Forecast ranked tailored equity portfolios

Buncic, Daniel and Stern, Cord (2018): Forecast ranked tailored equity portfolios.

Abstract

We use a dynamic model averaging (DMA) approach to construct forecasts of individual equity returns for a large cross-section of stocks contained in the SP500, FTSE100, DAX30, CAC40 and SPX30 headline indices, taking value, momentum, and quality factors as predictor variables. Fixing the set of ‘forgetting factors’ in the DMA prediction framework, we show that highly significant return forecasts relative to the historic average benchmark are obtained for 173 (281) individual equities at the 1% (5%) level, from a total of 895 stocks. These statistical forecast improvements also translate into considerable economic gains, producing out-of-sample R 2 values above 5% (10%) for 283 (166) of the 895 individual stocks. Equally weighted long only portfolios constructed from a ranking of the best 25% forecasts in each headline index can generate sizable returns in excess of a passive investment strategy in that index itself, even when transaction costs and risk taking are accounted for.

• Item Type: MPRA Paper
• Language: English
• Keywords: Active factor models, model averaging and selection, computational finance, quantitative equity investing, stock selection strategies, return-based factor models.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; F - International Economics > F3 - International Finance > F37 - International Finance Forecasting and Simulation: Models and Applications; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation

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