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We proposed classification models that utilize the result from theQuasi-Reversibility Method, which solves the Black-Scholes equation to forecastthe option prices one day in advance. Combining the minimizer from QRM with ourmachine learning classifications, we can classify the option as an increase ordecrease in value. Based on the different classifications of the options, wecan apply various trading strategies which we aim to figure out ways to improvethe results from QRM's extrapolations. To further test the viability of ourmodel, we collected 23548 options data from the real-world market for ourmodel, and we will then feed in the data along with the minimizer from QRM toform decision trees and random forests, which we will later test for accuracy,precision, and recall.

Solving the Stock Option Forecast problem by a numerical method for the Black-Scholes Equation with Machine Learning Classification Model