Earnings forecasting in a global stock selection model and efficient portfolio construction and management

Front. Appl. Math. Stat.

Saxena

2018

The allocation of scarce economic resources so as to maximize societal good is at the very core of human economic development. The key contribution of Markowitz [1] was to view this age-old activity in a scientifically rigorous manner, and bring three key elements to the forefront, namely, risk, return and correlations. Since the publication of Markowitz' seminal paper, investment professionals have expensed significant resources in identifying, understanding, and monetizing new sources of uncorrelated returns. This paper moves forward that narrative by focusing on Emerging Markets (EM) and demonstrating the effectiveness of earnings acceleration factors in building significantly better mean-variance optimized portfolios.

Section: Why Emerging Markets?mentioning

confidence: 99%

A Case Study of Forecasted Earnings Acceleration and Stock Selection in Global and Emerging Stock Markets

Front. Appl. Math. Stat.

Saxena

2018

“…The Axioma Statistical Risk Model, World-Wide Equity Risk Factor Model, AX-WW2.1, estimates 15 principal components to measure risk. See Guerard et al [14] for a comparison of Axioma Fundamental and statistically based risk models. Guerard et al [14] reported that the statistical model dominated the fundamental risk model in producing a higher set of returns for a given level of risk.…”

Section: Constructing Mean-variance Efficient Portfoliosmentioning

confidence: 99%

“…See Guerard et al [14] for a comparison of Axioma Fundamental and statistically based risk models. Guerard et al [14] reported that the statistical model dominated the fundamental risk model in producing a higher set of returns for a given level of risk. We use the Sungard APT risk model, which uses principal components in its estimation, in this analysis.…”

Section: Constructing Mean-variance Efficient Portfoliosmentioning

confidence: 99%

“…The regression techniques in this study include the Beaton-Tukey [1] Bisquare weighting procedure that produces regression weights for data 1 . The reader is referred to Bloch et al [12], Guerard et al [13], and Guerard et al [14]. These factors are statistically significant in univariate models (tilts) and in multiple-factor models (MFM).…”

Section: Introductionmentioning

confidence: 98%

“…These factors are statistically significant in univariate models (tilts) and in multiple-factor models (MFM). We briefly review the applied U.S. and Global equity investment research in Guerard et al [15], Guerard et al [13], and Guerard et al [14]. We test whether a meanvariance optimization technique using the portfolio variance as the relevant risk measure dominates the risk-return tradeoff curve using a variation of the optimization model that emphasizes systematic (or market) risk.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investing in Global Markets: Big Data and Applications of Robust Regression

Front. Appl. Math. Stat.

2016

Self Cite

In this analysis of the risk and return of stocks in global markets, we apply several applications of robust regression techniques in producing stock selection models and several optimization techniques in portfolio construction in global stock universes. We find that (1) that robust regression applications are appropriate for modeling stock returns in global markets; and (2) mean-variance techniques continue to produce portfolios capable of generating excess returns above transactions costs and statistically significant asset selection. We estimate expected return models in a global equity markets using a given stock selection model and generate statistically significant active returns from various portfolio construction techniques.Keywords: outliers, big data, robust regression, portfolio selection, portfolio management INVESTING IN GLOBAL MARKETS: BIG DATA, OUTLIERS, AND ROBUST REGRESSIONIn this study, we apply robust regression techniques to model stock returns and create stock selection models in a very large global stock universe. We employ Markowitz portfolio construction and optimization techniques to a global stock universe. We estimate expected return models in the global market using a given stock selection model and generate statistically significant active returns from various portfolio construction techniques. In the first section, we introduce the reader to the risk and return trade-off analysis. In second section, we introduce the reader to modeling expected returns and make extensive use of robust regression techniques. In the third section, we examine the relationship of the (traditional) Markowitz mean-variance (MV) portfolio construction model with a fixed upper bound on security weights. In fourth section, we discuss portfolio construction and simulation, and present the empirical results. In fifth section, we offer conclusions and a summary. We report that mean-variance techniques using robust regression-created expected returns continue to produce portfolios capable of generating excess returns above transactions costs and statistically significant asset selection in a global stock market offer the potential for high returns relative to risk. The stock selection model used in this analysis is the "public form" of the McKinley Capital Management proprietary model. The public form model produces highly statistically significant asset selection and similar factor risk exposures to our proprietary model.

The existence and persistence of financial anomalies: What have you done for me lately?

Markowitz²

2018

Fin Plan Rev

Self Cite

The purpose of this study is to document the existence, persistence, and effectiveness of publicly available variables linked to financial anomalies during the 1979–1999 time period with particular emphasis on earnings forecasts. It then tests whether these variables have held up through the 2003–2017 time period. We report three results: (a) many of the reported financial anomalies published in the 1979–1999 time period maintain their statistically significant active (or excess) returns; (b) the anomalies are larger in non‐U.S. markets than in the U.S.; and (c) reasonable transactions costs do not destroy the excess returns.