The Wharton Research Data Service (WRDS) data set and Factor Model Factors

Information wants to be free
Attributed to Stewart Brand

On the one hand information wants to be expensive, because it's so valuable. The right information in the right place just changes your life.
Steve Wozniak, co-founder of Apple Computer

Table of Contents

• Data Set Overview
• Detailed Data Source Notes
  • I/B/E/S (IBES)
  • CRSP/Compustat Corporate Value Factors
    • CRSP/Compustat Merged Database - Fundamentals Quarterly
    • Factor Density for CRSP/Compustat Quarterly Factors
    • 100% to 60% CRSP/Compustat Quarterly Corporate Factors with Explanation
• Corporate Factor Building Blocks
  • Cleaning up the Data
  • R code to process the Compustat Data
  • Fundamental Corporate Factors
  • R Code to Calculate the Value Factors

Introduction

This page started out as a set of notes that I wrote while I was working with the Wharton Research Data Service (WRDS) data set for my Masters presentation in Computational Finance and Risk Management Masters at the University of Washington, on November 22, 2013.

I am grateful that I had access to the WRDS databases. Without this access I would not have been able to do my Masters presentation work. This is a fantastic resource for graduate students and researchers.

Locating the data that I needed within the WRDS data bases and cleaning up the data so that it could be used in my Masters presentation proved to be extremely time consuming. At one point I worried that I would end up giving my Masters presentation on data preparation since this work had consumed so much time. I hope that these notes will help others navigate the WRDS data (and spend less time on the data and more on their research).

Pension funds, endowments, hedge funds and other investment groups spend tens of thousands, or hundreds of thousands of dollars a year on market data. Some of these "industry" data sources are available through the Wharton Research Data Service. I don't know if there are differences in the quality of the academic databases vs. those provided to industrial subscribers.

Apparently different academic subscribers have different levels of access. So these notes are not comprehensive: a subscription may have more or less data access than is noted here. Some of these data sources, like the Fama French data may be available elsewhere (e.g., Kenneth French's web site).

I have focused on data sets that are useful for building portfolio factor models based on corporate balance sheet data (earnings per share, etc...). To explore the WRDS data, I first used the S&P 100 stocks and then later, the S&P 500 stocks. Most of the detail here involves stocks in the CRSP/Compustat S&P 500 index, from 1998 to 2013.

The WRDS data can be difficult to navigate. Each data set has a variety of sub-data sets. Even after reading the documentation it can be difficult to understand what is contained in the data set or how complete it is. These notes reflect many hours spent exploring the WRDS data set.

Data Set Overview

In most cases the descriptions of the data sets consist of edited versions of the description provided on the WRDS web site.

• AuditAnalytics

  Audit Analytics provides detailed research on over 150,000 active audits and more than 10,000 accounting firms.

  Audit and Compliance datasets covers all SEC registrants. Audit Analytics data includes the tracking and analysis of public company disclosures related to the audit, compliance, governance, corporate actions and federal litigation.

• Bank Regulatory

  This database contains five databases for regulated depository financial institutions. These databases provide accounting data for bank holding companies, commercial banks, savings banks, and savings and loans institutions. The source of the data comes from the required regulatory forms filed for supervising purposes. Please see the Federal Reserve Bank of Chicago website for more information.

• Blockholders

  This dataset contains standardized data for blockholders of 1,913 companies. The data was cleaned from biases and mistakes usually observed in the standard source for this particular type of data. Blockholders' data is reported by firm for the period 1996-2001.

• Bureau van Dijk

  Bureau van Dijk (BvD) is widely recognized as a specialist provider of both public and private company information within the academic community.

