{ “cells”: [ { “cell_type”: “code”, “execution_count”: 1, “id”: “15888628-892a-406b-a4fd-bbc41de0cf20”, “metadata”: {}, “outputs”: [], “source”: [ “import pandas as pd\n”, “import random\n”, “import os\n”, “import glob\n”, “import re\n”, “from bs4 import BeautifulSoup\n”, “from near_regex import NEAR_regex\n”, “from tqdm import tqdm\n”, “import datetime as dt\n”, “import seaborn as sns\n”, “import matplotlib.pyplot as plt” ] }, { “cell_type”: “markdown”, “id”: “d426ec90-9158-4f16-a5ab-480d44970476”, “metadata”: {}, “source”: [ “## Some notes about missing files before we start\n”, “- Of the 505 firms listed in the wikipedia list for S&P500 firms, some of the firms did not file 10-Ks for the year 2019. For some it was because they had different corporate entities so they filed under some other ticker/CIK. Here is a summary of how 10-Ks were obtained.\n”, “- Firms not obtained using ticker symbol:\n”, “ - Count: 13\n”, “ - BBWI\n”, “ - BRK.B\n”, “ - BF.B\n”, “ - PARA\n”, “ - LUMN\n”, “ - VTRS\n”, “ - SBNY\n”, “ - OTIS\n”, “ - OGN\n”, “ - FRC\n”, “ - CEG\n”, “ - CARR\n”, “ - CCL\n”, “- Of these, 6 were downloaded using CIK:\n”, “ - CCL\n”, “ - BBWI\n”, “ - BRK.B\n”, “ - BF.B\n”, “ - PARA\n”, “ - LUMN\n”, “- The rest could not be downloaded due to filing dates falling outside of the range we are using for our analysis in this project. Some firms filed later in 2020, but if we use those ranges, we obtain 2020 10-Ks for other firms and it gets messy quickly.” ] }, { “cell_type”: “code”, “execution_count”: 2, “id”: “7858ce41-98f6-4eb9-9b70-b6917177a22d”, “metadata”: {}, “outputs”: [], “source”: [ “# load the sp500 accounting+risk measures dataset\n”, “sp500 = pd.read_csv("input/sp500firms.csv")\n”, “sp500a =pd.read_csv("output/sp500_accting_plus_textrisks.csv")\n”, “# drop some columns we don’t need so we can see some columns we need (some pre-EDA cleaning =) )\n”, “sp500a = sp500a.drop(columns=[‘Unnamed: 0’,’gvkey’,’_merge’,’SEC filings’,’Headquarters Location’,’Date first added’, ‘Founded’])\n”, “ccm = pd.read_stata("https://github.com/LeDataSciFi/ledatascifi-2022/blob/main/data/2019%20ccm_cleaned.dta?raw=true")\n”, “ccm\n”, “# load the 2019-2020 return data from the input foler\n”, “ccm_ret = pd.read_stata(‘input/2019-2020-stock_rets cleaned.dta’)\n”, “# convert the dates into date fromat\n”, “ccm_ret[‘date’] = pd.to_datetime(ccm_ret[‘date’].astype(str), format=’%Y%m%d’)\n”, “# convert non-integer and empty return values to 0\n”, “ccm_ret.loc[ccm_ret[‘ret’] == ‘’, ‘ret’] = 0.0\n”, “ccm_ret.loc[ccm_ret[‘ret’] == ‘C’, ‘ret’] = 0.0\n”, “ccm_ret.loc[ccm_ret[‘ret’] == ‘B’, ‘ret’] = 0.0\n”, “ccm_ret[‘ret_f’]=ccm_ret[‘ret’].astype(float) #convert ret to float\n”, “ccm_ret = ccm_ret.drop(columns=[‘ret’]) # ret_f replaces the ret column\n”, “#ccm_ret\n”, “# merge this dataset with the dataset containing sp500 accounting+risk measures dataset\n”, “sp500b = sp500a.merge(ccm_ret, left_on="Symbol", right_on="ticker", indicator=’exists’, validate="one_to_many")\n”, “# add a new column to the merged dataset that includes the week for each date (we have 52 weeks for the returns we are analyzing)\n”, “sp500b = sp500b.assign(week=sp500b[‘date’].dt.isocalendar().week)\n”, “# the sp500b dataset is the basis of our analysis \n”, “# and contains the accounting data, risk measures and daily returns for 355 SP500 firms” ] }, { “cell_type”: “markdown”, “id”: “2cb1c987-4cc3-4d65-9aa6-80bbe1b63361”, “metadata”: { “tags”: [] }, “source”: [ “## Description of Risk Measures\n”, “- Legal risks: This was chosen as a risk measure as it can be interpreted in multiple different ways, with different terms relating to different subcategories of legal risk. The 3 I measure here are Litigation Risk (LIT_exposure), Contractual Risks (CONT_exposure) and Non-Contractual Risks (NCR_exposure).\n”, “ - LIT_exposure \n”, “ - This measure of risk uses keywords relevant to a specific type of legal risk known as litigation risk. The number of occurrences of words such as ‘(lawsuit|court|settle|legal|litigation)’ being within a 10-word distance of ‘(payout|risk|violation|breach)’ found using near_regex() is used as the risk measure. The risk of being sued, having to pay out sums of money as settlements etc can affect investor confidence, especially when the future is less certain (as was the climate at the beginning of the pandemic).\n”, “ - This measure aims to capture specifically litigation risk and see how that relates to returns around the onset of the pandemic. Did firms who mentioned litigation-related risks in their 10-Ks experience lower returns than those that did not? Does this speak to lower/higher investor-confidence in a firm due to the extent of its exposure to litigation risks (if, for instance, firms with a higher risk measure experience relative lower returns during the first week of the pandemic)?\n”, “ - Statistical properties: 276 out of the 355 firms we had requisite data for had some score for this risk measure. The values for these range from 0-8, with a mean of 1.72, median of 1.0 and std of 1.63.\n”, “ - CONT_exposure\n”, “ - This measure of risk uses keywords relevant to a specific type of legal risk known as contractual risk. The number of occurrences of words such as ‘(legal|contract|terms|liabilities)’ being within a 10-word distance of ‘(breach|penalty|risk|failure)’ found using near_regex() is used as the risk measure. Contractual risk is incurred when there is some failure in fulfillment of contractual liabilities. Failure to meet terms of the contract, failure to provide services in compliance with the contract, failure to include risk mitigating clauses in the contract.\n”, “ - This measure aims to capture specifically contractual risk and see how that relates to returns around the onset of the pandemic. Did firms who mentioned contractual risks more in their 10-Ks experience lower returns than those that did not? Does this speak to lower/higher investor-confidence in a firm due to the extent of its exposure to contractual risks (if, for instance, firms with a higher risk measure experience relative lower returns during the first week of the pandemic)?\n”, “ - Statistical properties: 326 out of the 355 firms we had requisite data for had some score for this risk measure. The values for these range from 0-21, with a mean of 3.594, median of 3.0 and std of 2.974.\n”, “ - NCR_exposure\n”, “ - This measure of risk uses keywords relevant to a specific type of legal risk known as non-contractual risk. The number of occurrences of words such as ‘(copyright|trademark|patent|intellectual property)’ being within a 10-word distance of ‘(risk|violation|breach|infringe|misrepresent)’ found using near_regex() is used to measure the risk. Non-contractual risks represent certain damages caused to the competitor due to infringement of copyright or trademark done by your entity in the due course of your business proceedings.\n”, “ - This measure aims to capture specifically non-contractual risk and see how that relates to returns around the onset of the pandemic.\n”, “ - Statistical properties: 191 out of the 355 firms we had requisite data for had some score for this risk measure. The values for these range from 0-12, with a mean of 1.34 median of 1 and std of 1.92.\n”, “- Supply-chain risks: The most difficult challenge to business throughout the COVID-19 pandemic has been supply chain management. The number of occurrences of ‘(supply|supply chain|raw materials|sourcing)’ and ‘(shortage|recall|delay)’ within a 10-word range found using near_regex() is the risk measure. This measure aims to see whether firms who mentioned supply chain risks more before the pandemic were more exposed to these risks. We can see how the value of the measure correlates to the return for that firm. Statistical properties: 63 out of the 355 firms had some value for this risk measure. The values for these range from 0-5, with a mean of 0.25, median of 0 and std of 0.63.