Detecting Price Manipulation in the Financial Market

Yi Cao; Yuhua Li; SA Coleman; Ammar Belatreche; TM McGinnity

Detecting Price Manipulation in the Financial Market

Yi Cao
, Yuhua Li
, SA Coleman
, Ammar Belatreche
, TM McGinnity

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

Abstract

Market abuse has attracted much attention from financial regulators around the world but it is difficult to fully prevent. One of the reasons is the lack of thoroughly studies of the market abuse strategies and the corresponding effective market abuse approaches. In this paper, the strategies of reported price manipulation cases are analysed as well as the related empirical studies. A transformation is then defined to convert the time-varying financial trading data into pseudo-stationary time series, where machine learning algorithms can be easily applied to the detection of the price manipulation. The evaluation experiments conducted on four stocks from NASDAQ show a promising improved performance for effectively detecting such manipulation cases.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	77-84
Number of pages	8
Publication status	Published (in print/issue) - 27 Mar 2014
Event	IEEE Computational Intelligence for Financial Engineering and Economics 2014 - Canary Wharf, London Duration: 27 Mar 2014 → …

Conference

Conference	IEEE Computational Intelligence for Financial Engineering and Economics 2014
Period	27/03/14 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Computational approaches for detecting manipulations in capital markets
Cao, Y. (Author), Li, Y. (Supervisor), Mar 2015
Student thesis: Doctoral Thesis

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Cite this

@inproceedings{b399d81427c34edfb178437cdf698f04,

title = "Detecting Price Manipulation in the Financial Market",

abstract = "Market abuse has attracted much attention from financial regulators around the world but it is difficult to fully prevent. One of the reasons is the lack of thoroughly studies of the market abuse strategies and the corresponding effective market abuse approaches. In this paper, the strategies of reported price manipulation cases are analysed as well as the related empirical studies. A transformation is then defined to convert the time-varying financial trading data into pseudo-stationary time series, where machine learning algorithms can be easily applied to the detection of the price manipulation. The evaluation experiments conducted on four stocks from NASDAQ show a promising improved performance for effectively detecting such manipulation cases.",

author = "Yi Cao and Yuhua Li and SA Coleman and Ammar Belatreche and TM McGinnity",

year = "2014",

month = mar,

day = "27",

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booktitle = "Unknown Host Publication",

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address = "United States",

note = "IEEE Computational Intelligence for Financial Engineering and Economics 2014 ; Conference date: 27-03-2014",

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T1 - Detecting Price Manipulation in the Financial Market

AU - Cao, Yi

AU - Li, Yuhua

AU - Coleman, SA

AU - Belatreche, Ammar

AU - McGinnity, TM

PY - 2014/3/27

Y1 - 2014/3/27

N2 - Market abuse has attracted much attention from financial regulators around the world but it is difficult to fully prevent. One of the reasons is the lack of thoroughly studies of the market abuse strategies and the corresponding effective market abuse approaches. In this paper, the strategies of reported price manipulation cases are analysed as well as the related empirical studies. A transformation is then defined to convert the time-varying financial trading data into pseudo-stationary time series, where machine learning algorithms can be easily applied to the detection of the price manipulation. The evaluation experiments conducted on four stocks from NASDAQ show a promising improved performance for effectively detecting such manipulation cases.

AB - Market abuse has attracted much attention from financial regulators around the world but it is difficult to fully prevent. One of the reasons is the lack of thoroughly studies of the market abuse strategies and the corresponding effective market abuse approaches. In this paper, the strategies of reported price manipulation cases are analysed as well as the related empirical studies. A transformation is then defined to convert the time-varying financial trading data into pseudo-stationary time series, where machine learning algorithms can be easily applied to the detection of the price manipulation. The evaluation experiments conducted on four stocks from NASDAQ show a promising improved performance for effectively detecting such manipulation cases.

M3 - Conference contribution

SP - 77

EP - 84

BT - Unknown Host Publication

PB - IEEE

T2 - IEEE Computational Intelligence for Financial Engineering and Economics 2014

Y2 - 27 March 2014

ER -

Detecting Price Manipulation in the Financial Market

Abstract

Conference

UN SDGs

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Student theses

Computational approaches for detecting manipulations in capital markets

Cite this