stock_prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cce3852a",
   "metadata": {},
   "source": [
    "# Stock Prices\n",
    "\n",
    "_In physics it takes three laws to explain 99% of the data; in finance it takes more than 99 laws to explain about 3%_ - Andrew Lo \n",
    "\n",
    "Concepts:\n",
    "- Stock splits, dividends and delistings\n",
    "- Properties of stock returns\n",
    "\n",
    "__References:__\n",
    "\n",
    "- Fama, Eugene F., and Kenneth R. French. 1992. “The cross-section of expected stock returns.” The Journal of Finance 47 (2): 427–65.\n",
    "\n",
    "- Shumway, Tyler. 1997. The Delisting Bias in CRSP Data. The Journal of Finance, 52, 327-340. \n",
    "\n",
    "- Bali, Turan G, Robert F Engle, and Scott Murray. 2016. Empirical asset pricing: The cross section of stock returns. John Wiley & Sons.\n",
    "\n",
    "- Stulz, René M., 2018, \"The Shrinking Universe of Public Firms: Facts, Causes, and Consequences\", National Bureau of Economics, 2 (June 2018)\n",
    "\n",
    "- FRM Exam Book Part I Quantative Analysis Chapter 12\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc1013ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from finds.database import SQL, RedisDB\n",
    "from finds.structured import BusDay, CRSP, CRSPBuffer, Benchmarks\n",
    "from finds.utils import Finder, plot_date\n",
    "from secret import credentials, CRSP_DATE, paths\n",
    "VERBOSE = 0\n",
    "\n",
    "# %matplotlib qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb3f7e79",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# open database connections\n",
    "sql = SQL(**credentials['sql'], verbose=VERBOSE)\n",
    "rdb = RedisDB(**credentials['redis'])\n",
    "bd = BusDay(sql, endweek=3, verbose=VERBOSE)\n",
    "crsp = CRSP(sql, bd, rdb=rdb, verbose=VERBOSE)\n",
    "bench = Benchmarks(sql, bd, verbose=VERBOSE)\n",
    "find = Finder(sql)\n",
    "outdir = paths['scratch']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f86f0b",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Measuring stock returns\n",
    "\n",
    "Long time-series and broad cross-sections of US stock price data are best managed in a structured database. This facilitates linking across data sources, where data vendors or financial organizations may use different identifiers, and the tracking of identifier changes and corporate actions (e.g. splits, dividends, delistings and mergers) over time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80240183",
   "metadata": {},
   "source": [
    "### SQL \n",
    "\n",
    "Structured query language (SQL) is a standard language for database creation and manipulation. \n",
    "MySQL is a popular open-source package that uses structured query language (SQL) for data management. Python toolkits such as SQLAlchemy provide an interface for using an SQL database.\n",
    "The Python Pandas library also provides methods such as `pandas.read_sql` to query a database table and return in a DataFrame.\n",
    "\n",
    "SQL Cheatsheet:\n",
    "\n",
    "__Manage tables__\n",
    "\n",
    "CREATE DATABASE -- Creates a new database.\n",
    "\n",
    "CREATE TABLE -- Creates a new table. \n",
    "\n",
    "DELETE -- Delete data from a table.\n",
    "\n",
    "DROP COLUMN -- Deletes a column from a table.\n",
    "\n",
    "DROP DATABASE -- Deletes the entire database.\n",
    "\n",
    "DROP TABLE -- Deletes a table from a database.\n",
    "\n",
    "TRUNCATE TABLE -- Deletes the data but does not delete the table.\n",
    "\n",
    "\n",
    "\n",
    "__Querying a table__\n",
    "\n",
    "SELECT -- Used to select data from a database, which is then returned in a results set.\n",
    "\n",
    "SELECT DISTINCT -- Sames as SELECT, except duplicate values are excluded.\n",
    "\n",
    "SELECT INTO -- Copies data from one table and inserts it into another.\n",
    "\n",
    "UNIQUE -- This constraint ensures all values in a column are unique.\n",
    "\n",
    "FROM -- Specifies which table to select or delete data from.\n",
    "\n",
    "AS -- Renames a table or column with an alias value which only exists for the duration of the query.\n",
    "\n",
    "__Query conditions__\n",
    "\n",
    "WHERE -- Filters results to only include data which meets the given condition.\n",
    "\n",
    "AND -- Used to join separate conditions within a WHERE clause.\n",
    "\n",
    "BETWEEN -- Selects values within the given range.\n",
    "\n",
    "IS NULL -- Tests for empty (NULL) values.\n",
    "\n",
    "IS NOT NULL -- The reverse of NULL. Tests for values that aren’t empty / NULL.\n",
    "\n",
    "LIKE -- Returns true if the operand value matches a pattern.\n",
    "\n",
    "NOT -- Returns true if a record DOESN’T meet the condition.\n",
    " \n",
    "OR -- Used alongside WHERE to include data when either condition is true.\n",
    "\n",
    "__Organize results__\n",
    "\n",
    "ORDER BY -- Used to sort the result data in ascending (default) or descending order through the use of ASC or DESC keywords.\n",
    "\n",
    "GROUP BY -- Used alongside aggregate functions (COUNT, MAX, MIN, SUM, AVG) to group the results.\n",
    "\n",
    "__Join tables__\n",
    "\n",
    "INNER JOIN returns rows that have matching values in both tables.\n",
    "\n",
    "LEFT JOIN returns all rows from the left table, and the matching rows from the right table.- \n",
    "\n",
    "RIGHT JOIN returns all rows from the right table, and the matching records from the left table\n",
    "\n",
    "FULL OUTER JOIN returns all rows when there is a match in either left table or right table.\n",
    "\n",
    "### Redis\n",
    "\n",
    "Redis is an open-source, in-memory data structure store that can be used as a caching layer to store frequently accessed data and reduce the load on primary databases.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32c1cc6d",
   "metadata": {},
   "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>permno</th>\n",
       "      <th>name</th>\n",
       "      <th>item</th>\n",
       "      <th>permno</th>\n",
       "      <th>date</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>F-F_Momentum_Factor</td>\n",
       "      <td>0</td>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>19270131</td>\n",
       "      <td>0.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>F-F_Momentum_Factor</td>\n",
       "      <td>0</td>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>19270228</td>\n",
       "      <td>-0.0214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>F-F_Momentum_Factor</td>\n",
       "      <td>0</td>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>19270331</td>\n",
       "      <td>0.0361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>F-F_Momentum_Factor</td>\n",
       "      <td>0</td>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>19270430</td>\n",
       "      <td>0.0430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>F-F_Momentum_Factor</td>\n",
       "      <td>0</td>\n",
       "      <td>Mom(mo)</td>\n",
       "      <td>19270531</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    permno                 name item   permno      date     ret\n",
       "0  Mom(mo)  F-F_Momentum_Factor    0  Mom(mo)  19270131  0.0036\n",
       "1  Mom(mo)  F-F_Momentum_Factor    0  Mom(mo)  19270228 -0.0214\n",
       "2  Mom(mo)  F-F_Momentum_Factor    0  Mom(mo)  19270331  0.0361\n",
       "3  Mom(mo)  F-F_Momentum_Factor    0  Mom(mo)  19270430  0.0430\n",
       "4  Mom(mo)  F-F_Momentum_Factor    0  Mom(mo)  19270531  0.0300"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inner join of identifier tables\n",
    "\n",
    "# double up the % in a pandas string\n",
    "pd.read_sql(\"select * from benchident where name like '%%momentum%%'\", con=sql.engine)\n",
    "\n",
    "cmd = \"\"\"\n",
    "select * from benchident inner join benchmarks\n",
    "on benchident.permno = benchmarks.permno\n",
    "where benchident.name like '%%momentum%%'\n",
    "  and benchident.permno like '%%(mo)%%'\n",
    "limit 5\n",
    "\"\"\"\n",
    "pd.read_sql(cmd, con=sql.engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d8539c",
   "metadata": {},
   "source": [
    "### CRSP stocks data\n",
    "\n",
    "The Center for Research in Security Prices (CRSP) provides the most widely used data for academic research into US stocks. It includes both successful and unsuccessful entities, rather than just those that have \"survived\" over time. This helps avoid the pitfalls of focusing only on surviving entities which can lead to an overestimation of average performance and underestimation of risk.\n",
    "\n",
    "Following Fama and French (1992), only US-domiciled (`shrcd` in [10, 11]), exchange-listed (`exchcd` in [1, 2, 3]) stocks are typically considered for equities research. The universe is often divided into 10 market capitalization-based deciles (with the 10th decile comprising the smallest stocks) whose breakpoints are determined only from those\n",
    "stocks listed on the NYSE.  For a company with multiple classes securities, the combined market value of the company is used for all classes of its securities.  The shrout (shares outstanding) field in CRSP is in units of  thousands of shares. Hence all calculations and derivations (such as market capitalization) need to be multipled by 1000 to get the true value.\n",
    "\n",
    "\n",
    "Stulz (2018) and others hvae noted the shrinking universe of public US firms and increased concentration of mega-cap stocks in recent years. These observations are consistent with plots  of the market cap breakpoints of and the number of stocks in each decile by year. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e000fdf",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# retrieve universe of stocks annually from 1981\n",
    "start = bd.endyr(19731231)\n",
    "rebals = bd.date_range(start, CRSP_DATE, freq=12)\n",
    "univs = {rebal: crsp.get_universe(date=rebal) for rebal in rebals}\n",
    "num = dict()\n",
    "for date, univ in univs.items():\n",
    "    num[str(date//10000)] = {decile: sum(univ['decile']==decile)\n",
    "                             for decile in range(10, 0, -1)}\n",
    "num = DataFrame.from_dict(num, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e705ddf4",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot number of stocks in each size decile\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Number of stocks in universe by size decile')\n",
    "num.plot.bar(stacked=True, ax=ax, width=.8, alpha=0.4)\n",
    "#set_xtickbins(ax=ax, nbins=len(cap)//10)\n",
    "plt.legend(title='Size Decile', loc='upper left')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "869c5651",
   "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>comnam</th>\n",
       "      <th>ncusip</th>\n",
       "      <th>shrcls</th>\n",
       "      <th>ticker</th>\n",
       "      <th>permno</th>\n",
       "      <th>nameendt</th>\n",
       "      <th>shrcd</th>\n",
       "      <th>exchcd</th>\n",
       "      <th>siccd</th>\n",
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       "      <th>naics</th>\n",