\n”, “- COVID-19 Risk: This is an experimental measure of risk. Using near_regex() to count the number of occurrences of ‘(coronavirus|covid|sars-cov|pandemic)’ and ‘(challenge|issue|problem|threat|susceptible|risk|pose)’ within a 25-word distance as the measure, the aim is to see which firms saw and mentioned the risks related to the pandemic. We could then see their returns to see if they were more or less affected by the onset of the pandemic. Statistical properties: 31 out of the 355 firms had some value for this risk measure. The values ranged from 0-3 with a mean of 0.113, median of 0 and std of 0.395.\n”, “ “ ] }, { “cell_type”: “code”, “execution_count”: 3, “id”: “764f8838-82ff-4365-ae4d-dc972be4efae”, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “Count of non zeros in LIT_exposure : 276\n”, “Count of non zeros in CONT_exposure : 326\n”, “Count of non zeros in NCR_exposure : 191\n”, “Count of non zeros in SC_exposure : 63\n”, “Count of non zeros in COV_exposure : 31\n”, “ LIT_exposure CONT_exposure NCR_exposure SC_exposure COV_exposure\n”, “count 355.000000 355.000000 355.000000 355.000000 355.000000\n”, “mean 1.723944 3.594366 1.340845 0.250704 0.112676\n”, “std 1.630350 2.974272 1.922038 0.630414 0.395929\n”, “min 0.000000 0.000000 0.000000 0.000000 0.000000\n”, “25% 1.000000 2.000000 0.000000 0.000000 0.000000\n”, “50% 1.000000 3.000000 1.000000 0.000000 0.000000\n”, “75% 2.000000 5.000000 2.000000 0.000000 0.000000\n”, “max 8.000000 21.000000 12.000000 5.000000 3.000000\n” ] } ], “source”: [ “sp500a\n”, “print(‘Count of non zeros in LIT_exposure : ‘, (sp500a[‘LIT_exposure’] != 0).sum())\n”, “print(‘Count of non zeros in CONT_exposure : ‘, (sp500a[‘CONT_exposure’] != 0).sum())\n”, “print(‘Count of non zeros in NCR_exposure : ‘, (sp500a[‘NCR_exposure’] != 0).sum())\n”, “print(‘Count of non zeros in SC_exposure : ‘, (sp500a[‘SC_exposure’] != 0).sum())\n”, “print(‘Count of non zeros in COV_exposure : ‘, (sp500a[‘COV_exposure’] != 0).sum())\n”, “print(sp500a[["LIT_exposure", "CONT_exposure", "NCR_exposure", "SC_exposure", "COV_exposure"]].describe())” ] }, { “cell_type”: “markdown”, “id”: “68ab6c52-69d8-46df-9e59-4fe0187086ed”, “metadata”: {}, “source”: [ “## Validation Checks \n”, “- These measures are likely valid. However, there are some caveats. For instance, the COV_exposure measure is experimental as it seeks to measure how many firms proactively mentioned COVID-19 as a source of possible problems/threats and uses a number of terms that may coincidentally be within range. Examples of this include "problem", "threat", "issue", all of which can be used in a variety of ways. Words such as "susceptible", "pose" or "threat" could be more relevant. I wanted the measure to take into account a wide variety of words. There are also only 31 firms that have non zero values for this measure, meaning that there are very few actual cases where this occurs, making it harder to evaluate as a true measure of risk.\n”, “- The legal risk measures had the highest rate of occurrences (non-zero values among the firms). Since they each specific to a type of risk, they have different rates of occurrences. These are all likely valid because there are specific terms that apply to each category of legal risk. For instance, when talking about "copyright", firms are much more likely to be referring to non-contractual risk than contractual risks. Also words such as "infringe" are less likely to be used when talking about contractual risk compared to non-contractual risk. The only issues when measuring using near_regex() maybe the number of words between the firms. 10 may be too high or low, depending on a particular sentence (we will see examples of this below)\n”, “\n”, “### Matches\n”, “In explore_ugly.ipynb, some NEAR_regex() matches were printed for each risk. \n”, “\n”, “The following table consists of examples of matches for each risk measure (the code can be found in explore_ugly.ipynb)\n”, “\n”, “| Risk Measure|Example Matches| Firm | \n”, “| ———– | ———– | ———– |\n”, “| LIT_exposure| [(‘’, ‘’, ‘risk’, ‘litigation’)] | ABMD |\n”, “| |[(‘’, ‘’, ‘risk’, ‘legal’), (‘legal’, ‘violation’, ‘’, ‘’), (‘’, ‘’, ‘risk’, ‘litigation’)] | ACN |\n”, “| CONT_exposure| [(‘’, ‘’, ‘risk’, ‘terms’), (‘’, ‘’, ‘risk’, ‘liabilities’), (‘’, ‘’, ‘breach’, ‘contract’)] | MMM |\n”, “| |[(‘liabilities’, ‘failure’, ‘’, ‘’), (‘’, ‘’, ‘breach’, ‘terms’)] | ABMD |\n”, “| NCR_exposure| [(‘’, ‘’, ‘infringe’, ‘intellectual property’)] | MMM |\n”, “| |[(‘intellectual property’, ‘infringe’, ‘’, ‘’), (‘’, ‘’, ‘infringe’, ‘intellectual property’)] | ACN |\n”, “| SC_exposure| [(‘’, ‘’, ‘delay’, ‘supply’), (‘supply’, ‘delay’, ‘’, ‘’)] | AOS |\n”, “| |[(‘’, ‘’, ‘shortage’, ‘supply’)] | ABMD |\n”, “| COV_exposure| [(‘pandemic’, ‘risk’, ‘’, ‘’)] | CVX |\n”, “| | [(‘’, ‘’, ‘threat’, ‘pandemic’)] | CMG |\n”, “\n”, “- SC_exposure measures supply chain risk and is quite straightforward. A 10-word distance may again be too low or high depending on the sentence, but the firms with the highest risk score intuitively make sense. This is because companies that mention ‘(supply|supply chain|raw materials|sourcing)’ and ‘(shortage|recall|delay)’ together are obviously more concerned with supply chain risks and are likely to be heavily reliant on their supply chains for a majority of their business. Firms that had the highest SC_exposure risk values included Coca-Cola, Wabtec, Seagate etc. Most of the companies belong to Industrials, Information Technology, Health Care, Energy, Consumer Discretionary and Consumer Staples sectors. This makes sense intuitively because firms belonging to these sectors are in fact heavily reliant on global supply chains, which, if affected, pose serious risk to the firms’ business. Check the following output of firms sorted by highest measure of SC_exposure.” ] }, { “cell_type”: “code”, “execution_count”: 4, “id”: “61346fb3-9fe6-477d-9f3f-9cb284d7bb75”, “metadata”: {}, “outputs”: [ { “data”: { “text/html”: [ “<div>\n”, “\n”, “<table border="1" class="dataframe">\n”, “ <thead>\n”, “ <tr style="text-align: right;">\n”, “ <th></th>\n”, “ <th>Symbol</th>\n”, “ <th>Security</th>\n”, “ <th>GICS Sector</th>\n”, “ <th>GICS Sub-Industry</th>\n”, “ <th>CIK</th>\n”, “ <th>SC_exposure</th>\n”, “ <th>COV_exposure</th>\n”, “ <th>LIT_exposure</th>\n”, “ <th>CONT_exposure</th>\n”, “ <th>NCR_exposure</th>\n”, “ <th>…</th>\n”, “ <th>mb</th>\n”, “ <th>prof_a</th>\n”, “ <th>ppe_a</th>\n”, “ <th>cash_a</th>\n”, “ <th>xrd_a</th>\n”, “ <th>dltt_a</th>\n”, “ <th>invopps_FG09</th>\n”, “ <th>sales_g</th>\n”, “ <th>dv_a</th>\n”, “ <th>short_debt</th>\n”, “ </tr>\n”, “ </thead>\n”, “ <tbody>\n”, “ <tr>\n”, “ <th>77</th>\n”, “ <td>KO</td>\n”, “ <td>Coca-Cola</td>\n”, “ <td>Consumer Staples</td>\n”, “ <td>Soft Drinks</td>\n”, “ <td>21344</td>\n”, “ <td>5.0</td>\n”, “ <td>0.0</td>\n”, “ <td>5.0</td>\n”, “ <td>5.0</td>\n”, “ <td>1.0</td>\n”, “ <td>…</td>\n”, “ <td>3.522742</td>\n”, “ <td>0.137611</td>\n”, “ <td>0.141351</td>\n”, “ <td>0.129369</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.331404</td>\n”, “ <td>3.224640</td>\n”, “ <td>NaN</td>\n”, “ <td>0.079242</td>\n”, “ <td>0.351670</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>340</th>\n”, “ <td>WAB</td>\n”, “ <td>Wabtec</td>\n”, “ <td>Industrials</td>\n”, “ <td>Construction Machinery & Heavy Trucks</td>\n”, “ <td>943452</td>\n”, “ <td>4.0</td>\n”, “ <td>0.0</td>\n”, “ <td>2.0</td>\n”, “ <td>4.0</td>\n”, “ <td>1.0</td>\n”, “ <td>…</td>\n”, “ <td>1.261699</td>\n”, “ <td>0.082648</td>\n”, “ <td>0.087404</td>\n”, “ <td>0.031894</td>\n”, “ <td>0.011080</td>\n”, “ <td>0.228756</td>\n”, “ <td>0.926347</td>\n”, “ <td>NaN</td>\n”, “ <td>0.004313</td>\n”, “ <td>0.021606</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>291</th>\n”, “ <td>STX</td>\n”, “ <td>Seagate</td>\n”, “ <td>Information Technology</td>\n”, “ <td>Technology Hardware, Storage & Peripherals</td>\n”, “ <td>1137789</td>\n”, “ <td>3.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>3.0</td>\n”, “ <td>3.0</td>\n”, “ <td>…</td>\n”, “ <td>2.183781</td>\n”, “ <td>0.225774</td>\n”, “ <td>0.210355</td>\n”, “ <td>0.253348</td>\n”, “ <td>0.111536</td>\n”, “ <td>0.478672</td>\n”, “ <td>1.971957</td>\n”, “ <td>NaN</td>\n”, “ <td>0.080248</td>\n”, “ <td>0.000000</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>34</th>\n”, “ <td>ANET</td>\n”, “ <td>Arista</td>\n”, “ <td>Information Technology</td>\n”, “ <td>Communications Equipment</td>\n”, “ <td>1596532</td>\n”, “ <td>3.