Stock prediction by deep learning

Abstract

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. In this domain, fast denotes the capacity to acquire and analyse pertinent market data quickly enough for the results to remain actionable, as stock prices fluctuate within minutes or even seconds. Accurate denotes generating predictions with low forecasting error, ensuring that the expected price or return closely aligns with actual market movements. Effective decisions are those that enable investors or portfolio managers to choose appropriate buy, sell, or hold actions that improve returns or reduce risk. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock price is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. This research focuses on the prediction model for improving the performance of stock price prediction. The historical prices and financial news were be considered as inputs to this research and expect that they will improve the prediction efficiency of the models created.

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Identifiers

ORCID for Mehtabhorn Obthong: orcid.org/0000-0002-3869-578X

ORCID for Gary Wills: orcid.org/0000-0001-5771-4088

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