Abstract

Stock market manipulation detection is important for both investors and regulators. Being able to detect stock manipulation and preventing it gives investors the confidence in the market fairness and integrity. It also helps maintaining liquidity of the stocks and market efficiency. Implementing data mining algorithms in manipulation detection is a relatively recent technique but in the past few years there has been an increasing interest in it's applications in this domain. The benefit of monitoring manipulative trade behavior is that it can be implemented on live feed of stock data, which saves a lot of time in detecting stock price manipulation. This research implements machine learning algorithms in detecting trade manipulations where trade behaviors artificially impact the National Best Bid and Offer (NBBO) of traded stocks. Research methodology implemented is based on feature extraction using signal analysis, taking advantage of the similarity between physical signals measured by machines and raw financial data. Accordingly, Continuous Wavelet Transform (CWT) is applied on actual manipulation data for feature extraction, Principal Component Analysis (PCA) and factor analysis are used for dimensionality reduction and then Machine Learning Classifiers are trained and tested. Tick Bid/Ask Price and volume data of actual 15 manipulation cases published by the Security Exchange Center (SEC) was extracted from an online interface and labeled accordingly. This data was then used to train, and test 3 different classification models (XGBoost, KNN & SVM) and the outcome was compared accordingly. Results showed that introducing continuous wavelet transform enhances model accuracy, it increased precision results tremendously, while reducing recall values slightly. Adding PCA, reduced run time greatly, yet reduced the quality of some models prediction. Out of the three classifiers XGboost & KNN are showing the highest performance.

Department

Management Department

Degree Name

MS in Finance

Graduation Date

Spring 6-15-2021

Submission Date

1-31-2021

First Advisor

Medhat Hussanien

Second Advisor

Noha Youssef

Committee Member 1

Islam Azzam

Committee Member 2

Wael Abdallah

Extent

47 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Not necessary for this item

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