       "      <th>primexch</th>\n",
       "      <th>trdstat</th>\n",
       "      <th>secstat</th>\n",
       "      <th>permco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20040819</td>\n",
       "      <td>GOOGLE INC</td>\n",
       "      <td>38259P50</td>\n",
       "      <td>A</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>90319</td>\n",
       "      <td>20050818</td>\n",
       "      <td>11</td>\n",
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       "      <td>GOOG</td>\n",
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       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20050819</td>\n",
       "      <td>GOOGLE INC</td>\n",
       "      <td>38259P50</td>\n",
       "      <td>A</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>90319</td>\n",
       "      <td>20060720</td>\n",
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       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>518111</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20060721</td>\n",
       "      <td>GOOGLE INC</td>\n",
       "      <td>38259P50</td>\n",
       "      <td>A</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>90319</td>\n",
       "      <td>20080131</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>518112</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20080201</td>\n",
       "      <td>GOOGLE INC</td>\n",
       "      <td>38259P50</td>\n",
       "      <td>A</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>90319</td>\n",
       "      <td>20140402</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>519130</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20140403</td>\n",
       "      <td>GOOGLE INC</td>\n",
       "      <td>38259P70</td>\n",
       "      <td>C</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>14542</td>\n",
       "      <td>20150423</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>519130</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20140403</td>\n",
       "      <td>GOOGLE INC</td>\n",
       "      <td>38259P50</td>\n",
       "      <td>A</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>90319</td>\n",
       "      <td>20150423</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>519130</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20150424</td>\n",
       "      <td>GOOGLE INC</td>\n",
       "      <td>38259P70</td>\n",
       "      <td>C</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>14542</td>\n",
       "      <td>20151004</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>519190</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20150424</td>\n",
       "      <td>GOOGLE INC</td>\n",
       "      <td>38259P50</td>\n",
       "      <td>A</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>90319</td>\n",
       "      <td>20151004</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>519190</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20151005</td>\n",
       "      <td>ALPHABET INC</td>\n",
       "      <td>02079K10</td>\n",
       "      <td>C</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>14542</td>\n",
       "      <td>20180201</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>519190</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20151005</td>\n",
       "      <td>ALPHABET INC</td>\n",
       "      <td>02079K30</td>\n",
       "      <td>A</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>90319</td>\n",
       "      <td>20180201</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>519190</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20180202</td>\n",
       "      <td>ALPHABET INC</td>\n",
       "      <td>02079K10</td>\n",
       "      <td>C</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>14542</td>\n",
       "      <td>20231012</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>334419</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20180202</td>\n",
       "      <td>ALPHABET INC</td>\n",
       "      <td>02079K30</td>\n",
       "      <td>A</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>90319</td>\n",
       "      <td>20231012</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>334419</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20231013</td>\n",
       "      <td>ALPHABET INC</td>\n",
       "      <td>02079K10</td>\n",
       "      <td>C</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>14542</td>\n",
       "      <td>20231229</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>541511</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20231013</td>\n",
       "      <td>ALPHABET INC</td>\n",
       "      <td>02079K30</td>\n",
       "      <td>A</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>90319</td>\n",
       "      <td>20231229</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7375</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>541511</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>R</td>\n",
       "      <td>45483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date        comnam    ncusip shrcls ticker  permno  nameendt  shrcd  \\\n",
       "0   20040819    GOOGLE INC  38259P50      A   GOOG   90319  20050818     11   \n",
       "1   20050819    GOOGLE INC  38259P50      A   GOOG   90319  20060720     11   \n",
       "2   20060721    GOOGLE INC  38259P50      A   GOOG   90319  20080131     11   \n",
       "3   20080201    GOOGLE INC  38259P50      A   GOOG   90319  20140402     11   \n",
       "4   20140403    GOOGLE INC  38259P70      C   GOOG   14542  20150423     11   \n",
       "5   20140403    GOOGLE INC  38259P50      A  GOOGL   90319  20150423     11   \n",
       "6   20150424    GOOGLE INC  38259P70      C   GOOG   14542  20151004     11   \n",
       "7   20150424    GOOGLE INC  38259P50      A  GOOGL   90319  20151004     11   \n",
       "8   20151005  ALPHABET INC  02079K10      C   GOOG   14542  20180201     11   \n",
       "9   20151005  ALPHABET INC  02079K30      A  GOOGL   90319  20180201     11   \n",
       "10  20180202  ALPHABET INC  02079K10      C   GOOG   14542  20231012     11   \n",
       "11  20180202  ALPHABET INC  02079K30      A  GOOGL   90319  20231012     11   \n",
       "12  20231013  ALPHABET INC  02079K10      C   GOOG   14542  20231229     11   \n",
       "13  20231013  ALPHABET INC  02079K30      A  GOOGL   90319  20231229     11   \n",
       "\n",
       "    exchcd  siccd tsymbol   naics primexch trdstat secstat  permco  \n",
       "0        3   7375    GOOG  514191        Q       A       R   45483  \n",
       "1        3   7375    GOOG  518111        Q       A       R   45483  \n",
       "2        3   7375    GOOG  518112        Q       A       R   45483  \n",
       "3        3   7375    GOOG  519130        Q       A       R   45483  \n",
       "4        3   7375    GOOG  519130        Q       A       R   45483  \n",
       "5        3   7375   GOOGL  519130        Q       A       R   45483  \n",
       "6        3   7375    GOOG  519190        Q       A       R   45483  \n",
       "7        3   7375   GOOGL  519190        Q       A       R   45483  \n",
       "8        3   7375    GOOG  519190        Q       A       R   45483  \n",
       "9        3   7375   GOOGL  519190        Q       A       R   45483  \n",
       "10       3   7375    GOOG  334419        Q       A       R   45483  \n",
       "11       3   7375   GOOGL  334419        Q       A       R   45483  \n",
       "12       3   7375    GOOG  541511        Q       A       R   45483  \n",
       "13       3   7375   GOOGL  541511        Q       A       R   45483  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For assigning into size deciles, sum up the company's total market\n",
    "# capitalization instead of separate capital values each security if multiple classes\n",
    "names = find('ALPHABET', 'comnam').groupby('permno').tail(1).set_index('permno')\n",
    "find(names['permco'].iloc[0], identifier='permco')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "89ded42a",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                cap         capco  decile   nyse  siccd     prc   naics\n",
       "permno                                                                 \n",
       "14542   806824250.0  1.633510e+09       1  False   7375  140.93  541511\n",
       "90319   826685420.0  1.633510e+09       1  False   7375  139.69  541511"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ.loc[names.index]  # market caps of share class and total company"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6944656b",
   "metadata": {},
   "source": [
    "### Stock distributions\n",
    "\n",
    "In addition to gains from appreciated stock prices, adjusted for stock splits (`facpr` in CRSP), investors' total holding returns also include ordinary cash dividends (`divamt` in CRSP).\n",
    "\n",
    "The total holding return on an asset from $t-1$ to $t$ includes both price appreciation and any dividends paid on record date $t$: $R_t = \\dfrac{P_t + D_t - P_{t-1}}{P_{t-1}}$. Ex-dividend means a company's dividend allocations have been specified. The ex-dividend date or \"ex-date\" is usually one business day before the record date.\n",
    "\n",
    "The return of an asset over multiple periods is calculated using the product of the simple returns in each period: $1 + R_T = \\Pi_{t=1}^{T} (1 + R_t)$.  This implicitly assumes that dividends have been reinvested in the same asset.\n",
    "\n",
    "There are also continuously compounded returns, or log returns. These are computed as the difference of the natural logarithm of the price: $r_t = ln Pt - ln Pt - 1 \n",
    "\n",
    "\n",
    "### Yahoo Finance data\n",
    "\n",
    "- The historical \"close\" from Yahoo Finance is the split-adjusted price\n",
    "- The historical \"adjclose\" from Yahoo Finance is equal to the dividend- and split-adjusted price:\n",
    "  Daily changes of \"adjclose\" reflects the total holdings returns which includes the effect of both stock splits and cash dividends received.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "460c8da1",
   "metadata": {},
   "outputs": [
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       "      <td>108872000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200108</th>\n",
       "      <td>74.29</td>\n",
       "      <td>76.11</td>\n",
       "      <td>74.29</td>\n",
       "      <td>75.80</td>\n",
       "      <td>73.65</td>\n",
       "      <td>132079200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200109</th>\n",
       "      <td>76.81</td>\n",
       "      <td>77.61</td>\n",
       "      <td>76.55</td>\n",
       "      <td>77.41</td>\n",
       "      <td>75.21</td>\n",
       "      <td>170108400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240624</th>\n",
       "      <td>207.72</td>\n",
       "      <td>212.70</td>\n",
       "      <td>206.59</td>\n",
       "      <td>208.14</td>\n",
       "      <td>208.14</td>\n",
       "      <td>80727000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240625</th>\n",
       "      <td>209.15</td>\n",
       "      <td>211.38</td>\n",
