Predicting stock prices in bist: A reinforcement learning and sentimental analysis approach

Abstract

The stock exchange is an environment where the buying and selling of financial products take place through intermediaries. In financial markets, the most traded financial instruments are equity shares. Investors aim to enhance their profitability by investing in this financial instrument but due to the high volatility of stock prices, they are a high-risk financial product. For this reason, researchers from different fields have conducted studies on predicting stock prices. Many methods have been developed in studies on this topic from past to present. In literature, statistical methods such as AutoRegressive (AR), AutoRegressive Integrated Moving Average (ARIMA), AutoRegressive Conditional Heteroskedasticity (ARCH), Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) have been commonly used in research. Researchers have noted that sudden price fluctuations or unexpected events can disrupt prediction performance when employing these methods. Recently, numerous prediction models have emerged in research, leveraging machine learning and deep learning algorithms. In complex scenarios, datasets containing abrupt price changes and unexpected events have sometimes yielded improved results with these algorithms. In existing literature, variables derived from fundamental and technical analyses have been commonly employed. Additionally, sentiment analysis-based variables have been utilized. The overarching goal has been to capture the impact of geopolitical, global, and economic indicators on stock prices within these models. In research on predicting stock prices in the context of Borsa Istanbul, there are deficiencies in the existing literature, especially regarding the use of methods such as reinforcement learning, technical indicators and sentimental analysis in the same study. This study aims to address this gap by investigating predictive indicators and sentimental analysis along with various reinforcement learning techniques. Our research involves predicting price changes for specific stocks traded in Borsa Istanbul using deep reinforcement learning techniques (DQN, DDQN, DDDQN). The performance evaluation of these techniques was conducted, and a buy-and-hold strategy was used as a benchmark for comparison. For this study, shares of 8 companies traded on Borsa Istanbul were selected. Daily information of these stocks between January 2021 and March 2024 and technical indicators calculated from this information are used in the data. Apart from this information, sentimental analysis of the material event disclosures reported by the companies to the Public Disclosure Platform was made with the distilroberta model. A separate dataset was prepared for each company stock. DQN, DDQN, DDDQN algorithms were used in the study. When the test period results are examined, profit and reward performances differ on a stock basis. Notably, the DDQN algorithm stood out in securing rewards for six of the eight selected stocks. The results indicate that model performance varies significantly across different stocks. While the DDQN and DDDQN models were successful in certain situations, the Buy-and-Hold strategy proved more effective for some stocks. This suggests that model-based strategies can outperform Buy-and-Hold under certain conditions, but their effectiveness is largely dependent on the specific stock in question. Additionally, the impact of the Kap score on model performance was evaluated. It was observed that the inclusion of the cap score as an input generally enhanced the performance of the DQN, DDQN, and DDDQN models. Obtained results have led to conclusions regarding future studies.