Quasi-reversibility method and neural network machine learning for forecasting of stock option prices

Source abstract

This paper presents a novel way to predict options price for one day in advance, utilizing the method of Quasi-Reversibility for solving the Black-Scholes equation. The Black-Scholes equation is solved forwards in time, which is an ill-posed problem. Thus, Tikhonov regularization via the Quasi-Reversibility Method is applied. This procedure allows to forecast stock option prices for one trading day ahead of the current one. To enhance these results, the Neural Network Machine Learning is applied on the second stage. Real market data are used. Results of Quasi-Reversibility Method and Machine Learning method are compared in terms of accuracy, precision and recall.