sec_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Literature Review"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$r_{it} = B_1 T_{it} + I_{it} + P_t + M_t + e_i$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Macroeconomic Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>equal_equity</th>\n",
       "      <th>bill_1m</th>\n",
       "      <th>bill_30y</th>\n",
       "      <th>consump</th>\n",
       "      <th>inflation</th>\n",
       "      <th>low_bond</th>\n",
       "      <th>oil</th>\n",
       "      <th>produc</th>\n",
       "      <th>sp500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.11</td>\n",
       "      <td>4.25</td>\n",
       "      <td>10202.2</td>\n",
       "      <td>1.640043</td>\n",
       "      <td>6.04</td>\n",
       "      <td>79.48</td>\n",
       "      <td>94.2238</td>\n",
       "      <td>47.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>34.725072</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3.91</td>\n",
       "      <td>10689.3</td>\n",
       "      <td>3.156842</td>\n",
       "      <td>5.66</td>\n",
       "      <td>94.88</td>\n",
       "      <td>97.1387</td>\n",
       "      <td>26.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>35.756063</td>\n",
       "      <td>0.07</td>\n",
       "      <td>2.92</td>\n",
       "      <td>11050.6</td>\n",
       "      <td>2.069337</td>\n",
       "      <td>4.94</td>\n",
       "      <td>94.05</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>59.440359</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.45</td>\n",
       "      <td>11361.2</td>\n",
       "      <td>1.464833</td>\n",
       "      <td>5.10</td>\n",
       "      <td>97.98</td>\n",
       "      <td>101.9753</td>\n",
       "      <td>22.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>117.523283</td>\n",
       "      <td>0.03</td>\n",
       "      <td>3.34</td>\n",
       "      <td>11863.7</td>\n",
       "      <td>1.622223</td>\n",
       "      <td>4.85</td>\n",
       "      <td>93.17</td>\n",
       "      <td>105.1294</td>\n",
       "      <td>39.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date  equal_equity  bill_1m  bill_30y  consump  inflation  low_bond    oil  \\\n",
       "0  2009     34.000000     0.11      4.25  10202.2   1.640043      6.04  79.48   \n",
       "1  2010     34.725072     0.04      3.91  10689.3   3.156842      5.66  94.88   \n",
       "2  2011     35.756063     0.07      2.92  11050.6   2.069337      4.94  94.05   \n",
       "3  2012     59.440359     0.05      3.45  11361.2   1.464833      5.10  97.98   \n",
       "4  2013    117.523283     0.03      3.34  11863.7   1.622223      4.85  93.17   \n",
       "\n",
       "     produc  sp500  \n",
       "0   94.2238  47.22  \n",
       "1   97.1387  26.45  \n",
       "2  100.0000   4.95  \n",
       "3  101.9753  22.25  \n",
       "4  105.1294  39.72  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro = pd.read_csv('macro.csv')\n",
    "macro.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Industry Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>Date</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>comm</td>\n",
       "      <td>-41.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>comm</td>\n",
       "      <td>73.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>comm</td>\n",
       "      <td>33.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>comm</td>\n",
       "      <td>11.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>comm</td>\n",
       "      <td>36.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date Industry  Return\n",
       "0  2008     comm  -41.26\n",
       "1  2009     comm   73.76\n",
       "2  2010     comm   33.61\n",
       "3  2011     comm   11.61\n",
       "4  2012     comm   36.26"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry_data = pd.read_csv('bloomberg_data.csv')\n",
    "industry_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VC Activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Industry</th>\n",
       "      <th>total</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>comm</td>\n",
       "      <td>13092.02</td>\n",
       "      <td>15.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>comm</td>\n",
       "      <td>8252.16</td>\n",
       "      <td>14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>comm</td>\n",
       "      <td>8202.69</td>\n",
       "      <td>16.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>comm</td>\n",
       "      <td>10708.05</td>\n",
       "      <td>21.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>comm</td>\n",
       "      <td>19560.26</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date Industry     total    avg\n",
