Factor-based Trading Strategy for Index Rebalancing: Predicting Abnormal Returns using Logistic Classification

In this paper, we present an approach which examines whether there are persistent abnormal returns around the periods of index rebalancing, using a factor-based framework. By employing this methodology, we observe the yield of normal returns around these events, although the detected abnormal returns may not usually be statistically significant. Therefore, we conclude the study by addressing how one such supposed strategy for rebalance trading is systematic constructive by supervision of some predictive model for the candidates based on all available in the TSX universe for inclusion into the S&P Capped TSX Composite Index in about a month. Trade signals were constructed using a logistic binary classifier based on the same factors used by the index committee to determine the qualifications for inclusion in the index. The problem of a very low probability of out of context security selection for addition to the index is solved at the expense of random over-sampling. In the end, these trade signals were then used to create a hypothetical trading strategy that outperformed the back-test period.