Version 1
: Received: 2 October 2024 / Approved: 3 October 2024 / Online: 4 October 2024 (04:20:56 CEST)
How to cite:
Mandavalli, S. Factor-Based Trading Strategy for Index Rebalancing: Predicting Abnormal Returns Using Logistic Classification. Preprints2024, 2024100271. https://doi.org/10.20944/preprints202410.0271.v1
Mandavalli, S. Factor-Based Trading Strategy for Index Rebalancing: Predicting Abnormal Returns Using Logistic Classification. Preprints 2024, 2024100271. https://doi.org/10.20944/preprints202410.0271.v1
Mandavalli, S. Factor-Based Trading Strategy for Index Rebalancing: Predicting Abnormal Returns Using Logistic Classification. Preprints2024, 2024100271. https://doi.org/10.20944/preprints202410.0271.v1
APA Style
Mandavalli, S. (2024). Factor-Based Trading Strategy for Index Rebalancing: Predicting Abnormal Returns Using Logistic Classification. Preprints. https://doi.org/10.20944/preprints202410.0271.v1
Chicago/Turabian Style
Mandavalli, S. 2024 "Factor-Based Trading Strategy for Index Rebalancing: Predicting Abnormal Returns Using Logistic Classification" Preprints. https://doi.org/10.20944/preprints202410.0271.v1
Abstract
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.
Keywords
Index; Predictive Model; Returns; Yield
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
