Research on Identifying Stock Manipulation using GARCH Model

Pan, Wei; Qian, W.; He, Ying; Wang, Zhi-Xiu; Liu, Wei

doi:10.14569/ijacsa.2023.01403109

Cited by 1 publication

(1 citation statement)

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“…This ensemble method is predicated on the notion that combining insights from multiple, independently assessed partitions increases the detection accuracy by capturing a wider spectrum of potential fraudulent behaviors. The dataset underpinning our analysis contains four fundamental variables central to stock market analytics, as recognized by prior research [25][26][27]: price, return, volume, and trade count. A feature engineering process augments these variables with additional metrics that reveal the temporal dynamics of the market more comprehensively.…”

Section: Ensemble Approach Using K-partitioned Isolation Forestsmentioning

confidence: 99%

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Núñez Delafuente,

Astudillo,

Díaz

2024

Mathematics

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Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the dearth of labeled data and the inability to recognize novel manipulation tactics beyond those explicitly labeled. This study ventures into addressing these gaps by proposing a novel detection framework aimed at identifying suspicious hourly manipulation blocks through an unsupervised learning approach, thereby circumventing the limitations of data labeling and enhancing the adaptability to emerging manipulation strategies. Our methodology involves the innovative creation of features reflecting the behavior of stocks across various time windows followed by the segmentation of the dataset into k subsets. This setup facilitates the identification of potential manipulation instances via a voting ensemble composed of k isolation forest models, which have been chosen for their efficiency in pinpointing anomalies and their linear computational complexity—attributes that are critical for analyzing vast datasets. Evaluated against eight real stocks known to have undergone manipulation, our approach demonstrated a remarkable capability to identify up to 89% of manipulated blocks, thus significantly outperforming previous methods that do not utilize a voting ensemble. This finding not only surpasses the detection rates reported in prior studies but also underscores the enhanced robustness and adaptability of our unsupervised model in uncovering varied manipulation schemes. Through this research, we contribute to the field by offering a scalable and efficient unsupervised learning strategy for stock manipulation detection, thereby marking a substantial advancement over traditional supervised methods and paving the way for more resilient financial markets.

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Section: Ensemble Approach Using K-partitioned Isolation Forestsmentioning

confidence: 99%