Developing an Effective Model for Detecting Trade-Based Market Manipulation

K. Ganesh, Sanjay Mohapatra, Jose Joy Thoppan, M. Punniyamoorthy

ISBN 10: 1801173974 / ISBN 13: 9781801173971

Published by Emerald Publishing Limited, GB, 2021

About this Item

Stock market manipulation is detrimental to traders and corporations, causes unnecessary price fluctuations, and only benefits financial criminals. The research presented here determines an appropriate model to help identify stocks witnessing activities that are indicative of potential manipulation through three separate but related studies.In Developing an Effective Model for Detecting Trade-Based Market Manipulation, classifiers based on three different techniques namely discriminant analysis, a composite classifier based on Artificial Neural Network and Genetic Algorithm and support Vector Machines is proposed. The proposed models help investigators, with varying degree of accuracy, to arrive at a shortlist of securities which could be subject to further detailed investigation to detect the type and nature of the manipulation, if any.Following a fluid outline, Developing an Effective Model for Detecting Trade-Based Market Manipulation, introduces the topic, explores the aims and scopes of the research, before delving into the data and modelling to explore their application to the stock market to detect price manipulation.

Seller Inventory # LU-9781801173971

Bibliographic Details

• Title: Developing an Effective Model for Detecting Trade-Based Market Manipulation
• Publisher: Emerald Publishing Limited, GB
• Publication Date: 2021
• Language: English
• ISBN 10: 1801173974
• ISBN 13: 9781801173971
• Binding: Hardback
• Condition: New
• Weight: 294 grams

About this title

Synopsis

Stock market manipulation is detrimental to traders and corporations, causes unnecessary price fluctuations, and only benefits financial criminals. The research presented here determines an appropriate model to help identify stocks witnessing activities that are indicative of potential manipulation through three separate but related studies.

In Developing an Effective Model for Detecting Trade-Based Market Manipulation, classifiers based on three different techniques namely discriminant analysis, a composite classifier based on Artificial Neural Network and Genetic Algorithm and support Vector Machines is proposed. The proposed models help investigators, with varying degree of accuracy, to arrive at a shortlist of securities which could be subject to further detailed investigation to detect the type and nature of the manipulation, if any.

Following a fluid outline, Developing an Effective Model for Detecting Trade-Based Market Manipulation, introduces the topic, explores the aims and scopes of the research, before delving into the data and modelling to explore their application to the stock market to detect price manipulation.

About the Author

Dr. Jose Joy Thoppan is an Associate Professor at Saintgits Institute of Management, India, and has a PhD in capital markets from the National Institute of Technology, Tiruchirappalli. He holds an MBA in Finance and has served Tata Consultancy Services for 6 years where he was a business area specialist for the Trading, Clearing and Surveillance platform – TCS BaNCS Market Infrastructure.

Dr. M. Punniyamoorthy is Professor [HAG Scale] in Operations and Analytics at the National Institute of Technology, Tiruchirappalli, India. His research interests include Supply Chain performance, Supplier Selection, Technology Selection, Data Analytics, Data Science, Performance Measurement and Balanced Scorecard.

Dr. K. Ganesh is Senior Knowledge Expert and Global Lead of “manufacturing and supply chain management center of competence” (MSC CoC) at McKinsey & Company, Chennai, India.

Dr. Sanjay Mohapatra has more than 37 years of combined industry and academic experience. He was VP in three organizations (Polaris Lab, iSOFT Plc, JB Soft Inc.) and was heading Asia Pacific, Europe and USA. He has authored twenty eight books and seventy eight papers in Scopus indexed journals.