       "      <td>208.61</td>\n",
       "      <td>209.07</td>\n",
       "      <td>209.07</td>\n",
       "      <td>56713900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240626</th>\n",
       "      <td>211.50</td>\n",
       "      <td>214.86</td>\n",
       "      <td>210.64</td>\n",
       "      <td>213.25</td>\n",
       "      <td>213.25</td>\n",
       "      <td>66213200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240627</th>\n",
       "      <td>214.69</td>\n",
       "      <td>215.74</td>\n",
       "      <td>212.35</td>\n",
       "      <td>214.10</td>\n",
       "      <td>214.10</td>\n",
       "      <td>49772700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20240628</th>\n",
       "      <td>215.77</td>\n",
       "      <td>216.07</td>\n",
       "      <td>210.30</td>\n",
       "      <td>210.62</td>\n",
       "      <td>210.62</td>\n",
       "      <td>82475600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1129 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            open    high     low   close  adjClose       volume\n",
       "20200103   74.29   75.14   74.12   74.36     72.25  146322800.0\n",
       "20200106   73.45   74.99   73.19   74.95     72.83  118387200.0\n",
       "20200107   74.96   75.22   74.37   74.60     72.48  108872000.0\n",
       "20200108   74.29   76.11   74.29   75.80     73.65  132079200.0\n",
       "20200109   76.81   77.61   76.55   77.41     75.21  170108400.0\n",
       "...          ...     ...     ...     ...       ...          ...\n",
       "20240624  207.72  212.70  206.59  208.14    208.14   80727000.0\n",
       "20240625  209.15  211.38  208.61  209.07    209.07   56713900.0\n",
       "20240626  211.50  214.86  210.64  213.25    213.25   66213200.0\n",
       "20240627  214.69  215.74  212.35  214.10    214.10   49772700.0\n",
       "20240628  215.77  216.07  210.30  210.62    210.62   82475600.0\n",
       "\n",
       "[1129 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prices from Yahoo Finance\n",
    "from yahoo import get_price\n",
    "ticker = 'AAPL'\n",
    "date = 20200101\n",
    "df = get_price(ticker, start_date=date, verbose=VERBOSE)\n",
    "df.rename_axis(ticker)\n",
    "prices_columns = df.columns\n",
    "df.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a9b0828",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_price(ticker, start_date=date, verbose=VERBOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4603d82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'AAPL prices from Yahoo Finance'}>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "plot_date(df[['close', 'adjClose']], legend1=['close', 'adjClose'],\n",
    "          title=f\"{ticker} prices from Yahoo Finance\",\n",
    "          ls=':', fontsize=8, rotation=0, cn=0, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2efff77",
   "metadata": {},
   "source": [
    "__Dividends__\n",
    "\n",
    "Differences between daily changes in adjClose and close on ex-dates are estimates of dividend amounts (scaled by prior adjClose).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e4db039",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lookup AAPL's permno identifier\n",
    "find = Finder(sql)\n",
    "permno = find('AAPL').tail(1)['permno'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ce04bd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dividend yield is excess of change in adjClose over chance in close\n",
    "df['divyld'] = ((df['adjClose'] / df['adjClose'].shift())\n",
    "                / (df['close'] / df['close'].shift())) - 1\n",
    "# ex-dates inferred to be when amount is larger than rounding error\n",
    "exdt = df.index[abs(df['divyld']) > .0001]\n",
    "yahoo_div = df.loc[exdt, 'divyld']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc43b473",
   "metadata": {},
   "source": [
    "__Dividend amount per share__ from CRSP distributions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1375a603",
   "metadata": {},
   "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>divamt</th>\n",
       "      <th>retx</th>\n",
       "      <th>prc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exdt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20200207</th>\n",
       "      <td>0.770</td>\n",
       "      <td>-0.015928</td>\n",
       "      <td>320.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200508</th>\n",
       "      <td>0.820</td>\n",
       "      <td>0.021038</td>\n",
       "      <td>310.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200807</th>\n",
       "      <td>0.820</td>\n",
       "      <td>-0.024495</td>\n",
       "      <td>444.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20201106</th>\n",
       "      <td>0.205</td>\n",
       "      <td>-0.002856</td>\n",
       "      <td>118.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210205</th>\n",
       "      <td>0.205</td>\n",
       "      <td>-0.004586</td>\n",
       "      <td>136.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210507</th>\n",
       "      <td>0.220</td>\n",
       "      <td>0.003623</td>\n",
       "      <td>130.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210806</th>\n",
       "      <td>0.220</td>\n",
       "      <td>-0.006256</td>\n",
       "      <td>146.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20211105</th>\n",
       "      <td>0.220</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>151.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220204</th>\n",
       "      <td>0.220</td>\n",
       "      <td>-0.002950</td>\n",
       "      <td>172.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220506</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.003253</td>\n",
       "      <td>157.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220805</th>\n",
       "      <td>0.230</td>\n",
       "      <td>-0.002774</td>\n",
       "      <td>165.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20221104</th>\n",
       "      <td>0.230</td>\n",
       "      <td>-0.003600</td>\n",
       "      <td>138.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230210</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>151.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230512</th>\n",
       "      <td>0.240</td>\n",
       "      <td>-0.006791</td>\n",
       "      <td>172.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230811</th>\n",
       "      <td>0.240</td>\n",
       "      <td>-0.001011</td>\n",
       "      <td>177.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231110</th>\n",
       "      <td>0.240</td>\n",
       "      <td>0.021874</td>\n",
       "      <td>186.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          divamt      retx     prc\n",
       "exdt                              \n",
       "20200207   0.770 -0.015928  320.03\n",
       "20200508   0.820  0.021038  310.13\n",
       "20200807   0.820 -0.024495  444.45\n",
       "20201106   0.205 -0.002856  118.69\n",
       "20210205   0.205 -0.004586  136.76\n",
       "20210507   0.220  0.003623  130.21\n",
       "20210806   0.220 -0.006256  146.14\n",
       "20211105   0.220  0.002120  151.28\n",
       "20220204   0.220 -0.002950  172.39\n",
       "20220506   0.230  0.003253  157.28\n",
       "20220805   0.230 -0.002774  165.35\n",
       "20221104   0.230 -0.003600  138.38\n",
       "20230210   0.230  0.000928  151.01\n",
       "20230512   0.240 -0.006791  172.57\n",
       "20230811   0.240 -0.001011  177.79\n",
       "20231110   0.240  0.021874  186.40"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crsp_div = DataFrame(**sql.run(f\"\"\"\n",
    "SELECT divamt, exdt, retx, prc FROM dist INNER JOIN daily\n",
    "ON dist.permno = daily.permno AND dist.exdt = daily.date AND dist.exdt <= {CRSP_DATE}\n",
    "WHERE dist.permno={permno} AND dist.exdt>{date} AND dist.divamt > 0\n",
    "\"\"\".strip()))\n",
    "crsp_div = crsp_div.set_index('exdt').sort_index()\n",
    "crsp_div"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef04a5b6",
   "metadata": {},
   "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>divamt</th>\n",
       "      <th>yahoo_div</th>\n",
       "      <th>prc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exdt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20200207</th>\n",
       "      <td>0.770</td>\n",
       "      <td>0.772</td>\n",
       "      <td>320.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200508</th>\n",
       "      <td>0.820</td>\n",
       "      <td>0.822</td>\n",
       "      <td>310.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200807</th>\n",
       "      <td>0.820</td>\n",
       "      <td>0.822</td>\n",
       "      <td>444.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20201106</th>\n",
       "      <td>0.205</td>\n",
       "      <td>0.205</td>\n",
       "      <td>118.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210205</th>\n",
       "      <td>0.205</td>\n",
       "      <td>0.205</td>\n",
       "      <td>136.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210507</th>\n",
       "      <td>0.220</td>\n",
       "      <td>0.220</td>\n",
       "      <td>130.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210806</th>\n",
       "      <td>0.220</td>\n",
       "      <td>0.220</td>\n",
       "      <td>146.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20211105</th>\n",
       "      <td>0.220</td>\n",
       "      <td>0.220</td>\n",
       "      <td>151.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220204</th>\n",
       "      <td>0.220</td>\n",
       "      <td>0.220</td>\n",
       "      <td>172.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220506</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.230</td>\n",
       "      <td>157.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20220805</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.230</td>\n",
       "      <td>165.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20221104</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.230</td>\n",
       "      <td>138.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230210</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.230</td>\n",
       "      <td>151.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230512</th>\n",
       "      <td>0.240</td>\n",
       "      <td>0.240</td>\n",
       "      <td>172.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230811</th>\n",
       "      <td>0.240</td>\n",
       "      <td>0.240</td>\n",
       "      <td>177.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231110</th>\n",
       "      <td>0.240</td>\n",
       "      <td>0.240</td>\n",
       "      <td>186.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          divamt  yahoo_div     prc\n",
       "exdt                               \n",
       "20200207   0.770      0.772  320.03\n",
       "20200508   0.820      0.822  310.13\n",
       "20200807   0.820      0.822  444.45\n",
       "20201106   0.205      0.205  118.69\n",
       "20210205   0.205      0.205  136.76\n",
