Machine Learning-Based Financial Stock Market Trading Strategies Using Moving Average, Stochastic Relative Strength Index, and Price-Volume Actions for Indian and Malaysian Stock Markets

Abstract

This study explores the integration of machine learning (ML) techniques with traditional technical indicators to enhance financial stock market trading strategies in the Indian and Malaysian markets. By combining Moving Averages (SMA/EMA), Stochastic Relative Strength Index (Stochastic RSI), and Price-Volume actions (OBV, PVT, A/D Line), the proposed framework aims to improve the predictive accuracy and profitability of trading systems. The research applies supervised learning models, including Support Vector Machines (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) networks, to classify stock trends and generate trading signals. Empirical analysis based on historical data from the NSE, BSE, and Bursa Malaysia demonstrates that LSTM outperforms other models, achieving the highest accuracy (85.3%) and Sharpe ratio (1.45). The study highlights how the combination of trend-following indicators and ML models effectively minimizes false signals and enhances risk-adjusted returns. Further, comparative Backtesting results show that ML-driven strategies perform better in the Indian market due to higher liquidity and trading volume. The findings contribute to the growing literature on AI-assisted trading strategies and provide actionable insights for traders, analysts, and financial institutions. This research underscores the importance of feature engineering and model customization for adapting trading systems to different emerging market environments.