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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]

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]

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]

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]

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.)

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]

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]

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]

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]

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]

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.

The article analyzes whether retail investors acting together on social media are subject to federal market manipulation under the Securities and Exchange Act and U.S. Securities and Exchange Commission (SEC) Rule 10b-5.

In this article, the author reviews the European Union law regarding market abuse from a human rights perspective. Setting the foundation for the discussion from a historical perspective on the different legislative approaches on the issue of market abuse, the article continues with the ECHR’s and CJEU’s relevant case-law on the subject. Mainly, the right to a fair trial and the ne bis in idem rule are the fundamental rights that should be looked at closely when placing the market abuse regulations under scrutiny. Certain irregularities of the legislation are outlined. Also, the article tries to point out on which aspects improvements should be pursued.

In recent years, algorithmic-based market manipulation in stock and power markets has considerably increased, and it is difficult to identify all such manipulation cases. This causes serious challenges for market regulators. This work highlights and lists various aspects of the monitoring of stock and power markets, using as test cases the regulatory agencies and regulatory policies in diverse regions, including Hong Kong, the United Kingdom, the United States and the European Union. Reported cases of market manipulations in the regions are examined. In order to help establish a relevant digital regulatory system, this work reviews and categorizes the indicators used to monitor the stock and power markets, and provides an in-depth analysis of the relationship between the indicators and market manipulation. This study specifically compiles a set of 10 indicators for detecting manipulation in the stock market, utilizing the perspectives of return rate, liquidity, volatility, market sentiment, closing price and firm governance. Additionally, 15 indicators are identified for detecting manipulation in the power market, utilizing the perspectives of market power (also known as pricing power or market structure), market conduct and market performance. Finally, the study elaborates on the current challenges in the regulation of stock and power markets in terms of parameter performance, data availability and technical requirements. [ABSTRACT FROM AUTHOR]

Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network. [ABSTRACT FROM AUTHOR]

Abstract Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market, both directly influencing the price of an asset or indirectly by misinforming other market participants. Such behavior can be a source of systemic risk and increasing distrust for the market participants, consequences that call for viable countermeasures. Building on the foundations provided by the extant literature, this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018. The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies, including a fraudulent agent, initialized from empirical data and who performs market manipulation. The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume. Simulation results provide a satisfactory fit to historical data. Several price dips and volume anomalies are explained by the actions of the fraudulent trader, completing the known body of evidence extracted from blockchain activity. The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period; without this agent, it would have been very unlikely that the price had reached the heights as it did in late 2017. The insights gained from the model, especially the connection between liquidity and manipulation efficiency, unfold a discussion on how to prevent illicit behavior.

The article presents the results of the application of machine learning methods to identify customers prone to the use of insider information and market manipulation. The feature space was formed taking into account the specifics of the subject area. Cluster analysis techniques were used to markup data and to construct a training sample to identify features of anomalous objects. The key distinguishing features of atypical transactions were obtained and analyzed, an "atypical investor profile" was created. Ensemble algorithms of machine learning were used to build models for identifying transactions with possible misuse of insider information and signs of market manipulation. The practical significance of the study lies in the fact that the use of models will allow to effectively and timely identify atypical transactions, as well as unscrupulous investors involved in the misuse of insider information and market manipulation. The proposed approaches can be applied both in the work of the Bank of Russia and in the brokers' work, who should independently monitor the doubtful operations of their clients. [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 major 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. [ABSTRACT FROM AUTHOR]

This paper investigates both coin-specific and market-based factors that drive cryptocurrency pump-and-dump schemes. It analyzes a data set comprising 1457 pump events that occurred from January 3, 2018, to January 2, 2022. Empirical findings, derived from binary cross-sectional regression models, reveal several characteristics that increase the likelihood of cryptocurrencies being pumped. These include lower market capitalization, lower trading volume, greater social media popularity, increased developer activity, and fewer exchanges trading them. Furthermore, the study employs count time-series models to examine market-based factors. The results indicate that periods of higher volatility or uncertainty are associated with an increase in pre-announced pump-and-dump activities. Additionally, the analysis shows that macroeconomic factors and specific time-related effects – such as Sundays, certain months, and the COVID-19 period – are significant in explaining the frequency of pump occurrences. Based on these findings, the article discusses several targeted recommendations. • We explore coin-specific vs market-based factors that drive pump-and-dump schemes. • We use a data set of 1457 pump events that span over 4 years. • Low market cap and volume, and social media buzz explain pump-and-dump schemes. • Higher market volatility is positively linked with pump-and-dump activities. • Macro factors, specific days, and COVID-19 impact pump frequency. [ABSTRACT FROM AUTHOR]

