Enhancing Stock Market Forecasting with LLMs, Sentiment Analysis and Technical Indicators

Title

Enhancing Stock Market Forecasting with LLMs, Sentiment Analysis and Technical Indicators

Creator

Santhosh Kumar, S.; Manjunatha Swamy, C.; Balamurugan, M.

Description

Forecasting stock market trends remains a complex and demanding endeavor due to the intricate and dynamic nature of financial markets. This study explores the combination of sentiment analysis with technical indicators to improve the accuracy of stock price predictions. The research incorporates stock market and news data spanning from January 2015 to June 2023, ensuring a well-aligned and comprehensive dataset. Data was sourced using the Google News API and the Bombay Stock Exchange (BSE), followed by rigorous preprocessing, which involved handling missing values and standardizing sentiment scores for accuracy and consistency. To analyze sentiment, tools like VADER, TextBlob, and the Gemini-1.5-Flash API were employed, with sentiment scores aggregated at the stock level. Simultaneously, key technical indicators including the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, and Exponential Moving Averages were derived from stock price patterns. These diverse data points were integrated to predict 14-day closing stock prices, leveraging the Gemini1.5-Flash model for forecasting. The model s performance was assessed using various error metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results indicated strong predictive accuracy for stable stocks while pointing out challenges in forecasting highly volatile stocks. Ultimately, the findings suggest that combining sentiment analysis with technical indicators strengthens stock market trend predictions, offering a solid foundation for future advancements in real-time financial analytics. 2025 IEEE.

Source

Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Gemini-1.5-flash; multimodal data; sentiment analysis; Stock market prediction; technical indicators

Coverage

Santhosh Kumar S., Christ University, Dept. of CSE, Bangalore, India; Manjunatha Swamy C., Christ University, Dept. of CSE, Bangalore, India; Balamurugan M., Christ University, Dept. of CSE, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152118-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/InC465408.2025.11256269

https://www.scopus.com/pages/publications/105030333276?origin=resultslist