ItemConference Paper

Enhancing Stock Price Prediction: A Multi-Model Framework Integrating Technical Indicators and Sentiment Analysis

Title
Enhancing Stock Price Prediction: A Multi-Model Framework Integrating Technical Indicators and Sentiment Analysis
Creator
Prakasan, Adwaidh; Harimoorthy, Karthikeyan; Ganesh Kumar, R.
Description
This paper proposes a multi-model strategy that would improve the predictive power of stock prices by combining time-series analytics with external market indicators. The system allows five different base prediction methods; Long Short-Term Memory (LSTM), Enhanced Bidirectional LSTM (XLSTM), Support Vector Machine (SVM) which may use radial basis function (rbf), linear or polynomial (poly) kernels, Autoregressive integrated moving average (ARIMA), and Seasonal Autoregressive integrated moving average (SARIMA). A stacking procedure which uses linear regression as a meta-model together with a voting ensemble method is then employed to link these base models. The feature engineering is thorough, as it provides for general price and volume data, a battery of technical indicators (SMA10, SMA20, EMA 12, EMA 26, MACD elements, and RSI14) and a general sentiment indicator (summarised financial news). Sentiment analysis is performed by a pipeline that is trained using RoBERTa and yields discrete numerical values (0 negative, 1 neutral, 2 positive). The model's capability is very rigorously gauged by the conventional metrics Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy (DA). The real-world results demonstrate that the ensemble method is very efficient where the stacking arrangement leads to the lowest total MAPE of 0.6027 % MSFT and the highest directional Accuracy of 75.86 % GOOGL, thus, providing a strong evidence for the effectiveness of the thorough integration of heterogeneous machine-learning, statistical, and sentiment- analysis methods to produce the most accurate financial forecasts. 2026 IEEE.
Source
International Conference on Innovative Practices in Technology and Management, ICIPTM 2026;
Date
01-01-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
ARIMA; Ensemble Learning; Financial Forecasting; LSTM; Sentiment Analysis; Stock Price Prediction; Support Vector Machine (SVM); Technical Indicators; Time Series Analysis; XLSTM
Coverage
Prakasan A., Christ University, Department of Computer Science and Engineering, Bangalore, India; Harimoorthy K., Christ University, Department of Computer Science and Engineering, Bangalore, India; Ganesh Kumar R., Christ University, Department of Computer Science and Engineering, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-831954328-8;
Format
online
Language
English
Type
Conference paper

Citation

Prakasan, Adwaidh; Harimoorthy, Karthikeyan; Ganesh Kumar, R., “Enhancing Stock Price Prediction: A Multi-Model Framework Integrating Technical Indicators and Sentiment Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26044.

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