Machine Learning Driven Options Pricing Model for Changing Market Conditions

Ling Yuan; Jihua Chen

doi:10.54097/ta4f1674

Authors

Ling Yuan
Jihua Chen

DOI:

https://doi.org/10.54097/ta4f1674

Keywords:

Adaptive Options Pricing, Deep Learning, Market Sentiment

Abstract

This paper presents the design and implementation of an Adaptive Options Pricing Model utilizing deep learning techniques to enhance the accuracy of options pricing in volatile financial markets. Traditional models such as the Black-Scholes and Binomial frameworks have served as foundational tools in options pricing; however, they are constrained by assumptions of constant volatility and market efficiency, which often fail to reflect real-world dynamics. To address these limitations, the proposed model incorporates historical pricing data alongside real-time market sentiment derived from news articles and social media. By leveraging advanced deep learning architectures, particularly Long Short-Term Memory (LSTM) networks, the model dynamically adapts to changing market conditions, capturing complex, nonlinear relationships in the data. The performance of the model is evaluated against traditional pricing methods, demonstrating significant improvements in pricing accuracy and responsiveness to market fluctuations. This research contributes valuable insights for traders and risk managers seeking to optimize their strategies in the derivatives market.

Downloads

Download data is not yet available.

References

[1] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

[2] Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281-305.

[3] Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence, 14, 1137-1145.

[4] Brooks, C. (2014). Introductory Econometrics for Finance. Cambridge University Press.

[5] Cao, C., et al. (2019). A deep learning approach for options pricing. Journal of Financial Data Science, 1(1), 1-15.

[6] Zhang, Y., Chen, Y., & Huang, Y. (2019). Integrating historical price data and market sentiment for option pricing. Journal of Financial Engineering, 6(3), 195-215.

[7] Chollet, F. (2015). Keras. GitHub. https://github.com/fchollet/keras

[8] Dixon, M. F., Halperin, I., & Moller, H. (2020). Machine learning for option pricing. Quantitative Finance, 20(8), 1231-1242.

[9] Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.

[10] Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645-1680.

[11] Iglewicz, B., & Hoaglin, D. C. (1993). How to Detect and Handle Outliers. Sage Publications.

[12] Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.

[13] Krauss, C., et al. (2017). Deep neural networks for the prediction of stock prices and option pricing. Quantitative Finance, 17(5), 723-735.

[14] Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

[15] Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human-Centered Informatics, 5(1), 1-167.

[16] Merton, R. C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8(4), 323-361.

[17] Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139-1168.

[18] Wang, Y., & Zhang, X. (2019). A survey of sentiment analysis in financial markets. Journal of Financial Markets, 45, 1-18.

[19] Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79-82.

[20] Zhang, X., & Zhang, Y. (2018). A survey on deep learning in financial market prediction. Journal of Finance and Data Science, 4(1), 1-16.