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"Guerard, John"
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Introduction to financial forecasting in investment analysis
\"Forecasting--the art and science of predicting future outcomes--has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions.
Book
Mean-variance and mean-ETL optimizations in portfolio selection: an update
In this research update, we apply the Mean-Variance (MV) and Mean-Expected Tail Loss (ETL) portfolio optimization techniques on earnings forecasting and robust regression-based composite models. A time series model with multivariate normal tempered stable (MNTS) innovations is applied to generate the out-of-sample scenarios for the portfolio optimization. We report that (1) a composite variable of analysts’ forecasts, revisions, and direction of analysts’ revisions continues to produce value in portfolio construction; (2) robust regression-based models continue to produce meaningful active returns; and (3) the Mean-Variance and Mean-ETL portfolio optimizations produce statistically significant active returns, passing the Markowitz and Xu (Journal of Portfolio Management 21:1–60, 1994) data mining corrections test.
Journal Article
A further analysis of robust regression modeling and data mining corrections testing in global stocks
In this analysis of the risk and return of stocks in global markets, we build a reasonably large number of stock selection models and create optimized portfolios to outperform a global benchmark. We apply robust regression techniques, LAR regression, and LASSO regression modeling to estimate stock selection models. Markowitz-based optimization techniques is used in portfolio construction within a global stock universe. We apply the Markowitz–Xu data mining corrections test to a global stock universe. We find that (1) robust regression applications are appropriate for modeling stock returns in global markets; (2) weighted latent root regression robust regression techniques work as well as LAR and LASSO-Regressions in building effective stock selection models; (3) mean–variance techniques continue to produce portfolios capable of generating excess returns above transactions costs; and (4) our models pass several data mining tests such that regression models produce statistically significant asset selection for global stocks. Recent Sturdy-Regression modeling technique may offer the greatest potential for further research for statistically based stock selection modeling.
Journal Article
Earnings forecasting and mean–variance efficient portfolios in the United States
Guerard and Takano (J Investing 1, 48–54, 1992), Guerard et al. (Ann Oper Res 45, 91–108, 1993) and Bloch et al. (Jpn World Econ 5: 3–26, 1993) reported mean–variance efficient portfolios for the Japanese and U.S. equity markets that were composed 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. The optimized portfolios produced higher Sharpe Ratios than the benchmarks in Japan and the United States; the U.S. survivor-biased-free Sharpe Ratio was 1.20 whereas the benchmark was 0.96. Markowitz and Xu (J Portfolio Manag 21, 60–69. 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. We report updated US portfolio results for the 1995–2022 period that verifies the Guerard et al. (Ann Oper Res 45, 91–108, 1993) research and demonstrates that the Guerard and Markowitz post-publication, out-of-sample.
Journal Article
Financial Anomalies in Portfolio Construction and Management
Financial anomalies have been studied in the United States. Recent evidence suggests that financial anomalies have diminished in the United States and possibly in non-US portfolios. Have the anomalies changed, or are they persistent? Have historical and earnings forecasting data been a consistent, and highly statistically significant, source of excess returns? The authors test many financial anomalies of the 1980s and 1990s and report that several models and strategies continue to produce statistically significant excess returns. The authors test a large set in US and non-US markets over the past 25 years. They report that many of these fundamentals, earnings forecasts, revisions, and breadth and momentum strategies maintained their statistical significance during the 1996–2020 time period. Moreover, the earnings forecasting model and robust regression estimated composite model excess returns are greater in non-US and global markets than in US markets. TOPICS: Security analysis and valuation, global markets, statistical methods, performance measurement Key Findings ▪ The authors verify the continuity of financial anomalies in the post-publication period. ▪ The authors use composite modeling methodology to estimate expected returns. ▪ The authors use robust regression to address the outliers and issues in the data.
Journal Article
Automatic time series modeling and forecasting: a replication case study of forecasting real GDP, the unemployment rate and the impact of leading economic indicators
We test and report on time series modelling and forecasting using several US. Leading economic indicators (LEI) as an input to forecasting real US. GDP and the unemployment rate. These time series have been addressed before, but our results are more statistically significant using more recently developed time series modelling techniques and software. In this replication case study, we apply the Hendry and Doornik automatic time series PC-Give (AutoMetrics) methodology to the well-studied macroeconomic series, US. real GDP and the unemployment rate. The Autometrics system substantially reduces regression sum of squares measures relative to traditional variations on the random walk with drift model. The LEI are a statistically significant input to real GDP. A similar conclusion is found for the impact of the LEI and weekly unemployment claims series leading the unemployment rate series. We tested the forecasting ability of best univariate and best bivariate models over 60- and 120-period rolling windows and report considerable forecast error reductions. The adaptive averaging autoregressive model forecast ADA-AR and the adaptive learning forecast, ADL, produced the smallest root-mean-square errors and lowest mean absolute errors. Our results are greatly supportive of the significance for modeling and forecasting of the suggested input variables and they imply considerable improvements over all traditional benchmarks.
Journal Article
Global portfolio construction with emphasis on conflicting corporate strategies to maximize stockholder wealth
This study addresses stock selection modeling and portfolio construction and implementation in global and U.S. markets in the context of multi-objectives optimization framework. We will show how forecasted earnings acceleration factors can enhance returns in global and U.S. stock markets. We construct Markowitz portfolios for Global and U.S. domestic Markets that offer superior returns-to-risk ratios, relative to domestic portfolios. We show how stock repurchases and corporate exports can be estimated and implemented as the third objective to generate statistically significant excess returns in global and U.S. stock markets. It is particularly interesting to note the conflicting corporate strategies’ impacts on stockholder wealth.
Journal Article
Warning: SRI Need Not Kill Your Sharpe and Information Ratios—Forecasting of Earnings and Efficient SRI and ESG Portfolios
Using an earnings forecasting model is useful and produces statistically significant outperformance in US stock selection. This study finds that the incorporation of environmental, social, and governance (ESG) criteria can potentially enhance stockholder returns, holding risk constant under reasonable assumptions. The novel approach here uses a normalization of ESG strengths and weaknesses ratings, applied in both robust simply weighted and realistic optimized portfolio settings. The study confirms a now-classical no-cost result for the overall ESG criteria and—with human rights and corporate governance criteria—shows that SRI and ESG information can enhance portfolio returns in certain implementations. Thus, SRI and ESG investors may not necessarily have to expect lower portfolio returns and Sharpe ratios under all circumstances. TOPICS: ESG investing, portfolio theory, portfolio construction, performance measurement Key Findings • ESG measures can be used in conjunction with a statistically significant earnings forecasting efficiency tilt so that portfolio standard deviation and tracking errors decrease. • Decreased portfolio standard deviation and tracking error increase portfolio Sharpe and information ratios. Return-to-risk ratios rise with KLD SRI and ESG variables. • The incorporation of KLD human rights factors, including those related to indigenous peoples, and the inclusion of overall KLD concerns increase Sharpe ratios and information ratios. No longer are researchers merely concerned with asking, “Is there a cost to being socially responsible in investing?” across the board.
Journal Article