Abstract
LIBOR (London Inter Banking Offered Rate) is one of the most important indicators of global currency liquidity risk. LIBOR market, involving top 18 member banks (including HSBC, Citibank, Bank of Tokyo-Mitsubishi UFJ, Credit Suisse etc.) and thousands non-member banks crossing different continents, is the huge market for banks to keep liquidity and currency flow globally. Because LIBOR is so important, decided by so many huge banks together and impacted by both current demand and supply of monetary currency and the forecast of future market, therefore the prediction is quite challenging. This paper is to introduce genetic algorithm (“GA”) based bi-directional generative adversary network (“BiGAN”) to predict the LIBOR in USD. Both the pro and cons of the algorithm will be discussed, with fitness values and Mean Squared Error (“MSE”). 50 test cases are executed randomly to verify the performance of the predictions. The target variance between predication and actual value is no more than 0.015.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goodfellow, I.J., Pouget-Abadiey, J., Mirza, M., Xu, B., Warde-Farley, D., Ozairz, S., Courville, A., Bengio, Y.: Generative Adversarial Nets (2014)
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning (2016)
Abay, R., Gehly, S., Balage, S., Brown, M., Boyce, R.: Maneuver detection of space objects using Generative Adversarial Networks (2018)
Zamorski, M., Zieba, M.: Semi-supervised learning with Bidirectional GANs (2018)
Jaiswal, A., AbdAlmageed, W., Wu, Y., Natarajan, P.: Bidirectional Conditional Generative Adversarial Networks (2018)
Bao, R., Liang, S., Wang, Q.: Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference (2018)
Bang, D., Shim, H.: High Quality Bidirectional Generative Adversarial Networks (2018)
Hitawala, S.: Comparative Study on Generative Adversarial Networks (2018)
Chen, M., Denoyer, L.: Multi-view Generative Adversarial Networks (2019)
Acknowledgments
Here I would like to thank my supervisor, Dr. H F Ting, for his guidance and support through each step of the process, and his encouragement and inspiration to me to explore innovative ideas in technology with maximum freedom.
And thank my family for their daily taking-care.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tan, X. (2020). Genetic Algorithm Based Bi-directional Generative Adversary Network for LIBOR Prediction. In: Barolli, L., Okada, Y., Amato, F. (eds) Advances in Internet, Data and Web Technologies. EIDWT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 47. Springer, Cham. https://doi.org/10.1007/978-3-030-39746-3_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-39746-3_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39745-6
Online ISBN: 978-3-030-39746-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)