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>6.0</td>\n”, “ <td>8.0</td>\n”, “ <td>…</td>\n”, “ <td>4.020779</td>\n”, “ <td>0.200261</td>\n”, “ <td>0.030355</td>\n”, “ <td>0.650939</td>\n”, “ <td>0.110568</td>\n”, “ <td>0.019837</td>\n”, “ <td>3.671484</td>\n”, “ <td>NaN</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.162020</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>224</th>\n”, “ <td>MRNA</td>\n”, “ <td>Moderna</td>\n”, “ <td>Health Care</td>\n”, “ <td>Biotechnology</td>\n”, “ <td>1682852</td>\n”, “ <td>2.0</td>\n”, “ <td>3.0</td>\n”, “ <td>1.0</td>\n”, “ <td>4.0</td>\n”, “ <td>7.0</td>\n”, “ <td>…</td>\n”, “ <td>4.402403</td>\n”, “ <td>-0.323828</td>\n”, “ <td>0.181141</td>\n”, “ <td>0.694612</td>\n”, “ <td>0.312258</td>\n”, “ <td>0.083278</td>\n”, “ <td>4.156502</td>\n”, “ <td>NaN</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.026363</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”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>130</th>\n”, “ <td>FDX</td>\n”, “ <td>FedEx</td>\n”, “ <td>Industrials</td>\n”, “ <td>Air Freight & Logistics</td>\n”, “ <td>1048911</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>3.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>1.412708</td>\n”, “ <td>0.157583</td>\n”, “ <td>0.559326</td>\n”, “ <td>0.042626</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.305443</td>\n”, “ <td>1.014307</td>\n”, “ <td>NaN</td>\n”, “ <td>0.012554</td>\n”, “ <td>0.054832</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>129</th>\n”, “ <td>FAST</td>\n”, “ <td>Fastenal</td>\n”, “ <td>Industrials</td>\n”, “ <td>Building Products</td>\n”, “ <td>815556</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>1.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>5.881304</td>\n”, “ <td>0.317035</td>\n”, “ <td>0.333272</td>\n”, “ <td>0.046028</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.129003</td>\n”, “ <td>5.703049</td>\n”, “ <td>NaN</td>\n”, “ <td>0.131214</td>\n”, “ <td>0.169997</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>128</th>\n”, “ <td>FDS</td>\n”, “ <td>FactSet</td>\n”, “ <td>Financials</td>\n”, “ <td>Financial Exchanges & Data</td>\n”, “ <td>1013237</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>4.0</td>\n”, “ <td>3.0</td>\n”, “ <td>…</td>\n”, “ <td>7.216963</td>\n”, “ <td>0.319547</td>\n”, “ <td>0.084944</td>\n”, “ <td>0.247167</td>\n”, “ <td>0.137617</td>\n”, “ <td>0.368030</td>\n”, “ <td>7.009918</td>\n”, “ <td>NaN</td>\n”, “ <td>0.064131</td>\n”, “ <td>0.000000</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>126</th>\n”, “ <td>XOM</td>\n”, “ <td>ExxonMobil</td>\n”, “ <td>Energy</td>\n”, “ <td>Integrated Oil & Gas</td>\n”, “ <td>34088</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>3.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>1.286264</td>\n”, “ <td>0.087601</td>\n”, “ <td>0.716087</td>\n”, “ <td>0.008519</td>\n”, “ <td>0.003348</td>\n”, “ <td>0.085392</td>\n”, “ <td>0.896609</td>\n”, “ <td>NaN</td>\n”, “ <td>0.040408</td>\n”, “ <td>0.413213</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>354</th>\n”, “ <td>ZTS</td>\n”, “ <td>Zoetis</td>\n”, “ <td>Health Care</td>\n”, “ <td>Pharmaceuticals</td>\n”, “ <td>1555280</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>6.0</td>\n”, “ <td>9.0</td>\n”, “ <td>5.0</td>\n”, “ <td>…</td>\n”, “ <td>6.216815</td>\n”, “ <td>0.218709</td>\n”, “ <td>0.184409</td>\n”, “ <td>0.167518</td>\n”, “ <td>0.039584</td>\n”, “ <td>0.529320</td>\n”, “ <td>6.019250</td>\n”, “ <td>NaN</td>\n”, “ <td>0.027198</td>\n”, “ <td>0.080500</td>\n”, “ </tr>\n”, “ </tbody>\n”, “</table>\n”, “<p>355 rows × 48 columns</p>\n”, “</div>” ], “text/plain”: [ “ Symbol Security GICS Sector \\n”, “77 KO Coca-Cola Consumer Staples \n”, “340 WAB Wabtec Industrials \n”, “291 STX Seagate Information Technology \n”, “34 ANET Arista Information Technology \n”, “224 MRNA Moderna Health Care \n”, “.. … … … \n”, “130 FDX FedEx Industrials \n”, “129 FAST Fastenal Industrials \n”, “128 FDS FactSet Financials \n”, “126 XOM ExxonMobil Energy \n”, “354 ZTS Zoetis Health Care \n”, “\n”, “ GICS Sub-Industry CIK SC_exposure \\n”, “77 Soft Drinks 21344 5.0 \n”, “340 Construction Machinery & Heavy Trucks 943452 4.0 \n”, “291 Technology Hardware, Storage & Peripherals 1137789 3.0 \n”, “34 Communications Equipment 1596532 3.0 \n”, “224 Biotechnology 1682852 2.0 \n”, “.. … … … \n”, “130 Air Freight & Logistics 1048911 0.0 \n”, “129 Building Products 815556 0.0 \n”, “128 Financial Exchanges & Data 1013237 0.0 \n”, “126 Integrated Oil & Gas 34088 0.0 \n”, “354 Pharmaceuticals 1555280 0.0 \n”, “\n”, “ COV_exposure LIT_exposure CONT_exposure NCR_exposure … mb \\n”, “77 0.0 5.0 5.0 1.0 … 3.522742 \n”, “340 0.0 2.0 4.0 1.0 … 1.261699 \n”, “291 0.0 1.0 3.0 3.0 … 2.183781 \n”, “34 0.0 0.0 6.0 8.0 … 4.020779 \n”, “224 3.0 1.0 4.0 7.0 … 4.402403 \n”, “.. … … … … … … \n”, “130 0.0 1.0 3.0 0.0 … 1.412708 \n”, “129 0.0 1.0 1.0 0.0 … 5.881304 \n”, “128 0.0 1.0 4.0 3.0 … 7.216963 \n”, “126 0.0 1.0 3.0 0.0 … 1.286264 \n”, “354 0.0 6.0 9.0 5.0 … 6.216815 \n”, “\n”, “ prof_a ppe_a cash_a xrd_a dltt_a invopps_FG09 sales_g \\n”, “77 0.137611 0.141351 0.129369 0.000000 0.331404 3.224640 NaN \n”, “340 0.082648 0.087404 0.031894 0.011080 0.228756 0.926347 NaN \n”, “291 0.225774 0.210355 0.253348 0.111536 0.478672 1.971957 NaN \n”, “34 0.200261 0.030355 0.650939 0.110568 0.019837 3.671484 NaN \n”, “224 -0.323828 0.181141 0.694612 0.312258 0.083278 4.156502 NaN \n”, “.. … … … … … … … \n”, “130 0.157583 0.559326 0.042626 0.000000 0.305443 1.014307 NaN \n”, “129 0.317035 0.333272 0.046028 0.000000 0.129003 5.703049 NaN \n”, “128 0.319547 0.084944 0.247167 0.137617 0.368030 7.009918 NaN \n”, “126 0.087601 0.716087 0.008519 0.003348 0.085392 0.896609 NaN \n”, “354 0.218709 0.184409 0.167518 0.039584 0.529320 6.019250 NaN \n”, “\n”, “ dv_a short_debt \n”, “77 0.079242 0.351670 \n”, “340 0.004313 0.021606 \n”, “291 0.080248 0.000000 \n”, “34 0.000000 0.162020 \n”, “224 0.000000 0.026363 \n”, “.. … … \n”, “130 0.012554 0.054832 \n”, “129 0.131214 0.169997 \n”, “128 0.064131 0.000000 \n”, “126 0.040408 0.413213 \n”, “354 0.027198 0.080500 \n”, “\n”, “[355 rows x 48 columns]” ] }, “execution_count”: 4, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [ “sp500a.sort_values(by=’SC_exposure’, ascending=False)” ] }, { “cell_type”: “markdown”, “id”: “28571883-0ba3-4ca4-8bec-6a271980b920”, “metadata”: {}, “source”: [ “- The output ranks firms with highest COV_exposure ratings. It makes sense that the firms that were most concerned with the pandemic before it became a global phenomenon belonged to Health Care, Airlines, Information Technology (particularly "Semiconductors" Sub-Industry), Energy, Materials, Consumer Discretionary (particularly "Hotels, Resorts & Cruise Lines" Sub-Industry) as they felt most exposed to the effects of a potential global pandemic. This very specific, very experimental measure of risk actually captures, to some extent, risk exposure to COVID-19 based on whether firms mention the relevant terms in their 10-K.” ] }, { “cell_type”: “code”, “execution_count”: 5, “id”: “f72388d5-63bb-477e-93b5-e828c4703fc7”, “metadata”: {}, “outputs”: [ { “data”: { “text/html”: [ “<div>\n”, “\n”, “<table border="1" class="dataframe">\n”, “ <thead>\n”, “ <tr style="text-align: right;">\n”, “ <th></th>\n”, “ <th>Symbol</th>\n”, “ <th>Security</th>\n”, “ <th>GICS Sector</th>\n”, “ <th>GICS Sub-Industry</th>\n”, “ <th>CIK</th>\n”, “ <th>SC_exposure</th>\n”, “ <th>COV_exposure</th>\n”, “ <th>LIT_exposure</th>\n”, “ <th>CONT_exposure</th>\n”, “ <th>NCR_exposure</th>\n”, “ <th>…</th>\n”, “ <th>mb</th>\n”, “ <th>prof_a</th>\n”, “ <th>ppe_a</th>\n”, “ <th>cash_a</th>\n”, “ <th>xrd_a</th>\n”, “ <th>dltt_a</th>\n”, “ <th>invopps_FG09</th>\n”, “ <th>sales_g</th>\n”, “ <th>dv_a</th>\n”, “ <th>short_debt</th>\n”, “ </tr>\n”, “ </thead>\n”, “ <tbody>\n”, “ <tr>\n”, “ <th>224</th>\n”, “ <td>MRNA</td>\n”, “ <td>Moderna</td>\n”, “ <td>Health Care</td>\n”, “ <td>Biotechnology</td>\n”, “ <td>1682852</td>\n”, “ <td>2.0</td>\n”, “ <td>3.0</td>\n”, “ <td>1.0</td>\n”, “ <td>4.0</td>\n”, “ <td>7.0</td>\n”, “ <td>…</td>\n”, “ <td>4.402403</td>\n”, “ <td>-0.323828</td>\n”, “ <td>0.181141</td>\n”, “ <td>0.694612</td>\n”, “ <td>0.312258</td>\n”, “ <td>0.083278</td>\n”, “ <td>4.156502</td>\n”, “ <td>NaN</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.026363</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>228</th>\n”, “ <td>MPWR</td>\n”, “ <td>Monolithic Power Systems</td>\n”, “ <td>Information Technology</td>\n”, “ <td>Semiconductors</td>\n”, “ <td>1280452</td>\n”, “ <td>0.0</td>\n”, “ <td>2.0</td>\n”, “ <td>4.0</td>\n”, “ <td>2.0</td>\n”, “ <td>6.0</td>\n”, “ <td>…</td>\n”, “ <td>8.309925</td>\n”, “ <td>0.122786</td>\n”, “ <td>0.241723</td>\n”, “ <td>0.476170</td>\n”, “ <td>0.112672</td>\n”, “ <td>0.001153</td>\n”, “ <td>8.037213</td>\n”, “ <td>NaN</td>\n”, “ <td>0.070364</td>\n”, “ <td>0.532032</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>329</th>\n”, “ <td>UAL</td>\n”, “ <td>United Airlines</td>\n”, “ <td>Industrials</td>\n”, “ <td>Airlines</td>\n”, “ <td>100517</td>\n”, “ <td>0.0</td>\n”, “ <td>2.0</td>\n”, “ <td>1.0</td>\n”, “ <td>5.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>1.201453</td>\n”, “ <td>0.129916</td>\n”, “ <td>0.663892</td>\n”, “ <td>0.093973</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.348045</td>\n”, “ <td>0.789031</td>\n”, “ <td>NaN</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.104597</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>312</th>\n”, “ <td>TFX</td>\n”, “ <td>Teleflex</td>\n”, “ <td>Health Care</td>\n”, “ <td>Health Care Equipment</td>\n”, “ <td>96943</td>\n”, “ <td>0.0</td>\n”, “ <td>2.0</td>\n”, “ <td>1.0</td>\n”, “ <td>6.0</td>\n”, “ <td>3.0</td>\n”, “ <td>…</td>\n”, “ <td>3.293279</td>\n”, “ <td>0.113476</td>\n”, “ <td>0.086196</td>\n”, “ <td>0.047717</td>\n”, “ <td>0.018044</td>\n”, “ <td>0.310677</td>\n”, “ <td>3.008507</td>\n”, “ <td>NaN</td>\n”, “ <td>0.009957</td>\n”, “ <td>0.034881</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>106</th>\n”, “ <td>DLTR</td>\n”, “ <td>Dollar Tree</td>\n”, “ <td>Consumer Discretionary</td>\n”, “ <td>General Merchandise Stores</td>\n”, “ <td>935703</td>\n”, “ <td>0.0</td>\n”, “ <td>2.0</td>\n”, “ <td>4.0</td>\n”, “ <td>2.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>2.289558</td>\n”, “ <td>0.178399</td>\n”, “ <td>0.255185</td>\n”, “ <td>0.031264</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.315920</td>\n”, “ <td>1.950046</td>\n”, “ <td>NaN</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.000000</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”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>119</th>\n”, “ <td>EOG</td>\n”, “ <td>EOG Resources</td>\n”, “ <td>Energy</td>\n”, “ <td>Oil & Gas Exploration & Production</td>\n”, “ <td>821189</td>\n”, “ <td>1.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>6.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>1.729985</td>\n”, “ <td>0.202067</td>\n”, “ <td>0.838732</td>\n”, “ <td>0.054626</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.123662</td>\n”, “ <td>1.328347</td>\n”, “ <td>NaN</td>\n”, “ <td>0.015844</td>\n”, “ <td>0.231621</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>118</th>\n”, “ <td>ENPH</td>\n”, “ <td>Enphase</td>\n”, “ <td>Information Technology</td>\n”, “ <td>Electronic Components</td>\n”, “ <td>1463101</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>2.0</td>\n”, “ <td>3.0</td>\n”, “ <td>…</td>\n”, “ <td>5.128619</td>\n”, “ <td>0.164542</td>\n”, “ <td>0.054756</td>\n”, “ <td>0.415170</td>\n”, “ <td>0.056618</td>\n”, “ <td>0.157315</td>\n”, “ <td>NaN</td>\n”, “ <td>NaN</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.051194</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>117</th>\n”, “ <td>EMR</td>\n”, “ <td>Emerson</td>\n”, “ <td>Industrials</td>\n”, “ <td>Electrical Components & Equipment</td>\n”, “ <td>32604</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>1.0</td>\n”, “ <td>…</td>\n”, “ <td>2.591377</td>\n”, “ <td>0.192321</td>\n”, “ <td>0.177685</td>\n”, “ <td>0.072889</td>\n”, “ <td>0.022150</td>\n”, “ <td>0.208665</td>\n”, “ <td>2.273168</td>\n”, “ <td>NaN</td>\n”, “ <td>0.058984</td>\n”, “ <td>0.252403</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>116</th>\n”, “ <td>EA</td>\n”, “ <td>Electronic Arts</td>\n”, “ <td>Communication Services</td>\n”, “ <td>Interactive Home Entertainment</td>\n”, “ <td>712515</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>2.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>3.787274</td>\n”, “ <td>0.129284</td>\n”, “ <td>0.050017</td>\n”, “ <td>0.607904</td>\n”, “ <td>0.159987</td>\n”, “ <td>0.110975</td>\n”, “ <td>3.548721</td>\n”, “ <td>NaN</td>\n”, “ <td>0.000000</td>\n”, “ <td>0.000000</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>354</th>\n”, “ <td>ZTS</td>\n”, “ <td>Zoetis</td>\n”, “ <td>Health Care</td>\n”, “ <td>Pharmaceuticals</td>\n”, “ <td>1555280</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>6.0</td>\n”, “ <td>9.0</td>\n”, “ <td>5.0</td>\n”, “ <td>…</td>\n”, “ <td>6.216815</td>\n”, “ <td>0.218709</td>\n”, “ <td>0.184409</td>\n”, “ <td>0.167518</td>\n”, “ <td>0.039584</td>\n”, “ <td>0.529320</td>\n”, “ <td>6.019250</td>\n”, “ <td>NaN</td>\n”, “ <td>0.027198</td>\n”, “ <td>0.080500</td>\n”, “ </tr>\n”, “ </tbody>\n”, “</table>\n”, “<p>355 rows × 48 columns</p>\n”, “</div>” ], “text/plain”: [ “ Symbol Security GICS Sector \\n”, “224 MRNA Moderna Health Care \n”, “228 MPWR Monolithic Power Systems Information Technology \n”, “329 UAL United Airlines Industrials \n”, “312 TFX Teleflex Health Care \n”, “106 DLTR Dollar Tree Consumer Discretionary \n”, “.. … … … \n”, “119 EOG EOG Resources Energy \n”, “118 ENPH Enphase Information Technology \n”, “117 EMR Emerson Industrials \n”, “116 EA Electronic Arts Communication Services \n”, “354 ZTS Zoetis Health Care \n”, “\n”, “ GICS Sub-Industry CIK SC_exposure COV_exposure \\n”, “224 Biotechnology 1682852 2.0 3.0 \n”, “228 Semiconductors 1280452 0.0 2.0 \n”, “329 Airlines 100517 0.0 2.0 \n”, “312 Health Care Equipment 96943 0.0 2.0 \n”, “106 General Merchandise Stores 935703 0.0 2.0 \n”, “.. … … … … \n”, “119 Oil & Gas Exploration & Production 821189 1.0 0.0 \n”, “118 Electronic Components 1463101 0.0 0.0 \n”, “117 Electrical Components & Equipment 32604 0.0 0.0 \n”, “116 Interactive Home Entertainment 712515 0.0 0.0 \n”, “354 Pharmaceuticals 1555280 0.0 0.0 \n”, “\n”, “ LIT_exposure CONT_exposure NCR_exposure … mb prof_a \\n”, “224 1.0 4.0 7.0 … 4.402403 -0.323828 \n”, “228 4.0 2.0 6.0 … 8.309925 0.122786 \n”, “329 1.0 5.0 0.0 … 1.201453 0.129916 \n”, “312 1.0 6.0 3.0 … 3.293279 0.113476 \n”, “106 4.0 2.0 0.0 … 2.289558 0.178399 \n”, “.. … … … … … … \n”, “119 0.0 6.0 0.0 … 1.729985 0.202067 \n”, “118 1.0 2.0 3.0 … 5.128619 0.164542 \n”, “117 0.0 1.0 1.0 … 2.591377 0.192321 \n”, “116 1.0 2.0 0.0 … 3.787274 0.129284 \n”, “354 6.0 9.0 5.0 … 6.216815 0.218709 \n”, “\n”, “ ppe_a cash_a xrd_a dltt_a invopps_FG09 sales_g dv_a \\n”, “224 0.181141 0.694612 0.312258 0.083278 4.156502 NaN 0.000000 \n”, “228 0.241723 0.476170 0.112672 0.001153 8.037213 NaN 0.070364 \n”, “329 0.663892 0.093973 0.000000 0.348045 0.789031 NaN 0.000000 \n”, “312 0.086196 0.047717 0.018044 0.310677 3.008507 NaN 0.009957 \n”, “106 0.255185 0.031264 0.000000 0.315920 1.950046 NaN 0.000000 \n”, “.. … … … … … … … \n”, “119 0.838732 0.054626 0.000000 0.123662 1.328347 NaN 0.015844 \n”, “118 0.054756 0.415170 0.056618 0.157315 NaN NaN 0.000000 \n”, “117 0.177685 0.072889 0.022150 0.208665 2.273168 NaN 0.058984 \n”, “116 0.050017 0.607904 0.159987 0.110975 3.548721 NaN 0.000000 \n”, “354 0.184409 0.167518 0.039584 0.529320 6.019250 NaN 0.027198 \n”, “\n”, “ short_debt \n”, “224 0.026363 \n”, “228 0.532032 \n”, “329 0.104597 \n”, “312 0.034881 \n”, “106 0.000000 \n”, “.. … \n”, “119 0.231621 \n”, “118 0.051194 \n”, “117 0.252403 \n”, “116 0.000000 \n”, “354 0.080500 \n”, “\n”, “[355 rows x 48 columns]” ] }, “execution_count”: 5, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [ “sp500a.sort_values(by=’COV_exposure’, ascending=False)” ] }, { “cell_type”: “markdown”, “id”: “cf18752f-325f-461f-80db-23a222c6074e”, “metadata”: {}, “source”: [ “## Final sample for tests & EDA\n”, “- Our final sample here is the sp500b dataset, which contains all the data we need for our tests and analysis. This dataset includes all the relevant accounting data, the risk measures, the daily returns for 355 firms, Sector and Sub-industry information, the week number. This makes it extremely flexible. \n”, “- There are multiple entries for each firm to include daily data. This increases the number of observations to 130627\n”, “- I will be using different names such as "sp500c", "sp500e" etc when I perform changes. This is just how I like to do things. They all come from "sp500b"\n”, “#### Summary stats and a view of the dataset is shown below:” ] }, { “cell_type”: “code”, “execution_count”: 6, “id”: “cdd5a27f-de8e-4b3a-bb12-4ab0f4a701a3”, “metadata”: { “tags”: [] }, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “ CIK SC_exposure COV_exposure LIT_exposure \\n”, “count 1.306270e+05 130627.000000 130627.000000 130627.000000 \n”, “mean 7.690427e+05 0.256861 0.115443 1.696418 \n”, “std 5.534430e+05 0.635969 0.399799 1.595862 \n”, “min 1.800000e+03 0.000000 0.000000 0.000000 \n”, “25% 9.602100e+04 0.000000 0.000000 1.000000 \n”, “50% 8.750450e+05 0.000000 0.000000 1.000000 \n”, “75% 1.136869e+06 0.000000 0.000000 2.000000 \n”, “max 1.757898e+06 5.000000 3.000000 8.000000 \n”, “\n”, “ CONT_exposure NCR_exposure lpermno fyear \\n”, “count 130627.000000 130627.000000 130627.000000 130627.000000 \n”, “mean 3.579911 1.339179 53883.440866 2018.886187 \n”, “std 2.965733 1.934247 30181.631245 0.317585 \n”, “min 0.000000 0.000000 10104.000000 2018.000000 \n”, “25% 2.000000 0.000000 19561.000000 2019.000000 \n”, “50% 3.000000 1.000000 59248.000000 2019.000000 \n”, “75% 5.000000 2.000000 82686.000000 2019.000000 \n”, “max 21.000000 12.000000 93436.000000 2019.000000 \n”, “\n”, “ sic sic3 … invopps_FG09 sales_g \\n”, “count 130627.000000 130627.000000 … 123316.000000 0.0 \n”, “mean 4346.255016 434.410673 … 2.721212 NaN \n”, “std 1945.563187 194.585668 … 2.107415 NaN \n”, “min 100.000000 10.000000 … 0.405435 NaN \n”, “25% 2851.000000 285.000000 … 1.268946 NaN \n”, “50% 3812.000000 381.000000 … 2.203327 NaN \n”, “75% 5531.000000 553.000000 … 3.326861 NaN \n”, “max 8742.000000 874.000000 … 12.164233 NaN \n”, “\n”, “ dv_a short_debt permno prc \\n”, “count 130627.000000 128365.000000 130627.000000 130627.000000 \n”, “mean 0.025619 0.112199 53730.287077 146.664993 \n”, “std 0.027112 0.112126 30245.945452 252.629791 \n”, “min 0.000000 0.000000 10104.000000 3.120000 \n”, “25% 0.000000 0.026231 19502.000000 53.189999 \n”, “50% 0.020531 0.082587 58683.000000 92.910004 \n”, “75% 0.038010 0.153107 82686.000000 159.360001 \n”, “max 0.138594 0.761029 93436.000000 4037.770020 \n”, “\n”, “ shrout vwretd ret_f week \n”, “count 1.306270e+05 130627.000000 130627.000000 130627.000000 \n”, “mean 5.706217e+05 0.000740 0.000941 22.402543 \n”, “std 9.609968e+05 0.017512 0.028917 14.226850 \n”, “min 3.590000e+03 -0.118168 -0.538647 1.000000 \n”, “25% 1.284650e+05 -0.003516 -0.009315 11.000000 \n”, “50% 2.601800e+05 0.001289 0.001355 20.000000 \n”, “75% 5.750990e+05 0.006756 0.011548 33.000000 \n”, “max 7.695352e+06 0.091556 0.426136 52.000000 \n”, “\n”, “[8 rows x 48 columns]\n” ] }, { “data”: { “text/html”: [ “<div>\n”, “\n”, “<table border="1" class="dataframe">\n”, “ <thead>\n”, “ <tr style="text-align: right;">\n”, “ <th></th>\n”, “ <th>Symbol</th>\n”, “ <th>Security</th>\n”, “ <th>GICS Sector</th>\n”, “ <th>GICS Sub-Industry</th>\n”, “ <th>CIK</th>\n”, “ <th>SC_exposure</th>\n”, “ <th>COV_exposure</th>\n”, “ <th>LIT_exposure</th>\n”, “ <th>CONT_exposure</th>\n”, “ <th>NCR_exposure</th>\n”, “ <th>…</th>\n”, “ <th>ticker</th>\n”, “ <th>comnam</th>\n”, “ <th>primexch</th>\n”, “ <th>prc</th>\n”, “ <th>shrout</th>\n”, “ <th>vwretd</th>\n”, “ <th>_merge</th>\n”, “ <th>ret_f</th>\n”, “ <th>exists</th>\n”, “ <th>week</th>\n”, “ </tr>\n”, “ </thead>\n”, “ <tbody>\n”, “ <tr>\n”, “ <th>0</th>\n”, “ <td>MMM</td>\n”, “ <td>3M</td>\n”, “ <td>Industrials</td>\n”, “ <td>Industrial Conglomerates</td>\n”, “ <td>66740</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>3.0</td>\n”, “ <td>1.0</td>\n”, “ <td>…</td>\n”, “ <td>MMM</td>\n”, “ <td>3M CO</td>\n”, “ <td>N</td>\n”, “ <td>153.300003</td>\n”, “ <td>575600.0</td>\n”, “ <td>0.044398</td>\n”, “ <td>matched (3)</td>\n”, “ <td>0.064140</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>1</th>\n”, “ <td>MMM</td>\n”, “ <td>3M</td>\n”, “ <td>Industrials</td>\n”, “ <td>Industrial 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“ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>130622</th>\n”, “ <td>ZTS</td>\n”, “ <td>Zoetis</td>\n”, “ <td>Health Care</td>\n”, “ <td>Pharmaceuticals</td>\n”, “ <td>1555280</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>6.0</td>\n”, “ <td>9.0</td>\n”, “ <td>5.0</td>\n”, “ <td>…</td>\n”, “ <td>ZTS</td>\n”, “ <td>ZOETIS INC</td>\n”, “ <td>N</td>\n”, “ <td>85.900002</td>\n”, “ <td>480451.0</td>\n”, “ <td>0.004474</td>\n”, “ <td>matched (3)</td>\n”, “ <td>-0.003827</td>\n”, “ <td>both</td>\n”, “ <td>2</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>130623</th>\n”, “ <td>ZTS</td>\n”, “ <td>Zoetis</td>\n”, “ <td>Health Care</td>\n”, “ <td>Pharmaceuticals</td>\n”, “ <td>1555280</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>6.0</td>\n”, “ <td>9.0</td>\n”, “ <td>5.0</td>\n”, “ <td>…</td>\n”, “ <td>ZTS</td>\n”, “ <td>ZOETIS INC</td>\n”, “ <td>N</td>\n”, “ <td>120.279999</td>\n”, “ <td>474934.0</td>\n”, “ <td>-0.096841</td>\n”, “ <td>matched (3)</td>\n”, “ <td>-0.076687</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>130624</th>\n”, “ <td>ZTS</td>\n”, “ <td>Zoetis</td>\n”, “ <td>Health Care</td>\n”, “ <td>Pharmaceuticals</td>\n”, “ <td>1555280</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>6.0</td>\n”, “ <td>9.0</td>\n”, “ <td>5.0</td>\n”, “ <td>…</td>\n”, “ <td>ZTS</td>\n”, “ <td>ZOETIS INC</td>\n”, “ <td>N</td>\n”, “ <td>127.040001</td>\n”, “ <td>477558.0</td>\n”, “ <td>-0.007200</td>\n”, “ <td>matched (3)</td>\n”, “ <td>0.004904</td>\n”, “ <td>both</td>\n”, “ <td>36</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>130625</th>\n”, “ <td>ZTS</td>\n”, “ <td>Zoetis</td>\n”, “ <td>Health Care</td>\n”, “ <td>Pharmaceuticals</td>\n”, “ <td>1555280</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>6.0</td>\n”, “ <td>9.0</td>\n”, “ <td>5.0</td>\n”, “ <td>…</td>\n”, “ <td>ZTS</td>\n”, “ <td>ZOETIS INC</td>\n”, “ <td>N</td>\n”, “ <td>100.120003</td>\n”, “ <td>478772.0</td>\n”, “ <td>0.000550</td>\n”, “ <td>matched (3)</td>\n”, “ <td>0.011007</td>\n”, “ <td>both</td>\n”, “ <td>17</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>130626</th>\n”, “ <td>ZTS</td>\n”, “ <td>Zoetis</td>\n”, “ <td>Health Care</td>\n”, “ <td>Pharmaceuticals</td>\n”, “ <td>1555280</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>6.0</td>\n”, “ <td>9.0</td>\n”, “ <td>5.0</td>\n”, “ <td>…</td>\n”, “ <td>ZTS</td>\n”, “ <td>ZOETIS INC</td>\n”, “ <td>N</td>\n”, “ <td>133.229996</td>\n”, “ <td>474934.0</td>\n”, “ <td>-0.008552</td>\n”, “ <td>matched (3)</td>\n”, “ <td>-0.005375</td>\n”, “ <td>both</td>\n”, “ <td>9</td>\n”, “ </tr>\n”, “ </tbody>\n”, “</table>\n”, “<p>130627 rows × 60 columns</p>\n”, “</div>” ], “text/plain”: [ “ Symbol Security GICS Sector GICS Sub-Industry CIK \\n”, “0 MMM 3M Industrials Industrial Conglomerates 66740 \n”, “1 MMM 3M Industrials Industrial Conglomerates 66740 \n”, “2 MMM 3M Industrials Industrial Conglomerates 66740 \n”, “3 MMM 3M Industrials Industrial Conglomerates 66740 \n”, “4 MMM 3M Industrials Industrial Conglomerates 66740 \n”, “… … … … … … \n”, “130622 ZTS Zoetis Health Care Pharmaceuticals 1555280 \n”, “130623 ZTS Zoetis Health Care Pharmaceuticals 1555280 \n”, “130624 ZTS Zoetis