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The Black-Scholes Option pricing model (BSOPM) has long been in use forvaluation of equity options to find the prices of stocks. In this work, usingBSOPM, we have come up with a comparative analytical approach and numericaltechnique to find the price of call option and put option and considered thesetwo prices as buying price and selling price of stocks of frontier markets sothat we can predict the stock price (close price). Changes have been made tothe model to find the parameters strike price and the time of expiration forcalculating stock price of frontier markets. To verify the result obtainedusing modified BSOPM we have used machine learning approach using the softwareRapidminer, where we have adopted different algorithms like the decision tree,ensemble learning method and neural network. It has been observed that, theprediction of close price using machine learning is very similar to the oneobtained using BSOPM. Machine learning approach stands out to be a betterpredictor over BSOPM, because Black-Scholes-Merton equation includes risk anddividend parameter, which changes continuously. We have also numericallycalculated volatility. As the prices of the stocks goes high due tooverpricing, volatility increases at a tremendous rate and when volatilitybecomes very high market tends to fall, which can be observed and determinedusing our modified BSOPM. The proposed modified BSOPM has also been explainedbased on the analogy of Schrodinger equation (and heat equation) of quantumphysics.

Predicting the Stock Price of Frontier Markets Using Modified Black-Scholes Option Pricing Model and Machine Learning