Failed factors are a frustrating reality of investing. The dilemma investors constantly face is determining when to discard a factor that seems to have lost efficacy, and when to embrace one that seems to have become important. The Alpha Repair strategy we have described focuses on this issue.
The goal is to provide a systematic framework that removes failed strategies from the playing field until they prove worthy, while providing an opportunity for new strategies to join the team.
The problem is framed as one of asset allocation for factors, with the important twist that a large set of factors always compete for a place in a small set of factors that are used for stock selection.
The criterion for making the team of selected factors in a given month depends on the history of factor return, volatility, and correlation with other factor returns. The size of the team of factors is kept small so that as similar factors compete for a spot, only the best ones survive the process.
The success of this factor competition criterion exploits the persistence of correlation to counter the lack of persistence in the return to investment strategies. The process is dynamic, yet it does not demand high turnover of stocks. This approach is an algorithm applied to a database of factor returns.
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