Health Care Pharmaceuticals 1555280 \n”, “130625 ZTS Zoetis Health Care Pharmaceuticals 1555280 \n”, “130626 ZTS Zoetis Health Care Pharmaceuticals 1555280 \n”, “\n”, “ SC_exposure COV_exposure LIT_exposure CONT_exposure NCR_exposure \\n”, “0 0.0 0.0 0.0 3.0 1.0 \n”, “1 0.0 0.0 0.0 3.0 1.0 \n”, “2 0.0 0.0 0.0 3.0 1.0 \n”, “3 0.0 0.0 0.0 3.0 1.0 \n”, “4 0.0 0.0 0.0 3.0 1.0 \n”, “… … … … … … \n”, “130622 0.0 0.0 6.0 9.0 5.0 \n”, “130623 0.0 0.0 6.0 9.0 5.0 \n”, “130624 0.0 0.0 6.0 9.0 5.0 \n”, “130625 0.0 0.0 6.0 9.0 5.0 \n”, “130626 0.0 0.0 6.0 9.0 5.0 \n”, “\n”, “ … ticker comnam primexch prc shrout vwretd \\n”, “0 … MMM 3M CO N 153.300003 575600.0 0.044398 \n”, “1 … MMM 3M CO N 170.580002 575279.0 0.002394 \n”, “2 … MMM 3M CO N 165.169998 575051.0 -0.005643 \n”, “3 … MMM 3M CO N 146.399994 575261.0 -0.010146 \n”, “4 … MMM 3M CO N 179.779999 575051.0 0.001938 \n”, “… … … … … … … … \n”, “130622 … ZTS ZOETIS INC N 85.900002 480451.0 0.004474 \n”, “130623 … ZTS ZOETIS INC N 120.279999 474934.0 -0.096841 \n”, “130624 … ZTS ZOETIS INC N 127.040001 477558.0 -0.007200 \n”, “130625 … ZTS ZOETIS INC N 100.120003 478772.0 0.000550 \n”, “130626 … ZTS ZOETIS INC N 133.229996 474934.0 -0.008552 \n”, “\n”, “ _merge ret_f exists week \n”, “0 matched (3) 0.064140 both 11 \n”, “1 matched (3) 0.014874 both 37 \n”, “2 matched (3) -0.018423 both 49 \n”, “3 matched (3) -0.009338 both 16 \n”, “4 matched (3) -0.008767 both 3 \n”, “… … … … … \n”, “130622 matched (3) -0.003827 both 2 \n”, “130623 matched (3) -0.076687 both 11 \n”, “130624 matched (3) 0.004904 both 36 \n”, “130625 matched (3) 0.011007 both 17 \n”, “130626 matched (3) -0.005375 both 9 \n”, “\n”, “[130627 rows x 60 columns]” ] }, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [ “print(sp500b.describe())\n”, “sp500b” ] }, { “cell_type”: “code”, “execution_count”: 7, “id”: “e9e31d02-9571-4712-96a2-f5365404f5c9”, “metadata”: {}, “outputs”: [ { “data”: { “text/html”: [ “<style type="text/css">\n”, “</style>\n”, “<table id="T_54987">\n”, “ <thead>\n”, “ <tr>\n”, “ <th class="blank level0" > </th>\n”, “ <th class="col_heading level0 col0" >% missing</th>\n”, “ </tr>\n”, “ </thead>\n”, “ <tbody>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row0" class="row_heading level0 row0" >sales_g</th>\n”, “ <td id="T_54987_row0_col0" class="data row0 col0" >100.0</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row1" class="row_heading level0 row1" >largetaxlosscarry</th>\n”, “ <td id="T_54987_row1_col0" class="data row1 col0" >22.3</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row2" class="row_heading level0 row2" >smalltaxlosscarry</th>\n”, “ <td id="T_54987_row2_col0" class="data row2 col0" >22.3</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row3" class="row_heading level0 row3" >invopps_FG09</th>\n”, “ <td id="T_54987_row3_col0" class="data row3 col0" >5.6</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row4" class="row_heading level0 row4" >short_debt</th>\n”, “ <td id="T_54987_row4_col0" class="data row4 col0" >1.7</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row5" class="row_heading level0 row5" >Ch_Eqty</th>\n”, “ <td id="T_54987_row5_col0" class="data row5 col0" >0.0</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row6" class="row_heading level0 row6" >Ch_WC</th>\n”, “ <td id="T_54987_row6_col0" class="data row6 col0" >0.0</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row7" class="row_heading level0 row7" >CF</th>\n”, “ <td id="T_54987_row7_col0" class="data row7 col0" >0.0</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row8" class="row_heading level0 row8" >td_a</th>\n”, “ <td id="T_54987_row8_col0" class="data row8 col0" >0.0</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th id="T_54987_level0_row9" class="row_heading level0 row9" >td_mv</th>\n”, “ <td id="T_54987_row9_col0" class="data row9 col0" >0.0</td>\n”, “ </tr>\n”, “ </tbody>\n”, “</table>\n” ], “text/plain”: [ “<pandas.io.formats.style.Styler at 0x1a6e0d67310>” ] }, “execution_count”: 7, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [ “## More EDA and caveats\n”, “# We already dropped some columns from sp500a before merging with ccm_ret to make sp500b\n”, “# Here, we are checking for missing values that may cause issues with our analysis\n”, “(\n”, “ ( # these lines do the calculation - what % of missing values are there for each var\n”, “ sp500b.isna()\n”, “ .sum(axis=0)\n”, “ /len(sp500b)\n”, “ *100\n”, “ )\n”, “ .sort_values(ascending=False)[0:10]\n”, “ .to_frame(name=’% missing’)\n”, “ .style.format("{:.1f}")\n”, “)\n”, “# The table below shows that most/all of the relevant data is present in our data\n”, “# There are also more caveats regarding the risk measurements in the description section above” ] }, { “cell_type”: “markdown”, “id”: “a485fca4-fc08-4da1-9b0d-a9bdb0bc8d99”, “metadata”: {}, “source”: [ “### Exploring the correlation between the risk values and stock returns around key dates for the onset of COVID-19.” ] }, { “cell_type”: “code”, “execution_count”: 8, “id”: “e6ad3070-00d7-4f22-91b8-cccd30527ef9”, “metadata”: {}, “outputs”: [ { “data”: { “text/html”: [ “<div>\n”, “\n”, “<table border="1" class="dataframe">\n”, “ <thead>\n”, “ <tr style="text-align: right;">\n”, “ <th></th>\n”, “ <th>Symbol</th>\n”, “ <th>Security</th>\n”, “ <th>GICS Sector</th>\n”, “ <th>GICS Sub-Industry</th>\n”, “ <th>CIK</th>\n”, “ <th>SC_exposure</th>\n”, “ <th>COV_exposure</th>\n”, “ <th>LIT_exposure</th>\n”, “ <th>CONT_exposure</th>\n”, “ <th>NCR_exposure</th>\n”, “ <th>…</th>\n”, “ <th>ticker</th>\n”, “ <th>comnam</th>\n”, “ <th>primexch</th>\n”, “ <th>prc</th>\n”, “ <th>shrout</th>\n”, “ <th>vwretd</th>\n”, “ <th>_merge</th>\n”, “ <th>ret_f</th>\n”, “ <th>exists</th>\n”, “ <th>week</th>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>date</th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></th>\n”, “ <th></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>2020-03-09</th>\n”, “ <td>LYB</td>\n”, “ <td>LyondellBasell</td>\n”, “ <td>Materials</td>\n”, “ <td>Specialty Chemicals</td>\n”, “ <td>1489393</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>2.0</td>\n”, “ <td>3.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>LYB</td>\n”, “ <td>LYONDELLBASELL INDUSTRIES N V</td>\n”, “ <td>N</td>\n”, “ <td>59.610001</td>\n”, “ <td>333555.0</td>\n”, “ <td>-0.079317</td>\n”, “ <td>matched (3)</td>\n”, “ <td>-0.171968</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>2020-03-09</th>\n”, “ <td>MU</td>\n”, “ <td>Micron</td>\n”, “ <td>Information Technology</td>\n”, “ <td>Semiconductors</td>\n”, “ <td>723125</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>MU</td>\n”, “ <td>MICRON TECHNOLOGY INC</td>\n”, “ <td>Q</td>\n”, “ <td>45.970001</td>\n”, “ <td>1112000.0</td>\n”, “ <td>-0.079317</td>\n”, “ <td>matched (3)</td>\n”, “ <td>-0.106858</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>2020-03-09</th>\n”, “ <td>FBHS</td>\n”, “ <td>Fortune Brands</td>\n”, “ <td>Industrials</td>\n”, “ <td>Building Products</td>\n”, “ <td>1519751</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>1.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>FBHS</td>\n”, “ <td>FORTUNE BRANDS HOME & SECUR INC</td>\n”, “ <td>N</td>\n”, “ <td>56.169998</td>\n”, “ <td>139972.0</td>\n”, “ <td>-0.079317</td>\n”, “ <td>matched (3)</td>\n”, “ <td>-0.070341</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>2020-03-09</th>\n”, “ <td>ZBH</td>\n”, “ <td>Zimmer Biomet</td>\n”, “ <td>Health Care</td>\n”, “ <td>Health Care Equipment</td>\n”, “ <td>1136869</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>3.