An investor sentiment reward-based trading system using Gaussian inverse reinforcement learning algorithm

Investor sentiment has been shown as an important factor that influences market returns, and a number of profitable trading systems have been proposed by taking advantage of investor sentiment signals. In this paper, we aim to design an investor sentiment reward-based trading system using Gaussian inverse reinforcement learning method. Our hypothesis is that while markets interact with investor's sentiment, there exists an intrinsic mapping between investor's sentiment and market conditions revealing future market directions. We propose an investor sentiment reward based trading system aimed at extracting only signals that generate either negative or positive market responses. Such a reward extraction mechanism is based not only on market returns but also market volatility representing a succinct and robust feature space. The back-test results show that the proposed sentiment reward-based trading system is superior to various benchmark strategies on S&P 500 index and market-based ETFs as well as few other existing news sentiment-based trading signals. Moreover, we find that sentiment reward trading system is much more effective in a volatile market, but it is sensitive to transaction costs.