The author determines the peculiarities of privatisation processes in transformational economies compared with mature ones and considers the difference of the goal – to accelerate the development of the already existing stock market, and in fact, to create the market anew. Socio-economic transformations, privatisation of property and creation of market infrastructure are prerequisites but not a guarantee for the emergence of the domestic stock market as a specific institutional phenomenon, especially in the absence of adequate incentives for public equity. It is established that some local stock markets are degraded against the background of increasing size, globalisation, liquidity, and integration of world capital markets, including the Ukrainian one. It is stated that, unlike the neighbouring post-socialist states (firstly, Poland), the stock market has not been built in Ukraine as a basis for compelling attraction and allocation of capital. Paper substantiated that this is primarily a consequence of inefficient and protracted privatisation, mainly over-the-counter sale of shares, inconsistency of state and regulatory policy, optional iterations in the development of market infrastructure, the creation irrationality of privatised enterprises (even the smallest) exclusively in the form of open joint-stock companies, which die to their objective inability to raise public capital faced inadequate financial burden and coercion of listing on exchanges. The author identified the main problems of the Ukrainian stock market laid down during privatisation: excessively consolidated share capital structure, insecurity of minorities, meagre free-float and liquidity, conditional exchange pricing, the predominance of over-the-counter circulation of shares, etc. Emphasis is placed on the consequences of the attempt to implement the squeeze-out procedure in Ukraine. In the absence of market prices and the acquisition of control by dominant shareholders long before the legislative changes, the share buyback did not protect minorities. However, it led to significant investors’ losses, termination of circulation of shares of most issuers, even greater conditionality of indices, capitalisation and other indicators of market development. It is noted that the effectiveness of the announced state plans for the development of the stock market due to the privatisation of state property remains in doubt in the absence of prerequisites for balancing economic interests between market participants, the objectivity of pricing, incentives for public capital raising and effective institutional environment. It is concluded that in Ukraine, the focus on privatisation procedures in the stock market development has no prospects in the absence of adequate incentives for the public raising of capital.

Purpose: This study aims to analyze the practice of earnings management (EM) by the five largest banks in the Brazilian financial market through the manipulation of the loan loss provision (LLP). Methodology: The methodology used in this study recognizes (i) that the EM can result from both discretionary accounting records and actual decisions of companies and (ii) that, in the case of financial institutions, the calculation and registration of LLP do not comply with a detailed rule, but on general principles. Based on this recognition, it uses a sample composed of the five largest banks operating in the Brazilian market, in the period 2004-2019, with quarterly data, to estimate different panel specifications in order to test such as return, size and the use of financial derivatives affect the EM. Results: The research results suggest, from the identification of the random effects model, that the return and the use of derivatives have an inversely proportional influence, and that size has a proportional influence, on LLP, corroborating the thesis of the use of LLP as an instrument for earnings management by the largest Brazilian banks. Contributions of the Study: The general contribution of this study to the literature is in the discussion of LLP as a measure of earnings management by actual decisions and discretionary records in discretionary financial statements; the study also contributes to a better understanding of EM decisions in large Brazilian financial institutions. [ABSTRACT FROM AUTHOR]

This study investigates trade-based manipulations of capital market instruments. The dataset of the study was gathered from 22 cases of manipulation in Borsa Istanbul (BIST) that occurred in the period between 2010 and 2015. We propose a machine learning approach consisting of supervised machine learning classification models to detect trade-based manipulation from the daily data of manipulated stocks. As a result of this study, supervised machine learning techniques are proven to be successful at detecting trade-based manipulations in trading networks based on the measurement methods of accuracy, sensitivity, and F1 score. We found that our proposed model has an F1 score of 91%, 95% sensitivity, and 93% accuracy in market manipulation detection. [ABSTRACT FROM AUTHOR]

Since the adoption of the SEC's Rule 10b-21 in 1988, many researchers have been concerned over the effectiveness of short sales constraints in preventing manipulative trading in the derivatives market. We analyze whether options can be used as synthetic short sale instruments to manipulate stock prices before a seasoned equity offer. Due to the existence of strict short sales constraints in the equity market and market makers' anticipation of manipulative trading, it would be very costly for a manipulator to drive stock prices down artificially either by short selling in the equity market or by using synthetic short sales in the options market. Using a sample of 237 firms that issued SEOs on the NYSE and had options listed on any U.S. options exchange from April 2002 to December 2004, we show that potential manipulators in the options market tend to use put options as a trading vehicle during the SEO's pre-offer period. The results of our empirical tests support the predictions of our model. [ABSTRACT FROM AUTHOR]

Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market, both directly influencing the price of an asset or indirectly by misinforming other market participants. Such behavior can be a source of systemic risk and increasing distrust for the market participants, consequences that call for viable countermeasures. Building on the foundations provided by the extant literature, this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018. The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies, including a fraudulent agent, initialized from empirical data and who performs market manipulation. The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume. Simulation results provide a satisfactory fit to historical data. Several price dips and volume anomalies are explained by the actions of the fraudulent trader, completing the known body of evidence extracted from blockchain activity. The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period; without this agent, it would have been very unlikely that the price had reached the heights as it did in late 2017. The insights gained from the model, especially the connection between liquidity and manipulation efficiency, unfold a discussion on how to prevent illicit behavior. [ABSTRACT FROM AUTHOR]

The rapid increases of European Union Allowance (EUA) prices and very high market volatility, resulting mainly from the growing role of speculative entities, can contribute to forming a price bubble. This may cause the market instability and could have a implications on planning future reduction investments by European Union Emissions Trading Scheme (EU ETS) participants. That is why they need some kind of ‘safety valve’, an effective EU ETS instrument, which can be triggered when the situation requires it.

Objective: to consider approaches to countering the use of unfair transaction practices, in particular, market manipulations and insider trading within the functions and powers of the Bank of Russia.Methods: the work used a combination of methods of dialectical, empirical, comparative and logical research.Results: approaches to the definition of market manipulation are established, the main methods of market manipulation are considered. The activity of the Bank of Russia to identify unfair practices in the Russian financial market was studied; the amount of market manipulation cases was identified and analyzed. The approaches were shown, which exist within the framework of the financial market development strategy to counteract the use of unfair transactions practices, including the practices of market manipulation and the use of insider trading; promising areas of development in the specified areas were identified.Scientific novelty: the article consolidates the dynamics of the activities of the Bank of Russia, the probable problem areas and the potential in the field of countering the unfair transactions practices, including market manipulation and insider trading; the amount of identified cases of market manipulation is analyzed.Practical significance: the main provisions and conclusions of the article can be used in the further analysis of countering the use of unfair behavior practices in the financial market and in forming a glossary.

Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network.

In recent years, algorithmic-based market manipulation in stock and power markets has considerably increased, and it is difficult to identify all such manipulation cases. This causes serious challenges for market regulators. This work highlights and lists various aspects of the monitoring of stock and power markets, using as test cases the regulatory agencies and regulatory policies in diverse regions, including Hong Kong, the United Kingdom, the United States and the European Union. Reported cases of market manipulations in the regions are examined. In order to help establish a relevant digital regulatory system, this work reviews and categorizes the indicators used to monitor the stock and power markets, and provides an in-depth analysis of the relationship between the indicators and market manipulation. This study specifically compiles a set of 10 indicators for detecting manipulation in the stock market, utilizing the perspectives of return rate, liquidity, volatility, market sentiment, closing price and firm governance. Additionally, 15 indicators are identified for detecting manipulation in the power market, utilizing the perspectives of market power (also known as pricing power or market structure), market conduct and market performance. Finally, the study elaborates on the current challenges in the regulation of stock and power markets in terms of parameter performance, data availability and technical requirements.

The LIBOR (London Interbank Offered Rate) scandal, which led to the prosecution of both financial institutions and individuals for manipulating the primary interest rate benchmark in global financial markets, shows that the current top‐down approaches to regulatory rule enforcement cannot prevent or adequately detect financial market abuse. We argue that a bottom‐up approach, which relies on individuals acting in the interest of the common good, may be more effective in organizational environments that are duty‐ as well as incentive‐based. This approach encourages individuals to accept a degree of moral responsibility for their actions, and to some extent for the actions of others. We argue that properly motivated and instructed individuals can think and act better than they might otherwise do, despite behavioural biases. Nonetheless, there are circumstances under which the prevention of market manipulation may not ultimately serve the common good. [ABSTRACT FROM AUTHOR]

