Computational Approaches to Market Risk Prediction: A Comparison of Hidden Markov and Markov Models in the Pakistan Stock Exchange

Abstract

Market risk prediction is a critical aspect of financial decision-making, particularly in volatile stock markets such as the Pakistan Stock Exchange (PSX). This study examines the effectiveness of computational approaches in market risk prediction by comparing Hidden Markov Models (HMMs) and traditional Markov Models (MMs). While Markov Models rely on observable states and assume a memoryless stochastic process, HMMs incorporate latent variables, allowing for a more nuanced representation of market dynamics. The study employs historical PSX data, integrating price trends, trading volumes, and volatility indicators to assess the predictive accuracy of both models. Our findings suggest that HMMs outperform traditional Markov Models in capturing complex market behaviors, particularly during periods of economic uncertainty and sudden market shifts. The study highlights the superior ability of HMMs to model latent structures in financial time series, enabling investors and analysts to anticipate market risks more effectively. Furthermore, our research explores the implications of computational approaches in financial risk management, emphasizing the role of machine learning in refining stock market predictions. The comparative analysis contributes to the growing body of literature on quantitative finance and market risk assessment, offering insights into the potential adoption of HMMs in algorithmic trading and portfolio optimization. The findings of this study are relevant for policymakers, investors, and financial analysts seeking robust predictive models for market risk assessment in emerging economies. Future research directions include the integration of deep learning techniques with HMMs to further enhance predictive accuracy.