In the following we expand on research performed in previous articles by further exploring the effect and interpretation of the I Know First prediction measures and how these can be used for stock filtering. The analysis shows that as predictability and signal strength increase the average trade returns based on these indicators grow in a consistent, significant, and robust manner and that by daily selecting stocks with the highest predictabilities and signals average returns significantly above those of the S&P500 Index can be achieved.

Signal and Predictability

As described here the I Know First signals and predictabilities are generated on a daily basis for each stock and represent respectively the algorithm’s prediction regarding the direction and size of the return and the confidence in this prediction for various time horizons. These indicators are computed by the I Know First algorithm in response to the patterns and relationships learned from the historical data and matched to the current market environment. A variety of rules based on these indicators can be developed for trade execution and rebalancing on a daily basis for which a high predictability level and signal strength are key factors.

Mean Return per Trade

In the following we explore a very simple analysis of these predictions: for each trading day from January to June 2016 we filter stocks from the S&P500 universe by their short-term I Know First predictabilities and signals and compute their average close-to-close returns per trade. We use close-to-close returns since these are the changes the algorithm is actually predicting and refer to previously published articles that show how these predictions can be systematically utilized for open-to-close trading strategies. In this analysis the predictabilities are filtered by fixed levels and the signals by daily quantiles; the results are plotted below