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Authors' ORCIDs

Alpa Singh Rajput: https://orcid.org/0000-0003-1833-0537

Arpan Singh Rajput: https://orcid.org/0000-0001-5085-1573

Article Type

Research Article

Abstract

Predicting the stock market is never easy because it is influenced by many uncertain and constantly changing factors such as economic conditions, investor behaviour, and global events. Traditional models like the Crisp Markov Chain (CMC) try to predict market movements by using fixed probabilities for different states like bullish, bearish, or stagnant. However, real markets do not behave in such a strict way—they often move gradually between states, which these models fail to capture. To overcome this limitation, this study introduces a Fuzzy Markov Chain (FMC) model, where fuzzy logic is used to handle uncertainty and allow smoother transitions between market states. By using Triangular Membership Functions, the model can represent situations like ``slightly bullish'' or ``moderately bearish,'' making it closer to real market behaviour. In this work, both CMC and FMC models are developed and tested using historical data from the Nifty 50 index. Their performance is compared using Mean Squared Error (MSE). The results show that the FMC model performs better in both short-term (one day) and slightly longer-term (one week) predictions, with lower error values.Overall, the findings suggest that adding fuzzy logic makes the model more flexible, accurate, and better suited to handle the uncertainty of financial markets. This approach provides a more reliable way to understand and forecast stock market trends.

Keywords

Triangular membership function, Crisp Markov chain model, Fuzzy Markov chain model, Nifty 50 datasets

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