       "20210507   0.220      0.220  130.21\n",
       "20210806   0.220      0.220  146.14\n",
       "20211105   0.220      0.220  151.28\n",
       "20220204   0.220      0.220  172.39\n",
       "20220506   0.230      0.230  157.28\n",
       "20220805   0.230      0.230  165.35\n",
       "20221104   0.230      0.230  138.38\n",
       "20230210   0.230      0.230  151.01\n",
       "20230512   0.240      0.240  172.57\n",
       "20230811   0.240      0.240  177.79\n",
       "20231110   0.240      0.240  186.40"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare CRSP actual and Yahoo Finance estimated dividend amounts\n",
    "div = crsp_div.join(yahoo_div)\n",
    "div['yahoo_div'] = div['divyld'] * (div['prc'] / (1 + div['retx']))\n",
    "div[['divamt', 'yahoo_div', 'prc']].round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9463ead",
   "metadata": {},
   "source": [
    "__Split adjustment factors__ from CRSP\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "53a1cfea",
   "metadata": {},
   "outputs": [],
   "source": [
    "crsp_fac = DataFrame(**sql.run(f\"\"\"\n",
    "SELECT 1 + facpr as facpr, exdt FROM dist \n",
    "WHERE permno={permno} AND exdt>{date} AND facpr > 0.0\n",
    "\"\"\".strip())).set_index('exdt').sort_index(ascending=False).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14c577e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve CRSP raw prices around ex-dates\n",
    "crsp_prc = DataFrame(**sql.run(f\"\"\"\n",
    "SELECT date, prc from daily\n",
    "WHERE permno={permno} AND date>{bd.offset(crsp_fac.index[0],-5)}\n",
    "  AND date<{bd.offset(crsp_fac.index[-1],5)}\n",
    "\"\"\".strip())).set_index('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a41997",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "The Factor to Adjust Price, `facpr`, can be used to adjust prices for\n",
    "distributions (usually stock dividends and splits) so that stock prices\n",
    "before and after the distribution are comparable.  \n",
    "\n",
    "The historical cumulative adjust factors are computed by additing 1 to and then taking cumulative product from current to earlier time periods. This cumulative factor between two dates is divided into the earlier raw stock price to derive comparable split-adjusted prices. Hence to split-adjust CRSP raw prices\n",
    "- apply cumulative factor to raw prices before corresponding ex-date\n",
    "- back-fill to dates prior to ex-date\n",
    "- default factor after latest ex-date is 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e005336",
   "metadata": {},
   "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>prc</th>\n",
       "      <th>prc_adjusted</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20200825</th>\n",
       "      <td>499.30</td>\n",
       "      <td>124.825</td>\n",
       "      <td>124.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200826</th>\n",
       "      <td>506.09</td>\n",
       "      <td>126.522</td>\n",
       "      <td>126.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200827</th>\n",
       "      <td>500.04</td>\n",
       "      <td>125.010</td>\n",
       "      <td>125.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200828</th>\n",
       "      <td>499.23</td>\n",
       "      <td>124.808</td>\n",
       "      <td>124.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200831</th>\n",
       "      <td>129.04</td>\n",
       "      <td>129.040</td>\n",
       "      <td>129.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200901</th>\n",
       "      <td>134.18</td>\n",
       "      <td>134.180</td>\n",
       "      <td>134.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200902</th>\n",
       "      <td>131.40</td>\n",
       "      <td>131.400</td>\n",
       "      <td>131.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200903</th>\n",
       "      <td>120.88</td>\n",
       "      <td>120.880</td>\n",
       "      <td>120.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200904</th>\n",
       "      <td>120.96</td>\n",
       "      <td>120.960</td>\n",
       "      <td>120.960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             prc  prc_adjusted    close\n",
       "date                                   \n",
       "20200825  499.30       124.825  124.825\n",
       "20200826  506.09       126.522  126.522\n",
       "20200827  500.04       125.010  125.010\n",
       "20200828  499.23       124.808  124.808\n",
       "20200831  129.04       129.040  129.040\n",
       "20200901  134.18       134.180  134.180\n",
       "20200902  131.40       131.400  131.400\n",
       "20200903  120.88       120.880  120.880\n",
       "20200904  120.96       120.960  120.960"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare split-adjusted CRSP price to Yahoo Finance close price\n",
    "crsp_fac = crsp_fac.reindex(crsp_prc.index)\\\n",
    "                   .shift(-1)\\\n",
    "                   .bfill()\\\n",
    "                   .fillna(1)\\\n",
    "                   .join(crsp_prc)\n",
    "crsp_fac['prc_adjusted'] = crsp_fac['prc'] / crsp_fac['facpr']\n",
    "crsp_fac[['prc', 'prc_adjusted']].join(df['close'], how='left').round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4527504d",
   "metadata": {},
   "source": [
    "### Stock delistings\n",
    "\n",
    "An important feature of the CRSP database is that it is free of survivorship-bias.  It includes the historical records of stocks that have delisted from trading on the exchanges.\n",
    "\n",
    "In CRSP Monthly, the Delisting Return is calculated from the last\n",
    "month ending price to the last daily trading price if no other\n",
    "delisting information is available. In this case the delisting\n",
    "payment date is the same as the delisting date.  If the return is\n",
    "calculated from a daily price, it is a partial-month return. The\n",
    "partial-month returns are not truly Delisting Returns since they do\n",
    "not represent values after delisting, but allow the researcher to\n",
    "make a more accurate estimate of the Delisting Returns.\n",
    "\n",
    "Following Bali, Engle, and Murray (2016) and Shumway (1997): we can\n",
    "construct returns adjusted for delistings, which result when a\n",
    "company is acquired, ceases operations, or fails to meet exchange\n",
    "listing requirements. The adjustment reflects the partial month of\n",
    "returns to investors who bought the stock in the month before the\n",
    "delisting. For certain delisting codes ([500, 520, 551..574, 580,\n",
    "584]) where the delisting return is missing, a delisting return of -30%\n",
    "is assumed which reflects the average recovery amount after delisting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c14a4c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>prc</th>\n",
       "      <th>original_ret</th>\n",
       "      <th>retx</th>\n",
       "      <th>dlstcd</th>\n",
       "      <th>dlret</th>\n",
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       "    <tr>\n",
       "      <th>permno</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>10275</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>10418</th>\n",
       "      <td>20041130</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520</td>\n",
       "      <td>0.0509</td>\n",
       "      <td>0.0509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11194</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>233</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>0.0066</td>\n",
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       "    <tr>\n",
       "      <th>12010</th>\n",
       "      <td>20041130</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>587</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20459</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.0724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32897</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584</td>\n",
       "      <td>-0.2357</td>\n",
       "      <td>-0.2357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55589</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>-0.0114</td>\n",
       "      <td>-0.0114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64290</th>\n",
       "      <td>20041130</td>\n",
       "      <td>13.70</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67708</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69593</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331</td>\n",
       "      <td>0.0998</td>\n",
       "      <td>0.0998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69681</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584</td>\n",
       "      <td>-0.4853</td>\n",
       "      <td>-0.4853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70447</th>\n",
       "      <td>20041130</td>\n",
       "      <td>5.80</td>\n",
       "      <td>-0.2246</td>\n",
       "      <td>-0.2246</td>\n",
       "      <td>570</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.4572</td>\n",
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       "    <tr>\n",
       "      <th>75606</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520</td>\n",
       "      <td>-0.2466</td>\n",
       "      <td>-0.2466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75684</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76306</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "      <td>-0.0329</td>\n",
       "      <td>-0.0329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76691</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>582</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.0127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77838</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520</td>\n",
       "      <td>-0.0041</td>\n",
       "      <td>-0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79149</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>574</td>\n",
       "      <td>-0.2088</td>\n",
       "      <td>-0.2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79523</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.0043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80211</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>470</td>\n",
       "      <td>0.0186</td>\n",
       "      <td>0.0186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80714</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332</td>\n",
       "      <td>0.0337</td>\n",
       "      <td>0.0337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82491</th>\n",
       "      <td>20041130</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-0.0644</td>\n",
       "      <td>-0.0644</td>\n",
       "      <td>551</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83583</th>\n",
       "      <td>20041130</td>\n",
       "      <td>125.10</td>\n",
       "      <td>0.2810</td>\n",
       "      <td>0.2810</td>\n",
       "      <td>241</td>\n",
       "      <td>0.0624</td>\n",
       "      <td>0.3608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83702</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>587</td>\n",