  WRDS currently offers users with the following company information databases through BvD's Orbis product:

  • Amadeus: European public and private company information
  • Bankscope: Global banking information
  • Osiris: Globally listed, as well as major unlisted or delisted companies

• CBOE Indexes (various versions of the VIX index)

  The CBOE (Chicago Board Options Exchange) Volatility Index is a key measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices. The Vix Index was introduced in 1993 by Professor Robert E. Whaley of Duke University in his paper Derivatives on Market Volatility: Hedging Tools Long Overdue Journal of Derivatives 1 (Fall 1993), pp. 71-84. Since then, VIX has been considered by many to be the world's premier barometer of investor sentiment and market volatility.

  The New VIX still measures the market's expectation of 30-day volatility, but in a way that conforms to the latest thinking and research among industry practitioners. The New VIX is based on S&P 500 index option prices and incorporates information from the volatility "skew" by using a wider range of strike prices rather than just at-the-money series.

• Compustat from Standard and Poors

  This database includes:

  • North America

    This is a database of U.S. and Canadian fundamental and market information on active and inactive publicly held companies. It provides more than 300 annual and 100 quarterly Income Statement, Balance Sheet, Statement of Cash Flows, and supplemental data items.

  • Global

    This is a database of non-U.S. and non-Canadian fundamental and market information on more than 33,900 active and inactive publicly held companies with annual data history from 1987.

  • Bank
  • Historical Segments
  • Snapshot
  • Capital IQ

  Compustat has a wide variety of data, so it is difficult to characterize. Compustat includes daily and monthly security prices.

  For those building factor models, Compustat has a data section labeled Simplified Finance Statement Extract that includes monthly corporate factors. The data appears to have fewer missing values than the IBES data. Also, unlike IBES, the Compustat data is returned in columnar format (factors per month, with the factors as the columns) so software is not necessary to assemble a table of factors. However, when I queried for the S&P 100 stocks, values for only a few stocks where returned.

  Compustat also includes the components of various indices (e.g., computer software, chemicals, etc). It also includes index prices. It should be possible to construct beta values for the various indices from this data.

• CRSP

  Perhaps the most famous research database. The Center for Research in Security Prices (CRSP) maintains the most comprehensive collection of security price, return, and volume data for the NYSE, AMEX and NASDAQ stock markets. Additional CRSP files provide stock indices, beta- and cap-based portfolios, treasury bond and risk-free rates, mutual funds, and real estate data.

• CUSIP

  The CUSIP Master Files provide CUSIP numbers, standardized descriptions and additional data attributes for over 5 million corporate, municipal and government securities offered in North America.

• DMEF Academic Data (Direct Marketing Educational Foundation) Not of much interest to quants.

• Dow Jones

  The Dow Jones Averages are comprised of The Daily and Monthly Dow Jones Composite (DJA), as well as The Dow Jones Industrial (DJI), The Dow Jones Transportation (DJT), The Dow Jones Utility (DJU), The Dow 10, and The Dow 5.

• FDIC

  The Federal Deposit Insurance Corporation (FDIC) datasets contains historical financial data for all entities filing the Report of Condition and Income (Call Report) and some savings institutions filing the OTS Thrift Financial Report (TFR). These entities include commercial banks, savings banks, or savings and loans.

• Fama French & Liquidity Factors

  The Fama-French Portfolios are constructed from the intersections of two portfolios formed on size, as measured by market equity (ME), and three portfolios using the ratio of book equity to market equity (BE/ME) as a proxy for value. Returns from these portfolios are used to construct the Fama-French factors.

• Federal Reserve Bank

• GovPX - Tick-by-tick data after 2009.

  Data must be searched by CUSIP. It's not clear from the WRDS description how large an asset universe is covered or even what the assets are.

• I/B/E/S

  I/B/E/S (IBES) International Inc. created their Academic Research Program over 30 years ago to provide both summary and individual analyst forecasts of company earnings, cash flows, and other important financial items, as well as buy-sell-hold recommendations.

  The IBES values are returned with one value (Cash Flow per Share - CPS) on a line. Building a table from this data can be a challenge and I wrote some Java code to do this.

