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The sensors available in the smartphones are useful to explore a diverse range of city dynamics (e.g. noise pollution, road condition, traffic condition, etc.). The potential of the smartphone sensors coupled with their widespread availability help to emerge a new paradigm of sensing known as participatory sensing. It uses the power of smartphone equipped sensors to collect, store, and analyze data with high spatiotemporal granularity. In a participatory sensing based system, a task provider (also known as a crowdsourcer) may have a set of sensing tasks regarding different dynamics of a city. Here, adequate users' participation is necessary to acquire a sufficient amount of data which is a key factor for the participatory sensing based systems to provide good service quality. The task providers appoint a set of task executors (smartphone users i.e. participants of crowdsensing tasks) to execute those sensing tasks. But, existing works on sensing task allocation suffer from lack of good incentive mechanisms that are attractive for the task executors. In order to address this issue, in this paper, a double auction based incentive mechanism called TATA (Truthful Double Auction for Task Allocation) is proposed for participatory sensing. TATA performs fair allocation of tasks which is leading to efficient incentive mechanism. In the case of TATA, the fair allocation of sensing tasks of the task providers to the task executers indicates that the proposed double auction mechanism is able to satisfy the truthfulness property in order to resist market manipulation (i.e., untruthful bidding and asking). Specifically, TATA achieves all the desirable properties like individual rationality, truthfulness (i.e. incentive compatibility), budget balance, etc. TATA is also computationally efficient and yields high system efficiency. Additionally, the performance of the proposed incentive mechanism is evaluated and compared with the existing mechanisms through extensive simulations based on the real-world data from Amazon Mechanical Turk. TATA yields high utility and satisfaction for the task providers and executors as compared to the existing mechanisms. [ABSTRACT FROM AUTHOR]

Given a stream of financial transactions between traders in an e-market, how can we accurately detect fraudulent traders and suspicious behaviors in real time? Despite the efforts made in detecting these fraudsters, this field still faces serious challenges, including the ineffectiveness of existing methods for the complex and streaming environment of e-markets. As a result, it is still difficult to quickly and accurately detect suspected traders and behavior patterns in real-time transactions, and it is still considered an open problem. To solve this problem and alleviate the existing challenges, in this article, we propose FiFrauD, which is an unsupervised, scalable approach that depicts the behavior of manipulators in a transaction stream. In this approach, real-time transactions between traders are converted into a stream of graphs and, instead of using supervised and semi-supervised learning methods, fraudulent traders are detected precisely by exploiting density signals in graphs. Specifically, we reveal the traits of fraudulent traders in the market and propose a novel metric from this perspective, i.e., graph topology, time, and behavior. Then, we search for suspicious blocks by greedily optimizing the proposed metric. Theoretical analysis demonstrates upper bounds for FiFrauD's effectiveness in catching suspicious trades. Extensive experiments on five real-world datasets with both actual and synthetic labels demonstrate that FiFrauD achieves significant accuracy improvements compared with state-of-the-art fraud detection methods. Also, it can find various suspicious behavior patterns in a linear runtime and provide interpretable results. Furthermore, FiFrauD is resistant to the camouflage tactics used by fraudulent traders. [ABSTRACT FROM AUTHOR]

This article describes the GameStop short squeeze of January 2021, which was driven by retail investors from the online Reddit forum r/WallStreetBets. The short squeeze resulted in unprecedented market volatility and significant losses for institutional investors like hedge funds, leading to controversial trading restrictions by brokerages like Robinhood. This case study uses the GameStop saga to delve into key legal and ethical issues pertinent to an undergraduate business law class, including market manipulation, regulatory oversight/compliance, and conflicts of interest. It also examines fiduciary duties and class action lawsuits. By ex ploring these aspects, the study highlights the event's educational value in demonstrating many real-life examples of business law concepts while underscoring the need for robust regulatory frameworks in an era shaped by social media and technological advances. [ABSTRACT FROM AUTHOR]

