Reinganum's trading strategies revisited: Structuring profitable strategies based on updated filters
Purpose – The purpose of this paper is to reexamine the value, momentum and size factors employed in the trading strategy originally proposed by Reinganum. Also to enhance our understanding of the impact of these factors over time; to evaluate the effectiveness of these factors; to develop new strategies through a framework presented by Chan et al.; and to investigate the possibility of limiting the number of filters to allow for a larger universe of eligible stocks without hurting performance for both long and short strategies. Design/methodology/approach – Using data from 1970 to 1983, Reinganum developed a profitable trading strategy based on four (or nine) variables to select stocks. First this paper shows why it is increasingly difficult to implement his original trading strategy, then tests his strategy on 23 additional years of data through 2006, and compares it to similar strategies that incorporate straightforward modifications to his filters. The analysis is further extended to a long/short trading strategy similar to that of Chan et al. Findings – Using the Capital Asset Pricing Model, Fama-French's three-factor model and Carhart's four-factor model to evaluate returns following portfolio formation, significant and consistent alphas and portfolio sizes were found. Research limitations/implications – It is concluded that strategies that mix value, momentum and size filter rules can be developed to produce consistently negative or non-positive (vs solely positive) alphas. Originality/value – The paper shows that it is possible to form profitable short-only and long/short strategies and to increase the number of eligible stocks in the sample despite our modifications to Reinganum's variables. By creating strategies based on mixed filter rules to produce either consistently positive (or negative) risk-adjusted returns, it can be concluded that slightly different versions of a given filter rule – often much simpler versions – not only enhance return performance but also increase its effectiveness in terms of portfolio size.
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