ANN‐GA based model for stock market surveillance

Murugesan Punniyamoorthy and Jose Joy Thoppan

Journal of Financial Crime, 2013, vol. 20, issue 1, 52-66

Abstract: Purpose - This paper attempts to develop a hybrid model using advanced data mining techniques for the detection of Stock Price Manipulation. The hybrid model detailed in this article elucidates the application of a Genetic Algorithm based Artificial Neural Network to classify stocks witnessing activities that are suggestive of potential manipulation. Design/methodology/approach - Price, volume and volatility are used as the variables for this model to capture the characteristics of stocks. An empirical analysis of this model is carried out to evaluate its ability to predict stock price manipulation in one of the largest emerging markets – India, which has a large number of securities and significant trading volumes. Further, the article compares the performance of this hybrid model with a conventional standalone model based on Quadratic Discreminant Function (QDF). Findings - Based on the results obtained, the superiority of the hybrid model over the conventional model in its ability to predict manipulation in stock prices has been established. Research limitations/implications - The classification by the proposed model is agnostic of the type of manipulation – action‐based, information‐based or trade‐based. Practical implications - The market regulators can use these techniques to ensure that sufficient deterrents are in place to identify a manipulator in their market. This helps them carry out their primary function, namely, investor protection. These models will help effective monitoring for abnormal market activities and detect market manipulation. Social implications - Implementing this model at a regulator or SRO helps in strengthening the integrity and safety of the market. This strengthens investor confidence and hence participation, as the investors are made aware that the regulators implementing market manipulation detection techniques ensure that the markets they monitor are secure and protects investor interest. Originality/value - This is the first time a hybrid model has been used to detect market manipulation.

Keywords: Stock markets; Stock prices; Modelling; Market manipulation; Surveillance; Artificial neural networks; Genetic algorithms (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:jfcpps:13590791311287355

DOI: 10.1108/13590791311287355

Access Statistics for this article

Journal of Financial Crime is currently edited by Dr Li Hong Xing and Prof Barry Rider

More articles in Journal of Financial Crime from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().