TY - JOUR
T1 - Stock portfolio selection using learning-to-rank algorithms with news sentiment
AU - Song, Qiang
AU - Liu, Anqi
AU - Yang, Steve Y.
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/11/15
Y1 - 2017/11/15
N2 - 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&P 500 index return, the hedge fund industry average performance - HFRIEMN, and some sentiment-based approaches without learning-to-rank algorithm during the same period.
AB - 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&P 500 index return, the hedge fund industry average performance - HFRIEMN, and some sentiment-based approaches without learning-to-rank algorithm during the same period.
KW - Financial news sentiment
KW - Learning-to-rank
KW - Long-short strategy
KW - Stock portfolio selection
KW - Trading strategy
UR - http://www.scopus.com/inward/record.url?scp=85021208422&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021208422&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2017.02.097
DO - 10.1016/j.neucom.2017.02.097
M3 - Article
AN - SCOPUS:85021208422
SN - 0925-2312
VL - 264
SP - 20
EP - 28
JO - Neurocomputing
JF - Neurocomputing
ER -