This article raises the question of whether the European Insider Trading and Market Manipulation Regulation (Market Abuse Regulation—MAR) affected the behaviour of investors around the date of the final financial report announcement. The MAR is a relatively recent regulation in the European Union and provides stricter rules for information disclosure than the previous Market Abuse Directive. One can expect that this regulation should improve the information flow from companies to investors, lowering the volatility of stock returns and abnormal rate of return around the date of the final report. To verify this hypothesis, the panel regressions and ANOVA analysis were applied to the stock prices of 60 medium and large-size companies listed on the Warsaw Stock Exchange in the years 2013–2018. The analysis demonstrates that implementing the regulation has increased the average volatility of stock prices, whereas prices in the period before and after the publication period of annual financial reports remained unaffected. It indicates that the information provided was of lower quality, despite the increase in the information available to the public. Therefore, the anti-abusive regulation requires a modification of rules applied to small stock markets as they do not meet their objectives. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Bankrecht und Bankwirtschaft is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zeitschrift für Bankrecht und Bankwirtschaft is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We analyse the impact of the introduction of a new technology on the National Stock Exchange in India that allowed trading of stocks without the need to transfer paper share certificates (demat trading). We document a decrease in the bid-ask spread and an increase in trading volume following its introduction particularly for those stocks that were previously illiquid. We present evidence that suggests that the primary channel for the increase in liquidity was the elimination of the risk of being sold forged securities as the clearing system took on the risk of reimbursing buyers of forged shares at the introduction of demat trading. [ABSTRACT FROM AUTHOR]

Abnormal trading behavior in e-markets, also known as market manipulation, is done with the aim of deceiving traders in order to obtain unusual profits. These behaviors harm the proper functioning and integrity of the market and are, therefore, detrimental to trading activities. Although many studies have investigated this issue, their focus is on modeling the detection method, and they have neglected the processes that lead to manipulation. So that, to questions such as: What is the main reason for the success of these manipulations? What is the central idea behind manipulative strategies? Are manipulative schemes based on a specific theory? No answer has scientifically been given yet. Accordingly, there is a knowledge gap in the literature; this study aims to provide this complementary perspective and fill this gap. In this study, as a pioneer in this field, we develop the concept of the trading behavior's diffusion network. Then, we analyze and examine the diffusion process from the aspects of temporal dynamics of trading behaviors, diffusion network topology, diffusion patterns, and structural characteristics of the diffusion network. The results indicate significant differences in the diffusion process of manipulative behavior compared to regular trading behavior and random behavior. In addition, manipulators showed higher social influence (network centrality) in the diffusion network of manipulative behavior. Our findings bear significant implications for market supervisors and regular traders and provide a new dimension for researchers and supervisors in determining approaches and methods for detecting manipulation. [ABSTRACT FROM AUTHOR]

Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the dearth of labeled data and the inability to recognize novel manipulation tactics beyond those explicitly labeled. This study ventures into addressing these gaps by proposing a novel detection framework aimed at identifying suspicious hourly manipulation blocks through an unsupervised learning approach, thereby circumventing the limitations of data labeling and enhancing the adaptability to emerging manipulation strategies. Our methodology involves the innovative creation of features reflecting the behavior of stocks across various time windows followed by the segmentation of the dataset into k subsets. This setup facilitates the identification of potential manipulation instances via a voting ensemble composed of k isolation forest models, which have been chosen for their efficiency in pinpointing anomalies and their linear computational complexity—attributes that are critical for analyzing vast datasets. Evaluated against eight real stocks known to have undergone manipulation, our approach demonstrated a remarkable capability to identify up to 89% of manipulated blocks, thus significantly outperforming previous methods that do not utilize a voting ensemble. This finding not only surpasses the detection rates reported in prior studies but also underscores the enhanced robustness and adaptability of our unsupervised model in uncovering varied manipulation schemes. Through this research, we contribute to the field by offering a scalable and efficient unsupervised learning strategy for stock manipulation detection, thereby marking a substantial advancement over traditional supervised methods and paving the way for more resilient financial markets. [ABSTRACT FROM AUTHOR]

The cryptocurrency market cap has experienced a great increase in recent years. However, large price fluctuations demonstrate the need for governance structures and identify whether there are market manipulations. In this article, we conduct three analyses—social media data analysis, blockchain data analysis, and price bubble analysis—to investigate whether market manipulation exists on Bitcoin, Ethereum, and Dogecoin platforms. Social media data analysis aims to find the reasons for price fluctuations. Blockchain data analysis is used to find detailed behavior of the manipulators. Price bubble analysis is used to investigate the relation between price fluctuation and manipulators' behavior. By using the three analyses, we show that market manipulation exists on Bitcoin, Ethereum, and Dogecoin. However, market manipulation of Bitcoin is limited, and for most of Bitcoin's price fluctuations, we found other explanations. The price for Ethereum is the most sensitive to technical updates. Technical companies/teams usually hype some new concepts (e.g., ICO, DeFi), which causes a price spike. The price of Dogecoin has a high correlation with Elon Musk's X (formerly known as Twitter) activity, showing that influential individuals have the ability to manipulate its prices. In addition, the poor monetary liquidity of Dogecoin allows some users to manipulate its price. [ABSTRACT FROM AUTHOR]

