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

TL;DR

This paper introduces a hybrid approach combining a numerical solution to the Black-Scholes equation with machine learning classification to forecast stock option price movements, aiming to enhance trading strategies.

Contribution

It presents a novel integration of the Quasi-Reversibility Method with machine learning models for improved option price forecasting and trading decision support.

Findings

Collected and analyzed 23,548 real-world options data.

Developed decision trees and random forests for classification.

Aimed to improve accuracy, precision, and recall of forecasts.

Abstract

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