ANN model for Call Options Pricing using S&P 100 Market Data

Abstract

Stock options pricing is of key importance for markets and traders and is largely based on theoretical models like the Black-Scholes model. However, developments in machine learning open a novel, data-driven, perspective in contrast to the theoretical ones. This work explores the feasibility of artificial neural network model utilization for call option pricing, using the traditional Black-Scholes model as a benchmark. A multilayer perceptron model is trained to learn the Black-Scholes function and tested in real market call options data originating from thirty-five S&P 100 stocks. Findings demonstrate that artificial neural networks perform relatively well with market data and can be a valid data-driven approach for call option pricing, competitive to Black-Scholes. A unique contribution of this study is that testing data is not derived from the same distribution as training data, something common in existing works with similar models. Although further exploration and experimentation are required to reach the required robustness and become less ad hoc and data sensitive, datadriven pricing using artificial neural networks is a promising approach and can play a substantial role in option pricing.