• ISSM (The Institute for the Study of Security Markets)

  The Institute for the Study of Security Markets (ISSM) database contains tick-by-tick data covering the NYSE and AMEX between 1983 and 1992, and NASDAQ between 1987 and 1992. Each year of data is divided into two files, one for trades and one for quotes.

• Markit Trial (credit default swap database)

• PHLX (Philadelphia Stock Exchange)

  Currency and implied volatility option database.

• Penn World Tables (national (country) income data)

• Risk Metrics

  RiskMetrics (through ISS Governance Services) is a leader in corporate governance data. RiskMetrics delivers to data for the new methodology to WRDS in four datasets identified as:

  1. RiskMetrics Group Historical Governance
  2. Historical Directors data
  3. Voting Results data
  4. Shareholder Proposal data

• SEC Order Execution

  On November 15, 2000, the SEC adopted new rules aimed at improving public disclosure of order execution and routing practices. As a result of Rule 11Ac1-5, market centers that trade national market system securities must make monthly, electronic disclosures of basic information concerning their quality of executions on a stock-by-stock basis, including how market orders of various sizes are executed relative to the public quotes and information about effective spreads - the spreads actually paid by investors whose orders are routed to a particular market center. In addition, market centers must disclose the extent to which they provide executions at prices better than the public quotes to investors using limit orders.

  Data is available on WRDS through 2005, and is no longer updated.

• TAQ (Trade And Quote database)

  The Trade and Quote (TAQ) database contains intraday transactions data (trades and quotes) for all securities listed on the New York Stock Exchange (NYSE) and American Stock Exchange (AMEX), as well as Nasdaq National Market System (NMS) and SmallCap issues.

  The Reg SHO - NYSE Short Sales database contains data related to SEC Regulation SHO per the terms of the SRO requirement to make Short Sale transactions data available on a monthly basis for trade dates beginning January 2005 through the pilot period ending April 2006. (SROs are self-regulatory organizations in the securities industry, such as the NYSE, that create and enforce rules for its members based on the federal securities laws.)

• TRACE (OTC Corporate Bond and Agency Debt Bond Transaction Data)

• Thomson Reuters

  Mutual fund holdings, institutional holdings, insider data, stock ownership summary.

• Zacks Trial

  Analysts forecast database (e.g., Earnings per Share consensus, surprise, etc). The trial dataset is limited date range for 100 of the largest global companies.

Detailed Data Source Notes

As far as I can tell, of the data sets that I have access to, there are only two that contain corporate factor data: IBES and Compustat. The IBES data also includes analysts forcasts.

I/B/E/S (IBES)

The IBES data set can be selected in a variety of ways. The IBES Detail History - Actuals data set appears to be the most promising when it comes to corporate factors (e.g., earnings per share, cash flow per share). Here "actual" values are in contrast to the forecast values.

The IBES data is surprising in its ommissions. Almost every stock has periods where there are no values provided for Book Value per Share, Cash Flow per Share and Return on Equity. This makes this data set difficult or impossible to use. Given what IBES charges for data I can only hope that they provide better quality for their industry subscribers.

IBES Detail History - Actuals

The IBES database has quarterly stock factor data. I experimented with selecting the factors shown below:

• EPS - Earnings per share
• BPS - Book Value Per share
• CPS - Cash Flow Per Share
• CSH - Cash Earnings Per Share
• DPS - Dividend Per Share
• EBG - Earnings per share - before goodwill
• EBS - EBITDA per share
• FFO - Funds from operations per share
• GPS - GAAP/Earnings Per share - fully reported
• GRM - Gross margin (percent, GRM)
• OPR - Operating Profit (non-per share)
• ROA - Return on Assets (percent)
• ROE - Return on Equity (percent)
• SAL - Revenue (non per share)

The SAL factor (revinue non-per-share) does not seem to be a useful factor since it needs to be scaled by the number of shares or the corporate size (capitalization).

