A comparison of some aspects of the U.S. and Japanese equity markets

Bloch, M.; Guerard, John B.; Markowitz, Harry M.; Todd, Peter A.; Xu, Ganlin

doi:10.1016/0922-1425(93)90025-y

Cited by 75 publications

(47 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 12-month smoothing is consistent with the four-quarter smoothing in Bloch et al [12]. While EP and BP variables are significant in explaining returns, the majority of the forecast performance is attributable to other model variables, namely the relative earnings-to-price, relative cashto-price, relative sales-to-price, price momentum, and earnings forecast variables.…”

Section: Regression-based Expected Returns Modelingsupporting

confidence: 82%

“…The Markowitz team used relative variables, defined as the ratio of the absolute fundamental variable ratios divided by the 60-month averages of the fundamental variables. Bloch et al [12] reported a set of ∼200 simulations of United States and Japanese equity models. The models produced outof-sample statistically significant excess returns in the portfolios.…”

Section: Regression-based Expected Returns Modelingmentioning

confidence: 99%

“…Bloch et al [12] employed the Beaton-Tukey Bisquare procedure to weight observations identified as outliers. There are several methods that one can use to identify influential observations or outliers, which are often referred to as an analysis of influential observations 3 .…”

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: 98%

“…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%

See 4 more Smart Citations

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

Guerard

2016

Front. Appl. Math. Stat.

View full text Add to dashboard 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.

show abstract

Section: Regression-based Expected Returns Modelingsupporting

confidence: 82%

Section: Regression-based Expected Returns Modelingmentioning

confidence: 99%

Section: Regression-based Expected Returns Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Regression-based Expected Returns Modelingmentioning

confidence: 99%

See 3 more Smart Citations

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

Guerard

2016

Front. Appl. Math. Stat.

View full text Add to dashboard Cite

show abstract

Risk–Return Analysis

Markowitz¹

2010

Encyclopedia of Quantitative Finance

View full text Add to dashboard Cite

A risk–return analysis analyzes the trade‐off of some measure of risk and some measure of return. Almost universally, the measure of return used is the mean or expected value. Various measures of risk have been proposed including variance (or, equivalently, standard deviation), semivariance (which is the same as variance, but only considers the downside), absolute deviation, and probability of loss. This article considers the merits of each, how trade‐off curves (also known as efficient frontiers ) of these are computed, and how one gets (or can get) data for these.

show abstract

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

Guerard

Markowitz²

2018

Fin Plan Rev

View full text Add to dashboard 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.

show abstract

A comparison of some aspects of the U.S. and Japanese equity markets

Cited by 75 publications

References 15 publications

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

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

Risk–Return Analysis

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

Contact Info

Product

Resources

About