Predictive Modeling and Forecasting of Stock Prices Using Machine Learning

Abstract

Stock market prediction is always a challenging task to perform as financial time series data are non-linear, volatile and unpredictable. Classic models such as ARIMA and moving average sometimes do not describe the real-time variations; only deal with the mono-variable analysis. Even more sophisticated deep learning models such as LSTM, that are still proven to be beneficial for sequence learning, remain resource hungry, slow to train and often provide black box responses – harder to interpret or use in real-time systems. To solve these problems, a new forecasting investment model is introduced in this project that utilizes a machine learning techniques based on XGBoost (Extreme Gradient Boosting), which is known to be efficient and interpretable algorithm designed for modeling complex-structured relationships over varied data of the stock history. The system converts raw stock data (Open, High, Low, Close, Volume) pulled from the yfinance API into a feature-rich predictive dataset. Lag values, percentage return, Moving Averages (MA5 and MA10), Volatility pattern etc., are derived to ensure that the model has more in-depth knowledge about the market. The XGBoost model can predict the close price one day ahead with good accuracy, and is tested on MAE, RMSE and R² Score.The system is plug-and-play and can adjust to a given stock, handle multivariate data and update itself in real time without retraining from the beginning. This work enriches a transparent and accurate real-time prediction system for financial dashboards/investment platforms – an essential trade-off between precision, interpretability and speed in a coherent framework

Introduction

The study focuses on predicting stock market prices using machine learning, highlighting the importance of accurate forecasting for economic stability and investment decisions. Traditional statistical models like ARIMA and moving averages struggle with the nonlinear and dynamic nature of financial data, prompting a shift to AI and ML approaches. Ensemble methods, particularly XGBoost, offer a balance of accuracy, efficiency, and interpretability compared to deep learning models like LSTM, which are computationally heavy and less transparent.

The proposed system integrates real-time data collection (via APIs like yfinance), data preprocessing, and feature engineering to extract indicators such as moving averages, volatility, returns, and lag values. These features allow the model to capture short-term fluctuations and long-term trends. The XGBoost algorithm is trained on these enriched datasets with hyperparameter optimization to ensure accurate next-day closing price predictions. Model performance is evaluated using MAE, RMSE, and R² scores, with results visualized through comparative plots and feature importance charts to maintain explainability.

The methodology ensures a lightweight, scalable, and interpretable forecasting system, suitable for integration into financial platforms, dashboards, and decision-support systems. Literature review indicates that while deep learning models can be accurate, they are often slow and opaque, making ensemble approaches like XGBoost more practical for real-time, interpretable financial forecasting.

Overall, the system demonstrates how machine learning can automate, enhance, and clarify stock market prediction, providing investors and institutions with actionable insights for data-driven decision-making.

Conclusion

The Predictive Modelling and Forecasting of Stock Prices system effectively proves the effectiveness and stability of applying the XGBoost algorithm in financial forecasting. The model not only provides high accuracy but also guarantees interpretability and efficiency in computation, resolving the significant shortcomings of conventional and deep learning-based models. By efficient feature engineering, such as the incorporation of lag values, moving averages, and volatility measures, the system is able to make accurate next-day stock price predictions. Streaming data retrieval via APIs such as yfinance allows the model to be highly flexible in accommodating shifting market dynamics. The attained evaluation metrics validate the reliability and stability of the proposed model. This model can be extended to accommodate other financial metrics, sentiment analysis, and macroeconomic data integration to further improve prediction performance. Future activities involve creating dynamic dashboards for visualization, incorporating automated warning systems for major price variations, and hosting the model on cloud environments for scalability and multi-user input. In conclusion, the project creates a usable, interpretable, and real-time forecasting method that can facilitate intelligent decision-making in contemporary financial settings.

References

[1] Patel, M., Jariwala, K., and Chattopadhyay, C., \"An Approach Toward Stock Market Prediction and Portfolio Optimization in Indian Financial Sectors,\" IEEE Transactions on Computational Social Systems, vol. 12, no. 1, February 2025. [2] Roy, P. K., Kumar, A., and Singh, A., \"Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications,\" IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 1351, February 2024. [3] Hou, J., Dong, Z., Zhou, J., and Liu, Z., \"Discovering Predictable Latent Factors for Time Series Forecasting,\" IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 10, October 2024. [4] Rosa, M. J. A., Souza, M. R., Machado, C. L. S., Rigo, S. J., and Barbosa, J. L. V., \"Brazilian Stock Market Forecast with Heterogeneous Data Integration for a Set of Stocks,\" IEEE Latin America Transactions, vol. 23, no. 7, July 2025. [5] Doroslova?ki, K., and Gradojevic, N., \"A Novel Market Sentiment Analysis Model for Forecasting Stock and Cryptocurrency Returns,\" IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 9, September 2024. [6] Zhu, Q., Li, J., and Liu, S., \"Decomposition-Based Dynamic Inductive Graph Embedding Learning Method to Forecast Stock Trends,\" IEEE Transactions on Computational Social Systems, published August 30, 2024. [7] Li, S., Tang, G., Chen, X., and Lin, T., \"Stock Index Forecasting Using a Novel Integrated Model Based on CEEMDAN and TCN-GRU-CBAM,\" IEEE Access, published July 29, 2024. [8] Alam, K., Bhuiyan, M. H., Haque, I. U., Monir, M. F., and Ahmed, T., \"Enhancing Stock Market Prediction: A Robust LSTM-DNN Model Analysis on 26 Real-Life Datasets,\" IEEE Access, published July 29, 2024. [9] Jain, A., Singh, R., & Sharma, P., “Deep recurrent neural network based stock market price forecasting: An empirical study on major indices,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 3, pp. 1050-1062, March 2024. [10] Chen, L., Wang, Y., & Zhao, H., “Integrating high-frequency trading data and sentiment indicators for intraday stock price prediction,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 789-798, August 2024. [11] Stock Market Prediction With Transductive Long Short?Term Memory and Social Media Sentiment Analysis — Ali Peivandizadeh, Sima Hatami, Amirhossein Nakhjavani, Lida Khoshsima, Mohammad Reza Chalak Qazani, Muhammad Haleem, Roohallah Alizadehsani; IEEE Access, “Stock market prediction with Transductive LSTM and social media sentiment analysis”, Jan 2024. DOI: 10.1109/ACCESS.2024.3399548. [12] Jia, Y. “ResNLS: An improved model for stock price forecasting.” Computational Intelligence, 2024; (paper introduces a hybrid model emphasizing dependencies between adjacent stock prices). [13] Phuoc, T., Kim Anh, P. T., & Tam, P. H., et al. “Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam.” Humanities & Social Sciences Communications, vol. 11, article 393, 2024. [14] Radfar, E. “Stock market trend prediction using deep neural network via chart analysis: a practical method or a myth?” Humanities & Social Sciences Communications, vol. 12, article 662, 2025. [15] Bao, W. “Data-driven stock forecasting models based on neural networks.” Decision Analytics Journal (or similar journal), 2025. (Comprehensive review of NN-based stock forecasting covering 2015-2023). [16] Najem, R. “Toward an enhanced stock market forecasting with machine learning: challenges & insights.” Procedia Computer Science, 2024; (focus on ML algorithms, common challenges in forecasting stock markets).

Copyright

Copyright © 2025 R. Nivetha , S. Madhu Priyaa, R. S. Nivetha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.