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

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.

News media analytics in finance: a survey

“…Song et al . () use an ANN trained with learning‐to‐rank loss functions for their trading strategy. Li et al .…”

“…(), Smales () and Song et al . (), in addition to those utilising cross‐sectional portfolio analyses (which are directly interpretable as trading strategies).…”

Behavioural Economics Approach: Using Investors Sentiment Indicator for Financial Markets Forecasting

“…When trying to capture the patterns of investors' behaviour social media and web news can be very useful. The latest researches show the advantages of the methods that use investors' sentiments to predict the stock markets (Shahzad et al, 2017;Oliveira et al, 2016;Song et al, 2017;Renault, 2017;Guo et al, 2017;Chau et al, 2016). Most of works focus on short term (e.g.…”

Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC)

“…Mutual fund investment has played a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors (Song et al 2017). For example, according to the Investment Company Institute, the total mutual fund asset globally as at the end of 2016 was in excess of $40.4 trillion.…”

“…At the end of 2016, 22% of household financial assets were held in mutual funds. Their role in retirement savings was even more significant, since mutual funds accounted for roughly half of the assets in individual retirement accounts, 401(k) s and other similar retirement plans (Song et al 2017). In total, mutual funds are large investors in stocks and bonds.…”