Cryptocurrency, as one of the most successful applications of blockchain technology, has played a vital role in promoting the development of the digital economy. However, its anonymity, large scale of cryptographic transactions, and decentralization have also brought new challenges in identifying abnormal accounts and preventing abnormal transaction behaviors, such as money laundering, extortion, and market manipulation. Recently, some researchers have proposed efficient and accurate abnormal transaction detection based on machine learning. However, in reality, abnormal accounts and transactions are far less common than normal accounts and transactions, so it is difficult for the previous methods to detect abnormal accounts by training with such an imbalance in abnormal/normal accounts. To address the issues, in this paper, we propose a method for identifying abnormal accounts using topology analysis of cryptographic transactions. We consider the accounts and transactions in the blockchain as graph nodes and edges. Since the abnormal accounts may have special topology features, we extract topology features from the transaction graph. By analyzing the topology features of transactions, we discover that the high-dimensional sparse topology features can be compressed by using the singular value decomposition method for feature dimension reduction. Subsequently, we use the generative adversarial network to generate samples like abnormal accounts, which will be sent to the training dataset to produce an equilibrium of abnormal/normal accounts. Finally, we utilize several machine learning techniques to detect abnormal accounts in the blockchain. Our experimental results demonstrate that our method significantly improves the accuracy and recall rate for detecting abnormal accounts in blockchain compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Copyright of Jurist-Diction is the property of Universitas Airlangga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study focused on the phenomenon of political marketing, political communication, and their application in world politics during the post-truth era with an emphasis on the territory of the United States of America. The aim of the study was to identify several aspects of these disciplines during the dynamic technological progress which makes it possible to develop misleading manipulative techniques and spread disinformation. The emerging issues of the current state of the online sphere such as abuse of personal data for one's own political gain, dissemination of false news that radicalizes the public and threats to the security of free and fair elections and a democracy relate to other concerns such as a polarized society and electoral integrity. Politics has always been, and will continue to be, an infinite number of shades of grey. Absolute honesty and elimination of negative advertising or propaganda is neither realistic nor desirable, though we consider it important to strive for more ethical election campaigns. The conclusion of this article can be applied to other studies that will continue the research on the given topic and will respond to the current challenges of democracy. [ABSTRACT FROM AUTHOR]

This paper is evidently about the legal comparison between Indonesia and the U.S on their views on market manipulation. There are several similarities between the Capital Market Law and SEA 1934, not only in terms but also in the elements. Articles 91 and 92 of Capital Market Law seem to mimic Section 9(a) (1) and (2) of SEA 1934. As both statues states similar prohibition in creating a misleading trading appearance and the purpose of inducing sales. This means that elements and tests applicable in the U.S. should also be applicable in Indonesia. Section 10(b) of SEA 1934 and SEC Rule 10b-5 serves to further supplement the application of Section 9 with its broad anti-manipulation provisions. Articles 91 and 92 of Capital Market Law cover the sales and purchase of stocks affected by the alleged manipulation that occurs only in the securities exchange as evident by the wording “on a/the Securities Exchange”. This is also observed in Section 9(a) (1) and (2) of SEA 1934 wherein the scope is limited to transactions in the “national securities exchange”. What this implies is that over-the-counter and block sales transactions are not protected under the statutes mentioned above. This issue is not addressed under the Capital Market Law, however Section 10(b) of SEA 1934 and its implementing regulation SEC Rule 10b-5 addresses this issue. Section 10(b) of SEA 1934 allows for broader authority of law enforcement as it includes “any security registered on a national securities exchange or any security not so registered”. This implies protection for a wider scope of securities transactions.

