Predicting warrant prices using an artificial neural network model: Experimental comparison with Black Scholes Metron model


The main objective of this study is to build an artificial neural network (ANN) model to predict warrant prices in Vietnam with data collected from 2019 to 2021 from nearly 300 different warrants. The ANN model is applied on a case-by-case basis depending on the status of the ITM or OTM warrants to examine further the model's pricing performance of the proposed model's price relative to the actual warrant's price. In addition, to compare with the ANN model, the Black Scholes Merton (BS) model is also used for warrant pricing. The ANN model is built with structure of 3 hidden layers using ReLU activation and 1 hidden layer using Softplus activation. The research results show that the ANN model has a more significant error performance in the case of more significant data than in the other two cases. BS model, there is no specific conclusion that applying the model, in any case, will be more effective. Regarding performance comparison between the two models, the ANN model outperforms both the BS model.

DOI Code: 10.1285/i20705948v17n1p89

Keywords: ANN, Black Scholes, deep learning, machine learning, warrant prediction

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