Efficient global portfolios: Big data and investment universes

Guerard, John B.; Rachev, Svetlozar T.; Shao, Barret Pengyuan

doi:10.1147/jrd.2013.2272483

Cited by 53 publications

(46 citation statements)

References 34 publications

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“…The authors have stated historically, that the low price-to-earnings of Graham and Dodd [20] and Williams [21], model is a very substantial benchmark to out-perform. The authors rely upon three levels of testing, as noted in Guerard et al [13]; the first level is the information coefficients, ICs, where ranked subsequent stock returns are regressed vs. the ranked strategy, and the slope coefficient is the information coefficient (and its corresponding t-statistic determines its statistical significance); the second level is a full efficient frontier by varying the portfolio lambda, or level of risk-aversion; and the third level is the Markowitz and Xu [84] Data Mining Corrections test. We report the first two level tests in this analysis.…”

Section: The Data and Empirical Resultsmentioning

confidence: 99%

“…Guerard et al [13] applied the USER Model a large set of global stocks for the 1997-2011 time period. Guerard et al [13] refereed to the global expected returns model as the GLER Model. We can estimate an expanded stock selection model to use as an input of expected returns in an optimization analysis.…”

Section: Regression-based Expected Returns Modelingmentioning

confidence: 99%

“…One eliminates the latent vectors with non-predictive multicollinearity. Guerard et al [13] reported the effective ness of the WLRR models that made extensive use of outlier-induced collinearities, originally formulated in Webster et al [43], Gunst and Mason [44], Mason and Gunst [45], and Carrillo-Gambos and Gunst [46]. Maronna et al [9] surveyed robust statistics.…”

Section: Regression-based Expected Returns Modelingmentioning

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: 97%

“…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 4 more Smart Citations

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

Guerard

2016

Front. Appl. Math. Stat.

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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.

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Section: The Data and Empirical Resultsmentioning

confidence: 99%

Section: Regression-based Expected Returns Modelingmentioning

confidence: 99%

Section: Regression-based Expected Returns Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Guerard

2016

Front. Appl. Math. Stat.

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

Guerard

Markowitz²

2018

Fin Plan Rev

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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.

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Investing in Global Equity Markets

Guerard¹,

Deng²,

Gilliam³

et al. 2020

Wilmott

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In this analysis of the risk and return of stocks in global markets, we build several models of stock selection and create optimized portfolios to outperform a global benchmark. We apply several applications of robust regression techniques in producing stock selection models and several Markowitz‐based optimization techniques in portfolio construction in various global stock universes. We test separate Japanese and Chinese stock selection models because they are large markets, with large global benchmark weights or are frequently in the news. 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 (3) our models pass data mining tests such that the models produce statistically significant asset selection. We updated US, Non‐US, and emerging markets stock models during the 2003 through November 2018 time period, and report a continued highly statistically significant risk‐return trade‐off.

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Efficient global portfolios: Big data and investment universes

Cited by 53 publications

References 34 publications

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

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

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

Investing in Global Equity Markets

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