The influence of Covid-19 pandemic crisis on rates of return is analyzed in this paper in the light of possible data manipulation related to reporting systems provided by the administration in the USA, Turkey and Poland. The study used various methods of analyzing the relationship of a discrete, non-discrete and dichotomous data nature between the studied variables. As a result, the strongest reaction of the market was observed in Turkey followed by the USA and Poland. It can be concluded that the reaction of the surveyed markets was influenced by the data manipulations. The added value of the article is related to the use of various methods to study phenomena and detect the impact of data manipulation on the markets. [ABSTRACT FROM AUTHOR]

Regulators use securities market surveillance as an essential tool to guard against market manipulation, safeguard investors, and maintain the effectiveness of the markets. In an effort to thwart open market manipulation SEBI in consultation with the exchanges has put in place a unique surveillance mechanism known as Additional Surveillance Measures (ASM). The short-term impact of this surveillance measure on stock prices and trading volume is examined in this study, using event study methodology. The sample data consists of 245 instances, where companies were subject to ASM surveillance actions for a brief period. The results of the study show that the Cumulative Abnormal Return (CAR) on the stocks decreased after inclusion into STASM and the same persisted after exclusion also. However, the average abnormal volume (AAV) changed significantly in the pre- and post-event period. [ABSTRACT FROM AUTHOR]

Paul Samuelson and others suggested that Ricardo ('the richest economist in history') made a life-changing coup on the Stock Exchange after the Battle of Waterloo (1815), but archives reveal that Ricardo amassed his fortune more gradually, often by small profit rates upon large investments, as a jobber on the Stock Exchange and a contractor for seven British Loans. The 1815 Loan generated exceptional profits for Ricardo, but not the million sterling mentioned in unreliable legends about Ricardo or Rothschild. Such legends neglected the stock price statistics, the suboptimal timing of Ricardo's transactions in 1815 and the dominant role of the Baring-Angerstein consortium, which took a much larger part of the Loans than Ricardo's consortium. Insufficient attention was also paid to the special year 1813, when Britain exceptionally launched not one, but two very profitable Loans. After his large 1813 profits, Ricardo started contemplating his retirement and bought Gatcombe Park in 1814. Samuelson neglected this and overestimated the influence of 1815 events. Moreover, Samuelson claimed that today Ricardo might be prosecuted for using unscrupulous market manipulation and inside information. Archives and statistics show that Samuelson's allegations lack historical evidence. [ABSTRACT FROM AUTHOR]

For the "In This Issue" column: Trading costs play a central role in designing and implementing quantitative trading strategies. To quantify trading costs, optimal execution and trading algorithms rely on price impact models, such as the propagator model. Empirically, price impact is concave in trade sizes, leading to nonlinear models for which optimization problems are intractable and even qualitative properties, such as price manipulation, are poorly understood. This paper shows that, in the diffusion limit of small and frequent orders, the nonlinear model converges to a tractable linear model. In this high-frequency limit, a stochastic liquidity parameter approximates the original impact function's nonlinearity. This allows us to derive simple formulas for optimal trading strategies and sharp conditions on market volumes to rule out price manipulation. A detailed empirical study using high-frequency limit-order data illustrates the practical performance of the theoretical results. Optimal execution and trading algorithms rely on price impact models, such as the propagator model, to quantify trading costs. Empirically, price impact is concave in trade sizes, leading to nonlinear models for which optimization problems are intractable, and even qualitative properties, such as price manipulation, are poorly understood. However, we show that in the diffusion limit of small and frequent orders, the nonlinear model converges to a tractable linear model. In this high-frequency limit, a stochastic liquidity parameter approximates the original impact function's nonlinearity. We illustrate the approximation's practical performance using limit order data. [ABSTRACT FROM AUTHOR]

Market abuse encompasses trading based on insider information and market manipulation. Such unfair market activities can endanger the entire market and, in their most intense and most pronounced form, seriously disrupt market dynamics, conferring undue advantages to certain participants, thus undermining principles of fair competition. Therefore, when defining legal regulations in the field of market abuse prohibition in a country, understanding the morphology of the market is crucial. It is important to consider both positive and negative consequences for the country’s market economy, if the goal is to form a transparent, integrated and efficient market, which would be attractive to investors based on such characteristics and thereby contribute to the economic growth and development of the country. The Republic of Serbia still lacks sufficiently developed mechanisms and appropriate legal solutions necessary for the functioning of a market economy, but its advantage lies in the model, experience, and judicial practice of developed markets within the member states of the European Union.

