"Black-Scholes-Artificial Neural Network": A novel option pricing model

Abstract

Purpose: This study conducts a comparative study of various options pricing models and introduces a new model. Research methodology: This paper reviews eight option pricing models, including the Black-Scholes-Merton model (BSM), Monte Carlo simulation (MC), Heston, GARCH, Lattice, Jump Diffusion models (JDM), Normal Inverse Gaussian-Cox-Ingersoll-Ross Model, and a novel model called Black-Scholes-Artificial Neural Network (BSANN). The objective is to predict the European call and put options using a payoff calculation. The underlying asset is Khodro, a famous automobile producer company in Iran, for the last year. The daily prices were also used as historical data. The primary software used for the calculations and plots was MATLAB. An Excel option pricing toolbox was used to obtain more accurate and improved results. Results: Based on the results, it can be concluded that the proposed model, BS-ANN, provides the most accurate estimation with the lowest standard deviation. Limitations: There are several limitations to be considered when choosing an underlying asset. An important factor is the availability of sufficient data on the number of shared transactions. Another limitation of this study is the absence of trading halts. Additionally, caution is crucial when selecting an appropriate number of estimated parameters. Contribution: By utilizing the presented model, researchers, individuals, investors, and stock market analysts interested in trading can enhance their estimations. Novelty: The most significant novelty of this study is the presentation of a hybrid model incorporating unique features.