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More, Sumit Vijay (2024) Stock Market Prediction Using Financial News Sentiments and Technical Indicator Data with Machine Learning Models and LIME for Explainable Insights. Masters thesis, Dublin, National College of Ireland.
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Abstract
The stock market is comprised of many input factors, which include stock prices, news sentiments, and technical ones. The general problem of how to forecast stock prices has not been fully solved due to the constant fluctuations of shares. This research aims to enhance stock price forecasting based on the utilization of heterogeneous data and machine learning algorithms and make the black box prediction more interpretable for investors. The purpose is to improve the forecast precision and gain a deeper understanding of the opportunities for the markets. The study involves the quantitative data of the stock prices such as open, high, low, closing, volume, SMA (Simple Moving Average), EMA (Exponential Moving Average), RSI (Relative Strength Index), BBANDS (Bollinger Bands), and News sentiment score data. The methodology has dwelled into three widely used models RandomForestRegressor, SVR (Support Vector Regressor), LSTM (Long-short term memory) and their hyper-parameter tuned versions for better results. Among all the models, the fine-tuned SVR model has outperformed others. The fine-tined SVR model achieved an MSE (Mean Square Error) of 0.518 and an MAE (Mean Absolute Error) of 0.566. The integration of technical indicators and news sentiment scores along with the LIME (Local Interpretable Model-Agnostic Explanation) explanation can significantly benefit traders and financial analysts by providing more accurate predictions and explanations. The real-time data processing and potential biases in sentiment analysis are the challenges that can be explored further. The final objective of this research is to empower traders and financial analysts to make sound and data-backed decisions in the stock market.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Makki, Ahmed UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HG Finance > Investment Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HG Finance > Investment > Stock Exchange |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 20 Aug 2025 11:28 |
| Last Modified: | 20 Aug 2025 11:28 |
| URI: | https://norma.ncirl.ie/id/eprint/8592 |
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