Abstract
Accurate stock price prediction has an increasingly prominent role in a market where rewards and risks fluctuate wildly. Market control is a technique utilized by brokers to adjust the cost of budgetary resources. Recently there has been a significant increase in the use of artificial intelligence techniques in stock markets. Reinforcement learning has become particularly important in stock market forecasting. There is a need for modern techniques to improve share analysis and to detect unfair trading. Due to the high volatility and non-stationary nature of the stock market, forecasting the trend of financial time series remains a big challenge. This research explores, compares, and analyses the different artificial intelligent techniques used in predicting stock prices. The aim of this research is to give a comparative analysis to understand how to detect and analyze unfair trading and to detect price manipulation. Also, our aim is to explain how reinforcement deep learning could avoid and analyze the risks and unfair trading in stock market. The result of this study addresses current challenges of reducing the unfair trading across the stock. A successful and accurate prediction to the future stock prices ultimately results in profit maximization. Such prediction is important for many individuals including companies, traders, market participants, and data analysts. In conclusion, Reinforcement learning in the stock market is in its early development and a lot more research is needed to make it a reliable method in this field.
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AlSallami, N., Salah, R.M., Hossain, M., Altaf, S., Salahuddin, E., Kaur, J. (2023). Comparative Analysis: Accurate Prediction to the Future Stock Prices. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_13
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