Eventstudy Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminary work\n",
    "\n",
    "1. Load the eventstudy module and its dependencies: numpy and matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-19T08:31:41.408207Z",
     "start_time": "2021-03-19T08:31:41.387562Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1a10\n"
     ]
    }
   ],
   "source": [
    "import eventstudy as es\n",
    "print(es.__version__)\n",
    "from eventstudy import excelExporter\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Set the parameters needed for your events: the returns and Fama-French factors (using es.import_returns()and es.import_FamaFrench()):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-19T08:31:47.062726Z",
     "start_time": "2021-03-19T08:31:46.959271Z"
    }
   },
   "outputs": [],
   "source": [
    "es.Single.import_returns('example/returns_GAFAM.csv')\n",
    "es.Single.import_FamaFrench('example/famafrench.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: A single event\n",
    "\n",
    "As an introductory example, we will compute the event study analysis of the announcement of the first iphone, made by Steve Jobs during MacWorld exhibition, on January 7, 2007.\n",
    "\n",
    "1. Run the event study, here using the Fama-French 3-factor model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-19T08:43:43.027580Z",
     "start_time": "2021-03-19T08:43:42.982087Z"
    }
   },
   "outputs": [],
   "source": [
    "source = [{\n",
    "    \"security_ticker\": \"AAPL\",\n",
    "    \"market_ticker\": 'SPY',\n",
    "    \"event_date\": '2007-01-09',\n",
    "},\n",
    "{\n",
    "    \"security_ticker\": \"MSFT\",\n",
    "    \"market_ticker\": 'SPY',\n",
    "    \"event_date\": '2007-01-09'\n",
    "},\n",
    "{\n",
    "    \"security_ticker\": \"AAPL\",\n",
    "    \"market_ticker\": 'SPY',\n",
    "    \"event_date\": '2008-01-09'\n",
    "}]\n",
    "\n",
    "\n",
    "results = []\n",
    "results_full = []\n",
    "\n",
    "\n",
    "for item in source:\n",
    "    event = es.Single.market_model(\n",
    "        security_ticker = item['security_ticker'],\n",
    "        market_ticker = item['market_ticker'],\n",
    "        event_date = np.datetime64(item['event_date']),\n",
    "        event_window = (-2,10), \n",
    "        estimation_size = 300,\n",
    "        buffer_size = 30\n",
    "    )\n",
    "    \n",
    "    results.append(event.results(asterisks=False).tail(1))\n",
    "    results_full.append(event)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-19T08:44:45.421427Z",
     "start_time": "2021-03-19T08:44:44.522433Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for event in results_full:\n",
    "    event.plot(AR=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Display results:\n",
    "    - In a plot:\n",
    "        - grey area: confidence interval (here at 90%)\n",
    "        - blue line: CAAR\n",
    "        - black bars: AR (displayed because AR parameter is set as True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T09:12:54.024031Z",
     "start_time": "2020-09-28T09:12:53.681694Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "event.plot(AR=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Or in a table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T09:12:54.055104Z",
     "start_time": "2020-09-28T09:12:54.027675Z"
    }
   },
   "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>AR</th>\n",
       "      <th>Std. E. AR</th>\n",
       "      <th>CAR</th>\n",
       "      <th>Std. E. CAR</th>\n",
       "      <th>T-stat</th>\n",
       "      <th>P-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03174</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.083</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.083 **</td>\n",
       "      <td>0.03888</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.041</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.125 ***</td>\n",
       "      <td>0.04489</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.104 **</td>\n",
       "      <td>0.05019</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.079 *</td>\n",
       "      <td>0.05498</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.028</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.107 **</td>\n",
       "      <td>0.05939</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.06349</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.06734</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.07098</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.07445</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.07776</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.02245</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.08093</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AR  Std. E. AR        CAR  Std. E. CAR  T-stat  P-value\n",
       "-2  0.004     0.02245      0.004      0.02245    0.16     0.44\n",
       "-1 -0.003     0.02245        0.0      0.03174    0.01     0.50\n",
       "0   0.083     0.02245   0.083 **      0.03888    2.14     0.02\n",
       "1   0.041     0.02245  0.125 ***      0.04489    2.78     0.00\n",
       "2  -0.020     0.02245   0.104 **      0.05019    2.08     0.02\n",
       "3  -0.025     0.02245    0.079 *      0.05498    1.44     0.08\n",
       "4   0.028     0.02245   0.107 **      0.05939    1.80     0.04\n",