Copyright of Journal of Finance & Financial Law / Finanse i Prawo Finansowe is the property of Wydawnictwo Uniwersytetu Lodzkiego and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the effect on stock market efficiency and potential market manipulation of introducing a random ending time for the call auctions that start and end continuous trading on three equity markets. We find that the probability of a price dislocation at the end of the auction declines, indicating a lower risk of market manipulation. In addition, the variance ratio and market-adjusted return volatility measures decrease, suggesting a more efficient and less volatile price discovery process. We confirm a behavioral change in order submissions by observing the timing of order entry, amendments, and deletions on one of the exchanges for which we have access to order data. Overall, our results indicate that adding a random auction ending time can reduce the risk of stock market manipulation and improve price efficiency. [ABSTRACT FROM AUTHOR]

We present the first systematic approach to detect fake transactions on cryptocurrency exchanges by exploiting robust statistical and behavioral regularities associated with authentic trading. Our sample consists of 29 centralized exchanges, among which the regulated ones feature transaction patterns consistently observed in financial markets and nature. In contrast, unregulated exchanges display abnormal first significant digit distributions, size rounding, and transaction tail distributions, indicating widespread manipulation unlikely driven by a specific trading strategy or exchange heterogeneity. We then quantify the wash trading on each unregulated exchange, which averaged more than 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and user base), market conditions, and regulation. Overall, our study cautions against potential market manipulations on centralized crypto exchanges with concentrated power and limited disclosure requirements and highlights the importance of fintech regulation. This paper was accepted by David Simchi-Levi, Special Section of Management Science: Blockchains and Crypto Economics. Funding: This research was partly funded by the Ewing Marion Kauffman Foundation [Grant G-201907-6995], the National Natural Science Foundation of China [Grants 72192802, 72192800, and 72192801], Ripple's University Blockchain Research Initiative (UBRI), and the FinTech at Cornell Initiative. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.02709. [ABSTRACT FROM AUTHOR]

Purpose: This study explores the decision-making powers of Australian female consumers in the financial product market. More precisely, it examines how the integrative effects of rationality, emotions and personality traits influence the decision-making powers of Australian female consumers when making financial product purchase decisions. Design/methodology/approach: The study employs a quantitative research approach, utilising a survey strategy. The proposed conceptual model was tested using structural equation modelling (AMOS) on a valid 357 responses from Australian female consumers. Findings: The findings revealed that rationality, self-efficacy and impulsivity have a positive impact on the decision-making powers of Australian female consumers. Besides this, self-efficacy and anxiety had significant moderating effects on the decision-making power of Australian female consumers when buying financial products, whereas anger and impulsivity were found to have no moderating effects. Research limitations/implications: The study offers understanding on the role of emotions and personality traits in financial decision-making, which can help financial institutions design sound products and services that can also ensure consumers' overall well-being. Originality/value: Informed by the theoretical notions of the appraisal-tendency framework (ATF) and emotion-imbued choice model (EIC), the study makes a unique contribution by investigating the impact of rationality, emotions and personality traits on the decision-making powers of female consumers in the Australian financial product market. [ABSTRACT FROM AUTHOR]

This study aims to contribute to the existing literature by examining the relationship between corporate governance (CG) attributes and real-based earnings management (REM) in the context of an emerging market economy. The study employs a sample of 78 Egyptian Exchange (EGX)-listed companies covering the period from 2008 to 2017, yielding a total of 780 observations. To address dynamic endogeneity concerns between CG mechanisms and REM, the dynamic panel system-generalized method of moments (SGMM) estimator is used as the main analytical tool. The findings reveal that managerial and family ownership are negatively and significantly correlated with REM proxies, except for the ABCFO measure. By contrast, government and institutional ownership exhibit contrasting results, depending on the REM proxies used. The CG-EM relationship is influenced by several conflicting theoretical perspectives, including agency theory, institutional theory, stewardship theory, and resource dependence theory, resulting in inconsistent empirical findings. To the best of the authors' knowledge, this study is the first to detect Real-earnings manipulation practices (REM) in the Egyptian context using six models to confirm the validity, reliability, and robustness of the findings. Additionally, the study employs an advanced statistical technique that considers endogeneity, heteroscedasticity, and simultaneity in the relationship between CG mechanisms and earnings quality. The results highlight the importance of considering the institutional and legal context of a country when analyzing the impact of corporate governance mechanisms on earnings quality, as the practice and implementation of governance mechanisms vary across countries. [ABSTRACT FROM AUTHOR]