       "      <td>0.2667</td>\n",
       "      <td>0.2667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83995</th>\n",
       "      <td>20041130</td>\n",
       "      <td>26.58</td>\n",
       "      <td>0.2374</td>\n",
       "      <td>0.2374</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.2861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84047</th>\n",
       "      <td>20041130</td>\n",
       "      <td>16.35</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>241</td>\n",
       "      <td>-0.2661</td>\n",
       "      <td>-0.2318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86123</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86307</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86388</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584</td>\n",
       "      <td>-0.0337</td>\n",
       "      <td>-0.0337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86991</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87126</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0784</td>\n",
       "      <td>0.0784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87158</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87247</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88670</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>470</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89186</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331</td>\n",
       "      <td>0.0696</td>\n",
       "      <td>0.0696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89385</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231</td>\n",
       "      <td>0.2959</td>\n",
       "      <td>0.2959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89936</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0719</td>\n",
       "      <td>0.0719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89939</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date     prc  original_ret    retx  dlstcd   dlret     ret\n",
       "permno                                                                \n",
       "10275   20041130     NaN           NaN     NaN     584 -0.2703 -0.2703\n",
       "10418   20041130     NaN           NaN     NaN     520  0.0509  0.0509\n",
       "11194   20041130     NaN           NaN     NaN     233  0.0066  0.0066\n",
       "12010   20041130     NaN           NaN     NaN     587  0.0490  0.0490\n",
       "20459   20041130     NaN           NaN     NaN     231  0.0724  0.0724\n",
       "32897   20041130     NaN           NaN     NaN     584 -0.2357 -0.2357\n",
       "55589   20041130     NaN           NaN     NaN     233 -0.0114 -0.0114\n",
       "64290   20041130   13.70        0.0178  0.0178     233  0.0000  0.0178\n",
       "67708   20041130     NaN           NaN     NaN     233  0.0164  0.0164\n",
       "69593   20041130     NaN           NaN     NaN     331  0.0998  0.0998\n",
       "69681   20041130     NaN           NaN     NaN     584 -0.4853 -0.4853\n",
       "70447   20041130    5.80       -0.2246 -0.2246     570     NaN -0.4572\n",
       "75606   20041130     NaN           NaN     NaN     520 -0.2466 -0.2466\n",
       "75684   20041130     NaN           NaN     NaN     233  0.0094  0.0094\n",
       "76306   20041130     NaN           NaN     NaN     241 -0.0329 -0.0329\n",
       "76691   20041130     NaN           NaN     NaN     582  0.0127  0.0127\n",
       "77838   20041130     NaN           NaN     NaN     520 -0.0041 -0.0041\n",
       "79149   20041130     NaN           NaN     NaN     574 -0.2088 -0.2088\n",
       "79523   20041130     NaN           NaN     NaN     233  0.0043  0.0043\n",
       "80211   20041130     NaN           NaN     NaN     470  0.0186  0.0186\n",
       "80714   20041130     NaN           NaN     NaN     332  0.0337  0.0337\n",
       "82491   20041130    4.21       -0.0644 -0.0644     551  0.0689  0.0000\n",
       "83583   20041130  125.10        0.2810  0.2810     241  0.0624  0.3608\n",
       "83702   20041130     NaN           NaN     NaN     587  0.2667  0.2667\n",
       "83995   20041130   26.58        0.2374  0.2374     231  0.0394  0.2861\n",
       "84047   20041130   16.35        0.0467  0.0467     241 -0.2661 -0.2318\n",
       "86123   20041130     NaN           NaN     NaN     233  0.0059  0.0059\n",
       "86307   20041130     NaN           NaN     NaN     233  0.0223  0.0223\n",
       "86388   20041130     NaN           NaN     NaN     584 -0.0337 -0.0337\n",
       "86991   20041130     NaN           NaN     NaN     233  0.0106  0.0106\n",
       "87126   20041130     NaN           NaN     NaN     231  0.0784  0.0784\n",
       "87158   20041130     NaN           NaN     NaN     233  0.0086  0.0086\n",
       "87247   20041130     NaN           NaN     NaN     233  0.0046  0.0046\n",
       "88670   20041130     NaN           NaN     NaN     470  0.0013  0.0013\n",
       "89186   20041130     NaN           NaN     NaN     331  0.0696  0.0696\n",
       "89385   20041130     NaN           NaN     NaN     231  0.2959  0.2959\n",
       "89936   20041130     NaN           NaN     NaN     231  0.0719  0.0719\n",
       "89939   20041130     NaN           NaN     NaN     233  0.0059  0.0059"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show sample of original CRSP Monthly ret and dlret, and after adjustment            \n",
    "pd.read_sql('select * from monthly where dlstcd>100 and date=20041130', sql.engine)\\\n",
    "  .set_index('permno')\\\n",
    "  .rename(columns={'ret': 'original_ret'})\\\n",
    "  .join(crsp.get_ret(beg=20041101, end=20041130), how='left')\\\n",
    "  .round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2472f43e",
   "metadata": {},
   "source": [
    "## Properties of stock returns\n",
    "\n",
    "### Long-run market averages\n",
    "\n",
    "- total stock returns\n",
    "- dividend yield\n",
    "- shares trading turnover\n",
    "\n",
    "Equal-weighted time-series means of\n",
    "annual cap-weighted cross-sectional averages of universe stocks \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b24c91f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [1:10:45<00:00, 849.19s/it]\n"
     ]
    }
   ],
   "source": [
    "# Loop over the 20-year eras, and compute means of annual cap-weighted averages\n",
    "years = [2005, 1985, 1965, 1945, 1925]\n",
    "results = DataFrame(columns=['divyld', 'turnover', 'means'])\n",
    "for era in tqdm(years):\n",
    "    label = f\"{era+1}-{min(era+20, CRSP_DATE // 10000)}\"\n",
    "    divyld = {}\n",
    "    means = {}\n",
    "    turnover = {}\n",
    "    for year in bd.date_range(era, min(CRSP_DATE // 10000, era+20), freq=12):\n",
    "        univ = crsp.get_universe(bd.endyr(year))  # screen for universe stocks\n",
    "\n",
    "        # Retrieve and compute cap-weighted average of annual returns\n",
    "        cmd = f\"\"\"\n",
    "select permno, SUM(LOG(1+ret)) AS ret FROM daily\n",
    "WHERE date > {bd.endyr(year)} AND date <= {bd.endyr(year, 1)}\n",
    "GROUP BY permno\n",
    "\"\"\".strip()\n",
    "        data = pd.read_sql(cmd, sql.engine)\n",
    "        df = data.set_index('permno').join(univ['cap'], how='right').dropna()\n",
    "        means[year] = (np.exp(df['ret'])-1).dot(df['cap']) / df['cap'].sum()\n",
    "\n",
    "        # Retrieve and compute cap-weighted average of annualized turnover\n",
    "        cmd = f\"\"\"\n",
    "select permno, 252*AVG(vol/(shrout*1000)) AS turnover FROM daily\n",
    "WHERE date > {bd.endyr(year)} AND date <= {bd.endyr(year, 1)}\n",
    "GROUP BY permno\n",
    "\"\"\".strip()\n",
    "        data = pd.read_sql(cmd, sql.engine)\n",
    "        df = data.set_index('permno').join(univ['cap'], how='right').dropna()\n",
    "        turnover[year] = df['turnover'].dot(df['cap']) / df['cap'].sum()\n",
    "\n",
    "        # Retrieve and compute cap-weighted average of annual dividend amounts\n",
    "        cmd = f\"\"\"\n",
    "SELECT dist.permno as permno, SUM(daily.shrout * dist.divamt) AS divamt\n",
    "FROM dist INNER JOIN daily\n",
    "ON daily.permno = dist.permno AND daily.date = dist.exdt\n",
    "WHERE dist.divamt > 0 AND dist.exdt > {bd.endyr(year)} \n",
    "  AND dist.exdt <= {bd.endyr(year, 1)}\n",
    "GROUP BY permno\n",
    "\"\"\".strip()\n",
    "        data = pd.read_sql(cmd, sql.engine)\n",
    "        df = data.set_index('permno').join(univ['cap'], how='right').dropna()\n",
    "        divyld[year] = df['divamt'].sum() / df['cap'].sum()\n",
    "\n",
    "    results.loc[label, 'turnover'] = np.mean(list(turnover.values()))\n",
    "    results.loc[label, 'divyld'] = np.mean(list(divyld.values()))\n",
    "    results.loc[label, 'means'] = np.mean(list(means.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "763c5b3a",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot mean returns, dividend yield and turnover using both y-axes\n",
    "results = results.iloc[::-1]\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(results['means'], color=\"blue\")\n",
    "ax.plot(results['divyld'], color=\"green\")\n",
    "ax.legend(['average returns', 'dividend yield'], loc=\"center left\")\n",
    "ax.set_ylabel(\"Annual dividend yield and total stock returns\")\n",
    "bx = ax.twinx()\n",
    "bx.plot(results['turnover'], color=\"red\")\n",
    "bx.set_ylabel(\"Annual Turnover\")\n",
    "bx.legend(['turnover'], loc=\"center right\")\n",
    "plt.title(\"Long-Run Market Averages\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe326a4",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Moments pf individual stock returns\n",
    "\n",
    "\n",
    "- The volatility of an asset is usually measured using the standard deviation of the returns. The common practice is to report the annualized volatility using the square-root rule which assumes that variance scales linearly with time: e.g.\n",
    "daily by $\\sqrt{252}$, weekly by $\\sqrt{52}$, monthly by $\\sqrt{12}$\n",
    "\n",
    "- Means are computed from log returns, and annualized by multiplying by the respective number of periods in a year.  We use log returns $\\log{1+R_t}$ instead of simple returns $R_t$ to skirt around some of the issues related to translating between arithmetic and geometric averages. It has been shown that:\n",
    "  - By Jensen's inequality, the arithmetic mean is greater than the geometric mean\n",
    "  - Under the assumption of log-normality, the arithmetic mean exceeds the geometric means of returns by half the volatility.\n",
    "\n",
    "- A normal distribution is symmetric and thin-tailed, and so has no skewness or excess kurtosis. However, many return series are both skewed and fat-tailed (kurtosis in excess of 3). \n",
    "  - A left-skewed distribution is longer on the left side of its peak than on its right. In other words, a left-skewed distribution has a long tail on its left side, where the mean typically lies to the left of its median. Left skew is also referred to as negative skew.\n",
    "  - Right or positive skew has the opposite properties.\n",
    "\n",
    "\n",
