Random Walk Model for Stock Market Trend Analysis of Nepal

This study examines the applicability of the random walk hypothesis in the Nepal Stock Exchange (NEPSE), an emerging market marked by inefficiencies such as low liquidity, limited technological infrastructure, and external economic influences. Advanced stochastic models, including Markov chains, martingale techniques, and the Chapman-Kolmogorov equation, are employed to assess NEPSE's weak-form market efficiency. Analysing daily closing price data from 242 listed companies over five weeks, the study reveals significant inefficiencies. Transition matrices demonstrate a high likelihood of price states revisiting specific trends, with stationary vectors [0.4472, 0.5228, 0.0300] reflecting the probabilities of "High," "Low," and "Same" states, respectively. Variance ratio test further confirms deviations from randomness, highlighting inefficiencies such as serial correlation and non-random price fluctuations. These findings indicate that historical prices influence future movements, contradicting the random walk hypothesis. The results stress the need for improved infrastructure, enhanced regulatory frameworks, and investor education to increase market efficiency. By integrating specific numerical findings and inefficiencies, this study provides actionable insights for policymakers and investors while contributing to the academic discourse on emerging markets.