       "0  2009     comm  13092.02  15.05\n",
       "1  2010     comm   8252.16  14.66\n",
       "2  2011     comm   8202.69  16.24\n",
       "3  2012     comm  10708.05  21.33\n",
       "4  2013     comm  19560.26  37.91"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc_data = pd.read_csv('vc_data_new.csv')\n",
    "vc_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Industry</th>\n",
       "      <th>total</th>\n",
       "      <th>avg</th>\n",
       "      <th>equal_equity</th>\n",
       "      <th>bill_1m</th>\n",
       "      <th>bill_30y</th>\n",
       "      <th>consump</th>\n",
       "      <th>inflation</th>\n",
       "      <th>low_bond</th>\n",
       "      <th>oil</th>\n",
       "      <th>produc</th>\n",
       "      <th>sp500</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>comm</td>\n",
       "      <td>13092.02</td>\n",
       "      <td>15.050000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>4.25</td>\n",
       "      <td>10202.2</td>\n",
       "      <td>1.640043</td>\n",
       "      <td>6.04</td>\n",
       "      <td>79.48</td>\n",
       "      <td>94.2238</td>\n",
       "      <td>47.22</td>\n",
       "      <td>73.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>consumer</td>\n",
       "      <td>8232.73</td>\n",
       "      <td>15.560000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>4.25</td>\n",
       "      <td>10202.2</td>\n",
       "      <td>1.640043</td>\n",
       "      <td>6.04</td>\n",
       "      <td>79.48</td>\n",
       "      <td>94.2238</td>\n",
       "      <td>47.22</td>\n",
       "      <td>41.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>energy</td>\n",
       "      <td>5382.27</td>\n",
       "      <td>13.260000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>4.25</td>\n",
       "      <td>10202.2</td>\n",
       "      <td>1.640043</td>\n",
       "      <td>6.04</td>\n",
       "      <td>79.48</td>\n",
       "      <td>94.2238</td>\n",
       "      <td>47.22</td>\n",
       "      <td>54.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>tech</td>\n",
       "      <td>39075.09</td>\n",
       "      <td>9.085117</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>4.25</td>\n",
       "      <td>10202.2</td>\n",
       "      <td>1.640043</td>\n",
       "      <td>6.04</td>\n",
       "      <td>79.48</td>\n",
       "      <td>94.2238</td>\n",
       "      <td>47.22</td>\n",
       "      <td>80.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>health</td>\n",
       "      <td>11745.67</td>\n",
       "      <td>10.422067</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>4.25</td>\n",
       "      <td>10202.2</td>\n",
       "      <td>1.640043</td>\n",
       "      <td>6.04</td>\n",
       "      <td>79.48</td>\n",
       "      <td>94.2238</td>\n",
       "      <td>47.22</td>\n",
       "      <td>38.530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date  Industry     total        avg  equal_equity  bill_1m  bill_30y  \\\n",
       "0  2009      comm  13092.02  15.050000          34.0     0.11      4.25   \n",
       "1  2009  consumer   8232.73  15.560000          34.0     0.11      4.25   \n",
       "2  2009    energy   5382.27  13.260000          34.0     0.11      4.25   \n",
       "3  2009      tech  39075.09   9.085117          34.0     0.11      4.25   \n",
       "4  2009    health  11745.67  10.422067          34.0     0.11      4.25   \n",
       "\n",
       "   consump  inflation  low_bond    oil   produc  sp500  Return  \n",
       "0  10202.2   1.640043      6.04  79.48  94.2238  47.22  73.760  \n",
       "1  10202.2   1.640043      6.04  79.48  94.2238  47.22  41.225  \n",
       "2  10202.2   1.640043      6.04  79.48  94.2238  47.22  54.470  \n",
       "3  10202.2   1.640043      6.04  79.48  94.2238  47.22  80.550  \n",
       "4  10202.2   1.640043      6.04  79.48  94.2238  47.22  38.530  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_temp = pd.merge(vc_data, macro, on='Date')\n",
    "data = pd.merge(data_temp, industry_data, on=('Date', 'Industry'))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r = data['Return']\n",
    "\n",
    "total = data['total']\n",
    "avg = data['avg']\n",
    "\n",
    "inflation = data['inflation']\n",
    "consump = data['consump']\n",
    "produc = data['produc']\n",
    "oil = data['oil']\n",
    "bill_1m = data['bill_1m']\n",
    "bill_30y = data['bill_30y']\n",
    "low_bond = data['low_bond']\n",
    "equal_equity = data['equal_equity']\n",
    "sp500 = data['sp500']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VC Activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>Date</th>\n",
