American Call Options Pricing With Modular Neural Networks

TL;DR

This paper introduces a Modular Neural Network approach for American call options pricing, outperforming traditional models and simpler neural networks in accuracy across multiple stocks.

Contribution

The paper presents a novel Modular Neural Network model that effectively captures complex market dynamics for American options pricing, surpassing existing methods.

Findings

MNN outperforms traditional models in RMSE and nRMSE

MNN shows superior accuracy across multiple stocks

Experimental results validate the effectiveness of the modular approach

Abstract

An accurate valuation of American call options is critical in most financial decision making environments. However, traditional models like the Barone-Adesi Whaley (B-AW) and Binomial Option Pricing (BOP) methods fall short in handling the complexities of early exercise and market dynamics present in American options. This paper proposes a Modular Neural Network (MNN) model which aims to capture the key aspects of American options pricing. By dividing the prediction process into specialized modules, the MNN effectively models the non-linear interactions that drive American call options pricing. Experimental results indicate that the MNN model outperform both traditional models as well as a simpler Feed-forward Neural Network (FNN) across multiple stocks (AAPL, NVDA, QQQ), with significantly lower RMSE and nRMSE (by mean). These findings highlight the potential of MNNs as a powerful tool to improve the accuracy of predicting option prices.