Investing with Momentum: The Past, Present, and Future

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: A Review Fundamental Variables and Regression-based Expectedmentioning

confidence: 99%

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

Front. Appl. Math. Stat.

Saxena

2018

“…We use the Sungard APT risk model, which uses principal components in its estimation, in this analysis. The reader is referred to Guerard et al [15] for evidence supporting the APT risk model and portfolio optimization techniques. An extensive review of factor risk models can be found in Connor et al [82].…”

Section: Constructing Mean-variance Efficient Portfoliosmentioning

confidence: 99%

“…The models produced outof-sample statistically significant excess returns in the portfolios. Guerard et al [15] extended a stock selection model originally developed and estimated in Bloch et al [12] by adding price momentum variable, taking the price at time t-1 divided by the price 12 months ago, t-12, denoted PM, and the consensus (I/B/E/S) analysts' earnings forecasts and analysts' revisions composite analysts' efficiency variable (CTEF) to the stock selection model. Guerard [26] used the CTEF variable that is composed of forecasted earnings yield, EP, revisions, EREV, and direction of revisions, EB, identified as breadth, as created in Guerard et al [27] 2 .…”

Section: Regression-based Expected Returns Modelingmentioning

confidence: 99%

“…Guerard et al [15] referred to the stock selection model as a United States Expected Returns (USER) Model. Guerard et al [13] applied the USER Model a large set of global stocks for the 1997-2011 time period.…”

Section: Regression-based Expected Returns Modelingmentioning

confidence: 99%

“…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

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.