Abstract When evaluating the historical success of a strategy with any stock, the ratio of recent wins/losses alone does not provide a statistical edge. However, when correlating factors to strategy success are identified, their accuracy in recent history gives valuable insight into the probability of success for a given position. When these correlating factors are measured against each other there are clear advantages to using some metrics over others. When they are layered and used as decision making criteria, they become filters with an array of combinations. The filters can be used to select candidates (stocks) for strategy implementation. After evaluating and testing filters across 1-year and 5-year research periods, they individually and collectively resulted in improvements over the baseline with independent advantages and disadvantages. Backtesting revealed 3 successful filters that maintained generally independent candidate selection. Using a variable selection model that chooses which filter to implement based on recent success resulted in an increase in trade volume and net return and a decrease in drawdown and volatility.
Strategy Evaluation and Candidate Selection
Abstract When evaluating the historical success of a strategy with any stock, the ratio of recent wins/losses alone does not provide a statistical edge. However, when correlating factors to strategy success are identified, their accuracy in recent history gives valuable insight into the probability of success for a given position. When these correlating factors are measured against each other there are clear advantages to using some metrics over others. When they are layered and used as decision making criteria, they become filters with an array of combinations. The filters can be used to select candidates (stocks) for strategy implementation. After evaluating and testing filters across 1-year and 5-year research periods, they individually and collectively resulted in improvements over the baseline with independent advantages and disadvantages. Backtesting revealed 3 successful filters that maintained generally independent candidate selection. Using a variable selection model that chooses which filter to implement based on recent success resulted in an increase in trade volume and net return and a decrease in drawdown and volatility.