The promotion of both market fairness and efficiency has long been a goal of securities market regulators worldwide. Accelerated digital disruption and abusive trading behaviors, such as the GameStop mania, prompt regulatory changes. It is unclear how this "democratization" of trading power affects market fairness as economies cope with pandemic-driven shifts in basic systems. Excessive speculation and market manipulation undermine the quality of financial markets in the sense that they cause volatility and increase the pain of bubble and crash events. Thereby, they weaken public confidence in financial markets to fulfill their roles in proper capital allocation to irrigate the real economy and generate value for society. While previous studies have mostly focused on market efficiency, our study proposes a tool to improve market fairness, even under periods of stress. To encourage value generation and improve market quality, we advance a graduated Non-Value-Added Tax that we implement in an agent-based model that can realistically capture the properties of real-world financial markets. A profitable transaction is taxed at a higher rate if it does not enhance the efficiency measured by deviation from fundamentals. When an agent locks in profit not supported by fundamentals but driven by trend-following strategies, the generated profit is taxed at various rates under the Non-Value-Added Tax regime. Unlike existing financial transaction taxes, the non-value-added tax is levied on profit rather than on price or volume. We show that the proposed tax encourages profitable trades that add value to the market and discourages valueless profit-making. It significantly curtails volatility and prevents the occurrence of extreme market events, such as bubbles and crashes. [ABSTRACT FROM AUTHOR]

Most technical trading strategies use the official closing price for analysis. But what is the effect when the official closing price is subject to market manipulation? This paper answers this question by testing the difference of profitabilities between using the official closing price and the last tick price. The results show a significant improvement of profitability by using the last tick price over the official closing price based on a data set in Hong Kong from 2011 to 2018. [ABSTRACT FROM AUTHOR]

This Article offers a novel perspective on the implications of increasingly autonomous and "black box" algorithms, within the ramification of algorithmic trading, for the integrity of capital markets. Artificial intelligence (AI) and particularly its subfield of machine learning (ML) methods have gained immense popularity among the great public and achieved tremendous success in many real-life applications by leading to vast efficiency gains. In the financial trading domain, ML can augment human capabilities in price prediction, dynamic portfolio optimization, and other financial decision-making tasks. However, thanks to constant progress in the ML technology, the prospect of increasingly capable and autonomous agents to delegate operational tasks and even decision-making is now beyond mere imagination, thus opening up the possibility for approximating (truly) autonomous trading agents anytime soon. Given these spectacular developments, this Article argues that such autonomous algorithmic traders may involve significant risks to market integrity, independent from their human experts, thanks to self-learning capabilities offered by state-of-the-art and innovative ML methods. Using the proprietary trading industry as a case study, we explore emerging threats to the application of established market abuse laws in the event of algorithmic market abuse, by taking an interdisciplinary stance between financial regulation, law and economics, and computational finance. Specifically, our analysis focuses on two emerging market abuse risks by autonomous algorithms: market manipulation and "tacit" collusion. We explore their likelihood to arise in global capital markets and evaluate related social harm as forms of market failures. With these new risks in mind, this Article questions the adequacy of existing regulatory frameworks and enforcement mechanisms, as well as current legal rules on the governance of algorithmic trading, to cope with increasingly autonomous and ubiquitous algorithmic trading systems. We demonstrate how the "black box" nature of specific ML-powered algorithmic trading strategies can subvert existing market abuse laws, which are based upon traditional liability concepts and tests (such as "intent" and "causation"). We conclude by addressing the shortcomings of the present legal framework and develop a number of guiding principles to assist legal and policy reform in the spirit of promoting and safeguarding market integrity and safety. [ABSTRACT FROM AUTHOR]

How Bookies Can Outwit Sophisticated Bettors Sports-betting markets are based entirely on predictions. A bettor has to pick a winning contestant, and a market maker―a bookie―bets on the opponent. As bookies have to take the other side of every bet, it is of great value to understand the market making problem, that is, how to set the spread lines as "prices" for the bookies. Nevertheless, understanding of this problem is limited. Specifically, sophisticated bettors exist in the market, and a bookie can be manipulated by skillful bettors because of information asymmetry. In "Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors," Birge, Feng, Keskin, and Schultz study the market-making problem under information asymmetry and market manipulation. They show that, although many popular learning and pricing algorithms, such as Bayesian policies, are effective in learning, they are vulnerable to strategic manipulations. The authors propose a dynamic learning and pricing algorithm, called the inertial policy, that collects information from the market effectively but also protects the bookie from strategic manipulations. We study the profit-maximization problem of a market maker in a spread betting market. In this market, the market maker quotes cutoff lines for the outcome of a certain future event as "prices," and bettors bet on whether the event outcome exceeds the cutoff lines. Anonymous bettors with heterogeneous strategic behavior and information levels participate in the market. The market maker has limited information on the event outcome distribution, aiming to extract information from the market (i.e., "learning") while guarding against an informed bettor's strategic manipulation (i.e., "bluff-proofing"). We show that Bayesian policies that ignore bluffing are typically vulnerable to the informed bettor's strategic manipulation, resulting in exceedingly large profit losses for the market maker as well as market inefficiency. We develop and analyze a novel family of policies, called inertial policies, that balance the trade-off between learning and bluff-proofing. We construct a simple instance of this family that (i) enables the market maker to achieve a near-optimal profit loss and (ii) eventually yields market efficiency. [ABSTRACT FROM AUTHOR]

