Abstract
Recently, option price forecasting has become a popular financial issue. Being affected by many factors, option price forecasting remains a challenging problem. This paper proposes a new method to forecast the option price. The proposed method, termed as fuzzy time series-based neural network (FTSNN), is a hybrid method composed of a fuzzy time series model and a neural network model. In FTSNN, the fuzzy time series model is used to select a dataset for training the neural network model for prediction. The experiment results show that FTSNN outperforms several existing methods in terms of MAE and RMSE.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ko, P.-C.: Option valuation based on the neural regression model. Expert Syst. Appl. 36, 464–471 (2009)
Black, F., Scholes, M.: The Pricing of Options and Corporate Liabilities. J. Polit. Econ. 81, 637 (1973)
Grudnitski, G., Osburn, L.: Forecasting S&P and gold futures prices: An application of neural networks. J. Futures Mark. 13, 631–643 (1993)
Morelli, M.J., Montagna, G., Nicrosini, O., Treccani, M., Farina, M., Amato, P.: Pricing financial derivatives with neural networks. Physica A 338, 160–165 (2004)
Shin, T., Han, I.: Optimal signal multi-resolution by genetic algorithms to support artificial neural networks for exchange-rate forecasting. Expert Syst. Appl. 18, 257–269 (2000)
Panda, C., Narasimhan, V.: Forecasting exchange rate better with artificial neural network. J. Policy Model. 29, 227–236 (2000)
Lajbcygier, P.: Improving option pricing with the product constrained hybrid neural network. Neural Networks, IEEE Trans. Neural Netw. 15, 465–476 (2004)
Tseng, C.-H., Cheng, S.-T., Wang, Y.-H., Peng, J.-T.: Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices. Physica A 387, 3192–3200 (2008)
Wang, Y.-H.: Nonlinear neural network forecasting model for stock index option price: Hybrid GJR-GARCH approach. Expert Syst. Appl. 36, 564–570 (2009)
Yu, H.-K.: Weighted fuzzy time series models for TAIEX forecasting. Physica A 349, 609–624 (2005)
Cheng, C.-H., Chen, T.-L., Teoh, H.J., Chiang, C.-H.: Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Syst. Appl. 34, 1126–1132 (2008)
Leu, Y., Lee, C.-P., Jou, Y.-Z.: A distance-based fuzzy time series model for exchange rates forecasting. Expert Syst. Appl. 36, 8107–8114 (2009)
Lee, L.-W., Wang, L.-H., Chen, S.-M., Leu, Y.: Handling forecasting problems based on two-factors high-order fuzzy time series. Fuzzy Systems, IEEE Trans. Fuzzy Syst. 14, 468–477 (2006)
Chen, S.-M.: Forecasting Enrollments Based on High-Order Fuzzy Time Series. Cybern. Syst. 33, 1–16 (2002)
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series – Part I. Fuzzy Sets Syst. 54, 1–9 (1993)
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series – part II. Fuzzy Sets Syst. 62, 1–8 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leu, Y., Lee, CP., Hung, CC. (2010). A Fuzzy Time Series-Based Neural Network Approach to Option Price Forecasting. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds) Intelligent Information and Database Systems. ACIIDS 2010. Lecture Notes in Computer Science(), vol 5990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12145-6_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-12145-6_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12144-9
Online ISBN: 978-3-642-12145-6
eBook Packages: Computer ScienceComputer Science (R0)