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Link to original content: https://doi.org/10.1145/3490354.3494407

Probabilistic framework for modeling event shocks to financial time series | Proceedings of the Second ACM International Conference on AI in Finance

research-article

Probabilistic framework for modeling event shocks to financial time series

Authors:

Jianbo LiAuthors Info & Claims

ICAIF '21: Proceedings of the Second ACM International Conference on AI in Finance

Article No.: 42, Pages 1 - 8

https://doi.org/10.1145/3490354.3494407

Published: 04 May 2022 Publication History

Abstract

In financial market, certain types of stochastic events are intrinsically impactful to the prediction of financial times series, such as stock return, while few existing research attempts have been made to incorporate stochastic event modeling to time series modeling in a principled way. In this paper, we present a pioneering study that fills this gap. In particular, we introduce a generic probabilistic model that captures 1) the inter-dependencies among stochastic events, and 2) the impact of these events on time series. To this end, we extend multivariate Hawkes process (MHP) and proximal graphical event model (PGEM) and apply this framework to modeling two financial events, companies' quarterly revenue releases and updates of consensus prediction of quarterly revenue, and their impacts on the mean and correlation structures of future stock return. Our model not only improves prediction of financial time series, but also promotes AI trust for finance by revealing the causal relationship among the events. Extensive experimental results based on real financial market data validate the effectiveness of our models in learning event impact and improving investment decision by incorporating stochastic event impacts.

References

[1]

O. O. Aalen, O. Borgan, and H. K. Gjessing. 2008. Survival and Event History Analysis: A Process Point of View. Springer Science & Business Media, New York, NY, USA.

[2]

Y. Aït-Sahalia, J. Cacho-Diaz, and R. J. Laeven. 2015. Modeling financial contagion using mutually exciting jump processes. Journal of Financial Economics 117, 3 (2015), 585--606.

[3]

E. Bacry, I. Mastromatteo, and J.-F. Muzy. 2015. Hawkes processes in finance. Market Microstructure and Liquidity 1, 01 (2015), 1550005.

[4]

L. Bauwens, S. Laurent, and J. VK Rombouts. 2006. Multivariate GARCH models: a survey. Journal of applied econometrics 21, 1 (2006), 79--109.

[5]

D. Bhattacharjya, D. Subramanian, and T. Gao. 2018. Proximal graphical event models. In Advances in Neural Information Processing Systems. Montreal, Canada, 8136--8145.

[6]

F. Black and M. Scholes. 2019. The pricing of options and corporate liabilities. In World Scientific Reference on Contingent Claims Analysis in Corporate Finance: Volume 1: Foundations of CCA and Equity Valuation. World Scientific, 3--21.

[7]

T. Bollerslev, R. F. Engle, and D. B. Nelson. 1994. ARCH models. Handbook of econometrics 4 (1994), 2959--3038.

[8]

D. R. Cox and P. A. W. Lewis. 1972. Multivariate point processes. In Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 3: Probability Theory. 401--448.

[9]

J. Fan, Y. Fan, and J. Lv. 2008. High dimensional covariance matrix estimation using a factor model. Journal of Econometrics 147, 1 (2008), 186--197.

[10]

R. C. Grinold and R. N. Kahn. 2000. Active portfolio management. McGraw Hill New York.

[11]

A. G. Hawkes. 1971. Spectra of some self-exciting and mutually exciting point processes. Biometrika 58, 1 (1971), 83--90.

[12]

J. C. Hull. 2003. Options futures and other derivatives. Pearson Education India.

[13]

Kazi T Islam, Christian R Shelton, Juan I Casse, and Randall Wetzel. 2017. Marked point process for severity of illness assessment. In Machine Learning for Healthcare Conference. 255--270.

[14]

W. Jiang. 2020. Applications of deep learning in stock market prediction: recent progress. arXiv:2003.01859 [q-fin.ST]

[15]

J. Liu, Y. Chen, K. Liu, and J. Zhao. 2017. Attention-Based Event Relevance Model for Stock Price Movement Prediction. In Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence. 37--49.

[16]

R. C. Merton. 1976. Option pricing when underlying stock returns are discontinuous. Journal of financial economics 3, 1--2 (1976), 125--144.

[17]

M. Nickel and M. Le. 2020. Learning Multivariate Hawkes Processes at Scale. arXiv preprint arXiv:2002.12501 (2020).

[18]

J.-L. Prigent. 2001. Option pricing with a general marked point process. Mathematics of Operations Research 26, 1 (2001), 50--66.

Digital Library

[19]

S. Xiao, J. Yan, M. Farajtabar, Le Song, X. Yang, and H. Zha. 2017. Joint modeling of event sequence and time series with attentional twin recurrent neural networks. arXiv preprint arXiv:1703.08524 (2017).

[20]

Shuai Xiao, Junchi Yan, Mehrdad Farajtabar, Le Song, Xiaokang Yang, and Hongyuan Zha. 2019. Learning time series associated event sequences with recurrent point process networks. IEEE transactions on neural networks and learning systems (2019).

[21]

S. Xiao, J. Yan, X. Yang, H. Zha, and S. M Chu. 2017. Modeling the intensity function of point process via recurrent neural networks. In Thirty-First AAAI Conference on Artificial Intelligence.

[22]

H. Xu, D. Luo, and H. Zha. 2017. Learning Hawkes processes from short doubly-censored event sequences. In Proceedings of the International Conference on Machine Learning (ICML). 3831--3840.

[23]

D. Zhou, L. Zheng, Y. Zhu, J. Li, and J. He. 2020. Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW '20). Association for Computing Machinery, New York, NY, USA, 2230--2240.

[24]

K. Zhou, H. Zha, and L. Song. 2013. Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes. In Artificial Intelligence and Statistics. 641--649.

[25]

K. Zhou, H. Zha, and L. Song. 2013. Learning triggering kernels for multidimensional Hawkes processes. In Proceedings of the International Conference on Machine Learning (ICML). 1301--1309.

Cited By

Boyd AChang YMandt SSmyth PEvans RShpitser I(2023)Inference for mark-censored temporal point processesProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3625856(226-236)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3625856

Index Terms

Probabilistic framework for modeling event shocks to financial time series
1. Mathematics of computing
  1. Probability and statistics
    1. Multivariate statistics
    2. Stochastic processes

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cover image ACM Conferences

ICAIF '21: Proceedings of the Second ACM International Conference on AI in Finance

November 2021

450 pages

ISBN:9781450391481

DOI:10.1145/3490354

General Chairs:
Anisoara Calinescu
University of Oxford, UK
,
Lukasz Szpruch
The Alan Turing Institute and University of Edinburgh, UK

Copyright © 2021 ACM.

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Published: 04 May 2022

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MIT-IBM Watson AI Lab
Refinitiv - LSEG
Wells Fargo

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ICAIF'21: 2nd ACM International Conference on AI in Finance

November 3 - 5, 2021

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Cited By

Boyd AChang YMandt SSmyth PEvans RShpitser I(2023)Inference for mark-censored temporal point processesProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3625856(226-236)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3625856

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