Purpose: The Regulatory Technology (RegTech) is said to be the use of information technology for regulatory monitoring, reporting and compliance. It is used to solve regulatory and compliance issues more effectively and efficiently. Regulators with the digitization of regulation and datafication of processes would get empowered to manage volumes of data. RegTech would assist them in understanding innovative products, transactions, risks, reporting and any market manipulation activities in real time. For successful use of RegTech, the regulatory framework of a country should be comprehensive to address issues that may arise in the use of RegTech. Thus, the purpose of this article is to analyze the adequacy of the Saudi Arabian legal framework to address RegTech adoption. Design/methodology/approach: The researcher using logical analysis method analyzed the available laws and interpreted the law to see the applicability and the adequacy of laws to regulate the use of RegTech in Saudi Arabia. The content analysis was also used in this research to analyze the literature. This analysis helped to explain the available literature on the research topic and its relevancy and the gap in literature. Findings: The analysis using the logical and content methodologies shows that Saudi Arabia has general law to address some of the issues that might arise in the adoption of RegTech. Nonetheless, amending some of the existing laws or introducing guidelines could help better uplift of RegTech and similar technologies in Saudi Arabia. Originality/value: As businesses and regulatory authorities embrace technology for better and efficient delivery of services and products in Saudi Arabia, the research is timely to analyze the adequacy of the laws in Saudi Arabia for adoption of RegTech. In the use of RegTech, issues related to privacy, due diligence, accountability and transparency could arise, however, there is a dearth of literature in these areas relating to technology adoption. [ABSTRACT FROM AUTHOR]

The article outlines the background of antirust laws and the cases that shaped the treatment of corporations in the U.S. Topics discussed include trends on nonenforcement of the Sherman Act that lead to increased prices of consumer goods and corporate profits under capitalism, status of corporate consolidation and corporate power, and ways the legal community can step up to protect the public from market manipulation and mitigate the effects of monopolies constraining working-class consumers.

Artificial Intelligence (AI) is playing a significant role in our social and daily life for technological development. The efficient integration of AI into business organizations or firms is improving performance and efficiency on a significant scale. Nowadays, artificial intelligence (AI) is assisting criminal activities or committing crimes through autonomous activity related to business, e-commerce, and market manipulation. AI could be used to aid criminal activity or, in certain situations, a mistake has resulted in criminal activity and most of the fields lack strong legislative safeguards due to the regulatory challenges imposed by the special nature of AI-Crime (AIC). The paper has examined the application and criminalization of AI in business and firm management. As we will have to strengthen the legislation for managing, preventing, and punishing AI-based business-related crimes, the last segment of the paper has suggested recommendations to counter the regulatory challenges. [ABSTRACT FROM AUTHOR]

We employ NASDAQ order level data to analyze intraday trading at option expirations and cross-market price pressure spillover. We observe more fleeting orders in optionable stocks on option expiration versus non-expiration days. The relation between NBBO proximity to strike prices and fleeting order direction, the relation between option Open Interest and fleeting order direction, as well as their placement outside NBBO suggest spoofing and price manipulation rather than a simple search for latent liquidity. We show that fleeting orders impact subsequent NBBO and increase likelihood of stock prices crossing option strike prices on option expiration days. [ABSTRACT FROM AUTHOR]

Processing fluency or the subjective experience of ease that consumers can experience when processing information is a prominent construct in consumer research. Despite its prevalence, however, its measurement has been inconsistent. The present research addresses this methodological gap in literature by developing and testing a scale for assessing the subjective experience of processing fluency. This scale demonstrates strong evidence of convergent and discriminant validity, reliability, and nomological validity across different processing fluency manipulations and marketing contexts. Use of this scale will allow marketing practitioners and academicians to consistently measure a psychological state that is known to have ubiquitous effects on downstream consumer outcomes including trust, attitude, and choice. Researchers can administer this four-item scale by having participants indicate their agreement (1 = strongly disagree, 7 = strongly agree) on whether a given marketing communication (e.g., ad copy) is (a) difficult to process, (b) difficult to read, (c) takes a long time to process, and (d) difficult to understand. [ABSTRACT FROM AUTHOR]

