Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices

This paper addresses challenges relating to applying data mining techniques to detect stock price manipulations and extends previous results by incorporating the analysis of intraday trade prices in addition to closing prices for the investigation of trade-based manipulations. In particular, this work extends previous results on the topic by analysing empirical evidence in normal and manipulated hourly data and the particular characteristics of intraday trades within suspicious hours. Furthermore, the analytical models described in this paper reinforce the results of previous market manipulation studies that are based on traditional statistical and econometrical methods providing an alternative portfolio of methods and techniques originating from the data mining and knowledge discovery areas. With the application of the analytical approach described in this paper, it is possible to identify new fraud manipulation pattern characteristics encoded as decision trees which can be readily employed in fraud detection systems. The paper also proposes a number of policy recommendations towards increasing the effectiveness of the operational processes executed by stock exchange fraud departments and regulatory authorities.

► We apply data mining techniques to detect trade-based stock price manipulations.
► We extend previous studies by analysing patterns in closing and intraday prices.
► We report patterns as decision trees ready to be used in fraud detection efforts.
► The paper also proposes a number of policy recommendations.
► Results confirm the importance of monitoring changes in trading volume and volatility.