    "The Jarque-Bera test statistic can be used to test whether the sample skewness and kurtosis are compatible with an assumption that the returns are normally distributed. When returns are normally distributed, the skewness is asymptotically normally distributed with a variance of 6, so (skewness)$^2/6$ has a $\\chi^2_1$\n",
    "distribution. Meanwhile, the kurtosis is asymptotically normally distributed with mean 3 and variance of 24, and so (kurtosis - 3)$^2/24$ also has a $\\chi^2_1$\n",
    "distribution. These two statistics are\n",
    "asymptotically independent (uncorrelated), and so their sum is $~\\chi^2_2$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3f84015f",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "# helper function\n",
    "def get_moments(freq, annualize, years, stride=20):\n",
    "    \"\"\"helper to summarize moments of individual stock returns\"\"\"\n",
    "    out = {}\n",
    "    years = sorted(years)\n",
    "    max_era = min(CRSP_DATE, bd.endyr(max(years) + stride)) # end year of last era\n",
    "\n",
    "    # Use StocksBuffer to cache monthly returns for entire era\n",
    "    if freq not in ['d', 'w']:\n",
    "        monthly = CRSPBuffer(stocks=crsp, dataset='monthly', fields=['ret'],\n",
    "                             beg=bd.endyr(years[0]), end=max_era)\n",
    "    for era, next_era in zip(years, years[1:] + [max_era]):\n",
    "        allstocks = DataFrame()\n",
    "        anystocks = DataFrame()\n",
    "        univ_year = bd.endyr(era)  # universe as of end of previous calendar year\n",
    "\n",
    "        # Loop over calendar years in era\n",
    "        endyrs = bd.date_range(bd.endyr(era), bd.endyr(next_era), freq=freq)\n",
    "        for beg, end in tqdm(bd.date_tuples(endyrs)):\n",
    "            \n",
    "            # Update the investment universe every calendar year\n",
    "            if bd.endyr(beg) != univ_year:\n",
    "                univ = crsp.get_universe(univ_year)\n",
    "                univ_year = bd.endyr(beg)\n",
    "\n",
    "                # Use StocksBuffer to cache daily returns for the calendar year\n",
    "                if freq == 'd':\n",
    "                    daily = CRSPBuffer(stocks=crsp, dataset='daily', fields=['ret'],\n",
    "                                       beg=bd.offset(beg, -1), end=bd.endyr(end))\n",
    "                    \n",
    "            # retrieve returns\n",
    "            if freq == 'd':\n",
    "                ret = daily.get_ret(beg=beg, end=end).reindex(univ.index)\n",
    "            elif freq == 'w':\n",
    "                ret = crsp.get_ret(beg=beg, end=end).reindex(univ.index)\n",
    "            else:    # monthly or coarser\n",
    "                ret = monthly.get_ret(beg=beg, end=end).reindex(univ.index)\n",
    "\n",
    "            # horizontally stack returns\n",
    "            if allstocks.empty:\n",
    "                allstocks = DataFrame(ret.rename(end))\n",
    "            else:   # every year                 \n",
    "                allstocks = allstocks.join(ret.rename(end), how='inner')\n",
    "            anystocks = anystocks.join(ret.rename(end), how='outer')  # any year\n",
    "\n",
    "        anystocks = anystocks.dropna(axis=0, how='all')        # drop empty rows    \n",
    "        out[f\"{str(era)[:4]}-{int(str(next_era)[:4]) - 1}\"] = {\n",
    "            f\"VolAnnualized\": np.nanmedian(\n",
    "                np.nanstd(allstocks, axis=1, ddof=0)) * np.sqrt(annualize),\n",
    "            f\"Skewness\": np.nanmedian(\n",
    "                scipy.stats.skew(allstocks, nan_policy='omit', axis=1)),\n",
    "            f\"ExcessKurtosis\": np.nanmedian(\n",
    "                scipy.stats.kurtosis(allstocks, nan_policy='omit', axis=1)),\n",
    "            f\"Count\": len(allstocks),\n",
    "            f\"MeanAnnualized\": np.nanmedian(\n",
    "                np.nanmean(np.log(1 + allstocks), axis=1)) * annualize,\n",
    "            f\"Mean (all stocks)\": np.nanmedian(\n",
    "                np.nanmean(np.log(1 + anystocks), axis=1)) * annualize,\n",
    "            f\"Count (all stocks)\": len(anystocks),\n",
    "        }\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "72b6cc88",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5963/5963 [10:37<00:00,  9.35it/s]  \n",
      "100%|██████████| 5237/5237 [10:41<00:00,  8.16it/s] \n",
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      "100%|██████████| 240/240 [00:18<00:00, 13.14it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n",
      "100%|██████████| 240/240 [00:25<00:00,  9.28it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n",
      "100%|██████████| 240/240 [00:27<00:00,  8.61it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n",
      "100%|██████████| 240/240 [00:25<00:00,  9.32it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n",
      "100%|██████████| 216/216 [00:18<00:00, 11.37it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n",
      "100%|██████████| 20/20 [00:01<00:00, 10.24it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n",
      "100%|██████████| 20/20 [00:01<00:00, 10.47it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n",
      "100%|██████████| 20/20 [00:02<00:00,  8.93it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n",
      "100%|██████████| 20/20 [00:02<00:00,  9.70it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n",
      "100%|██████████| 18/18 [00:01<00:00,  9.14it/s]\n",
      "/home/terence/env3.11/lib/python3.11/site-packages/pandas/core/internals/blocks.py:393: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Collect results for different intervals and eras\n",
    "intervals = [('d', 'daily', 252), ('w', 'weekly', 52), ('e', 'monthly', 12),\n",
    "             (12, 'annual', 1)]          # different returns sampling frequencies\n",
    "years = [2005, 1985, 1965, 1945, 1925]   # 20-year periods\n",
    "results = DataFrame()\n",
    "#results = pd.read_json(outdir / 'moments.json')\n",
    "#results = results.set_index(results.columns[0])\n",
    "for order, (freq, interval, annualize) in enumerate(intervals):\n",
    "    df = DataFrame(get_moments(freq, annualize, years, stride=20)).reset_index()\n",
    "    df['interval'] = interval\n",
    "\n",
    "    # set index so that rows can be ordered by moment then freq\n",
    "    df.index = [f\"{moment}{order}\" for moment in range(len(df))]\n",
    "    results = pd.concat((results, df))  # accumulate df to results\n",
    "    results.reset_index().to_json(outdir / 'moments.json')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "de1dbcdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "out = results.sort_index().rename(columns={'index':'moment'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc9ec300",
   "metadata": {},
   "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></th>\n",
       "      <th>1925-1944</th>\n",
       "      <th>1945-1964</th>\n",
       "      <th>1965-1984</th>\n",
       "      <th>1985-2004</th>\n",
       "      <th>2005-2022</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interval</th>\n",
       "      <th>moment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>daily</th>\n",
       "      <th>VolAnnualized</th>\n",
       "      <td>0.543</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly</th>\n",
       "      <th>VolAnnualized</th>\n",
       "      <td>0.540</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly</th>\n",
       "      <th>VolAnnualized</th>\n",
       "      <td>0.553</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual</th>\n",
       "      <th>VolAnnualized</th>\n",
       "      <td>0.553</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily</th>\n",
       "      <th>Skewness</th>\n",
       "      <td>0.909</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly</th>\n",
       "      <th>Skewness</th>\n",
       "      <td>0.932</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly</th>\n",
       "      <th>Skewness</th>\n",
       "      <td>1.257</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual</th>\n",
       "      <th>Skewness</th>\n",
       "      <td>0.751</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily</th>\n",
       "      <th>ExcessKurtosis</th>\n",
       "      <td>14.887</td>\n",
       "      <td>5.446</td>\n",
       "      <td>4.926</td>\n",
       "      <td>8.749</td>\n",
       "      <td>13.268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly</th>\n",
       "      <th>ExcessKurtosis</th>\n",
       "      <td>7.535</td>\n",
       "      <td>2.645</td>\n",
       "      <td>2.916</td>\n",
       "      <td>4.384</td>\n",
       "      <td>6.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly</th>\n",
       "      <th>ExcessKurtosis</th>\n",
       "      <td>6.073</td>\n",
       "      <td>1.074</td>\n",
       "      <td>1.648</td>\n",
       "      <td>2.080</td>\n",
       "      <td>2.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual</th>\n",
       "      <th>ExcessKurtosis</th>\n",
       "      <td>0.220</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-0.051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         1925-1944  1945-1964  1965-1984  1985-2004  2005-2022\n",
       "interval moment                                                               \n",
       "daily    VolAnnualized       0.543      0.267      0.337      0.392      0.437\n",
       "weekly   VolAnnualized       0.540      0.254      0.347      0.366      0.407\n",
       "monthly  VolAnnualized       0.553      0.244      0.340      0.355      0.381\n",
       "annual   VolAnnualized       0.553      0.296      0.382      0.379      0.360\n",
       "daily    Skewness            0.909      0.315      0.528      0.339      0.395\n",
       "weekly   Skewness            0.932      0.528      0.627      0.343      0.281\n",
       "monthly  Skewness            1.257      0.484      0.597      0.381      0.307\n",
       "annual   Skewness            0.751      0.582      0.628      0.684      0.423\n",
       "daily    ExcessKurtosis     14.887      5.446      4.926      8.749     13.268\n",
       "weekly   ExcessKurtosis      7.535      2.645      2.916      4.384      6.589\n",
       "monthly  ExcessKurtosis      6.073      1.074      1.648      2.080      2.306\n",
       "annual   ExcessKurtosis      0.220     -0.115      0.105      0.132     -0.051"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show medians of the moments of individual stock returns\n",
    "out[~out['moment'].str.startswith(\"Count\") &\n",
    "    ~out['moment'].str.startswith(\"Mean\")].set_index(['interval', 'moment']).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dc117af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1925-1944</th>\n",
       "      <th>1945-1964</th>\n",
       "      <th>1965-1984</th>\n",
       "      <th>1985-2004</th>\n",
       "      <th>2005-2022</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interval</th>\n",
       "      <th>moment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>daily</th>\n",
       "      <th>Count</th>\n",