       "      <th>total</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2012.500000</td>\n",
       "      <td>15786.570500</td>\n",
       "      <td>18.129323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.320477</td>\n",
       "      <td>8999.206916</td>\n",
       "      <td>8.580243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>3670.200000</td>\n",
       "      <td>7.267166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2010.750000</td>\n",
       "      <td>8714.187500</td>\n",
       "      <td>11.248385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2012.500000</td>\n",
       "      <td>13011.700000</td>\n",
       "      <td>15.552434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.250000</td>\n",
       "      <td>20949.505000</td>\n",
       "      <td>23.267500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>39075.090000</td>\n",
       "      <td>41.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date         total        avg\n",
       "count    40.000000     40.000000  40.000000\n",
       "mean   2012.500000  15786.570500  18.129323\n",
       "std       2.320477   8999.206916   8.580243\n",
       "min    2009.000000   3670.200000   7.267166\n",
       "25%    2010.750000   8714.187500  11.248385\n",
       "50%    2012.500000  13011.700000  15.552434\n",
       "75%    2014.250000  20949.505000  23.267500\n",
       "max    2016.000000  39075.090000  41.960000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Industry Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>17.282333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.611165</td>\n",
       "      <td>27.715829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>-42.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>5.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>18.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>34.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>80.550000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date     Return\n",
       "count    45.000000  45.000000\n",
       "mean   2012.000000  17.282333\n",
       "std       2.611165  27.715829\n",
       "min    2008.000000 -42.560000\n",
       "25%    2010.000000   5.220000\n",
       "50%    2012.000000  18.510000\n",
       "75%    2014.000000  34.240000\n",
       "max    2016.000000  80.550000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Macroeconomic Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>equal_equity</th>\n",
       "      <th>bill_1m</th>\n",
       "      <th>bill_30y</th>\n",
       "      <th>consump</th>\n",
       "      <th>inflation</th>\n",
       "      <th>low_bond</th>\n",
       "      <th>oil</th>\n",
       "      <th>produc</th>\n",
       "      <th>sp500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2012.50000</td>\n",
       "      <td>94.024398</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>3.27375</td>\n",
       "      <td>11714.175000</td>\n",
       "      <td>1.541686</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>75.28875</td>\n",
       "      <td>101.387350</td>\n",
       "      <td>21.965000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.44949</td>\n",
       "      <td>59.621841</td>\n",
       "      <td>0.280153</td>\n",
       "      <td>0.57587</td>\n",
       "      <td>1092.179388</td>\n",
       "      <td>0.870647</td>\n",
       "      <td>0.517547</td>\n",
       "      <td>23.66646</td>\n",
       "      <td>3.994098</td>\n",
       "      <td>15.713838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2009.00000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>2.59000</td>\n",
       "      <td>10202.200000</td>\n",
       "      <td>0.118627</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>43.29000</td>\n",
       "      <td>94.223800</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2010.75000</td>\n",
       "      <td>35.498315</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>2.87750</td>\n",
       "      <td>10960.275000</td>\n",
       "      <td>1.196187</td>\n",
       "      <td>4.817500</td>\n",
       "      <td>50.26500</td>\n",
       "      <td>99.284675</td>\n",
       "      <td>12.832500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2012.50000</td>\n",
       "      <td>88.481821</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>3.13000</td>\n",
       "      <td>11612.450000</td>\n",
       "      <td>1.543528</td>\n",
       "      <td>4.970000</td>\n",
       "      <td>86.32500</td>\n",
       "      <td>102.540550</td>\n",
       "      <td>20.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.25000</td>\n",
       "      <td>144.411788</td>\n",