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>ZBH</td>\n”, “ <td>ZIMMER BIOMET HOLDINGS INC</td>\n”, “ <td>N</td>\n”, “ <td>111.260002</td>\n”, “ <td>206404.0</td>\n”, “ <td>-0.079317</td>\n”, “ <td>matched (3)</td>\n”, “ <td>-0.103465</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>2020-03-09</th>\n”, “ <td>BWA</td>\n”, “ <td>BorgWarner</td>\n”, “ <td>Consumer Discretionary</td>\n”, “ <td>Auto Parts & Equipment</td>\n”, “ <td>908255</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>1.0</td>\n”, “ <td>1.0</td>\n”, “ <td>…</td>\n”, “ <td>BWA</td>\n”, “ <td>BORGWARNER INC</td>\n”, “ <td>N</td>\n”, “ <td>26.049999</td>\n”, “ <td>206410.0</td>\n”, “ <td>-0.079317</td>\n”, “ <td>matched (3)</td>\n”, “ <td>-0.097679</td>\n”, “ <td>both</td>\n”, “ <td>11</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”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ <td>…</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>2020-03-13</th>\n”, “ <td>XRAY</td>\n”, “ <td>Dentsply Sirona</td>\n”, “ <td>Health Care</td>\n”, “ <td>Health Care Supplies</td>\n”, “ <td>818479</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>5.0</td>\n”, “ <td>4.0</td>\n”, “ <td>7.0</td>\n”, “ <td>…</td>\n”, “ <td>XRAY</td>\n”, “ <td>DENTSPLY SIRONA INC</td>\n”, “ <td>Q</td>\n”, “ <td>40.459999</td>\n”, “ <td>221439.0</td>\n”, “ <td>0.085502</td>\n”, “ <td>matched (3)</td>\n”, “ <td>0.013273</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>2020-03-13</th>\n”, “ <td>ABT</td>\n”, “ <td>Abbott</td>\n”, “ <td>Health Care</td>\n”, “ <td>Health Care Equipment</td>\n”, “ <td>1800</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>ABT</td>\n”, “ <td>ABBOTT LABORATORIES</td>\n”, “ <td>N</td>\n”, “ <td>81.650002</td>\n”, “ <td>1763433.0</td>\n”, “ <td>0.085502</td>\n”, “ <td>matched (3)</td>\n”, “ <td>0.092454</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>2020-03-13</th>\n”, “ <td>MAR</td>\n”, “ <td>Marriott International</td>\n”, “ <td>Consumer Discretionary</td>\n”, “ <td>Hotels, Resorts & Cruise Lines</td>\n”, “ <td>1048286</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>2.0</td>\n”, “ <td>2.0</td>\n”, “ <td>0.0</td>\n”, “ <td>…</td>\n”, “ <td>MAR</td>\n”, “ <td>MARRIOTT INTERNATIONAL INC NEW</td>\n”, “ <td>Q</td>\n”, “ <td>95.610001</td>\n”, “ <td>324215.0</td>\n”, “ <td>0.085502</td>\n”, “ <td>matched (3)</td>\n”, “ <td>0.020275</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>2020-03-13</th>\n”, “ <td>IR</td>\n”, “ <td>Ingersoll Rand</td>\n”, “ <td>Industrials</td>\n”, “ <td>Industrial Machinery</td>\n”, “ <td>1699150</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>2.0</td>\n”, “ <td>2.0</td>\n”, “ <td>…</td>\n”, “ <td>IR</td>\n”, “ <td>INGERSOLL RAND INC</td>\n”, “ <td>N</td>\n”, “ <td>23.000000</td>\n”, “ <td>205212.0</td>\n”, “ <td>0.085502</td>\n”, “ <td>matched (3)</td>\n”, “ <td>0.077788</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ <tr>\n”, “ <th>2020-03-13</th>\n”, “ <td>GRMN</td>\n”, “ <td>Garmin</td>\n”, “ <td>Consumer Discretionary</td>\n”, “ <td>Consumer Electronics</td>\n”, “ <td>1121788</td>\n”, “ <td>0.0</td>\n”, “ <td>0.0</td>\n”, “ <td>1.0</td>\n”, “ <td>1.0</td>\n”, “ <td>1.0</td>\n”, “ <td>…</td>\n”, “ <td>GRMN</td>\n”, “ <td>GARMIN LTD</td>\n”, “ <td>Q</td>\n”, “ <td>77.339996</td>\n”, “ <td>190687.0</td>\n”, “ <td>0.085502</td>\n”, “ <td>matched (3)</td>\n”, “ <td>0.069018</td>\n”, “ <td>both</td>\n”, “ <td>11</td>\n”, “ </tr>\n”, “ </tbody>\n”, “</table>\n”, “<p>1750 rows × 59 columns</p>\n”, “</div>” ], “text/plain”: [ “ Symbol Security GICS Sector \\n”, “date \n”, “2020-03-09 LYB LyondellBasell Materials \n”, “2020-03-09 MU Micron Information Technology \n”, “2020-03-09 FBHS Fortune Brands Industrials \n”, “2020-03-09 ZBH Zimmer Biomet Health Care \n”, “2020-03-09 BWA BorgWarner Consumer Discretionary \n”, “… … … … \n”, “2020-03-13 XRAY Dentsply Sirona Health Care \n”, “2020-03-13 ABT Abbott Health Care \n”, “2020-03-13 MAR Marriott International Consumer Discretionary \n”, “2020-03-13 IR Ingersoll Rand Industrials \n”, “2020-03-13 GRMN Garmin Consumer Discretionary \n”, “\n”, “ GICS Sub-Industry CIK SC_exposure \\n”, “date \n”, “2020-03-09 Specialty Chemicals 1489393 0.0 \n”, “2020-03-09 Semiconductors 723125 0.0 \n”, “2020-03-09 Building Products 1519751 0.0 \n”, “2020-03-09 Health Care Equipment 1136869 0.0 \n”, “2020-03-09 Auto Parts & Equipment 908255 0.0 \n”, “… … … … \n”, “2020-03-13 Health Care Supplies 818479 0.0 \n”, “2020-03-13 Health Care Equipment 1800 0.0 \n”, “2020-03-13 Hotels, Resorts & Cruise Lines 1048286 0.0 \n”, “2020-03-13 Industrial Machinery 1699150 0.0 \n”, “2020-03-13 Consumer Electronics 1121788 0.0 \n”, “\n”, “ COV_exposure LIT_exposure CONT_exposure NCR_exposure … \\n”, “date … \n”, “2020-03-09 0.0 2.0 3.0 0.0 … \n”, “2020-03-09 0.0 0.0 0.0 0.0 … \n”, “2020-03-09 0.0 1.0 1.0 0.0 … \n”, “2020-03-09 0.0 0.0 3.0 0.0 … \n”, “2020-03-09 0.0 1.0 1.0 1.0 … \n”, “… … … … … … \n”, “2020-03-13 0.0 5.0 4.0 7.0 … \n”, “2020-03-13 0.0 0.0 0.0 0.0 … \n”, “2020-03-13 0.0 2.0 2.0 0.0 … \n”, “2020-03-13 0.0 1.0 2.0 2.0 … \n”, “2020-03-13 0.0 1.0 1.0 1.0 … \n”, “\n”, “ ticker comnam primexch prc \\n”, “date \n”, “2020-03-09 LYB LYONDELLBASELL INDUSTRIES N V N 59.610001 \n”, “2020-03-09 MU MICRON TECHNOLOGY INC Q 45.970001 \n”, “2020-03-09 FBHS FORTUNE BRANDS HOME & SECUR INC N 56.169998 \n”, “2020-03-09 ZBH ZIMMER BIOMET HOLDINGS INC N 111.260002 \n”, “2020-03-09 BWA BORGWARNER INC N 26.049999 \n”, “… … … … … \n”, “2020-03-13 XRAY DENTSPLY SIRONA INC Q 40.459999 \n”, “2020-03-13 ABT ABBOTT LABORATORIES N 81.650002 \n”, “2020-03-13 MAR MARRIOTT INTERNATIONAL INC NEW Q 95.610001 \n”, “2020-03-13 IR INGERSOLL RAND INC N 23.000000 \n”, “2020-03-13 GRMN GARMIN LTD Q 77.339996 \n”, “\n”, “ shrout vwretd _merge ret_f exists week \n”, “date \n”, “2020-03-09 333555.0 -0.079317 matched (3) -0.171968 both 11 \n”, “2020-03-09 1112000.0 -0.079317 matched (3) -0.106858 both 11 \n”, “2020-03-09 139972.0 -0.079317 matched (3) -0.070341 both 11 \n”, “2020-03-09 206404.0 -0.079317 matched (3) -0.103465 both 11 \n”, “2020-03-09 206410.0 -0.079317 matched (3) -0.097679 both 11 \n”, “… … … … … … … \n”, “2020-03-13 221439.0 0.085502 matched (3) 0.013273 both 11 \n”, “2020-03-13 1763433.0 0.085502 matched (3) 0.092454 both 11 \n”, “2020-03-13 324215.0 0.085502 matched (3) 0.020275 both 11 \n”, “2020-03-13 205212.0 0.085502 matched (3) 0.077788 both 11 \n”, “2020-03-13 190687.0 0.085502 matched (3) 0.069018 both 11 \n”, “\n”, “[1750 rows x 59 columns]” ] }, “execution_count”: 8, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [ “# slice the dataset by date\n”, “sp500c = sp500b.set_index([‘date’]).sort_values(by=’date’,ascending=True)\n”, “# Get the firms’ daily returns for the week of Mar 9 - Mar 13\n”, “sp500c = sp500c.loc["2020-03-09":"2020-03-13"]\n”, “sp500c\n”, “# 355 firms x 5 trading days’ data = 1750 observations in sp500c dataset” ] }, { “cell_type”: “code”, “execution_count”: 9, “id”: “deb9b5f7-5fa0-436f-bd06-f81ac1cd5ed9”, “metadata”: {}, “outputs”: [ { “data”: { “image/png”: 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\n”, “text/plain”: [ “<Figure size 600x600 with 3 Axes>” ] }, “metadata”: {}, “output_type”: “display_data” } ], “source”: [ “sp500e = sp500c\n”, “# weekly return for 3/9 to 3/13\n”, “sp500e = (sp500c\n”, “ # compute gross returns for each asset (and get the week var)\n”, “ .assign(ret_week = 1+sp500c[‘ret_f’])#,\n”, “ #week = sp500c[‘date’].dt.isocalendar().week)\n”, “ # for each firm\n”, “ .groupby(["Symbol"])\n”, “ # cumulate the returns\n”, “ [‘ret_week’].prod()\n”, “ # subtract one\n”, “ -1\n”, “ ).to_frame()\n”, “\n”, “sp500e = sp500e.merge(sp500c, left_on="Symbol",right_on="Symbol")\n”, “# sp500e has the cumulative return "ret_week" for the week\n”, “# Each weekly return value is repeated 5 times for each firm because this dataset also includes daily data\n”, “sp500e\n”, “# search_mask=["Industrials"]\n”, “# sp500e = sp500e[sp500e[‘GICS Sector’].str.contains(‘|’.join(search_mask))]\n”, “sns.set(rc= {‘figure.figsize’: (5,5),"figure.dpi":100})\n”, “#ax = sns.scatterplot(data=sp500e,x="LIT_exposure",y=’ret_week’,style="Symbol")\n”, “ax = sns.jointplot(x="LIT_exposure",y="ret_week", data=sp500e)\n”, “#ax.legend(bbox_to_anchor=(1.02, 0.15), loc="lower left", ncol=3)\n”, “#ax.legend([],[], frameon=False)\n”, “#ax.set(title=’Risk and Return (Week 3/9-3/13)’,ylabel=’Litigation Exposure’,xlabel=’Return’,)# xlim=(-1,0.1))” ] }, { “cell_type”: “markdown”, “id”: “f3e9ac7d-ab5d-48da-9398-e21c971770c9”, “metadata”: {}, “source”: [ “In this section of the analysis, a heavy focus is placed on firms that experienced more than a -20% loss in value. This is because almost all firms experienced a negative return, so simply looking at those firms would not yield any useful insights.