Title: FinBERT and LSTM-based novel model for stock price prediction using technical indicators and financial news

Authors: Gourav Bathla; Sunil Gupta

Addresses: Department of Computer Engineering and Applications, GLA University, Mathura, India ' Department of Cybernetics, School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, India

Abstract: Stock price movement is highly nonlinear, volatile, and complex. Traditional machine learning techniques are employed by researchers for stock price prediction, but due to shallow architecture, adequate accuracy is not achieved. In this paper, recently introduced bidirectional encoder representations from transformers (BERT) and long short-term memory (LSTM) hybrid model is utilised for stock price prediction. BERT model is used for financial news sentiment analysis. The sentiment score is merged with technical indicators of stock prices. In our approach, FinBERT is used which is specifically trained on financial corpus. Stock market prices were highly fluctuated in March 2020 due to lockdown. Thus, it is essential to predict high variation which existing works have not experienced due to lack of availability of highly fluctuated dataset. In our approach, the effect of financial news on stock indexes is analysed. Experiment analysis validates that our proposed approach outperforms existing approaches significantly.

Keywords: financial market; stock market; deep learning; data analytics; BERT; long short-term memory; LSTM; MAPE.

DOI: 10.1504/IJEBR.2024.139286

International Journal of Economics and Business Research, 2024 Vol.28 No.1, pp.1 - 16

Received: 14 Sep 2021
Accepted: 06 Dec 2021
Published online: 01 Jul 2024 *

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