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Hedging via Opinion-based Pair Trading Strategy

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Hsin-Hsi ChenAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 69 - 70

https://doi.org/10.1145/3366424.3382701

Published: 20 April 2020 Publication History

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Abstract

Risk is an important component when constructing a trading strategy. However, most of the previous works that make the price movement prediction on the basis of the opinions on social media platforms do not take the risk into consideration. In order to hedge the market-risk, we propose an idea of an opinion-based pair trading strategy. Comparing with the task setting of the previous works, our experimental results show that the neural network models with the pair-wise task setting perform better in both accuracy and profitability metrics. That introduces a new research direction for future researches on opinion-based price movement predictions.

References

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Yumo Xu and Shay B. Cohen. 2018. Stock Movement Prediction from Tweets and Historical Prices. In ACL.

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Kuo WChang WDai TChen YChang H(2022)Improving Pairs Trading Strategies Using Two-Stage Deep Learning Methods and Analyses of Time (In)variant Inputs for Trading PerformanceIEEE Access10.1109/ACCESS.2022.320405610(97030-97046)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3204056
Chen CHuang HChen HChen CHuang HChen H(2021)FinTech ApplicationsFrom Opinion Mining to Financial Argument Mining10.1007/978-981-16-2881-8_6(73-87)Online publication date: 21-May-2021
https://doi.org/10.1007/978-981-16-2881-8_6

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Hedging via Opinion-based Pair Trading Strategy

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Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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Association for Computing Machinery

New York, NY, United States

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Kuo WChang WDai TChen YChang H(2022)Improving Pairs Trading Strategies Using Two-Stage Deep Learning Methods and Analyses of Time (In)variant Inputs for Trading PerformanceIEEE Access10.1109/ACCESS.2022.320405610(97030-97046)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3204056
Chen CHuang HChen HChen CHuang HChen H(2021)FinTech ApplicationsFrom Opinion Mining to Financial Argument Mining10.1007/978-981-16-2881-8_6(73-87)Online publication date: 21-May-2021
https://doi.org/10.1007/978-981-16-2881-8_6

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Pair Trading with Machine Learning Strategy in China Stock Market

An Effective Correlation-Based Pair Trading Strategy Using Genetic Algorithms

Hedging International Foreign Exchange Risks via Option Based Portfolio Insurance

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