Quant Predictor

Abstract

Quantitative analysis of historical data plays a financial decision-making processes by utilizing advanced techniques to detect patterns, predict trends, and guide trading strategies. Conventional statistical methods often struggle to effectively capture the intricate and ever-changing nature of financial markets. challenge by introducing novel methodologies for forecasting stock market movements, short-term price predictions. The suggested framework integrates historical stock data with customized, and MACD to enrich the feature set. Various models, Forest, and LSTM neural network, are trained and assessed using RMSE and R² metrics. The findings indicate that although regression models provide interpretability, LSTM stands out in capturing temporal relationships and market volatility. This research underscores the opportunity to combine machine learning with financial analysis promptness of investment decision-making.

Introduction

The rapid evolution of global financial markets and the availability of high-frequency data have driven a shift from traditional econometric methods to machine learning (ML) techniques for forecasting market trends. ML models offer greater precision, flexibility, and scalability, making them valuable for quantitative investment strategies (“quant”). Supervised machine learning, particularly deep learning models like LSTM, has shown promise in capturing complex temporal dependencies in stock price data, outperforming traditional regression approaches.

This study presents a comparative framework evaluating several supervised ML models—Linear Regression, Support Vector Regression (SVR), Random Forest, and LSTM neural networks—for stock price prediction using historical and technical indicators (e.g., Moving Average, RSI, MACD, Bollinger Bands). Data from Yahoo Finance (2015-2023) was preprocessed with scaling and lag features to improve model performance.

Results show that LSTM achieved the highest accuracy (R² = 0.85), followed by Random Forest (R² = 0.78) and SVR (R² = 0.73), while Linear Regression lagged behind (R² = 0.62). LSTM also yielded the lowest prediction errors (RMSE and MAE), indicating superior forecasting ability. Random Forest offered reliable results with moderate computational cost, and SVR required careful tuning. Linear Regression was limited by its inability to model nonlinear stock behaviors.

The study concludes that ML models, especially LSTM and Random Forest, significantly enhance stock market forecasting accuracy, supporting better financial decision-making and risk management. The choice of model depends on specific needs like interpretability, speed, and computational resources.

Conclusion

This research introduces a methodical trends using supervised machine learning techniques. improve the precision and dependability of short-term financial forecasts. The research is founded on the premise that analyzing historical market data quantitatively, in conjunction with state-of-the-art AI algorithms, can provide valuable perspectives into future price shifts and investment possibilities. The proposed methodology integrates a mix of technical indicators and diverse Forest, and LSTM Neural Networks, to assess and anticipate stock price variations. Through the application of structured data preprocessing, feature engineering, and thorough model training, the framework converts raw financial information into actionable observations. The framework\'s effectiveness is showcased through evaluation outcomes. Among the models assessed, LSTM displayed remarkable accuracy by effectively understanding temporal relationships, while Random Forest offered a good blend of performance and interpretability. Crucial metrics such as R², RMSE, and MAE were utilized to objectively assess and compare model effectiveness. Machine learning models have demonstrated adeptness in capturing the nonlinear patterns and inherent volatility of financial markets, outperforming traditional statistical methods. This highlights the viability of the proposed framework as a scalable and efficient solution for quantitative financial analysis. Furthermore, the system\'s modular design enables adaptability in choosing and optimizing models according to particular investment goals, data accessibility, or operational limitations. The structure is ideal for incorporating into practical financial systems, providing foresightful analysis for traders, analysts, and automated trading mechanisms. The ability to tailor the process for different stocks, indices, or commodities boosts its effectiveness of market sectors. Future advancements may broaden the scope to encompass ensemble learning methods, real-time prediction mechanisms, and outlets and social media platforms, augmenting the range of features.

References

[1] Zhang, Y., Aggarwal, S., & Srivastava, A. (2017). Forecasting stock market trends using ARIMA models. Journal of Financial Data Science, 3(1), 44–54. [2] S. Yadav, V. Tomar, and P. Mishra, “Real-time stock price forecasting using LSTM with attention mechanism,” IEEE Access, vol. 11, pp. 21782–21793, 2023. [3] Y. Liu and X. Huang, “An overview of Dl methods for predicting stock prices,” Computational Economics, vol. 60, no. 2, pp. 559–580, 2022. [4] D. Das and M. Chen, “Utilizing DL for market prediction through sentiment analysis,” Computer Science, vol. 192, pp. 2212–2221, 2021. [5] A. Joshi and N. Singh, “Comparative study of ML for stock forecasting,” Procedia Computer Science, vol. 173, pp. 400–409, 2020.

Copyright

Copyright © 2025 Mr. Tateni Deviprasad, Mrs. Sharvani V. 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.