UTILIZING BERT AND CNN-LSTM IN STOCK PRICE PREDICTION USING DATA SENTIMENT ANALYSIS AND TECHNICAL ANALYSIS OF STOCK AND COMMODITY

ABSTRACT

The COVID-19 pandemic undoubtedly has affected people's lifestyles and stock investment activities. The government's policies to deal with the pandemic have an impact on increasing the number of investors in the stock market. Apart from profits, there are also risks associated with investing in stocks. To reduce the risk required analysis for stock price predictions. The data often used are stock data, commodity prices, and social media. The application of deep learning and natural language processing can help investors to process data. This paper proposes Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for technical analysis predicting stock prices using stock and commodity price data and urges BERT for sentiment analysis using social media data. The CNN-LSTM method has the best performance compared to the other four methods. The results showed that the performance of this method was the best, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were the smallest, and R Square (R2) was the largest. The BERT method has the best classification performance using 5-epochs, Weight Macro Avg, Weighted Avg, Accuracy, and the highest F1-Score. CNN-LSTM and BERT are more appropriate to predict stock prices and give investors suggestions to make stock investment decisions based on technical analysis and sentiment analysis. © 2023 ICIC International. All rights reserved.