Assessing four decades of global research studies on stock market manipulations: a sceintometric analysis

Akram, Tooba; Ramakrishnan, Suresh; Naveed, Muhammad

doi:10.1108/jfc-08-2020-0163

Cited by 7 publications

(1 citation statement)

References 54 publications

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“…1. In general, stock market manipulation is defined as an illegal practice that attempts to raise or lower stock prices by masking it as an active trade [16,17]. Nevertheless, there is no universally accepted definition of stock market manipulation due to the unclear legal meanings and inconsistent terms that are used in economics and finance literature.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence

Zulkifley¹,

Munir²,

Sukor³

et al. 2023

Computers, Materials &Amp; Continua

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A well-managed financial market of stocks, commodities, derivatives, and bonds is crucial to a country's economic growth. It provides confidence to investors, which encourages the inflow of cash to ensure good market liquidity. However, there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor. These illegal trading activities are surely prohibited according to the rules and regulations of every country's stock market. It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient. However, the complexity of manipulation cases has increased significantly, coupled with high trading volumes, which makes the manual observations of such cases by human operators no longer feasible. As a result, many intelligent systems have been developed by researchers all over the world to automatically detect various types of manipulation cases. Therefore, this review paper aims to comprehensively discuss the state-of-theart methods that have been developed to detect and recognize stock market manipulation cases. It also provides a concise definition of manipulation taxonomy, including manipulation types and categories, as well as some of the output of early experimental research. In summary, this paper provides a thorough review of the automated methods for detecting stock market manipulation cases.

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Section: Introductionmentioning

confidence: 99%

A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence

Zulkifley¹,

Munir²,

Sukor³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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A systematic literature review of cyber-security data repositories and performance assessment metrics for semi-supervised learning

et al. 2023

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In Machine Learning, the datasets used to build models are one of the main factors limiting what these models can achieve and how good their predictive performance is. Machine Learning applications for cyber-security or computer security are numerous including cyber threat mitigation and security infrastructure enhancement through pattern recognition, real-time attack detection, and in-depth penetration testing. Therefore, for these applications in particular, the datasets used to build the models must be carefully thought to be representative of real-world data. However, because of the scarcity of labelled data and the cost of manually labelling positive examples, there is a growing corpus of literature utilizing Semi-Supervised Learning with cyber-security data repositories. In this work, we provide a comprehensive overview of publicly available data repositories and datasets used for building computer security or cyber-security systems based on Semi-Supervised Learning, where only a few labels are necessary or available for building strong models. We highlight the strengths and limitations of the data repositories and sets and provide an analysis of the performance assessment metrics used to evaluate the built models. Finally, we discuss open challenges and provide future research directions for using cyber-security datasets and evaluating models built upon them.

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Capital market manipulation and regulatory compliance – a bibliometric analysis of scholarly research in the post-2000 era

Singh,

Sarva,

Gupta

2024

QRFM

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Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.

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Assessing four decades of global research studies on stock market manipulations: a sceintometric analysis

Cited by 7 publications

References 54 publications

A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence

A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence

A systematic literature review of cyber-security data repositories and performance assessment metrics for semi-supervised learning

Capital market manipulation and regulatory compliance – a bibliometric analysis of scholarly research in the post-2000 era

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