       "      <td>0.145000</td>\n",
       "      <td>3.56500</td>\n",
       "      <td>12454.400000</td>\n",
       "      <td>1.747367</td>\n",
       "      <td>5.240000</td>\n",
       "      <td>94.25750</td>\n",
       "      <td>104.570350</td>\n",
       "      <td>29.767500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.00000</td>\n",
       "      <td>179.101266</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>4.25000</td>\n",
       "      <td>13393.400000</td>\n",
       "      <td>3.156842</td>\n",
       "      <td>6.040000</td>\n",
       "      <td>97.98000</td>\n",
       "      <td>105.141800</td>\n",
       "      <td>47.220000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  equal_equity   bill_1m  bill_30y       consump  inflation  \\\n",
       "count     8.00000      8.000000  8.000000   8.00000      8.000000   8.000000   \n",
       "mean   2012.50000     94.024398  0.180000   3.27375  11714.175000   1.541686   \n",
       "std       2.44949     59.621841  0.280153   0.57587   1092.179388   0.870647   \n",
       "min    2009.00000     34.000000  0.030000   2.59000  10202.200000   0.118627   \n",
       "25%    2010.75000     35.498315  0.040000   2.87750  10960.275000   1.196187   \n",
       "50%    2012.50000     88.481821  0.060000   3.13000  11612.450000   1.543528   \n",
       "75%    2014.25000    144.411788  0.145000   3.56500  12454.400000   1.747367   \n",
       "max    2016.00000    179.101266  0.850000   4.25000  13393.400000   3.156842   \n",
       "\n",
       "       low_bond       oil      produc      sp500  \n",
       "count  8.000000   8.00000    8.000000   8.000000  \n",
       "mean   5.093750  75.28875  101.387350  21.965000  \n",
       "std    0.517547  23.66646    3.994098  15.713838  \n",
       "min    4.440000  43.29000   94.223800   1.800000  \n",
       "25%    4.817500  50.26500   99.284675  12.832500  \n",
       "50%    4.970000  86.32500  102.540550  20.060000  \n",
       "75%    5.240000  94.25750  104.570350  29.767500  \n",
       "max    6.040000  97.98000  105.141800  47.220000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      r   R-squared:                       0.797\n",
      "Model:                            OLS   Adj. R-squared:                  0.736\n",
      "Method:                 Least Squares   F-statistic:                     13.11\n",
      "Date:                Thu, 29 Mar 2018   Prob (F-statistic):           3.50e-08\n",
      "Time:                        19:17:27   Log-Likelihood:                -146.90\n",
      "No. Observations:                  40   AIC:                             313.8\n",
      "Df Residuals:                      30   BIC:                             330.7\n",
      "Df Model:                           9                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept        0.9795      0.804      1.218      0.233      -0.663       2.622\n",
      "total            0.0008      0.000      3.327      0.002       0.000       0.001\n",
      "avg              0.5584      0.256      2.185      0.037       0.036       1.080\n",
      "inflation        5.6268      5.341      1.053      0.301      -5.282      16.536\n",
      "consump         -0.0286      0.027     -1.060      0.297      -0.084       0.027\n",
      "produc           3.9919      3.002      1.330      0.194      -2.140      10.124\n",
      "oil             -0.4832      0.464     -1.041      0.306      -1.431       0.464\n",
      "bill_1m         26.7456     21.645      1.236      0.226     -17.460      70.951\n",
      "bill_30y        -9.0187     10.635     -0.848      0.403     -30.739      12.702\n",
      "low_bond        -7.4737      8.933     -0.837      0.409     -25.717      10.770\n",
      "equal_equity    -0.1035      0.355     -0.292      0.773      -0.828       0.621\n",
      "sp500            1.4024      0.686      2.045      0.050       0.002       2.803\n",
      "==============================================================================\n",
      "Omnibus:                        1.492   Durbin-Watson:                   2.133\n",
      "Prob(Omnibus):                  0.474   Jarque-Bera (JB):                1.216\n",
      "Skew:                          -0.420   Prob(JB):                        0.545\n",
      "Kurtosis:                       2.847   Cond. No.                     1.21e+21\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 1.22e-32. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.ols(formula=\"r ~ total + avg + inflation + consump + produc + oil + bill_1m + bill_30y + low_bond + equal_equity + sp500\", data=data).fit()\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  }
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