Journal of Financial Regulation and Compliance最新文献

Purpose

This paper examines the impact of Central Bank Digital Currencies (CBDCs), regulated stablecoins and tokenized traditional assets on the cryptocurrency market, following the guidelines set by the Basel Committee. This study aims to analyze the implications for secure storage, cross-border transfers and necessary investments.

Design/methodology/approach

The paper uses a policy analysis approach to assess the potential effects of the Basel Committee’s regulations on CBDCs, regulated stablecoins and tokenized traditional assets. It explores their impact on the cryptoasset market, strategies of central and commercial banks, payment systems and risk management.

Findings

The adoption of CBDCs, regulated stablecoins and tokenized traditional assets is expected to grow rapidly in the coming years. It raises concerns about secure storage, cross-border transfers and required investments. Central banks are likely to introduce CBDCs and authorize stablecoin issuance, aiming for efficient monetary policies and risk management. Basel III regulations may lead to asset tokenization by banks, reducing asset size and increasing fee-based income.

Originality/value

This paper provides insights into the potential impact of the Basel Committee's regulations on CBDCs, regulated stablecoins and tokenized traditional assets. It contributes to the understanding of the evolving cryptoasset market and the strategies of central and commercial banks in adopting these technologies. The findings offer valuable information for policymakers, regulators and market participants in navigating the changing landscape of digital assets.

Purpose

This paper aims to examine how fear of missing out (FOMO) and investment intention mediate the relationship between behavioral biases and investment decisions of retail investors in the Indian stock market.

Design/methodology/approach

The present research comprises two cross-sectional quantitative studies, where Study A involves data from 405 self-employed and Study B involves 393 salaried investors. Data was attained through questionnaires – the partial least squares structural equation modeling was used for data analysis.

Findings

The outcomes show that herding, overconfidence and loss aversion bias significantly impact investment intention and FOMO on both studies. Furthermore, the outcomes also indicate that herding and loss aversion bias significantly influence investment decisions in studies (A and B); however, overconfidence bias insignificantly affects the investment decisions in Study A. Besides, the results also reveal a substantial relationship between FOMO, investment intention and investment decision.

Practical implications

The findings of this paper assist practitioners (financial analysts and retail investors) in considering the various ways of analyzing investment decision outcomes by considering the joint effect of several biases.

Originality/value

This paper is an initial attempt to propose a new theoretical framework and empirically examine the impact of behavioral biases on investment decisions by considering the FOMO and investment intention among self-employed and salaried investors. This study also contributes to the behavioral finance literature; other researchers may find it valuable to attain their goals.

Purpose

The purpose of this paper is to assess the latest directives of the European Parliament and the Council – MiFID II and MiFIR – on markets in financial instruments in response to the growth of dark pools in European equity markets.

Design/methodology/approach

This paper examines the impact of the new regulatory packages on European equity markets by identifying areas where the legislation is effective and comparing these changes in EU legislation with US legislation on dark pools.

Findings

This paper find that the MiFID II and MiFIR directives, implemented by the European Securities and Markets Authority to address these concerns, have reduced information asymmetry between market participants, thereby increasing competition between regulated markets and alternative trading facilities.

Research limitations/implications

Increased competition can improve market quality, which has practical implications for financial market regulation and policy formulation.

Originality/value

These findings are novel in the existing literature on high frequency trading through dark pools. They improve the understanding of dark trading and its impact on competition and market efficiency. In addition, this research can assist policymakers in designing effective financial market regulation. The economic analysis of legislation also helps regulators assess the impact of new legal provisions on the functioning of capital markets.

Purpose

Although the consequences of illicit financial outflows on the economies of the world continue to exert adverse impacts on many economies of the world, explanations regarding specific drivers of the illicit outflows remain divergent in the literature. This study aims to investigate the effect of financial liberalisation on illicit financial outflows in Africa. Furthermore, the study also examines the effect of macroeconomic stability and institutional quality on illicit outflows.

