SFPAD: Sentiment-driven Firm Pricing Anomaly Detection algorithm and application

Senyuan Zheng; Vincent C.S. Lee; Zhaolin Guan

doi:10.1109/BigData55660.2022.10020562

SFPAD: Sentiment-driven Firm Pricing Anomaly Detection algorithm and application

Senyuan Zheng, Vincent C.S. Lee, Zhaolin Guan

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

In January of 2021, the stock price of GameStop Corp ascended 20 times under the influence of social media in a mere two weeks' time, becoming the first-ever case of sentiment-driven short squeeze. This caused certain stock prices to deviate significantly from their fundamental value, and hence, had an impact on the market efficiency. To help finance policymakers, regulators, and investment analysts to prepare for cases alike in the future, our study investigates a Machine Learning method of detecting sentiment-driven firm pricing anomaly using data collected from the GameStop frenzy. The proposed method combines anomaly pattern detection with sentiment analysis, which the latter is used to identify conditions that are considered crucial to the presence of sentiment-driven pricing. In our experiments, the adapted Machine Learning (SFPAD) method was able to outperform traditional detection technique when applied to historical time series.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	3567-3576
Number of pages	10
ISBN (Electronic)	9781665480451
ISBN (Print)	9781665480468
DOIs	https://doi.org/10.1109/BigData55660.2022.10020562
Publication status	Published - 2022
Event	IEEE International Conference on Big Data (Big Data) 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022 https://ieeexplore.ieee.org/xpl/conhome/10020192/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Big Data (Big Data) 2022
Abbreviated title	Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10020192/proceeding (Proceedings)

Keywords

Anomaly pattern detection
Machine Learning
Sentiment analysis
Sentiment-driven Pricing
Streaming data

Access to Document

10.1109/BigData55660.2022.10020562

Cite this

Zheng, S., Lee, V. C. S., & Guan, Z. (2022). SFPAD: Sentiment-driven Firm Pricing Anomaly Detection algorithm and application. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data (pp. 3567-3576). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BigData55660.2022.10020562

Zheng, Senyuan ; Lee, Vincent C.S. ; Guan, Zhaolin. / SFPAD : Sentiment-driven Firm Pricing Anomaly Detection algorithm and application. Proceedings - 2022 IEEE International Conference on Big Data. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2022. pp. 3567-3576

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title = "SFPAD: Sentiment-driven Firm Pricing Anomaly Detection algorithm and application",

abstract = "In January of 2021, the stock price of GameStop Corp ascended 20 times under the influence of social media in a mere two weeks' time, becoming the first-ever case of sentiment-driven short squeeze. This caused certain stock prices to deviate significantly from their fundamental value, and hence, had an impact on the market efficiency. To help finance policymakers, regulators, and investment analysts to prepare for cases alike in the future, our study investigates a Machine Learning method of detecting sentiment-driven firm pricing anomaly using data collected from the GameStop frenzy. The proposed method combines anomaly pattern detection with sentiment analysis, which the latter is used to identify conditions that are considered crucial to the presence of sentiment-driven pricing. In our experiments, the adapted Machine Learning (SFPAD) method was able to outperform traditional detection technique when applied to historical time series.",

keywords = "Anomaly pattern detection, Machine Learning, Sentiment analysis, Sentiment-driven Pricing, Streaming data",

author = "Senyuan Zheng and Lee, {Vincent C.S.} and Zhaolin Guan",

note = "Funding Information: ACKNOWLEDGMENTS We acknowledge Monash University for the opportunity provided to take on this project. We acknowledge using Pandas, Numpy, Matplotlib, Datetime, Scikit-learn, tslearn, FastDTW, YFinance, Twint, Praw, Psaw in our project. Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE International Conference on Big Data (Big Data) 2022, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10020562",

language = "English",

isbn = "9781665480468",

pages = "3567--3576",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/10020192/proceeding",

}

Zheng, S, Lee, VCS & Guan, Z 2022, SFPAD: Sentiment-driven Firm Pricing Anomaly Detection algorithm and application. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 3567-3576, IEEE International Conference on Big Data (Big Data) 2022, Osaka, Japan, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10020562

SFPAD: Sentiment-driven Firm Pricing Anomaly Detection algorithm and application. / Zheng, Senyuan; Lee, Vincent C.S.; Guan, Zhaolin.
Proceedings - 2022 IEEE International Conference on Big Data. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2022. p. 3567-3576.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - SFPAD

T2 - IEEE International Conference on Big Data (Big Data) 2022

AU - Zheng, Senyuan

AU - Lee, Vincent C.S.

AU - Guan, Zhaolin

N1 - Funding Information: ACKNOWLEDGMENTS We acknowledge Monash University for the opportunity provided to take on this project. We acknowledge using Pandas, Numpy, Matplotlib, Datetime, Scikit-learn, tslearn, FastDTW, YFinance, Twint, Praw, Psaw in our project. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - In January of 2021, the stock price of GameStop Corp ascended 20 times under the influence of social media in a mere two weeks' time, becoming the first-ever case of sentiment-driven short squeeze. This caused certain stock prices to deviate significantly from their fundamental value, and hence, had an impact on the market efficiency. To help finance policymakers, regulators, and investment analysts to prepare for cases alike in the future, our study investigates a Machine Learning method of detecting sentiment-driven firm pricing anomaly using data collected from the GameStop frenzy. The proposed method combines anomaly pattern detection with sentiment analysis, which the latter is used to identify conditions that are considered crucial to the presence of sentiment-driven pricing. In our experiments, the adapted Machine Learning (SFPAD) method was able to outperform traditional detection technique when applied to historical time series.

AB - In January of 2021, the stock price of GameStop Corp ascended 20 times under the influence of social media in a mere two weeks' time, becoming the first-ever case of sentiment-driven short squeeze. This caused certain stock prices to deviate significantly from their fundamental value, and hence, had an impact on the market efficiency. To help finance policymakers, regulators, and investment analysts to prepare for cases alike in the future, our study investigates a Machine Learning method of detecting sentiment-driven firm pricing anomaly using data collected from the GameStop frenzy. The proposed method combines anomaly pattern detection with sentiment analysis, which the latter is used to identify conditions that are considered crucial to the presence of sentiment-driven pricing. In our experiments, the adapted Machine Learning (SFPAD) method was able to outperform traditional detection technique when applied to historical time series.

KW - Anomaly pattern detection

KW - Machine Learning

KW - Sentiment analysis

KW - Sentiment-driven Pricing

KW - Streaming data

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U2 - 10.1109/BigData55660.2022.10020562

DO - 10.1109/BigData55660.2022.10020562

M3 - Conference Paper

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SN - 9781665480468

SP - 3567

EP - 3576

BT - Proceedings - 2022 IEEE International Conference on Big Data

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

Y2 - 17 December 2022 through 20 December 2022

ER -

Zheng S, Lee VCS, Guan Z. SFPAD: Sentiment-driven Firm Pricing Anomaly Detection algorithm and application. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2022. p. 3567-3576 doi: 10.1109/BigData55660.2022.10020562

SFPAD: Sentiment-driven Firm Pricing Anomaly Detection algorithm and application

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Keywords

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