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