\n”, “In the figure above if we look at -20% returns for the week, firms with a "LIT_exposure" value >= 1 make up majority of the points on the scatterplot. There are also extreme cases with highly negative returns for firms that have a high "LIT_exposure value. However, the inverse is also true (LIT_exposure = 8, Return > -20%)” ] }, { “cell_type”: “code”, “execution_count”: 10, “id”: “23e9ad8d-ee76-4c0e-bde3-6b58b006a71e”, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “\n” ] }, { “data”: { “text/plain”: [ “[Text(0.5, 1.0, ‘Risk and Return (Week 3/9-3/13)’),\n”, “ Text(0, 0.5, ‘Return’),\n”, “ Text(0.5, 0, ‘Contractual Exposure’)]” ] }, “execution_count”: 10, “metadata”: {}, “output_type”: “execute_result” }, { “data”: { “image/png”: 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\n”, “text/plain”: [ “<Figure size 500x500 with 1 Axes>” ] }, “metadata”: {}, “output_type”: “display_data” } ], “source”: [ “print()\n”, “#sns.set(rc= {‘figure.figsize’: (20,15),"figure.dpi":500})\n”, “ax = sns.scatterplot(data=sp500e,y="ret_week",x=’CONT_exposure’,style="Symbol")\n”, “#ax.legend(bbox_to_anchor=(1.02, 0.15), loc="lower left", ncol=3)\n”, “plt.legend([],[], frameon=False)\n”, “ax.set(title=’Risk and Return (Week 3/9-3/13)’,ylabel=’Return’,xlabel=’Contractual Exposure’)#, xlim=(-.5,6))” ] }, { “cell_type”: “markdown”, “id”: “3daa0a68-6060-4846-95a3-7081a7a3a56c”, “metadata”: {}, “source”: [ “CONT_exposure plotted against return shows that there is some correlation between the two. Majority of the firms with high negative returns (returns less than or equal to 20%), had a non-zero value for CONT_exposure. Furthermore, firms with a value of between 3-10 all experienced -30% return over the week or more. This suggests that a high CONT_exposure value, which indicates that a firm considers their ability to fulfill contractual obligations to be at risk, may be correlated with low investor confidence in times of uncertainty.” ] }, { “cell_type”: “code”, “execution_count”: 11, “id”: “0e598d29-d76a-4302-a926-ea665e06fde3”, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “\n” ] }, { “data”: { “text/plain”: [ “[Text(0.5, 1.0, ‘Risk and Return (Week 3/9-3/13)’),\n”, “ Text(0, 0.5, ‘Return’),\n”, “ Text(0.5, 0, ‘Non-Contractual Exposure’)]” ] }, “execution_count”: 11, “metadata”: {}, “output_type”: “execute_result” }, { “data”: { “image/png”: 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\n”, “text/plain”: [ “<Figure size 500x500 with 1 Axes>” ] }, “metadata”: {}, “output_type”: “display_data” } ], “source”: [ “print()\n”, “ax = sns.scatterplot(data=sp500e,y="ret_week",x=’NCR_exposure’,style="Symbol")\n”, “#ax.legend(bbox_to_anchor=(1.02, 0.15), loc="lower left", ncol=3)\n”, “plt.legend([],[], frameon=False)\n”, “ax.set(title=’Risk and Return (Week 3/9-3/13)’,ylabel=’Return’,xlabel=’Non-Contractual Exposure’)#, xlim=(-.5,6))” ] }, { “cell_type”: “markdown”, “id”: “115617fc-3c1f-4a7f-9a72-4e57aec75a82”, “metadata”: {}, “source”: [ “The correlation between NCR_exposure and Return seems to be stronger. A large proportion of high negative returns also had high NCR_exposure values” ] }, { “cell_type”: “code”, “execution_count”: 12, “id”: “f3adbe4a-a8bc-4c88-9689-7926b1924185”, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “\n” ] }, { “data”: { “text/plain”: [ “[Text(0.5, 1.0, ‘Risk and Return (Week 3/9-3/13)’),\n”, “ Text(0, 0.5, ‘Return’),\n”, “ Text(0.5, 0, ‘Supply Chain Risk Exposure’)]” ] }, “execution_count”: 12, “metadata”: {}, “output_type”: “execute_result” }, { “data”: { “image/png”: 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\n”, “text/plain”: [ “<Figure size 500x500 with 1 Axes>” ] }, “metadata”: {}, “output_type”: “display_data” } ], “source”: [ “print()\n”, “ax = sns.scatterplot(data=sp500e,y="ret_week",x=’SC_exposure’,style="Symbol")\n”, “#ax.legend(bbox_to_anchor=(1.02, 0.15), loc="lower left", ncol=3)\n”, “plt.legend([],[], frameon=False)\n”, “ax.set(title=’Risk and Return (Week 3/9-3/13)’,ylabel=’Return’,xlabel=’Supply Chain Risk Exposure’)#, xlim=(-.5,6))” ] }, { “cell_type”: “markdown”, “id”: “d3dcc089-e03c-4a54-816a-dc538ec12d32”, “metadata”: {}, “source”: [ “SC_risk and Return also have a strong correlation as a large proportion of high negative returns also had high SC_exposure values. Some firms with high negative returns had no SC_exposure value, but this can be accounted for. For instance, they may have other risks (such as Contractual risk which had a similar firm with a positive CONT_exposure value) or risks that were not measured in this project.” ] }, { “cell_type”: “code”, “execution_count”: 13, “id”: “9fd1b521-cecd-4be5-9861-db6afc12e580”, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “\n” ] }, { “data”: { “text/plain”: [ “[Text(0.5, 1.0, ‘Risk and Return (Week 3/9-3/13)’),\n”, “ Text(0, 0.5, ‘Return’),\n”, “ Text(0.5, 0, ‘COVID-19 Risk Exposure’)]” ] }, “execution_count”: 13, “metadata”: {}, “output_type”: “execute_result” }, { “data”: { “image/png”: 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\n”, “text/plain”: [ “<Figure size 500x500 with 1 Axes>” ] }, “metadata”: {}, “output_type”: “display_data” } ], “source”: [ “print()\n”, “ax = sns.scatterplot(data=sp500e,y="ret_week",x=’COV_exposure’,style="Symbol")\n”, “#ax.legend(bbox_to_anchor=(1.02, 0.15), loc="lower left", ncol=3)\n”, “plt.legend([],[], frameon=False)\n”, “ax.set(title=’Risk and Return (Week 3/9-3/13)’,ylabel=’Return’,xlabel=’COVID-19 Risk Exposure’)#, xlim=(-.5,6))” ] }, { “cell_type”: “markdown”, “id”: “216ea84e-5ace-4a5e-b3be-bd7b1bbd1edd”, “metadata”: {}, “source”: [ “The COV_exposure risk measure does not show a very strong correlation with negative Returns. A majority of the firms that experienced a high negative return over the week (less than or equal to -20%) had a COV_exposure value of 0. However, there were firms that experienced -30%,-50% and ~-28% or -29% returns with COV_exposure values of 1,1, and 3 respectively.” ] }, { “cell_type”: “code”, “execution_count”: 14, “id”: “c777913d-ad98-49ca-aa97-2a8bf0667802”, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “[Text(0.5, 1.0, ‘Risk and Return (Week 3/9-3/13)’),\n”, “ Text(0, 0.5, ‘Return’),\n”, “ Text(0.5, 0, ‘Contractual Exposure’)]” ] }, “execution_count”: 14, “metadata”: {}, “output_type”: “execute_result” }, { “data”: { “image/png”: 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“text/plain”: [ “<Figure size 10000x7500 with 1 Axes>” ] }, “metadata”: {}, “output_type”: “display_data” } ], “source”: [ “# Additonal visualization of Risk measure and return by industry/sector \n”, “# compute weekly returns for each firm and add to the analysis dataset \n”, “sp500e = sp500c\n”, “# weekly return for 3/9 to 3/13\n”, “sp500e = (sp500c\n”, “ # compute gross returns for each asset (and get the week var)\n”, “ .assign(R = 1+sp500c[‘ret_f’])#,\n”, “ #week = sp500c[‘date’].dt.isocalendar().week)\n”, “ # for each firm\n”, “ .groupby(["Symbol"])\n”, “ # cumulate the returns\n”, “ [‘R’].prod()\n”, “ # subtract one\n”, “ -1\n”, “ ).to_frame()\n”, “#weekly_ret.T.plot()\n”, “\n”, “sp500e = sp500e.merge(sp500c, left_on="Symbol",right_on="Symbol")\n”, “search_mask=["Health Care","Industrials"]\n”, “sp500e = sp500e[sp500e[‘GICS Sector’].str.contains(‘|’.join(search_mask))]\n”, “sns.set(rc= {‘figure.figsize’: (20,15),"figure.dpi":500})\n”, “ax = sns.scatterplot(data=sp500e,y="R",x=’CONT_exposure’,style="GICS Sector",hue="GICS Sub-Industry")\n”, “\n”, “ax.set(title=’Risk and Return (Week 3/9-3/13)’,ylabel=’Return’,xlabel=’Contractual Exposure’)#, xlim=(-.5,6))” ] } ], “metadata”: { “kernelspec”: { “display_name”: “Python 3 (ipykernel)”, “language”: “python”, “name”: “python3” }, “language_info”: { “codemirror_mode”: { “name”: “ipython”, “version”: 3 }, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.9.7” } }, “nbformat”: 4, “nbformat_minor”: 5 }