Earnings forecasting in a global stock selection model and efficient portfolio construction and management
Introduction
Expected returns on assets are a key input in the mean–variance portfolio selection process. One can estimate models of expected returns by using earnings expectations data, price momentum variables, and reported financial data. In this analysis, we construct and estimate a global stock selection model by using these data for the period from January 1997 to December 2011. Earnings expectations information has been being rewarded in global stocks for the past fifteen years or so, and we expect it to continue to be the primary variable driving global stocks. Despite the recent volatility of the momentum factor, momentum is still associated statistically with security returns, and can be used with other factors to rank stocks for purchase. A composite model of earnings expectations information, value, and momentum factors is estimated for global stocks in order to identify potentially mispriced stocks. In addition, the regression-weighting of factors enhanced the information coefficients relative to equally-weighted factors. Analysts’ forecast and momentum variables are dominant in the regression-based composite model of expected returns. We create portfolios for the period January 1997–December 2011, and simulate portfolio returns which we compare with a set of global stock benchmark returns.
We begin with a review of the literature on stock selection models in Section 2. In Section 3, we discuss the testing of a composite model of stock selection, incorporating earnings forecast information. We use an APT-based multi-factor risk model to create efficient portfolios in Section 4. In Section 5, we present and estimate the data mining corrections test. In Section 6, we discuss the relevance of the “alpha alignment factor” and show its relevance. Section 7 presents our summary and conclusions.
Section snippets
A literature review of expected returns modeling and stock selection models
There are many different approaches to security valuation and the creation of expected returns. One seeks to select expected returns inputs that are associated statistically with stock returns. The correlation coefficient between the strategy and the subsequent returns is referred to as the information coefficient, IC (Grinold & Kahn, 1999). The expected returns input normally consists of variables that are denoted anomalies, which can be used as inputs to the portfolio construction process in
Building and testing stock selection models
How does one develop, estimate, and test a global stock selection model? We use the Guerard et al. (2013) database of global stocks included on the FactSet database during the period January 1997–December 2011. The number of stocks grows to approximately 16,000 during the period 1997–2011. Guerard et al.’s (2013) universe was restricted to global stocks that were covered by at least two analysts, which reduced the number to approximately 7000–8000 stocks. One can survey the academic and
Efficient APT portfolio construction
The mean–variance (MV59) portfolio construction and management can be summarized as: where is the expected return vector, is the variance–covariance matrix, is the portfolio weights, and is the risk-return tradeoff parameter. The estimation of is usually done by a multifactor model, in which the individual stock return of security at time , dropping the subscript for time, may be written like this:
The nonfactor, or asset-specific, return
A further test of data mining corrections
In the practical world of Wall Street, it is conventional wisdom to cut your historical backtested excess returns in half; that is, if your backtested excess return (the portfolio geometric mean return less the geometric mean of the benchmark) was 6%, or 600 basis points, an investment manager and/or a client might expect 3% excess returns in the future. How do we justify this cutoff?
Markowitz and Xu (1994) proposed three statistical models for estimating the cutoff, which are close to half.
In
The alpha alignment factor: an application to global earnings forecasting
Several practitioners have decided to perform a “post-mortem” analysis of mean–variance portfolios, attempted to understand the reasons for the deviation of ex-post performances from ex-ante targets, and used their analysis to suggest enhancements to mean–variance optimization inputs, in order to overcome the discrepancy. Lee and Stefek (2008) and Saxena and Stubbs (2012) define this as a factor alignment problem (FAP), which arises as a result of the complex interactions between the factors
Conclusions
Investing based on analysts’ expectations, fundamental data, and momentum variables is a good investment strategy in the long-run. Stock selection models often use momentum, analysts’ expectations, and fundamental data. We find support for composite modeling using these sources of data, as well as evidence supporting the use of APT multi-factor models for portfolio construction and risk control. We develop and estimate three levels of testing for stock selection and portfolio construction. The
Acknowledgments
The authors appreciate the comments of Martin J. Gruber, the referee, Anureet Saxena, and Robert Stubbs. Research conversations with Bernell Stone and Mustafa Gultekin enhanced our analysis. Vishnu Anand and Chris Martin of Axioma were extremely helpful in helping the authors use Axioma, particularly Chris’ work on limiting the numbers of securities (names) in the portfolios. Any errors remaining are the responsibility of the authors.
John B. Guerard is Director of Quantitative Research at McKinley Capital Management, in Anchorage, Alaska. He earned his AB in Economics from Duke University, his MA in Economics from the University of Virginia, MSIM from the Georgia Institute of Technology, and Ph.D. in Finance from the University of Texas, Austin. Mr. Guerard has published several monographs, including Quantitative corporate finance (Springer, 2007, with Eli Schwartz) and Introduction to financial forecasting in investment
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John B. Guerard is Director of Quantitative Research at McKinley Capital Management, in Anchorage, Alaska. He earned his AB in Economics from Duke University, his MA in Economics from the University of Virginia, MSIM from the Georgia Institute of Technology, and Ph.D. in Finance from the University of Texas, Austin. Mr. Guerard has published several monographs, including Quantitative corporate finance (Springer, 2007, with Eli Schwartz) and Introduction to financial forecasting in investment analysis (Springer, 2013), and edited The handbook of portfolio construction: contemporary applications of Markowitz techniques (Springer, 2010). John serves an Associate Editor of the Journal of Investing and the International Journal of Forecasting.
Harry Markowitz is the Chief Architect at Guidedchoice.com in San Diego, CA, and a quantitative research scientific advisor at McKinley Capital Management LLC in Anchorage, AK. He is a Nobel Laureate and the father of modern portfolio theory, and was named “Man of the Century” by Pensions and Investments magazine. He has received the prestigious John von Neumann Theory Prize for his work in portfolio theory, sparse matrix techniques, and the SIMSCRIP programming language. Dr. Markowitz earned his Ph.D. from the University of Chicago.
GanLin Xu is the Chief Technology Officer of Guidedchoice.com, where he leads a team of finance, economics, and mathematics experts to develop Guidedchoice’s financial methodology, software, and systems. Dr. Xu earned his Ph.D. from Carnegie Mellon University. His research on portfolio theory has been published in various journals. He is a quantitative research scientific advisor at McKinley Capital Management LLC in Anchorage, AK.