Predicting stock market using natural language processing

Karlo Puh and Marina Bagić Babac

American Journal of Business, 2023, vol. 38, issue 2, 41-61

Abstract: Purpose - Predicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market. Design/methodology/approach - In this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested. Findings - Experimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result. Originality/value - This study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.

Keywords: Stock market prediction; Machine learning; Time-series analysis; Recurrent neural network; Natural language processing; BERT; GRU (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:ajbpps:ajb-08-2022-0124

DOI: 10.1108/AJB-08-2022-0124

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