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Stock portfolio selection using learning-to-rank algorithms with news sentiment

Song, Qian, Liu, Anqi and Yang, S.Y. 2017. Stock portfolio selection using learning-to-rank algorithms with news sentiment. Neurocomputing 264 , pp. 20-28. 10.1016/j.neucom.2017.02.097

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Abstract

In this study, we apply learning-to-rank algorithms to design trading strategies using relative performance of a group of stocks based on investors' sentiment toward these stocks. We show that learning-to-rank algorithms are effective in producing reliable rankings of the best and the worst performing stocks based on investors' sentiment. More specifically, we use the sentiment shock and trend indicators introduced in the previous studies, and we design stock selection rules of holding long positions of the top 25% stocks and short positions of the bottom 25% stocks according to rankings produced by learning-to-rank algorithms. We then apply two learning-to-rank algorithms, ListNet and RankNet, in stock selection processes and test long-only and long-short portfolio selection strategies using 10 years of market and news sentiment data. Through backtesting of these strategies from 2006 to 2014, we demonstrate that our portfolio strategies produce risk-adjusted returns superior to the S&P500 index return, the hedge fund industry average performance - HFRIEMN, and some sentiment-based approaches without learning-to-rank algorithm during the same period.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Publisher: Elsevier
ISSN: 0925-2312
Date of First Compliant Deposit: 26 February 2018
Date of Acceptance: 13 February 2017
Last Modified: 16 Aug 2019 12:45
URI: http://orca.cf.ac.uk/id/eprint/109453

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