       "5  -0.024     0.02245      0.083      0.06349    1.30     0.10\n",
       "6  -0.058     0.02245      0.024      0.06734    0.36     0.36\n",
       "7  -0.011     0.02245      0.014      0.07098    0.19     0.42\n",
       "8  -0.016     0.02245     -0.002      0.07445   -0.03     0.49\n",
       "9  -0.018     0.02245     -0.021      0.07776   -0.27     0.40\n",
       "10 -0.002     0.02245     -0.023      0.08093   -0.28     0.39"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event.results(decimals=[3,5,3,5,2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T09:19:15.662839Z",
     "start_time": "2020-09-28T09:19:15.627286Z"
    }
   },
   "outputs": [],
   "source": [
    "event.to_excel('export.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: An  of events\n",
    "\n",
    "The eventstudy package offer three different entry points. The list of events can be provided using a csv file, a plain python text or a python list.\n",
    "\n",
    "In this second example, based on the same data and preliminary work, we will compute an event study on GAFA annual report (10-K form) releases.\n",
    "\n",
    "### Data preparation\n",
    "\n",
    "The first step is to gather the data about the events and to format it. For the purpose of this example we will use the csv file methods. The csv file must contains all information needed to compute the model.\n",
    "\n",
    "The event study will be computed using the market model. According to the API documentation aside from event study parameters which will be set globally for all events (event_window, estimation_size and buffer_size), the market model needs the security_ticker, the market_ticker and the event_date parameters.\n",
    "\n",
    "Thus, the csv file should contain three columns with the exact same names as the parameter names.\n",
    "\n",
    "**Note:** The csv file must use comma (‘,’) as separator.\n",
    "\n",
    "### Event study computation\n",
    "\n",
    "To create an event study analysis of an aggregate of events, we will use the Multiple class. This class is designed following the same spirit and structure of the Single class.\n",
    "\n",
    "Run the event study using the csv importer method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-09T07:57:35.326436Z",
     "start_time": "2020-03-09T07:57:35.220000Z"
    }
   },
   "outputs": [],
   "source": [
    "release_10K = es.Multiple.from_csv(\n",
    "    path = '10K.csv', # the path to the csv file created\n",
    "    event_study_model = es.Single.market_model,\n",
    "    event_window = (-5,+10),\n",
    "    estimation_size = 200,\n",
    "    buffer_size = 30,\n",
    "    date_format = '%d/%m/%Y',\n",
    "    ignore_errors = True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The event_study_model parameter must point to a function. This function can either be a model function provided by the Single class or a user-defined function.\n",
    "\n",
    "When the ignore_errors parameter is set to True (which is its default value), the event study analysis will be computed regardless of any error encounter. Event triggering an error will be remove of the sample. Use error_report method to get the full list of error and their explanation. Errors are often coming from data unavailability.\n",
    "\n",
    "Set the ignore_errors to False if you prefer the analysis to stop at the first error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-09T07:57:35.343313Z",
     "start_time": "2020-03-09T07:57:35.328896Z"
    }
   },
   "outputs": [],
   "source": [
    "print(release_10K.error_report())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display results:\n",
    "In a plot:\n",
    "\n",
    "*grey area: confidence interval (here defined by the user at 95%); blue line: CAAR*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-09T07:57:35.629721Z",
     "start_time": "2020-03-09T07:57:35.350549Z"
    }
   },
   "outputs": [],
   "source": [
    "release_10K.plot(confidence=.95)\n",
    "plt.show() # use standard matplotlib function to display the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or in a table:\n",
    "\n",
    "**Note:** Asterisks are added automatically to highlight the level of significance (Significance level: *** at 99%, ** at 95%, * at 90%). You can remove asterisks by setting asterisks parameter at False.\n",
    "\n",
    "decimals is a list of integer setting for each column (except index) the rounding decimal. You can also set one integer (e.g. decimals = 3) if you want all columns to be rounded the same.\n",
    "\n",
    "See the documentation on this results function for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-09T07:57:35.655903Z",
     "start_time": "2020-03-09T07:57:35.632702Z"
    }
   },
   "outputs": [],
   "source": [
    "release_10K.results(decimals=[3,5,3,5,2,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-09T07:53:50.235310Z",
     "start_time": "2020-03-09T07:53:50.209995Z"
    }
   },
   "source": [
    "Get CAR distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-09T07:57:35.734165Z",
     "start_time": "2020-03-09T07:57:35.674343Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "release_10K.get_CAR_dist(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Loop in event\n",
    "\n",
