AIC:                        -3.060e+04\n",
       "Df Residuals:                   19233   BIC:                        -3.057e+04\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept           0.0208      0.012      1.727      0.084        -0.003     0.044\n",
       "smi                 0.0003   2.87e-05     11.329      0.000         0.000     0.000\n",
       "np.log(cap_usd)    -0.0007      0.001     -1.214      0.225        -0.002     0.000\n",
       "np.log(pb)          0.0018      0.001      1.655      0.098        -0.000     0.004\n",
       "==============================================================================\n",
       "Omnibus:                     3249.117   Durbin-Watson:                   1.889\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            32631.960\n",
       "Skew:                           0.511   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.298   Cond. No.                         939.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['pb'] = dat['cap_usd'] / dat['common_equity']\n",
    "m3 = smf.ols('ret_0_1_m ~ smi + np.log(cap_usd) + np.log(pb)', data=dat).fit()\n",
    "m3.summary()"
   ]
  }
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