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Programming in R Performance measurement of Growth and Value Stocks

By Rajat Agarwal, Gabriel Benedickt, Jishuo Ma, Helene Schröcksnadel, David Stein

Folder structure: o The folder aHer unzipping has the following structure:

o ProgrammingR_Project o Data -‐> Data files used in the enOre program o Saves -‐> Directory for saving Outputs of the program o Scripts -‐> Directory for the main program files o PresentaOon -‐> ExplanaOon of the program as a presentaOon

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How to run the program

Before running the scripts: o Make sure you update the staOc variables to your need

o  E.g. !!! Update the file paths if run on a different computer

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o Structure of the scripts: o To ensure userfriendlyness and prevent changing mulOple lines, staOc variables are defined at the top. You only need to change them once.

o To prevent searching through many lines of codes, ALL funcOons are defined following the staOc variables

o The sequenOal execuOon code is followed aHer the funcOons

o Structure:

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Running the scripts: o  cleaningData.R o  processingData.R o  Running processingData.R ist NOT necessary as all it does is: Take the

COMPUSTAT data (/data/NA_Quart_1999-‐2014_Raw2.csv) and clean it for companies which have INCOMPLETE Ome series data. It then outputs a list of companies (saved in: /saves/cleanedNames.txt)

o  This .txt can be used to do a query again in COMPUSTAT. But instead of downloading data for the enOre database, one can put in this list and download the data for thepreselected stocks, which have complete Ome series data.

o  The cleaned data (/data/NA_Clean_Quart_1999-‐2014_AJEXQ-‐ATQ-‐CEQQ-‐CSHOQ-‐EPSPXQ-‐NIQ-‐DVPSPQ-‐PRCCQ.csv) is used in processingData.R

o  All the data handling and computaOons take place in processingData.R o  For recreaOng the results, running processingData.R ist sufficient

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Overview

I. The TheoreOcal Concept 1. Growth versus Value Stocks 2. Data 3. Method

II. The Programm 1. Data PreparaOon 2. ClassificaOons: Growth – Value & Company Size 3. EvaluaOng Performance and Plolng

III. Conclusion, Outlook

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Growth vs. Value Stocks Which Investment Strategy is opOmal? Growth Stock o  High-‐quality, successful company, seeks to achieve high earnings growth o  Company may have the potenOal to achieve high earnings growth o  Tends to invest earnings back into the company -‐> zero or low dividend yield Value Stock o  Company fallen out of favor in the marketplace o  Bargain-‐priced compared with bookvalue, liquidaOon value o  Much lower priced than similar companies in the same industry o  Lower price may reflect investor reacOon to company problems, which may raise

doubts about the company‘s longterm prospects The Primary measures to define growth and value stocks are: Price-‐to-‐Earnings RaOo (in per share values) Price-‐to-‐Book RaOo (in per share values)

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Growth vs. Value Stocks

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Growth – Stock (Growth Inves=ng)

Value – Stock (Value Inves=ng )

CharacterisOcs

High

P/E Ra=o (Price-‐to-‐Earning) P/B Ra=o (Price-‐to-‐Bookvalue) Companies with rapidly growing sales and earnings, higher-‐than-‐average valuaOon

Low

P/E Ra=o (Price-‐to-‐Earning) P/B Ra=o (Price-‐to-‐Bookvalue) Companies with lower-‐than-‐average sales, earnings and growth, tend to have higher dividend yields

Risks

Growth may not always be realized, unforseen events

Intrinsic Value may never be realized, Market may have correctly priced

Growth vs. Value Inves=ng The ra=onale behind Growth Inves=ng – Thomas Rowe Price Jr. o  Strong past performance indicates conOnued performance o  Stocks will sell at higher price as companies conOnues to grow o  No assurance forecast will be atained o  Growth Stocks tend to lead bull markets

The ra=onale behind Value Inves=ng – Warren Buffet, Michael Larson o  Out-‐of-‐favor company with sOll good fundamentals o  Investors probably overreacted to negaOve events o  Buy in the lower-‐priced stocks and sell later when companies bounce back, value recognized by

other investors. o  Limited transparency, Market may have priced correctly o  Value Stocks oHen cyclical industries, tend to do well early in an economic recovery

Mixed evidence in Research o  Fama & French – Value Stocks have higher returns than Growth Stocks, difference on average

returns, global poruolios, 1975-‐1995 is 7.6% per year, internaOonal CAPM cannot explain the value premium

o  Bauman, Conover & Miller – 1990-‐2000, internaOonal stock markets, Value Stocks outperform Growth Stocks, except for the smallest firms, strong firm-‐size effect,

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Data

Source, Warton Research Data Services (WRDS) Range, All listed Northamerican companies, US$ Timeframe, 1999 – 2014, Quarterly Data Variables

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AJEXQ Adjustment Factor (for price of shares)

ATQ Total Assets (Bookvalue)

CSHOQ Common Shares Outstanding

NIQ Net Income (Earning)

