A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments

TL;DR

This paper introduces a deep reinforcement learning model that combines sentiment analysis and knowledge graphs to predict stock market directions, demonstrating improved accuracy on Turkish bank stocks.

Contribution

It presents a novel integration of social knowledge graphs, sentiment analysis with BerTurk, and deep Q-learning for stock direction prediction, which is a new approach in this domain.

Findings

The model achieved remarkable prediction accuracy on case study stocks.

Sentiment analysis with BerTurk improved the model's performance.

Knowledge graph integration enhanced the understanding of user relations.

Abstract

Stock market prediction has been an important topic for investors, researchers, and analysts. Because it is affected by too many factors, stock market prediction is a difficult task to handle. In this study, we propose a novel method that is based on deep reinforcement learning methodologies for the direction prediction of stocks using sentiments of community and knowledge graph. For this purpose, we firstly construct a social knowledge graph of users by analyzing relations between connections. After that, time series analysis of related stock and sentiment analysis is blended with deep reinforcement methodology. Turkish version of Bidirectional Encoder Representations from Transformers (BerTurk) is employed to analyze the sentiments of the users while deep Q-learning methodology is used for the deep reinforcement learning side of the proposed model to construct the deep Q network. In order to demonstrate the effectiveness of the proposed model, Garanti Bank (GARAN), Akbank (AKBNK), T\"urkiye \.I\c{s} Bankas{\i} (ISCTR) stocks in Istanbul Stock Exchange are used as a case study. Experiment results show that the proposed novel model achieves remarkable results for stock market prediction task.