Stock price manipulation detection using a computational neural network model

Leangarun, Teema; Tangamchit, Poj; Thajchayapong, Suttipong

doi:10.1109/icaci.2016.7449848

Cited by 17 publications

(24 citation statements)

References 2 publications

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“…In the figure, from left to right we find the labels Macro 1 and Macro 2 for our methodology tested using the macrofeatures described in Leangarun et al. () and Öğut et al. (), respectively, and Norm.…”

Section: Methodsmentioning

confidence: 82%

“…Additionally, Leangarun et al. () decided to extract level 1 data from TAQ data and used NNs to detect spoofing trading. They reported the NN cannot achieve good results with the proposed methodology.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, we have defined Macro 1 as the macrofeatures used in Leangarun et al. (), which are the maximum and minimum mid‐price per day, the opening mid‐price, and the total volume traded in the day. Also, we have tested the macrofeatures defined as Macro 2 as those used in Öğut et al.…”

Section: Methodsmentioning

confidence: 99%

“…In Leangarun, Tangamchit, and Thajchayapong (), the authors study the detection of trade‐based manipulation through neural networks (NNs) for two strategies, pump and dump and spoofing; however, there is a confusion of terminology on how spoofing actually works. The definition used in this study does not coincide in what is expressed in Aktas (), but could be more linked to what Scopino () describes as spoofing: “a trader creates the false appearance of liquidity or activity in the market by submitting bids and offers—i.e., orders for trades–that are briefly visible to other traders until the orders are canceled before trades can be executed.” This last definition may suggest that spoofing orders attract the opposite trading side rather than pushing it away.…”

Section: Related Literaturementioning

confidence: 99%

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Order flow dynamics for prediction of order cancelation and applications to detect market manipulation

2019

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In this work, a methodology is proposed to detect and predict the intention of cancelation of a large order at an optimal event‐time horizon by analyzing real order flow market data. We achieve this by reconstructing the full history of the limit order book and formulate the case as a binary classification supervised learning problem. The results presented in this study suggest that using the information at the microstructure level of the order book is highly efficient for predicting and detecting the cancelation of the large order than using information at the macrostructure level, and that predicting the cancelation is marginally outperformed by the detection case. With this, we make a step forward in identifying potential orders related to price manipulation but the results can be used by institutional traders to anticipate adversary market impact produced by large orders.

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Section: Methodsmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

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Order flow dynamics for prediction of order cancelation and applications to detect market manipulation

2019

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“…In our work [1], we considered a challenging scenario where we attempt to use less-detailed level 1 data to create a neural model for detecting manipulations even though using level 2 data is more accurate. The results showed that this can be done in pump-and-dump, in which price data reflects the intention of the manipulator.…”

Section: Introductionmentioning

confidence: 99%

Stock Price Manipulation Detection Based on Mathematical Models

Leangarun¹,

Tangamchit²,

Thajchayapong³

2016

IJTEF

Self Cite

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Abstract-We investigated two popular scenarios of stock price manipulations: pump-and-dump and spoof trading. Pump-and-dump is a procedure to buy a stock and push its price up. Then, the manipulator dumps all of the stock he holds to make a profit. Spoof trading is a procedure to trick other investors that a stock should be bought or sold at the manipulated price. We proposed mathematical definitions based on level 2 data for both scenarios, and used them to generate a training set consisting of buy/sell orders in an order book of 10 depths. Order cancellations, which are important indicators for price manipulation, are also visible in this level 2 data. In this paper, we considered a challenging scenario where we attempted to use less-detailed level 1 data to detect manipulations even though using level 2 data is more accurate. First, we implemented feedforward neural network models that have level 1 data, containing less-detailed information (no information about order cancellation), but is more accessible to investors as an input. The neural network model achieved 88.28% accuracy for detecting pump-and-dump but it failed to model spoof trading effectively. Therefore, we further investigate the two-dimensional Gaussian model and show that it can detect spoof trading using level 2 data as input.Index Terms-Stock price manipulation, pump-and-dump, spoof trading, neural network. I. INTRODUCTIONStock market gathers participations from all kinds of investors. Millions of buy/sell orders enters the market every day. Stock price fluctuates due to several factors, mainly from the profit that the company can make. However, there are some investors who attempt to get benefits from the stock market using irregular trade behaviors that affect the stock price. Some of these attempts are illegal. The control of these irregular trade behaviors is difficult due to the large amount of trade data.Automatic computer algorithms for detecting price manipulation are the solution to this problem. It can scan large amount of price data and spot manipulations in a short time. Price manipulation can be divided into three categories: trade-based, information-based, and action-based. This research discusses a mathematical model that classifies trade-based price manipulations from normal trades in stock markets. Two types of manipulations are investigated: pump-and-dump and spoof trading. Pump-and-dump is an action of buying stock, making the price to go higher, and then selling to others for a profit. Spoof trading is an action of sending passive orders in large volume to trick others that the stock should be sold at that price. After the manipulators secure enough benefits from that artificial price, they cancel their passive orders. These actions allow the manipulators to sell their stock at a price higher than usual.The effectiveness of manipulation detection depends on how much the information we have. We rely on using the price data that buyers and sellers sent to the market. The trade data can be classified into two levels. Level 1...

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A comprehensive review on insider trading detection using artificial intelligence

Priyadarshi,

Kumar

2024

J Comput Soc Sc

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Stock price manipulation detection using a computational neural network model

Cited by 17 publications

References 2 publications

Order flow dynamics for prediction of order cancelation and applications to detect market manipulation

Order flow dynamics for prediction of order cancelation and applications to detect market manipulation

Stock Price Manipulation Detection Based on Mathematical Models

A comprehensive review on insider trading detection using artificial intelligence

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