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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.

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.

This study aims to diagnose the global key contributors in the stock market manipulation studies during the past four decades.

The database search is based on the terms used in the existing body of knowledge. Using the bibliometric tools and techniques on the Scopus database, the study assessed and analysed the productivity of research studies, as well as the influence of the authors, publications, journals, affiliated institutions and countries.

This paper finds the USA as the leading country investigating this area, almost capturing 40% of the research studies in finance, moreover, a huge number of co-authors. Financial crises in the late 1990s and 2008 is observed as one of the main reasons for this intriguing research. The Journal of Finance is spotted as the most persuasive journal with the highest cite score and an unprecedented number of citations. The analysis of keywords engendered that most of the stock market manipulation studies are event-based studies. Seminally unique scientometric analysis revealed that the significance of stock market manipulation was mainly captured by event-based studies, insider trading and pump and dump schemes studies. However, much remained untapped to articulate the bridging scope of technology and media with stock market behaviour and manipulations.

The research only includes the Scopus database, however, incorporates 81% relevant study.

This study reckons that technology-based manipulations are emerging themes in this research field which invites the applied research to have productive outcomes.

The intriguing study incorporates a maximum number of the relevant literature and used a comprehensive technique for the selection of dataset in Scopus.

In this article, we consider the evolution of the US stock market from the 1770s through the early 20th century. Adopting an institutional lens, we conceive of the stock market as an institutional field constituted by socially constructed cultural logics and myths. We focus on the role of the US government as an actor embedded in the stock market field and sharing in the prevailing field logics. Tracking the dominant logics of the stock market field at different historical periods, we examine how these logics impacted government regulatory action upon the stock market, and how those government regulations affected the subsequent logics of the stock market field. Our research included both quantitative content analysis of articles in historical newspapers and qualitative historical analysis of multiple primary and secondary accounts of stock market problems and solutions across more than 150 years. We document how government regulatory action both reflects and shapes the logics of the stock market field.

The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.

The purpose of this paper is to examine the influence of major non-economic events, such as the announcement of Greek national parliamentary elections during the period 2000-2009, and search for stock manipulation and methods to detect and recover ill gotten assets. The Financial Sector in Greece is one of the most important and fast growing sectors during recent years and accounts to about 16.17-17.74 per cent of gross domestic product. The ten largest Greek banks listed in the Athens Stock Exchange, accounted to 38.34 per cent of the whole capitalisation of the Athens Stock Exchange during year end 2009.

By using event study methodology and Market Model and analyzing data of all Greek bank stocks prices listed in Athens Stock Exchange, before and after the announcement of four Greek national parliamentary elections during period 2000-2009, we find interesting results about stock market manipulation.

Using daily data from the Athens Stock Exchange, the results of this paper claim that the four Greek national parliamentary elections during the period 2000-2009, had no statistically significant effect on the Greek banks stocks. The results show that Cumulative Average Abnormal Returns (CAARs) were slightly positive or negative for Greek banks’ stocks, but not statistically significant in 5 and 10 per cent confidence levels. Results show no manipulation effect in banks’ stocks even if single-party governments in Greece caused elections early, sudden or even opportunistic timing, having an incentive to attempt to manipulate stocks to increase their chances of re-election.

Results show that CAARs were slightly positive or negative for Greek banks stocks, but not statistically significant in 5 and 10 per cent confidence levels, but when illicit funds or assets have been acquired from stock manipulation, as small as can be, then one fact remains constant. Proceeds from illicit activities must be disguised in some way to avoid being discovered and then being recovered. Especially, during current the financial crisis, debt crisis and the extraordinary liquidity support measures taken by the European Central Bank (ECB), International Monetary Fund (IMF) and European Commission to support Greek economy, using methods to detect and recover ill gotten assets are extremely important. Indirect methods such as net worth analysis, bank deposit analysis, expenditure method or sources and application of funds analysis, to detect ill gotten assets, and then when ill gotten income and assets from bank stock manipulation are found, a restraining order or court order will help to recovery assets by freezing and finally confiscating them by two types of forfeiture – criminal and civil forfeitures. Establishing a code of conduct informing employees of the risks and consequences of insider trading, creating a culture of honesty and high ethics and implementing Controlled Foreign Corporation legislation to cope with off-shore companies trading, can help to recover ill gotten assets.

The paper examines if there is banks stocks manipulation around announcement of Greek national parliamentary elections during the period 2000-2009; suggesting methods to detect and recover ill gotten assets and improving the current position of the Greek economy. Findings offer important positive implications for investors, political analysts and society as a whole, as Greek banks stocks show that they are not subject to political risk and manipulation and that there are methods to detect and recover ill gotten assets. A stable bank sector is prerequisite for economy growth.

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.

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.

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.