Journal of Investment Strategies

Risk.net

Dynamic signal selection strategies

Dilip B. Madan, Yazid M. Sharaiha and Pål Sundsøy

  • The problem of selecting a small number of predictors from a large set that varies dynamically over time is addressed.
  • The selection is based on measures of dependency between each variable and the target variable.
  • Eight measures of dependency are introduced for the purpose including three new measures based on gamma, beta and bilateral gamma marginals.
  • The different dependency selections are observed to deliver a variety of performance levels in the design of trading strategies.

This paper concerns the selection of a small number of predictors from a much larger set of potential stock signals. Selection is based on choosing predictors with high levels of dependency in stock returns. Eight different models of pairwise dependency are employed, including the Gaussian, t , Clayton, Frank and Gumbel copulas. In addition, predictive factors and returns are mapped to gamma, beta and standard bilateral gamma marginals with dependency constructed by using the magnitude of fractional common components from the same distributions. Each dependency model is used to select predictors. The predictions are used both directly and as required returns to build a measure for the value of an invested dollar. Stocks are ranked by these two metrics daily. For the mean-reversion strategy, we take short positions in a quarter of the top-ranked stocks and long positions in a quarter of the bottom-ranked stocks. The positioning is reversed under momentum. Trading performance results are presented for a variety of dependencies and sectors over the period from June 2006 to January 2021. The selection procedures are observed to deliver a reasonable set of trading strategies.

 

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