John B. Guerard

Is There a Cost to Being Socially Responsible in Investing

JOHN B. GUERARD, JR. is senior vice president and director of quantitative research at Vantage Global Advisors in New York, where he estimates equity selection models. He was previously a f m l t y member at the University of Virginia and Lehigh University, and in the quantitative investment research group at Drexel, Burnham, Lambert and Daiwa Security Tnst Company. Mr. Guerard holds an A . B . in economicsjom Duke University, an M.A. in economicsfrom the University of Virginia, and a Ph. D. injnatuefrom the University of Texas. ,

Investing with Momentum: The Past, Present, and Future

Stock selection models often use momentum, analysts’ expectations, and fundamental data. We found support for composite modeling using these sources of data for U.S. equities during the 1998–2007 period. We found additional evidence to support the use of Barra and APT multifactor models for portfolio construction and risk control. Three levels of testing of stock selection and portfolio construction models were developed and estimated. We created portfolios for the January 1998–December 2007 period. We report three conclusions: 1) Momentum investing was rewarded by the market in the United States from December 1928–December 2007; 2) Momentum can be combined with reported fundamental data—such as earnings, book value, cash flow, and sales—and analysts’ earnings forecast revisions in a stock selection model to identify mispriced securities; 3) the portfolio returns of the multifactor risk-controlled portfolio returns allow us to reject the data mining corrections test null hypothesis.

The development of efficient portfolios in Japan with particular emphasis on sales and earnings forecasting

In this study, we show that earnings forecasting creates an index-tracking portfolio that dominates the historical model trade-off curve. We find that using Toyo Keizai earnings forecasts improves geometric means by over 300 basis points compared to the historical model. Weighted latent root regression is used in this study to create portfolios that have outperformed the Japanese market in backtest and in real-time performance.

Quantitative Stock Selection in Japan and the United States: Some Past and Current Issues

Stock selection models have been, and can be, effectively employed in Japan to deliver excess returns. In 1992, in the initial year of this Journals publication, Guerard and Takano (1992) reported mean-variance efficient portfolios for the Japanese and U.S. equity markets, and show that the use of a regression-weighted composite model of earnings, book value, cash flow, sales, and their relative variables outperformed their respective equity benchmarks by approximately 400 basis points annually. Markowitz and Xu (1994) tested the composite model strategy and found that its excess returns were statistically significant from a variety of models tested, and the composite model strategy was not the result of data mining. In this analysis, the Guerard and Takano stock selection models are updated through 2001 using a similar Japanese-only database. The sophisticated regression model continues to produce the highest information coefficients in Japan and the U.S. We include a consensus analyst derived forecast, revisions, and breadth variable, and show its contribution to stock selection. Several Japanese-only analyst forecasting model results are similar to U.S.-only results. The use of the Global Compustat database allows the construction of global variables that produce similar stock selection models. The effectiveness of these quantitative models has not lessened during the 1992-2003 period.

Global Stock Selection Modeling and Efficient Portfolio Construction and Management

Stock selection models often use analysts’ expectations, momentum, and fundamental data. The authors found support for composite modeling using these sources of data for global stocks from 1997 through 2011. They found additional evidence to support the use of SunGard APT multifactor models for portfolio construction and risk control. Three levels of testing of stock selection and portfolio construction models are developed and estimated. They create portfolios for January 1997 through December 2011. They report three conclusions: 1) Analysts’ forecast information has been rewarded by the global market from January 1997 through December 2011; 2) analysts’ forecasts can be combined with reported fundamental data, such as earnings, book value, cash flow, and sales, and momentum, in a stock selection model to identify mispriced securities; and 3) the portfolio returns of the multifactor risk-controlled portfolios allow the rejection of the null hypothesis for the data-mining corrections test.

Stock-Selection Modeling and Data Mining Corrections: Long-Only Versus 130/30 Models

This study addresses several aspects of stock selection, portfolio construction, and data mining corrections and hypothesis testing of excess returns. Mean-variance, equally actively weighted, tracking-error-at-risk, and 130/30 portfolios are created and tests are conducted to find out whether the excess returns of these portfolios are statistically different from the average models that could have been used to build portfolios. Knowledge of earnings forecasts is an important input to the portfolio construction process. The portfolios constructed fulfill a global growthmandate, and the strategies work in EAFE plus Canada and the U.S. universes. The excess returns produced by the models are statically different from the average models used. The 130/30 strategy dominates the long-only strategy. Moreover, evidence is presented to show that these portfolios can be implemented and produce excess returns in the world of business.

