Abstract
This paper presents a two-stage Machine Learning (ML) framework for signal-based portfolio optimization in the National Stock Exchange (NSE) of India. Using a dataset of 20 major equities from 2020 to 2025, the study evaluates eleven predictive models, including regularized linear regressions, tree-based methods, and hybrid Deep Learning (DL) architectures such as Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Long Short-Term Memory–Multilayer Perceptron (LSTM-MLP). In the first stage, a 60-day sliding window is used to generate one-day-ahead return forecasts, which are then stabilized through a 10-period smoothing mechanism to reduce short-term prediction noise. In the second stage, these forecasts are transformed into cross-sectional investment signals and incorporated into a portfolio optimization framework. The results indicate that hybrid DL models achieve better predictive performance than traditional approaches in capturing non-linear market dynamics. Overall, the proposed framework provides a practical approach for converting model forecasts into portfolio decisions and may contribute to improved risk-adjusted performance.