Unlike the Compustat data, which is returned in tabular form, the IBES data is returned with one factor per line. This is shown below. The column names are:

OFTIC,TICKER,PENDS,MEASURE,PDICITY,VALUE,ANNDATS
AEP,AEP,12/31/2004,EPS,QTR,0.42,01/27/2005
AEP,AEP,03/31/2005,EPS,QTR,0.88,04/28/2005
AEP,AEP,06/30/2005,EPS,QTR,0.61,07/29/2005
AEP,AEP,09/30/2005,EPS,QTR,0.95,10/27/2005
AEP,AEP,12/31/2005,EPS,QTR,0.29,02/01/2006
AEP,AEP,03/31/2006,EPS,QTR,0.96,04/27/2006
AEP,AEP,06/30/2006,EPS,QTR,0.44,07/27/2006
AEP,AEP,03/31/2006,BPS,QTR,23.82,04/28/2006
...
AEP,AEP,06/30/2006,BPS,QTR,23.8,07/27/2006
AEP,AEP,09/30/2006,BPS,QTR,24.05,10/31/2006
AEP,AEP,12/31/2006,BPS,QTR,23.83,01/30/2007
AEP,AEP,03/31/2007,BPS,QTR,23.94,04/26/2007
AEP,AEP,06/30/2007,BPS,QTR,24.22,07/31/2007
AEP,AEP,09/30/2007,BPS,QTR,24.76,10/24/2007
AEP,AEP,12/31/2007,BPS,QTR,25.31,01/29/2008
AEP,AEP,03/31/2008,BPS,QTR,,04/28/2008

To allow this data be used in an environment like R the data needs to be in a tabular format, like that shown below:

Note that NA has been filled in for missing values.

I wrote a Java program (ConvertData.java) to convert the WRDS IBES value per line data into the table shown above (a downloadable form can be found here).

The WRDS query interface allows you to select a file on your local system that contains the stock ticker symbols for the query. I used a file that contained the S&P 100 stocks (note that the composition of the S&P 100 and other indexes change over time):

You can download the stocks in list form here.

Although the IBES search has a check box for each of the factors above, many of them have no data or many missing values (for example, CPH, EBG, FFO, GRM). In some cases, not all time periods have all of the factors selected in the query (e.g., values are not supplied as empty values, but are omitted entirely for the time period).

I tried selecting fewer factors and got a data set with fewer missing values. For some stocks there are still enough missing values to make this data set problematic.

The IBES time series with the fewest missing values is the earnings per share actual value. However, not all dates are covered.

S&P 500 Index Components

Compustat Monthly Updates - Index Constituents

Stocks are added and removed from the S&P 500 over time. When modeling portfolio performance with historical data it is important to use the historical S&P 500 to avoid survivor bias. Also, some stocks, like Google, have existed only relatively recently.

In order to download the historical S&P 500, the index for various time periods must be calculated. Some stocks do not exist anymore and are replaced by CRSP/Compustat codes. A file of GVKEY values must be generated to download the factors discussed here. See Building the S&P 500 Constituents. The web page include a link to R code that will build the index and the GVKEY file.

CRSP/Compustat Corporate Value Factors

Forrester Research analyst Sucharita Mulpuru told the Times that Amazon was "the teacher's pet of Wall Street," because no other company in the world "has the consistently abominable rate of profitability they do and yet has the stratospheric valuation they do."
http://www.salon.com/2013/10/25/why_wall_street_cant_quit_amazon

The raw numbers would seem to indicate that Microsoft is killing it while Amazon is floundering. But Wall Street disagrees: On Friday morning Microsoft's stock price hovered around $35 per share while Amazon rose to $357.
http://www.salon.com/2013/10/25/what_microsoft_profits_say_about_amazon_losses/Microsoft

Of the databases available on WRDS, the CRSP/Compustat combined database seems to be the most extensive database of fundamental corporate information. This is the core data that I used for my Masters presentation.

CRSP/Compustat Merged Database - Fundamentals Quarterly

This data consists of corporate factors from the quarterly corporate filings.

