A Multilayer Perceptron Learning Architecture for Black-Scholes Call Options Pricing

This work explores whether an artificial neural network trained with synthetic call option pricing data can perform well on semi-real market data. The approach that is followed is based on a multilayer perceptron using a large synthetic dataset for training and a semi-real dataset from market data simulating top ten S&P 100 stocks. The baseline for errors and price estimations is the Black-Sholes model. Results, demonstrate the overall capability of the network to learn the Black-Scholes function using synthetic data, and estimate call option prices with high level of accuracy, competitively to Black-Sholes model. This is a valuable result for practitioners who might want to use machine learning paradigms for option pricing in various assets and markets. Future work will include feature selection and advanced sampling for training, plus alternative network architectures and further experimentation with hyperparameters. The most important outcome from the work is that machine learning models can be used from practitioners as an alternative method for option pricing.