Further Analysis of Efficient Portfolios with the USER Data

In this study, we show that earnings forecasting and price momentum strategies complement fundamental stock selection strategies such that a composite model can be effectively implemented using both enhanced index-tracking portfolios and traditional mean–variance portfolios. The mean–variance optimization model produces statistically significant asset selection portfolios that dominate less-aggressive enhanced index-tracking portfolio construction models. We show that portfolios that use tracking error in risk optimization techniques produce a superior risk–return trade-off than traditional mean–variance optimization techniques. A portfolio manager should use a data mining corrections test to minimize the probability that the models selected resulted from a near-random process.

The Development of Mean-Variance-Efficient Portfolios in Japan and the U.S.

Stock selection models have been, and can be, effectively employed in Japan to deliver excess returns. In 1992, in the initial year of this Journal’s publication, Guerard and Takano (1992) reported mean-variance efficient portfolios for the Japanese and U.S. equity markets, and show that the use of a regression-weighted composite model of earnings, book value, cash flow, sales, and their relative variables outperformed their respective equity benchmarks by approximately 400 basis points annually. Markowitz and Xu (1994) tested the composite model strategy and found that its excess returns were statistically significant from a variety of models tested, and the composite model strategy was not the result of data mining. Guerard (2006) updated the Guerard and Takano stock selection models are updated through 2003 using a similar Japanese-only database. The sophisticated regression model continued to produce the highest information coefficients in Japan and the US. Guerard included a consensus analyst derived forecast, revisions, and breadth variable, and show its contribution to stock selection. Several Japanese-only analyst forecasting model results are similar to US-only results. The use of the Global Compustat database allowed the construction of global variables that produce similar stock selection models. The effectiveness of these quantitative models had not lessened during the 1992-2003 period. In this update to celebrate 25 years of the Journal of Investing, the author uses a commercially available global database, FactSet, for the 2002-June 2016 time period to address stock selection composite models, mean-variance efficient portfolios in Japan and the U.S., and reports three results: (1) the original stock selection continues to be effective in Japan and the U.S. in 2002-6/2016; (2) the mean-variance efficient portfolios outperformed in Japan and the U.S. in 2002-6/2016; and (3) the Guerard and Takano stock selection model is not the result of data mining. The author reports three additional results: (1) the weighted latent root regression Guerard and Takano can be enhanced by using the Tukey optimal influence function; (2) the mean-variance efficient portfolios failed in Japan and the U.S. in 1996-2000 with the PCAP and Global Compustat databases and 2006-2007, 2012-2013, and YTD 2016 with the FactSet databases, but work in the vast majority of the years; and (3) the Guerard and Takano portfolios outperformed in 14 of 17 years in backtest, 82.4%, of the yeas whereas the Japan model portfolios outperformed in 9 of 14.5 years, or 66.7%, of the years, post-publication and the U.S. model outperformed in 9.5 of 13.5 years, or 70%, post-publication, respectively!

Why Your Next Japanese Portfolio Should be Market-Neutral — and How

is a principal ofAPT Investment Management, Inc., in New York. He was prm’ously professor o f economics, econometrics, and Jinance at Northwestern University Kellogg Graduate School o f Management, as well as senior vice president o f the New York Futures Exchange. Mr. Blin has sewed as a consultant to governments, leading corporations, securities Jirms, and exchanges in the US. and abroad. He holds a 13h.D.from Purdue University.

Forecasting earnings composite variables, financial anomalies, and efficient Japanese and U.S. portfolios

Abstract In this study we address the creation of efficient portfolios with particular emphasis on earnings forecast and value strategies in Japan and the U.S. We show that market-neutral portfolios produce much higher returns for a given level of risk than merely creating efficient (long) portfolios. Thus use of a multi-factor risk model is useful for the 1988–1997 period for creating market-neutral portfolios and one can create the market-neutral equity selection and portfolio construction models. We find that the inclusion of consensus I/B/E/S forecasts, revisions, and momentum substantially increases the market-neutral portfolio average annual returns of Japanese and U.S. portfolios. A value-only model works quite well in a Japanese market-neutral strategy and the use of I/B/E/S forecasts significantly enhances the returns; however, in the U.S., we find that the I/B/E/S forecasts are necessary for an effective market-neutral strategy.