       "      <td>305</td>\n",
       "      <td>606</td>\n",
       "      <td>877</td>\n",
       "      <td>1219</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly</th>\n",
       "      <th>Count</th>\n",
       "      <td>305</td>\n",
       "      <td>606</td>\n",
       "      <td>877</td>\n",
       "      <td>1219</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly</th>\n",
       "      <th>Count</th>\n",
       "      <td>305</td>\n",
       "      <td>606</td>\n",
       "      <td>877</td>\n",
       "      <td>1219</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual</th>\n",
       "      <th>Count</th>\n",
       "      <td>305</td>\n",
       "      <td>606</td>\n",
       "      <td>877</td>\n",
       "      <td>1219</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily</th>\n",
       "      <th>Count (all stocks)</th>\n",
       "      <td>1082</td>\n",
       "      <td>2448</td>\n",
       "      <td>10089</td>\n",
       "      <td>16273</td>\n",
       "      <td>9050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly</th>\n",
       "      <th>Count (all stocks)</th>\n",
       "      <td>1082</td>\n",
       "      <td>2447</td>\n",
       "      <td>10084</td>\n",
       "      <td>16271</td>\n",
       "      <td>9042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly</th>\n",
       "      <th>Count (all stocks)</th>\n",
       "      <td>1082</td>\n",
       "      <td>2448</td>\n",
       "      <td>10088</td>\n",
       "      <td>16273</td>\n",
       "      <td>9049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual</th>\n",
       "      <th>Count (all stocks)</th>\n",
       "      <td>1082</td>\n",
       "      <td>2448</td>\n",
       "      <td>10088</td>\n",
       "      <td>16273</td>\n",
       "      <td>9049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             1925-1944  1945-1964  1965-1984  1985-2004  \\\n",
       "interval moment                                                           \n",
       "daily    Count                     305        606        877       1219   \n",
       "weekly   Count                     305        606        877       1219   \n",
       "monthly  Count                     305        606        877       1219   \n",
       "annual   Count                     305        606        877       1219   \n",
       "daily    Count (all stocks)       1082       2448      10089      16273   \n",
       "weekly   Count (all stocks)       1082       2447      10084      16271   \n",
       "monthly  Count (all stocks)       1082       2448      10088      16273   \n",
       "annual   Count (all stocks)       1082       2448      10088      16273   \n",
       "\n",
       "                             2005-2022  \n",
       "interval moment                         \n",
       "daily    Count                    1579  \n",
       "weekly   Count                    1579  \n",
       "monthly  Count                    1579  \n",
       "annual   Count                    1579  \n",
       "daily    Count (all stocks)       9050  \n",
       "weekly   Count (all stocks)       9042  \n",
       "monthly  Count (all stocks)       9049  \n",
       "annual   Count (all stocks)       9049  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show counts of stocks\n",
    "out[out['moment'].str.startswith(\"Count\")].set_index(['interval', 'moment']).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7265483a",
   "metadata": {},
   "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></th>\n",
       "      <th>1925-1944</th>\n",
       "      <th>1945-1964</th>\n",
       "      <th>1965-1984</th>\n",
       "      <th>1985-2004</th>\n",
       "      <th>2005-2022</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interval</th>\n",
       "      <th>moment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>daily</th>\n",
       "      <th>MeanAnnualized</th>\n",
       "      <td>0.040</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly</th>\n",
       "      <th>MeanAnnualized</th>\n",
       "      <td>0.046</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly</th>\n",
       "      <th>MeanAnnualized</th>\n",
       "      <td>0.047</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual</th>\n",
       "      <th>MeanAnnualized</th>\n",
       "      <td>0.047</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily</th>\n",
       "      <th>Mean (all stocks)</th>\n",
       "      <td>0.052</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly</th>\n",
       "      <th>Mean (all stocks)</th>\n",
       "      <td>0.061</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly</th>\n",
       "      <th>Mean (all stocks)</th>\n",
       "      <td>0.063</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual</th>\n",
       "      <th>Mean (all stocks)</th>\n",
       "      <td>0.061</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            1925-1944  1945-1964  1965-1984  1985-2004  \\\n",
       "interval moment                                                          \n",
       "daily    MeanAnnualized         0.040      0.104      0.089      0.099   \n",
       "weekly   MeanAnnualized         0.046      0.109      0.087      0.100   \n",
       "monthly  MeanAnnualized         0.047      0.108      0.088      0.099   \n",
       "annual   MeanAnnualized         0.047      0.108      0.088      0.099   \n",
       "daily    Mean (all stocks)      0.052      0.114      0.059      0.009   \n",
       "weekly   Mean (all stocks)      0.061      0.118      0.057      0.010   \n",
       "monthly  Mean (all stocks)      0.063      0.118      0.059      0.009   \n",
       "annual   Mean (all stocks)      0.061      0.116      0.056      0.008   \n",
       "\n",
       "                            2005-2022  \n",
       "interval moment                        \n",
       "daily    MeanAnnualized         0.059  \n",
       "weekly   MeanAnnualized         0.057  \n",
       "monthly  MeanAnnualized         0.059  \n",
       "annual   MeanAnnualized         0.059  \n",
       "daily    Mean (all stocks)      0.006  \n",
       "weekly   Mean (all stocks)      0.004  \n",
       "monthly  Mean (all stocks)      0.007  \n",
       "annual   Mean (all stocks)      0.006  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show medians of mean individual stock returns\n",
    "out[out['moment'].str.startswith(\"Mean\")].set_index(['interval', 'moment']).round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f46604",
   "metadata": {},
   "source": [
    "### Correlations\n",
    "\n",
    "The linear correlation estimator (also known as Pearson’s correlation) measures the linear relationship between two variables. Alternative measures can help capture nonlinear dependence:\n",
    "- rank correlation (or Spearman's correlation) is the linear correlation estimator applied to the ranks of the observations. It measures monotoric correlation and is less sensitive to outliers.\n",
    "- Kendall's $\\tau$ measures the relative frequency of concordant and discordant pairs of two random variables X and Y. Concordant pairs agree about the relative position of X and Y, while pairs that disagree about the order are discordant. This statistic is robust to outliers and works well with skewed or non-normally distributed data.\n",
    "  \n",
    "Computing these measures between the value-weighted and equal-weighted total daily market returns suggests that the high estimated linear correlation may be sensitive to outliers, compared to estimators that are less.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ddf0555a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>permno</th>\n",
       "      <th>vwretd</th>\n",
       "      <th>ewretd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19260102</th>\n",
       "      <td>0.004297</td>\n",
       "      <td>0.002941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19260104</th>\n",
       "      <td>-0.001357</td>\n",
       "      <td>0.001036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19260105</th>\n",
       "      <td>-0.004603</td>\n",
       "      <td>-0.005856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19260106</th>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.000888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19260107</th>\n",
       "      <td>0.003907</td>\n",
       "      <td>0.004258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231222</th>\n",
       "      <td>0.001708</td>\n",
       "      <td>0.003380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231226</th>\n",
       "      <td>0.004208</td>\n",
       "      <td>0.005969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231227</th>\n",
       "      <td>0.001604</td>\n",
       "      <td>0.001316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231228</th>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.001506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231229</th>\n",
       "      <td>-0.002798</td>\n",
       "      <td>-0.003538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25798 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "permno      vwretd    ewretd\n",
       "date                        \n",
       "19260102  0.004297  0.002941\n",
       "19260104 -0.001357  0.001036\n",
       "19260105 -0.004603 -0.005856\n",
       "19260106  0.000537  0.000888\n",
       "19260107  0.003907  0.004258\n",
       "...            ...       ...\n",
       "20231222  0.001708  0.003380\n",
       "20231226  0.004208  0.005969\n",
       "20231227  0.001604  0.001316\n",
       "20231228  0.000415  0.001506\n",
       "20231229 -0.002798 -0.003538\n",
       "\n",
       "[25798 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve CRSP value- and equal-weighted total market returns\n",
    "retd = bench.get_series(['vwretd', 'ewretd'], field='ret').dropna()\n",
    "retd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4762be8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kendall</th>\n",
       "      <th>spearman</th>\n",
       "      <th>pearson</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Measures of correlations</th>\n",
       "      <td>0.8147</td>\n",
       "      <td>0.9462</td>\n",
       "      <td>0.9628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          kendall  spearman  pearson\n",
       "Measures of correlations   0.8147    0.9462   0.9628"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute alternative measures of correlation\n",
    "DataFrame(dict(kendall=scipy.stats.kendalltau(retd['vwretd'], retd['ewretd'])[0],\n",
    "               spearman=scipy.stats.spearmanr(retd['vwretd'], retd['ewretd'])[0],\n",
    "               pearson=scipy.stats.pearsonr(retd['vwretd'], retd['ewretd'])[0]),\n",
    "               index=['Measures of correlations']).round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "099becdc",
   "metadata": {},
   "source": [
    "## Stock Delistings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c20d5c",
   "metadata": {},
   "source": [
