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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.

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).

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.

The classification by the proposed model is agnostic of the type of manipulation – action‐based, information‐based or trade‐based.

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.

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.

This is the first time a hybrid model has been used to detect market manipulation.

The purpose of this study is to review and evaluate the salient features of stock market manipulation in Malaysia. The research questions used are: Who was involved? How it happened? What were the consequences?

This study has been conducted using content and thematic analysis. This study includes multiple sources of information to help establish the stylized facts and it uses cases that have been prosecuted in Malaysia for 2005-2015.

This study presents arguments and empirical data supporting the view that the stock market manipulation was conducted by those in a privileged position and with access to information. Ethical failure, involving greed, self-interest, dishonesty and a preoccupation with a quick profit, could explain why stock market manipulation happened. Manipulation harms legitimate investors, as share prices and earnings of companies are affected.

A better understanding about the prevalence, characteristics and consequences of the market manipulation problems will be useful for stakeholders, investors and policymakers in the financial industry for promoting and maintaining a fair, efficient and transparent stock market.

The originality of this paper lies in examining and presenting interpretations based on contemporary phenomenon within the real-life context of Malaysia. There is little study or literature that focuses on Malaysia, especially in examining stock market manipulation by integrating finance and management perspectives to form a comprehensive understanding of the issue.

Although the financial markets are regulated by robust systems and rules that control their efficiency and try to protect investors from various manipulation schemes, markets still suffer from frequent attempts to mislead or misinform investors in order to generate illegal profits. The impetus to effectively and systematically address such schemes presents many challenges to academia, industry and relevant authorities. This paper aims to discuss these issues.

The paper describes a case study on fraud detection using data mining techniques that help analysts to identify possible instances of touting based on spam e‐mails. Different data mining techniques such as decision trees, neural networks and linear regression are shown to offer great potential for this emerging domain. The application of these techniques is demonstrated using data from the Pink Sheets market.

Results strongly suggest the cumulative effect of “stock touting” spam e‐mails is key to understanding the patterns of manipulations associated with touting e‐mail campaigns, and that data mining techniques can be used to facilitate fraud investigations of spam e‐mails.

The approach proposed and the paper's findings could be used retroactively to help the relevant authorities and organisations identify abnormal behaviours in the stock market. It could also be used proactively to warn analysts and stockbrokers of possible cases of market abuse.

This research studies the relationships between the cumulative volume of spam touts and a number of financial indicators using different supervised classification techniques. The paper aims to contribute to a better understanding of the market manipulation problem and provide part of a unified framework for the design and analysis of market manipulation systems.

The paper aims to investigate order-based manipulation that consists of order-placing strategies.

Using the bid and ask record provided by Hong Kong Exchanges and Clearing Limited, a Level II dataset, the paper develops a methodology to obtain cancelled orders during regular trading hours. The paper examines the cancelled orders and potential order-based manipulation activities, as well as the corresponding behavior of different groups of stocks.

Empirical results show that the relationship between order cancellation and order-based manipulation is strong and deserves more attention.

The methodology can also be used by regulators and authorities to monitor suspicious activities in the market. This paper also suggests that analysis on high-frequency data does improve the understanding of trading activities in the stock market.

This paper aims to show that market power exists in financial markets and analyze how spoofing is used by a high-frequency trader to build market power by taking advantage of both behavioral weaknesses of individual investors and microstructural loopholes of trading venues.

After showing that market power exists in the financial market, this paper centers around the question of how market power is constructed in the financial market. To sharpen the answer to the question, the paper compares the conditions needed for market power construction in the financial market with those needed in the goods market. The paper selects spoofing, the frequently used order-based tactic in high-frequency trading strategies, to analyze in detail how spoof orders can ignite herding with market power building as the essence. The Flash Crash that occurred in the New York Stock Exchange on May 6, 2010 provides an excellent case of market power construction exhibited in spoofing.

The behavioral mechanism of market power construction in the case of spoofing is perception alignment. It becomes effective when two necessary conditions are met: the spoof trader takes advantage of the incomplete order display set up by the exchange; and the behavioral weaknesses exhibited by numerous individual investors. In addition to these key conditions, this paper finds other ones for market power to be created in the financial market. They are easier, quicker, more secret, more flexible and less risky relative to the conditions for market power building in the goods market.

The detailed analysis points to the behavioral mechanism, i.e. perception alignment, and microstructural mechanism, i.e. incomplete order display, that could be responsive to regulation.

The originality of the findings is to uncover the mechanism of spoofing in taking advantage of behavioral biases of individual investors. The value is to gain more complete understanding of the essence of herding caused by spoofing.

The purpose of this paper is to examine the market reactions and its determinants of the releasing of restricted non‐tradable shares and to provide some useful information for the coming releasing peak of IPO‐restricted shares in China.

The paper employs event study and empirical analysis.

It was found that the cumulative abnormal return during the releasing windows is significantly negative, and firm quality, agency problems, and the market trading activity play important roles in explaining the negative market relations. This evidence shows that the cumulative abnormal returns during the releasing windows are positively associated with firm performance, assets turnover ratio, assets quality and trading turnover ratio, and are negatively associated with market‐to‐book ratio, financial leverage, the local government or private character of the ultimate ownership controller, and sum of trading on the announcement day.

The paper's value to investors is to show that one should choose firms with good financial position, not controlled by local government or private, and refer to the market trading activity in releasing windows. The paper's value to regulation parties is that they should regulate disclosure quality of financial reports, and avoid arbitrage due to information asymmetry during the releasing process to reduce the negative wealth effects to investors.

The purpose of this paper is to investigate the July 2007 introduction of a pre‐close call auction on the New Zealand stock market and its effect on share pricing quality and market manipulation.

Market quality was tested using the methodology of Pagano and Schwartz, which is based on changes in market model R²s. Closing price manipulation is detected by comparing mean bid‐ask spread characteristics of the periods before and after the introduction of the pre‐close call auction.

The closing call auction improves the quality of share pricing and reduces the incidence of market manipulation.

The paper confirms the effectiveness of the changes made to the method of closing the market for all firms in the market.

The paper extends knowledge of the effectiveness of closing call‐auctions. It is the first study in a low‐liquidity market and of shares with very low liquidities. Such markets have lower pricing quality and are more vulnerable to market manipulation. The study establishes the effectiveness of closing auctions in this environment.