Result: Improving neural network based option price forecasting

Title:
Improving neural network based option price forecasting
Authors:
TZASTOUDIS, Vasilios S, THOMAIDIS, Nikos S, DOUNIAS, George D
Source:
Advances in artificial intelligence (4th Helenic [i.e. Hellenic] Conference on AI, SETN 2006, Heraklion, Crete, Greece, May 18-20, 2006)0SETN 2006. :378-388
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 16 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Bourse valeurs, Stock exchange, Bolsa valores, Contrat, Contract, Contrato, Fixation prix, Pricing, Fijación precios, Fonction objectif, Objective function, Función objetivo, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Modèle Black Scholes, Black Scholes model, Modelo Black Scholes, Prévision, Forecasting, Previsión, Réseau multicouche, Multilayer network, Red multinivel, Réseau neuronal, Neural network, Red neuronal, Tarification, Tariffication, Tarificación, Volatilité, Volatility, Volatibilidad
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Decision and Management Engineering Laboratory, Dept. of Financial Engineering & Management, University of the Aegean, 31 Fostini Str, 821 00, Chios, Greece
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19152088
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19152088
Database:
PASCAL Archive

Further Information

As is widely known, the popular Black & Scholes model for option pricing suffers from systematic biases, as it relies on several highly questionable assumptions. In this paper we study the ability of neural networks (MLPs) in pricing call options on the S&P 500 index; in particular we investigate the effect of the hidden neurons in the in- and out-of-sample pricing. We modify the Black & Scholes model given the price of an option based on the no-arbitrage value of a forward contract, written on the same underlying asset, and we derive a modified formula that can be used for our purpose. Instead of using the standard backpropagation training algorithm we replace it with the Levenberg-Marquardt approach. By modifying the objective function of the neural network, we focus the learning process on more interesting areas of the implied volatility surface. The results from this transformation are encouraging.