    "An important feature of the CRSP database is that it is free of survivorship-bias.  It includes the historical records of stocks that have delisted from trading on the exchanges.\n",
    "\n",
    "In CRSP Monthly, the Delisting Return is calculated from the last\n",
    "month ending price to the last daily trading price if no other\n",
    "delisting information is available. In this case the delisting\n",
    "payment date is the same as the delisting date.  If the return is\n",
    "calculated from a daily price, it is a partial-month return. The\n",
    "partial-month returns are not truly Delisting Returns since they do\n",
    "not represent values after delisting, but allow the researcher to\n",
    "make a more accurate estimate of the Delisting Returns.\n",
    "\n",
    "Following Bali, Engle, and Murray (2016) and Shumway (1997): we can\n",
    "construct returns adjusted for delistings, which result when a\n",
    "company is acquired, ceases operations, or fails to meet exchange\n",
    "listing requirements. The adjustment reflects the partial month of\n",
    "returns to investors who bought the stock in the month before the\n",
    "delisting. For certain delisting codes ([500, 520, 551..574, 580,\n",
    "584]) where the delisting return is missing, a delisting return of -30%\n",
    "is assumed which reflects the average recovery amount after delisting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2442a21",
   "metadata": {},
   "outputs": [
    {
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       "      <th>10275</th>\n",
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       "      <th>11194</th>\n",
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       "      <td>0.0066</td>\n",
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       "      <th>12010</th>\n",
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       "      <td>587</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0490</td>\n",
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       "    <tr>\n",
       "      <th>20459</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.0724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32897</th>\n",
       "      <td>20041130</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584</td>\n",
       "      <td>-0.2357</td>\n",
       "      <td>-0.2357</td>\n",
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       "    <tr>\n",
       "      <th>55589</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>-0.0114</td>\n",
       "      <td>-0.0114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64290</th>\n",
       "      <td>20041130</td>\n",
       "      <td>13.70</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67708</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0164</td>\n",
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       "    <tr>\n",
       "      <th>69593</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331</td>\n",
       "      <td>0.0998</td>\n",
       "      <td>0.0998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69681</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584</td>\n",
       "      <td>-0.4853</td>\n",
       "      <td>-0.4853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70447</th>\n",
       "      <td>20041130</td>\n",
       "      <td>5.80</td>\n",
       "      <td>-0.2246</td>\n",
       "      <td>-0.2246</td>\n",
       "      <td>570</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.4572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75606</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520</td>\n",
       "      <td>-0.2466</td>\n",
       "      <td>-0.2466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75684</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76306</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "      <td>-0.0329</td>\n",
       "      <td>-0.0329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76691</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>582</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.0127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77838</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520</td>\n",
       "      <td>-0.0041</td>\n",
       "      <td>-0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79149</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>574</td>\n",
       "      <td>-0.2088</td>\n",
       "      <td>-0.2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79523</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.0043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80211</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>470</td>\n",
       "      <td>0.0186</td>\n",
       "      <td>0.0186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80714</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332</td>\n",
       "      <td>0.0337</td>\n",
       "      <td>0.0337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82491</th>\n",
       "      <td>20041130</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-0.0644</td>\n",
       "      <td>-0.0644</td>\n",
       "      <td>551</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83583</th>\n",
       "      <td>20041130</td>\n",
       "      <td>125.10</td>\n",
       "      <td>0.2810</td>\n",
       "      <td>0.2810</td>\n",
       "      <td>241</td>\n",
       "      <td>0.0624</td>\n",
       "      <td>0.3608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83702</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>587</td>\n",
       "      <td>0.2667</td>\n",
       "      <td>0.2667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83995</th>\n",
       "      <td>20041130</td>\n",
       "      <td>26.58</td>\n",
       "      <td>0.2374</td>\n",
       "      <td>0.2374</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.2861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84047</th>\n",
       "      <td>20041130</td>\n",
       "      <td>16.35</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>241</td>\n",
       "      <td>-0.2661</td>\n",
       "      <td>-0.2318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86123</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86307</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86388</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584</td>\n",
       "      <td>-0.0337</td>\n",
       "      <td>-0.0337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86991</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87126</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0784</td>\n",
       "      <td>0.0784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87158</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87247</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88670</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>470</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89186</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331</td>\n",
       "      <td>0.0696</td>\n",
       "      <td>0.0696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89385</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231</td>\n",
       "      <td>0.2959</td>\n",
       "      <td>0.2959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89936</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0719</td>\n",
       "      <td>0.0719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89939</th>\n",
       "      <td>20041130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date     prc  original_ret    retx  dlstcd   dlret     ret\n",
       "permno                                                                \n",
       "10275   20041130     NaN           NaN     NaN     584 -0.2703 -0.2703\n",
       "10418   20041130     NaN           NaN     NaN     520  0.0509  0.0509\n",
       "11194   20041130     NaN           NaN     NaN     233  0.0066  0.0066\n",
       "12010   20041130     NaN           NaN     NaN     587  0.0490  0.0490\n",
       "20459   20041130     NaN           NaN     NaN     231  0.0724  0.0724\n",
       "32897   20041130     NaN           NaN     NaN     584 -0.2357 -0.2357\n",
       "55589   20041130     NaN           NaN     NaN     233 -0.0114 -0.0114\n",
       "64290   20041130   13.70        0.0178  0.0178     233  0.0000  0.0178\n",
       "67708   20041130     NaN           NaN     NaN     233  0.0164  0.0164\n",
       "69593   20041130     NaN           NaN     NaN     331  0.0998  0.0998\n",
       "69681   20041130     NaN           NaN     NaN     584 -0.4853 -0.4853\n",
       "70447   20041130    5.80       -0.2246 -0.2246     570     NaN -0.4572\n",
       "75606   20041130     NaN           NaN     NaN     520 -0.2466 -0.2466\n",
       "75684   20041130     NaN           NaN     NaN     233  0.0094  0.0094\n",
       "76306   20041130     NaN           NaN     NaN     241 -0.0329 -0.0329\n",
       "76691   20041130     NaN           NaN     NaN     582  0.0127  0.0127\n",
       "77838   20041130     NaN           NaN     NaN     520 -0.0041 -0.0041\n",
       "79149   20041130     NaN           NaN     NaN     574 -0.2088 -0.2088\n",
       "79523   20041130     NaN           NaN     NaN     233  0.0043  0.0043\n",
       "80211   20041130     NaN           NaN     NaN     470  0.0186  0.0186\n",
       "80714   20041130     NaN           NaN     NaN     332  0.0337  0.0337\n",
       "82491   20041130    4.21       -0.0644 -0.0644     551  0.0689  0.0000\n",
       "83583   20041130  125.10        0.2810  0.2810     241  0.0624  0.3608\n",
       "83702   20041130     NaN           NaN     NaN     587  0.2667  0.2667\n",
       "83995   20041130   26.58        0.2374  0.2374     231  0.0394  0.2861\n",
       "84047   20041130   16.35        0.0467  0.0467     241 -0.2661 -0.2318\n",
       "86123   20041130     NaN           NaN     NaN     233  0.0059  0.0059\n",
       "86307   20041130     NaN           NaN     NaN     233  0.0223  0.0223\n",
       "86388   20041130     NaN           NaN     NaN     584 -0.0337 -0.0337\n",
       "86991   20041130     NaN           NaN     NaN     233  0.0106  0.0106\n",
       "87126   20041130     NaN           NaN     NaN     231  0.0784  0.0784\n",
       "87158   20041130     NaN           NaN     NaN     233  0.0086  0.0086\n",
       "87247   20041130     NaN           NaN     NaN     233  0.0046  0.0046\n",
       "88670   20041130     NaN           NaN     NaN     470  0.0013  0.0013\n",
       "89186   20041130     NaN           NaN     NaN     331  0.0696  0.0696\n",
       "89385   20041130     NaN           NaN     NaN     231  0.2959  0.2959\n",
       "89936   20041130     NaN           NaN     NaN     231  0.0719  0.0719\n",
       "89939   20041130     NaN           NaN     NaN     233  0.0059  0.0059"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(**sql.run('select * from monthly where dlstcd>100 and date=20041130'))\\\n",
    "    .set_index('permno')\\\n",
    "    .rename(columns={'ret': 'original_ret'})\\\n",
    "    .join(crsp.get_ret(beg=20041101, end=20041130), how='left')\\\n",
    "    .round(4)"
   ]
  }
 ],
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