Value Factors Do Not Forecast Returns for S&P 500 Stocks

This web page is associated with a paper titled Value Factors Do Not Forecast Returns for S&P 500 Stocks. The work described in this paper investigates how effective corporate value factors can be in selecting S&P 500 stocks for an investment portfolio. As the title of this web page suggests, the answer is "not very effective".

The PDF for the paper can be found here.

This paper grew out of my Masters thesis presentation for my Masters degree in Computational Finance and Risk Management through the University of Washington. I gave my thesis presentation on November 22, 2013 and was awarded a Masters degree in December 2013. I continued to refine the work that I presented, which resulted in this paper.

This paper is reproducible research. The paper is written using Knitr, which combines R and the typesetting language LaTex. Using R and RStudio the PDF for the paper can be regenerated from the document source and data. The code to generate every table and diagram in the document is included in the Knitr source code.

The data used in this paper consists of approximately fifteen years of corporate quarterly report data. Through my Masters program I had access to the Wharton Research Data Service (WRDS). The CRSP/Compustat data sets, which I used in this work, are available from WRDS. Unfortunately redistribution of this data is prohibited, so I cannot include the data here.

Working with WRDS and the CRSP/Compustat data and cleaning it up so that it can be used in historial back tests is a very time consuming process. I have tried to document my work with this data so that the data set can be reproduced by anyone with access to this data. See The Wharton Research Data Service (WRDS) data set and Factor Model Factors

Open Source Corporate Value Factor Data
The Quandl site publishes corporate factor (fundamental) data for approximately 15,000 stocks. The fundamental data can be accessed via a Web API. Quandl does not have as much history as WRDS, but for the time period where they have data they are an attractive alternative to the CRSP/Compustat data which is both costly and missing values.

Knitr and R Source

Ian Kaplan
March 2014
Last revised:

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