PRCCQ Share Price

Method

1.  Cleaning Data, accounOng for missing values 2.  Reading Data, Ome series 3.  Three subsets based on company capitalisaOon (in billion $)

small < 2 | medium 2-‐10 | large > 10

4.  IdenOfying Growth and Value ClassificaOon using P/E RaOo ClassificaOon using P/B RaOo

5.  EvaluaOng Performance The getReturns() funcOon

6.  Plolng and comparing Programming in R 11

Cleaning Data

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gvkey (Company ID)

fyear

1234 1999

1234 2000

1234 2001

1234 2002

1234 2003

1234 2004

5678 2001

5678 2002

5678 2003

rawCompNames: Aggregate( ... , by=list( ... $gvkey), FUN=sum):

gvkey (Company

ID)

Sum(gvkey) Sum(fyear)

1234 4936 12009

5678 10712 6006

-‐> All Companies having a sum(fyear) == 12009 Have complete data. All others have incomplete data

Time series not matching

Cleaning Data

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§  Keep selected Companies in the Dataset §  Companies with complete quarterly observaOons for the years 1999-‐2014 §  Calculate the maximal „quarterly number“ in the case of complete observaOons §  Calculate the „quarterly number“ for each company §  Compare and Select

Processing Data

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AllMarketData

CategorizaOon

CategorizaOon

CategorizaOon

SmallCap

MidCap

LargeCap

Small Growth

Small Value

Mid Growth

Mid Value Large Growth

Large Value

Processing Data

§  Read the cleaned Data §  Use the cleaned Data to calculate the P/E RaOo §  Use the cleaned Data to calculate the P/B RaOo §  (for later CategorisaOon into Growth and Value Stocks)

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Classify based on capitaliza=on

§  Use the first observaOon for every company, the first quarter in the first year and

check for NAs (Just to be sure)

Create subset SmallCap: companies with capitalisaOon < 2 billion §  The same procedure is repeated for medium sized companies (midCap 2-‐10 billion)

and large companies (largeCap > 10billion)

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Iden=fy Growth and Value

§  Using the subset smallCap §  order the company entries in this dataset according to the value of the P/E RaOo §  Determine Growth Stocks for the predefined (staOc variable) percenOle §  Determine Value Stocks for the predefined percenOle §  Same procedure for the subsets midCap and largeCap

Same procedure using P/B for all subsets smallCap, midCap and largeCap

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Evaluate Performance

§  Evaluate the performance of each of the six category of

Stocks using the funcOon getReturns()

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The getReturns() func=on

Clean the data one last Ome -‐> cleanDataFinally() Calculate the market weighted returns of individual stocks

Calculate the total return of the enOre collecOon of stocks

returnStock =P(t)

P(t = 0)×individualMarketCaptotalMarketCap

returnPortfolio = returnStockAllCompanies∑

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Company idenOfier Price Market Cap

InputData IndividualCompanyData

Company 1 (1050)

Company 2 (1257)

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returnStock =P(t)

P(t = 0)×individualMarketCaptotalMarketCap = individualMarketCap∑

+ + Company 1 Loop 1

Company 2 Loop 2

Company 3 Loop 3

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returnStock =P(t)


Loop 1 Loop 2 Loop 3 Loop 4 Loop 5 returnIndexBasePrice

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returnStock =P(t)


returnPortfolio = returnStockAllCompanies∑

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CleanDataFinally()

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§  Use the result from previous cleaning and account for missing values „NAs“ §  Replace „NAs“ with the next following value in the row if possible -‐> EsOmateion of the

NA with the next closest data point in Ome

Results & Conclusion

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In most of the Omes Value stocks clearly outperform growth stocks -‐> An investor in the past (1999) should have persued an investment strategy in Value stocks in order to gain the highest returns. However: 1.  The difiniOons of Value and Growth stocks may differ from person

to person 2.  Also the cleaning processes could have eliminated material data

for the analysis 3.  The analysis is of historical data and thus is to be used cauOously

for predicOons. The analysis shows that value outperforms growth in a persiod of 1999 – 2014.

Sources

# htp://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html # WDRS selecOon # htp://stocks.about.com/od/invesOngphilisophies/a/Groval061405.htm # htp://stackoverflow.com/quesOons/3695677/how-‐to-‐find-‐common-‐elements-‐from-‐mulOple-‐vectors # htp://stackoverflow.com/quesOons/12389318/dollar-‐sign-‐before-‐a-‐variable # htp://stackoverflow.com/quesOons/14860078/plot-‐mulOple-‐lines-‐data-‐series-‐each-‐with-‐unique-‐color-‐in-‐r # htp://stackoverflow.com/quesOons/2564258/plot-‐2-‐graphs-‐in-‐same-‐plot-‐in-‐r # htp://stackoverflow.com/quesOons/14883238/adding-‐simple-‐legend-‐to-‐plot-‐in-‐r

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