On public, permissionless blockchains like Ethereum, space is scarce and crypto traders must compete to use it for executing their transactions. That means those who control this space in the form of blocks resemble landlords who can extract rent. "Maximal Extractable Value" (MEV) refers to trading strategies that exploit the ability to decide what transactions go into a block. Those who control the contents of a block (validators) can obtain rents not only for including transactions in a block, but also for ordering them in profitable ways--say, by letting transactions "front-run" others. Since rising to prominence in 2019, MEV has quickly become a major market phenomenon, generating over $1 billion in profit since 2020, while affecting tens of billions of dollars in transaction value. MEV is often condemned. Techniques like "sandwich attacks" which involve trading ahead of other users' trades, have been described as toxic, fraudulent, manipulative--even theft. However, this broad denunciation of MEV is too quick, as the technical nuances of how each kind of MEV extraction operates are determinative of the legal risk it entails. The legality of MEV extraction under U.S. financial laws has yet to be subject to sustained scholarly analysis, and the present Article aims to fill this gap. We undertake the first systematic analysis of how U.S. securities and commodities law, particularly the broad antimanipulation rules wielded by the SEC (Rule 10b-5) and CFTC (Rule 180.1), apply to core MEV extraction techniques on Ethereum. In so doing, the Article confronts how basic notions of fairness and trust play out differently in a world of discretionary transaction ordering in crypto markets compared to the first-come first-serve world of traditional finance. Behaviors that might seem outrageous off-chain look very different when examined in light of how blockchains actually work. Nonetheless, this Article argues that some forms of MEV extraction entail a significant risk of market manipulation liability. Focusing on sandwiching in particular, we argue that there is a route for courts that adopt a moralized lens, focused on behavior that exploits privileged control over financial infrastructure, to find sandwiching impermissibly manipulative. We argue, further, that the legal hazards are even greater when it comes to sandwiching private transactions, which more clearly involves a heightened trust relationship, as well as disruptive schemes like oracle manipulation, wherein MEV is part of an independently manipulative strategy. Nonetheless, we argue, this alone does not mean a sweeping ban on MEV is necessarily a desirable policy. It remains unclear whether a strict ban on MEV sandwiching, for instance, would be prudent, given the unknowns about the net effects of MEV extraction and behavioral impact that a ban on MEV sandwiching would entail. [ABSTRACT FROM AUTHOR]

Abstract With the increasing popularity of cryptocurrency, many people are interested in cryptocurrency investments, but have so far hesitated. Many others have made investments without adequate preparation. To help interested investors improve their understanding of cryptocurrency and make rational investment decisions, it is important to study their concerns and motivations and to draw upon experienced investors’ experiences and practices. Therefore, we surveyed crypto investors and inexperienced potential investors interested in trading cryptocurrency (n = 395). Our results showed that extreme price volatility is the primary incentive and a substantial obstacle to market participation. Fraud risks, lack of personal funds, insufficient knowledge, and difficulty identifying credible information sources are also common barriers. Our findings highlight the need to build trustworthy exchange platforms and integrate educational features. Based on the reported concerns and experiences, we (1) identify learning components for new investors, and (2) formulate design recommendations for beginner-friendly exchange platforms. [ABSTRACT FROM AUTHOR]

The popularity of cryptocurrencies has skyrocketed in recent years, with blockchain technologies enabling the development of new digital assets. However, along with their advantages, such as lower transaction costs, increased security, and transactional transparency, cryptocurrencies have also become susceptible to various forms of market manipulation. The pump and dump (P&D) scheme is of significant concern among these manipulation tactics. Despite the growing awareness of P&D activities in cryptocurrency markets, a comprehensive survey is needed to explore the detection methods. This paper aims to fill this gap by reviewing the literature on P&D detection in the cryptocurrency world. This survey provides valuable insights into detecting and classifying P&D schemes in the cryptocurrency market by analyzing the selected studies, including their definitions and the taxonomies of P&D schemes, the methodologies employed, their strengths and weaknesses, and the proposed solutions. Presented here are insights that can guide future research in this field and offer practical approaches to combating P&D manipulations in cryptocurrency trading. [ABSTRACT FROM AUTHOR]

We examine whether and how options trading activity curtails real activities manipulation. Using a large sample of U.S. firms suspected of earnings manipulation, we document that an active options trading market significantly reduces real activities manipulation. We confirm our findings by using 2SLS analyses and alternative research designs. Our findings are also robust to using alternative proxies for options trading activity. Further, we find that the deterring impact of options trading on real activities manipulation is more pronounced among firms with low institutional ownership, among firms in highly competitive industries, and among small and young firms. Overall, our findings show that an active options market discourages managers from engaging in real activities manipulation, as informed options trading helps stock prices better reflect adverse consequences of real activities manipulation. Our findings highlight the benefits of options market development in reducing value-destroying activities and thus provide policy, practice, and research implications. [ABSTRACT FROM AUTHOR]