Corporations in different industrial sections differ in fundamental ways. A transportation company (trucking, railroad) has large capital equipment expenditures and depreciation, but no research and development expenses. A pharmaceutical company has research and develpment costs, capital costs for drug manufacture and depreciation. A software company has research and development costs but low capital expenses (outside of buildings and computers). Each of these companies will have different corporate factors.

Corporate reports contain a staggering number of categories. For those of us without the benefit of MBA or CPA graduate degrees, some of these factors are confusing. Another problem is that in many cases there are missing factors.

Factor Density for CRSP/Compustat Quarterly Factors

The tables below show the density of the factors, relative to the S&P 500 from January 1998 to September 2013. For example, [100.00:95.00] means that between 95 and 100 percent of the values are present (e.g., not NA).

In some cases the fact values are missing is valid. For example, not all stocks have dividends, so it is reasonable that dividend values are missing for some stocks.

[100.00:95.00]

[95.00:90.00]

[90.00:85.00]

[85.00:80.00]

[80.00:75.00]

[75.00:70.00]

[70.00:65.00]

[65.00:60.00]

[60.00:55.00]

[55.00:50.00]

[50.00:45.00]

[45.00:40.00]

[40.00:35.00]

[35.00:30.00]

[30.00:25.00]

[25.00:20.00]

[20.00:15.00]

[15.00:10.00]

[10.00:5.00]

[5.00:0.00]

100% to 60% CRSP/Compustat Quarterly Corporate Factors with Explanation

In most cases factors that don't have at least 2/3s of their values are not going to be useful. The tables below list the factors, with the description provided by Compustat.

[100.00:95.00]

[95.00:90.00]

[90.00:85.00]

[85.00:80.00]

[80.00:75.00]

[75.00:70.00]

[70.00:65.00]

[65.00:60.00]

Corporate Factor Building Blocks

Most of the Compustat corporate factors listed in the table below are used to build the factors described in Chapter 5 of Quantitative Equity Portfolio Management by Qian, Hua and Sorensen. As the table shows, most of these factors have few missing values. In some cases, dividends (DVPSPQ), missing values are not unexpected, since not all companies pay dividends.

Note:
In the CRSP/Compustat data the REVTQ (quarterly revenue) and SALEQ (quarterly sales) values seem to be the same. There are fewer missing values for SALEQ, so this value is used rather than REVTQ.

The distribution of NA (not present) values after interpolation for quarterly values is shown below:

Additional Metadata

In some cases the date defined in the datadate column is incorrect. To allow cleaning and filtering software to fix the dates the metadata fields (from CRSP/Compustat) should be included.

If you want additional verification you can also include the DATAFQTR column in the data. This value (e.g., 1998Q1, 1998Q2) mirrors the fyear and fqtr columns.

The GGROUP and GSECTOR are Global Industry Classification Standard values

Cleaning up the Data

In our recollection, we have never encountered a large, real-world dataset that was directly usable without data cleaning.
Scaling Big Data Mining Infrastructure: The Twitter Experience by Jimmy Lin and Dmitriy Ryaboy, SIGKDD Explorations, V14, Issue 2, 2012

Fixing Date Values

The table below of Google (GOOG) data shows the correct format for the dates. Note that the date in the datadate field mirrors the year and quarter in the fyearq and fqtr columns.

The data below for Mylan Inc (MYL) shows an example where the date in the datadate column is wrong. I have tried to check this data against finance.yahoo.com data. It is hard to exactly match up the values, but it does apepar that the fyearq and fqtr close price is the correct one.

Along with being inaccurate, this date problem causes an error in R's indexes since some datadate values are repeated.

The CRSP/Compustat Merged Database - Fundamentals Quarterly data is extracted from the quarterly reports that public companies, traded on US exchanges, are required to file with the SEC. There are many factors and not all apply to all companies, so some will naturally be zero (blank).

There are other data values that should be reported and present for all companies. However, there are cases where these critical values are missing in the CRSP/Compustat data.