Abstract This study is the first attempt to empirically analyse stock market manipulation on the Nigerian Stock Exchange and its consequences on economic performance. The empirical investigation employs a broad data set of 186 actual manipulation cases indicted by the Nigerian Security and Exchange Commission between 2002 and 2016. We embrace market microstructure analysis and the event study method to understand how various manipulation techniques impact on market measures, and the Error Correction Model to evaluate their economic effects. Manipulation is found to distort market efficiency measures (such as market capitalization, value traded ratio and All-Share Index) and genuine traders are forced to exit the market to avoid possible trading with a manipulator. Such huge divestment and the subsequent financial risk weaken the ability of the Stock Exchange market to improve economic performance, creating negative consequences at the post-manipulation period. Essentially, economic variables (such as the GDP) react negatively to manipulative trading. This finding is insightful and a prompt to the Securities and Exchange Commission to design policy responses towards deterring and prosecuting unfair trading practices or other activities that contravene the market rules.

Market manipulation includes, inter alia, a practice that interferes or attempts to interfere with the free and fair operation of the securities and financial markets by creating an artificial, false or misleading appearance of the price of, or market for, the relevant securities, commodities or financial instruments. Consequently, market manipulation is treated as an offence in many countries, including Zimbabwe. For instance, market manipulation is expressly prohibited under the Securities Act 17 of 2004 (Chapter 24: 25) as amended (Securities Act 2004). In light of this and for the purposes of this article, the adequacy of the statutory prohibition on market manipulation in Zimbabwe will be examined. Accordingly, selected key elements, types, examples, penalties and definitional aspects of the market manipulation offence under the Securities Act 2004 are discussed. This is done to unpack and examine the adequacy of the Securities Act 2004 in relation to the combating of market manipulation in the Zimbabwean financial markets. It is hoped that the recommendations enumerated in this article will enable policy makers to develop optimal regulatory measures that promote investor protection and effectively combat market manipulation in the Zimbabwean financial markets.

The authors intend to stress elements that should be considered by public authorities in the effort to atribute administrative and criminal responsibilities in cases of coordinated actions of investors, combined in social networks, with the objective of causing an increase in the prices of securities. For that purpose, the article reviews the scarce bibliography and the rare existing jurisprudence. After examining the fundamentals of the prohibition of capital market manipulation, as well as its manifestations, the authors mention how modern forms of communication via social networks have affected the production and dissemination of information related to securities. They then analyze the combined actions on social networks in light of the administrative violation of price manipulation and the criminal offense of market manipulation. In the end they conclude for the possibility of administrative and penal accountability of the leaders of this type of action. [ABSTRACT FROM AUTHOR]

Digital advertising markets have evolved into a complex system with multiple interdependent actors interacting across the supply and demand chains. Google has emerged as a systemic actor in the digital advertising ecosystem. The company's presence within each layer of the digital advertising value chain, combined with the opacity and complexity of the market mechanisms, creates dependency challenges for business users. Google is also a choice architect that shapes users' experiences on its platform's business domains, including the experiences of the ads they are exposed to. Therefore, the company is uniquely positioned to hypernudge users towards specific market outcomes; it has the ability to steer within markets whilst following its economic imperatives. Positioning digital advertising by Google within the hypernudging framework provides a new lens for studying its potential for influencing digital advertising market dynamics and individual users. Hypernudging refers to one of the most sophisticated data-driven nudging processes that allow for dynamically personalised user steering, where (when executed perfectly) the right user is reached with the right message, by the right means, at the right time, as many times as needed. By examining local search advertising on Google Maps and multi-channel integrated advertising campaigns, this article shows that both could constitute a form of hypernudging. As the welfare effects of hypernudging are inconclusive, these processes are not considered to be intrinsically problematic. However, with potential intermediation bias at play, hypernudging may lead to dangers of systemic market manipulation and limitation of consumer choice. Once consumer harm is present, some forms of hypernudging may fall within the realm of competition law relevant practices. Competition authorities may examine biased market intermediation as exclusionary abuse; by the same token, by focusing on direct harm to consumers, they may explore the exploitative abuse route. However, as it is Google's systemic position on the advertiser- and user-sides of the market that is the source of hypernudging, effects felt on both sides are not only inseparable, they are mutually reinforcing. Thus, only once we zoom out and take a holistic view of these both sides, the full picture of the impact of hypernudging emerges, requiring one to potentially step outside the realms of the traditional competition law assessment. [ABSTRACT FROM AUTHOR]