    "The true strengh of this package is to use python. You can create complex algorithm to compute programatically event stuy analyses. From the same data as above, we will compute an event study of 10-K form releases for each company.\n",
    "\n",
    "### Data preparation\n",
    "This time, we will use the from_list method which set event list using a python list. This let the user manipulate the list of event before computing event studies. This list must contains all parameters needed to compute the selected model (here we will use the Fama-French 3-factor model). The Fama-French factors have to be set using the eventstudy.Single.import_FamaFrench method (see preliminary step)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-09T07:57:35.810779Z",
     "start_time": "2020-03-09T07:57:35.754067Z"
    }
   },
   "outputs": [],
   "source": [
    "events_db = [\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2019-10-31')},\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2018-11-05')},\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2017-11-03')},\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2016-10-26')},\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2015-10-28')},\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2014-10-27')},\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2013-10-30')},\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2012-10-31')},\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2011-10-26')},\n",
    "    {'security_ticker': 'AAPL', 'market_ticker': 'SPY', 'event_date': np.datetime64('2010-10-27')},\n",
    "    {'security_ticker': 'GOOG', 'market_ticker': 'SPY', 'event_date': np.datetime64('2020-02-04')},\n",
    "    {'security_ticker': 'GOOG', 'market_ticker': 'SPY', 'event_date': np.datetime64('2019-02-05')},\n",
    "    {'security_ticker': 'GOOG', 'market_ticker': 'SPY', 'event_date': np.datetime64('2018-02-06')},\n",
    "    {'security_ticker': 'GOOG', 'market_ticker': 'SPY', 'event_date': np.datetime64('2017-02-03')},\n",
    "    {'security_ticker': 'GOOG', 'market_ticker': 'SPY', 'event_date': np.datetime64('2016-02-11')},\n",
    "    {'security_ticker': 'FB', 'market_ticker': 'SPY', 'event_date': np.datetime64('2020-01-30')},\n",
    "    {'security_ticker': 'FB', 'market_ticker': 'SPY', 'event_date': np.datetime64('2019-01-31')},\n",
    "    {'security_ticker': 'FB', 'market_ticker': 'SPY', 'event_date': np.datetime64('2018-02-01')},\n",
    "    {'security_ticker': 'FB', 'market_ticker': 'SPY', 'event_date': np.datetime64('2016-01-28')},\n",
    "    {'security_ticker': 'FB', 'market_ticker': 'SPY', 'event_date': np.datetime64('2015-01-29')},\n",
    "    {'security_ticker': 'FB', 'market_ticker': 'SPY', 'event_date': np.datetime64('2014-01-31')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2020-01-31')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2019-02-01')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2018-02-02')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2017-02-10')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2016-01-29')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2015-01-30')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2013-01-30')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2012-02-01')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2011-01-28')},\n",
    "    {'security_ticker': 'AMZN', 'market_ticker': 'SPY', 'event_date': np.datetime64('2010-01-29')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2019-08-01')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2018-08-03')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2017-08-02')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2016-07-28')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2015-07-31')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2014-07-31')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2013-07-30')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2012-07-26')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2011-07-28')},\n",
    "    {'security_ticker': 'MSFT', 'market_ticker': 'SPY', 'event_date': np.datetime64('2010-07-30')}\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Event studies computation\n",
    "Using a list comprehension, the full list of events can be filtered to create one list per company’s ticker. Then we can compute for each company the event study using the Fama-French 3 factor model and store each event study in a releases dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-09T07:57:36.031752Z",
     "start_time": "2020-03-09T07:57:35.817167Z"
    }
   },
   "outputs": [],
   "source": [
    "tickers = ['GOOG', 'AAPL', 'FB', 'AMZN', 'MSFT']\n",
    "releases = dict()\n",
    "\n",
    "for ticker in tickers:\n",
    "    events = [event for event in events_db if event['security_ticker']==ticker]\n",
    "    \n",
    "    releases[ticker] = es.Multiple.from_list(\n",
    "        events,\n",
    "        es.Single.FamaFrench_3factor,\n",
    "        event_window= (-5,+10)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-09T07:57:37.497893Z",
     "start_time": "2020-03-09T07:57:36.038670Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for ticker, event in releases.items():\n",
    "    print(ticker)\n",
    "    event.plot()\n",
    "    plt.show()\n",
    "    display(event.results())\n",
    "    display(event.get_CAR_dist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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