Before the Compustat data can be used, it must be processed to add critical values that are missing. Adding interpolated values is better than a missing value, but this may result in estimation error.

• CSHOQ - Common shares outstanding

  This value must always be present since it is used to calculate the market value (MV) and enterprise value (EV) for a company. If this value is missing, it is filled in by interpolation from surrounding values.

• PRCCQ - Quarterly close price.

  If the stock is still trading the quarterly close price can be obtained from finance.yahoo.com. If the stock is no longer trading the value is filled in by interpolating from neighboring values

• SALEQ - quarterly sales

  This is a critical value for calculating EBITDA. If values are missing they are filled in by interpolation.

• CEQQ - common equity
  LTQ - libilities (total)
  NIQ - Net income
  

  If there are fewer than four sequential missing values, the values are filled in by interpolation.

Filling in missing stock values

The R package tseries includes a function, get.hist.quote, which can be used to fetch stock values form Internet sources like finance.yahoo.com.

The historical S&P 500 includes stocks for companies that have been purchased by other companies or have otherwise gone out of business (Lehman Brothers, for example). The tickers for these stocks have been replaced by CRSP/Compustat symbols. If these symbols are passed to the get.hist.quote function there will be an error. This error is not an exception that can be caught, if the quote cannot be read. This will cause the R script to terminate.

#
# Return TRUE if the stock, on date, can be read from Yahoo
#

canGetStockInfo = function(ticker, date)
{
  start = date
  end = date
  url <- paste("http://chart.yahoo.com/table.csv?s=", ticker, 
               format(start, "&a=%m&b=%d&c=%Y"), 
               format(end, "&d=%m&e=%d&f=%Y"), "&g=d&q=q&y=0&z=",
	       ticker, "&x=.csv", sep = "")
  page = getURL(url) 
  ix = grep(pattern="404 Not Found", x = page)
  readURL = (length(ix) == 0)
  return(readURL)
}

R code to process the Compustat Data

Fundamental Corporate Factors from Quantitative Equity Portfolio Management

The factors in this section are discussed in Chapter 5 of Quantitative Equity Portfolio Management by Qian, Hua and Sorensen, Chapman and Hall, 2007. These factors are synthesized from the Compustat quarterly data (except for the earnings forecast, which can be obtained from IBES).

In Appendex A5.1 of Qian et al there is a description of how these factors are calculated from the Compustat data. Unfortunately the current Compustat data available from WRDS in 2013 has a different structure. Qian et al use an obsolete Compustat quarterly data schema, is described here.

I have listed below the calculation I have used for the selected Qian et al factors.

• MV = market value
  

  MV = CSHOQ (shares outstanding) * PRCCQ (share price)

  
  

• EV = Enterprise Value - The enterprise value is what it would cost to buy the company. This includes the market capitalization, preferred stock or debt (a purchaser must pay of the debt to buy the company). Cash and cash equivalents is subtracted since once the purchaser owns the company the cash can be used to pay back part of the cost of purchase.

  EV = [MV + ((debt in current liabilities) + (long term debt total) + (Preferred/Preference Stock (Capital))] - (Cash and Short-Term Investments) EV = (MV + DLCQ + DLTTQ + PSTKQ) - CHEQ

  
  
• tax rate

  Interest paid is multiplied by the tax rate to produce a credit, since corporations can deduct interest.

  Qian et al calculate the cash flow numerator as: CFO + (interest expense x (1 - tax rate)). The tax rate is not available in the current Compustat as a percentage.

  As it turns out, the corporate tax rate is complex and a lot of ink has been spilled on this topic. See, for example, John R. Graham's web page discussing this.

  Prof. Graham provides the tax rates for a huge number of stocks, from 1980 to 2012. However, his data has missing values (for example, some, but not all stocks have Compustat GVKEY values).

  I have calculated tax rates by dividing the quarterly taxes paid by the pretax income.
  

  tax_rate = TXTQ / PIQ

  The income tax distribution is shown below (after cleaning up the data):

  

  The mean is 28% which turns out to the the mean reported tax rate in the US. Note that the mean actual taxes paid by corporations in the US is less than half of this (~12%).

