 FinBERT and LSTM-based novel model for stock price prediction using technical indicators and financial newsGourav Bathla and Sunil Gupta International Journal of Economics and Business Research, 2024, vol. 28, issue 1, 1-16 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. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijecbr:v:28:y:2024:i:1:p:1-16 Access Statistics for this article More articles in International Journal of Economics and Business Research from Inderscience Enterprises Ltd |
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