Intraday markets can be organized as continuous trading or discrete auction sessions. While many studies have attempted to compare the liquidity of these two models, additional external factors specific to each system, such as the balancing market design and number of bidding zones, affect overall market liquidity. In this regard, the Spanish hybrid intraday markets seem like an excellent case study to compare the two market models. Since the two intraday models are implemented in the same system (the Spanish one), the same conditions apply to their implementation. However, a direct comparison of liquidity is still challenging due to two factors: (1) differences exist in market architecture (timing, pricing scheme, bidding formats, etc.) between the two models, which create preferences among market players for one or the other; (2) the opportunistic behavior of market players in the system responding to the market price signals may affect the liquidity dynamics. We demonstrate the relevance of these two factors coming into play in the Spanish intraday markets, first carrying out a qualitative analysis of the market architecture of both models and then empirically analyzing a market manipulation attempt, which we refer to as the 15:10 rush. Our analysis points towards the need for more efficient regulation governing the interaction of the continuous intraday market with intraday auction markets and the potential risks from increased algorithmic trading. [ABSTRACT FROM AUTHOR]

This paper models how chatroom traders, forming a coalition via social media platforms, influence the stock price in the presence of large and strategic short sellers. The economic consequences of this dynamic game are studied in an equilibrium framework with strategic trading. Various equilibrium phenomena arise, including price bubbles, short squeezes, forced liquidations, and precautionary savings by the large trader. Media groups discipline the large trader's incentive to short sell, but it can either increase or decrease market allocational efficiency. Additionally, it uniformly improves social welfare under the belief-neutral welfare criterion. [ABSTRACT FROM AUTHOR]

In this study, we test whether earnings management has a positive impact on information asymmetry as well as whether earnings management has a negative impact on stock return synchronicity to investigate how discretionary accrual earnings management affects the imbalance of information and the co-movement of stock prices in Vietnam. We utilise the Pooled OLS (OLS), Random Effects (RE), Fixed Effects (FE), and System GMM models to evaluate our dataset collected from 356 non-financial companies listed on the Hochiminh City Stock Exchange (HOSE) spanning from 2012 to 2021. We find that in Vietnamese market earnings manipulations through accrual based falsify the market and cause information asymmetry leading to adverse effects on market liquidity and stock price synchronicity. Additionally, our findings exhibit greater co-movements between stock prices and earnings management at the larger firms with long incorporation history and are audited by Big Four Audit firms due to their credibility. These findings are particularly useful for foreign investors in making investment decisions as we found that their influences on earnings management in Vietnamese market is limited. [ABSTRACT FROM AUTHOR]

Theoretical background: Free float (hereinafter referred to as FF) refers to the ratio of shares held by small investors (less than 5%) to all shares in a company. FF investors are generally unrelated to each other or to ma-jor shareholders and they constantly review the company’s current stock market valuation and thus improve the stock market efficiency. This means that the higher the FF, the potentially higher liquidity and better valuation of the company’s shares. A low FF recalls the institution of protecting the rights of minority shareholders, calls into question the sense of maintaining a public company status, and raises the potential risk of incorrect valuation. The literature generally lacks studies referring to all these issues as well as FF statistics in Poland. Purpose of the article: The purpose of the article is to sum up the term “free float”, analyze FF statistics on the Polish capital market, indicate the link between FF and market liquidity, identify potential risks associated with listed companies having low FF and to determine whether it makes sense for the strategic investor to maintain public company status with low FF. Research methods: Theoretical analysis (including analysis of capital market laws) and statistical analysis. Main findings: The research confirmed that the relationship between FF and stock market liquidity is positive, but only considering FF in nominal terms. On the Polish capital market, as the nominal level of FF increases, market capitalization and trading liquidity increase largely and the average market spread decreases slightly. The article also points out important risks associated with low FF conditioning low liquidity, such as the risk of stock manipulation and the risk of incorrect company valuation. Potential areas for changing the law on squeeze-out/sell out institutions due to the inadequacy of the FF percentage were also pointed out.

We investigate the cost of debt effects for firms that manage earnings per share (EPS) through abnormal share repurchases. Although prior research finds a significant cost of debt decrease for firms that meet earnings benchmarks, our results suggest that firms using the abnormal share repurchase strategy realize no cost of debt decrease associated with meeting earnings benchmarks. We find some evidence of a smaller decrease in cost of debt associated with measures of abnormal decreases in cash flows but weak evidence for measures that are cash flow increasing. We also find that the effect of using abnormal stock repurchases to meet earnings benchmarks leads to smaller reductions in the cost of debt when compared with the cost reduction when earnings benchmarks are met through accruals management. This study extends prior literature regarding the effects on the cost of debt through alternative strategies to meet earnings benchmarks and will be of interest to managers as they consider the impact of their managerial decisions. [ABSTRACT FROM AUTHOR]

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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-the-art 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. [ABSTRACT FROM AUTHOR]