• CFO2EV - Cash Flow from Operations to Enterprise Value
  

  Cash flow is "cash flow from operating activities". This value is not available directly on a quarterly basis. However, Compustat does include a year-to-date value, OANCFY. By subtracting quarter t from quarter t+1 the quarterly value can be arrived at.

  CFO2EV = (Operating Activities Net Cash Flow) + ((Interest and Related Expense) x (1 - tax_rate)) ΔCFO = OANCFYt+1 - OANCFYt CFO2EV = CFO + (XINTQ x (1 - tax_rate))

• RONA - Return on net operating assets

  ASSETS = (common equity) + ((debt in current liabilities) + (long term debt total) + (Preferred/Preference Stock (Capital))] - (Cash and Short-Term Investments) ASSETS = (CEQQ + DLCQ + DLTTQ + PSTKQ) - CHEQ RONA = [(Income Before Extraordinary Items) + ((Interest and Related Expense) x (1 - tax_rate))]/ASSETS RONA = [IBQ + (XINTQ x (1 - tax_rate))]/ASSETS

• EBITDA2EV - Earnings before Interest, Taxes, Depreciation and Amortization to Enterprise Value

  The EBITDA value is only available in the annual Compustat data. EBITDA can be calculated as:

  EBITDA = sales - (cost of goods) - (Selling, General, and Administrative Expense ) EBITDA = SALEQ - COGSQ - XSGAQ EBITDA2EV = EBITDA / EV

  
  

• E2PFY0 - Trailing 12-month earnings to market capitalization

  E2PFY0 = (Income Before Extraordinary Items - Available for Common)/MV E2PFY0 = IBCOMQ/MV

• E2PFY1 - Earnings yield of IBES's EPS (earnings per share) consensus estimate of the next fiscal year.
  This estimate can be obtained form the WRDS IBES data (Detail History - Detail File with Actuals)

• BB2P -Net buyback to market capitalization
  

  BB2P = ([Cash Dividend] + [Total Shares Repurchased]) - (Sale of Common an Preferred Stock)/MV BB2P = [(DVY + PRSTKCY) - SSTKY]/MV

  
  
• BB2EV - net external financing to enterprise value
  

  EQ_REPO = equity repurchase EQ_REPO = ([Total Shares Repurchased] - [Sale of Common and Preferred Stock]) EQ_REPO = PRSTKCY - SSTKY DEBT_REPO = [Long-Term Debt - Reduction] - [Changes in Current Debt] - [Long-Term Debt - Issuance] DEBT_REPO = DLTRY - DLCCHY - DLTISY BB2EV = [([Cash Dividend] + EQ_REPO) - DEBT_REPO]/EV BB2EV = [(DVY + EQ_REPO) - DEBT_REPO]/EV

• B2P - Book to market capitalization

  B2P = CEQQ (common/ordinary equity) / MV (market value)

• S2EV - sales-to-enterprise value
  

  S2EV = SALEQ (sales) / EV

  
  

Preprocessing Factor Data

In the CRSP/Compustat data the yearly values are "year-to-date" values. For example, if cash grows by 2 million a quarter, in the first quarter the cash will be 2 million, then 4 million in the second quarter, 6 million in the third quarter and 8 million in the fourth quarter. If the progression continues, then in the first quarter of the next year the cash will again be 2 million.

The yearly factors must be preprocessed to generate the quarterly values before they are used. The yearly factors used above are:

DLTISY
DLTRY
OANCFY

Distribution of the Value Factors

The plots below show the distributions of the standardized value factors synthesized from the corporate values from the S&P 500 stock universe over the period 1998 through 2013 (a distribution is standardized by subtracting the mean and dividing the result by the standard deviation).

R Code to Calculate the Value Factors

Ian Kaplan
October 2013
Last revised: February 2014

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