In recent two decades, artificial neural networks have been extensively used in many
business applications. Despite the growing number of research papers, only few studies …

Neural networks have been used as a nonparametric method for option pricing and hedging
since the early 1990s. Far over a hundred papers have been published on this topic. This …

The motivation for this paper is to introduce a hybrid neural network architecture of Particle
Swarm Optimization and Adaptive Radial Basis Function (ARBF–PSO), a time varying …

This work examines a deep learning approach to complement investors' practices for the
identification of pairs-trading opportunities among cointegrated stocks. We refer to the …

Option pricing has become one of the quite important parts of the financial market. As the
market is always dynamic, it is really difficult to predict the option price accurately. For this …

This paper addresses the interpretability problem of non-parametric option pricing models
by using the explainable artificial intelligence (XAI) approach. We study call options written …

In this study, we generate 50 Multi-layer Perceptons, 50 Radial Basis Functions, 50 Higher
Order Neural Networks and 50 Recurrent Neural Network and we explore their utility in …

Option pricing based on data‐driven methods is a challenging task that has attracted much
attention recently. There are mainly two types of methods that have been widely used …

During the recent decades, option pricing became an important topic in computational
finance. The main issue is to obtain a model of option prices that reflects price movements …

This paper proposes an optimal hedging strategy in the presence of market frictions using
the Long Short Term Memory Recurrent Neural Network (LSTM-RNN) method, which is a …