Earnings forecasting in a global stock selection model and efficient portfolio construction and management

Stock selection models often use analysts’ expectations, momentum, and fundamental data. We found support for composite modeling using these sources of data for global stocks during 1997-2011. We found additional evidence to support the use of SunGard APT and Axioma multi-factor 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 January 1997-December 2011. We report three conclusions: (1) analyst’s forecasts information has been rewarded by the global market from January 1997-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 multi-factor risk-controlled portfolios allow us to reject the null hypothesis for the data mining corrections test. The earnings forecasting variable dominates our composite model in terms of its impact on stock selection.

Earnings Forecasting in a Global Stock Selection Model and Efficient Portfolio Construction and Management

Effectiveness of earnings forecasts in efficient global portfolio construction

We extract information on relative shopping interest from Google search volume and provide a genuine and economically meaningful approach to directly incorporate this data into a portfolio optimization technique. By generating a firm ranking based on a Google search volume metric, we can predict future sales and thus generate excess returns in a portfolio exercise. The higher the (shopping) search volume for a firm the higher we rank the company in the optimization process. For a sample of firms in the fashion industry, our results demonstrate that shopping interest exhibits predictive content which can be exploited in a real‐time portfolio strategy yielding robust Alphas around 5.5%.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.2547

Valuable information in early sales proxies: The use of Google search ranks in portfolio optimization

Development of an efficient method to approximate the risk measure of Lower Partial Moment of the first order

A note on the integration of the alpha alignment factor and earnings forecasting models in producing more efficient Markowitz Frontiers

The Development and Evolution of Mean-Variance Efficient Portfolios in the US and Japan: 30 Years After the Markowitz and Ziemba Applications

This article addresses stock selection modeling and portfolio selection and implementation in EMs. The authors view EM investing as a special case of global investing, demonstrating how stock-selection models in EMs with price momentum and forecast earnings acceleration factors can enhance returns. The authors construct index-enhanced portfolios for EMs that offer superior return-to-risk ratios relative to domestic portfolios. On the basis of back test and in real-time performance, the authors show how an EM portfolio using forecast earnings and price momentum anomalies can be developed, estimated, and implemented to generate statistically significant excess returns.

Stock Selection Modeling and Portfolio Selection in Emerging Markets

The early 1990s was a period of expanding interest in Japanese financial research. In 1993, John Mulvey co-edited a Special Issue, entitled “Financial Engineering”, in the Annals of Operations Research. In that issue, Guerard, Takano, and Yamane (1993) reported mean-variance efficient portfolios for the Japanese and U.S. equity markets and showed that the use of a regression-weighted composite model of earnings, book value, cash flow, sales, and their relative variables and forecasted earnings, outperformed their respective equity benchmarks by approximately 400 basis points annually. Markowitz and Xu (1994) tested the composite model strategy and found. C. F. Lee edited a JAI Press volume, Advances in Pacific Basin Business, Economics, and Finance, created as a result of a Spring 1993 Rutgers Conference, in which Guerard, Suzuki, and Bide (1995) reported that Toyo Keizai earnings forecasts enhanced Japanese stock selection models, relative to the use of historical earnings. The two articles produced models in which the excess returns were statistically significant from a variety of models tested, and the composite model strategy was not the result of data mining. The Markowitz and Xu (1994) data mining corrections test had been developed to address data mining issues. Thirty years after the issue, we report factor backtesting results and robust regression modeling in creating optimized Japanese portfolio results for the December 1996 - December 2022 period, a combination of methods and the latest commercially available multi-factor models for portfolio selection. Our results suggest that stock selection models can be effectively employed to deliver excess returns. The authors believe that financial anomalies exist, persist, and most likely will exist and can be profitably exploited. 

The Development and Evolution of Mean-Variance Efficient Portfolios in Japan: 30 Years After

The development and evolution of mean-variance efficient portfolios in the US and Japan: 30 years after the Markowitz and Ziemba applications

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A note on the integration of the alpha alignment factor and earnings forecasting models in producing more efficient Markowitz Frontiers

The Development and Evolution of Mean-Variance Efficient Portfolios in the US and Japan: 30 Years After the Markowitz and Ziemba Applications

Stock Selection Modeling and Portfolio Selection in Emerging Markets

The Development and Evolution of Mean-Variance Efficient Portfolios in Japan: 30 Years After

The development and evolution of mean-variance efficient portfolios in the US and Japan: 30 years after the Markowitz and Ziemba applications