Title: Option pricing using data-driven machine learning approaches: empirical evidence from Indian financial market

Authors: Prem Vaswani; M. Padmaja; Kirubakaran Jayaprakasam

Addresses: Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015, India ' Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015, India ' Department of Management Studies, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015, India

Abstract: Option pricing has always been seen as a black box, as it is highly complex, fierce, dynamic, and volatile. This study explored the application of machine learning (ML) models like stochastic gradient descent (SGD), decision tree, random forest, XGBoost, and artificial neural networks (ANN) to forecast the option price of NIFTY 50 index in Indian financial market. And the study determines the best model in comparison to the Black-Scholes Merton (BSM) model in forecasting the option prices. The study has applied a methodology called multi-model option-pricing neutral intelligence grid-search cross-validation assessment (MONICA) to value index options. The findings can help the traders and investors with their buy/sell strategies by comparing the forecasted value with the quoted value. The consistent and robust results from subsets using the same inputs and significance of Diebold and Mariano (DM) test statistics validates the outperformance of random forest and ANN approaches.

Keywords: option prices; NIFTY index; Black-Scholes model; BSM; random forest; artificial neural network; ANN; DM test; stochastic gradient descent; SGD.

DOI: 10.1504/IJBIR.2026.150772

International Journal of Business Innovation and Research, 2026 Vol.39 No.1, pp.34 - 55

Received: 05 Aug 2022
Accepted: 27 Oct 2022
Published online: 23 Dec 2025 *

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