Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach. (arXiv:2112.02095v1 [q-fin.TR])

The feasibility of making profitable trades on a single asset on stock
exchanges based on patterns identification has long attracted researchers.
Reinforcement Learning (RL) and Natural Language Processing have gained
notoriety in these single-asset trading tasks, but only a few works have
explored their combination. Moreover, some issues are still not addressed, such
as extracting market sentiment momentum through the explicit capture of
sentiment features that reflect the market condition over time and assessing
the consistency and stability of RL results in different situations. Filling
this gap, we propose the Sentiment-Aware RL (SentARL) intelligent trading
system that improves profit stability by leveraging market mood through an
adaptive amount of past sentiment features drawn from textual news. We
evaluated SentARL across twenty assets, two transaction costs, and five
different periods and initializations to show its consistent effectiveness
against baselines. Subsequently, this thorough assessment allowed us to
identify the boundary between news coverage and market sentiment regarding the
correlation of price-time series above which SentARL’s effectiveness is



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