Design/methodology/approach

To achieve the objectives, the study uses a dynamic panel system generalised method of moments technique to analyse annual data from the period 1995 to 2015 of 22 African countries.

Findings

The results show that financial liberalisation helps to reduce illicit capital outflows. Furthermore, improved institutional quality is associated with lower levels of capital outflows, thus affirming the theoretical expectations that stable political environment boost investor confidence. Overall, the study show that financial liberalisation reduces illicit outflows. However, liberalisation without sound macroeconomic stability and institutional quality may avail opportunities for illicit outflows.

Research limitations/implications

The main limitation of the study was lack of data that spans periods beyond 2015 for most of the variables on financial illicit flows. The available data sources could not test the objectives beyond 2015.

Originality/value

Current literature on the relationship between financial liberalisation and illicit fund outflows are generally conducted in the context implications on economic growth. However, beyond economic growth, financial liberalisation may impact on illicit financial outflows. Furthermore, other institutional and macroeconomic dynamics may influence illicit financial outflow, especially for developing economies in Africa.

Purpose

Financial services firms have a significant societal responsibility to prevent issues. The three lines model helps them do that though faces challenges in its effectiveness. This paper aims to offer a behavioural perspective on these challenges and practical solutions to help improve the model and herewith better prevent issues.

Design/methodology/approach

The authors detail key behavioural pitfalls and underlying psychological mechanisms that hinder the effectiveness of the model. The authors illustrate these with examples from the corporate practice, alluding to the behavioural patterns and drivers identified in the academic and consultancy work. The authors conclude with offering practical solutions how to enhance the effectiveness of the model.

Findings

The authors discuss common ineffective intergroup behaviours between the controllers (here: internal audit) and the controlled (here: the audited business or 2nd line functions): the controllers responding to issues with increased scrutiny; the controlled dismissing the feedback and challenging the issues raised; and the controlled and the controller competing for power. The root causes of these ineffective intergroup behaviours include: psychological defence mechanisms, social categorisation and collective beliefs about intrusiveness. The offered solutions range from actions the controllers can take, actions the controlled can take and actions both can take to improve the effectiveness of the model in practice.

Originality/value

The authors argue that the behavioural perspective on the effectiveness of the model is a blind spot and largely omitted from organisations’ agendas. This paper adds this behavioural perspective to help organisations improve the effectiveness of the model.

Purpose

Compliance risk management in the banking sector is crucial because of its multifaceted nature and its potential repercussions on reputation and financial stability. This study aims to present a systematic approach to mapping compliance risks, leveraging the risk self-control assessment (RSCA) method. Integrating quantitative and qualitative assessment techniques and drawing on human expertise and industry best practices.

Design/methodology/approach

The authors followed a methodology that involves conducting semistructured interviews with banking compliance professionals and analyzing professional frameworks to gather pertinent information. This comprehensive approach allows to obtain a holistic view of the risks involved and to refine the risk analysis process.

Findings

A step-by-step guide to map compliance risk within banks is proposed. The authors offer a rigorous process to enhance compliance risk management practices within the banking sector, providing actionable insights to effectively navigate regulatory complexities and safeguard financial stability.

Practical implications

The insights from this paper will guide compliance officers and risk managers to better assess and monitor compliance risk within their organizations.

Originality/value

To the best of the authors’ knowledge, this may be the first thorough paper that tackles the topic of mapping compliance risk in banks, from identifying areas of risk to corrective measures after drawing up the risk map, using the RSCA approach.

Purpose

This study aims to investigate the influence of banking consolidations on bank stability in Sub-Saharan African (SSA) countries for the period 2003–2019, following a series of bank mergers and acquisitions (M&As) in the region and whether regulation-induced bank M&As affect banking sector stability.

Design/methodology/approach

The fixed effect panel regression model is used to understand the influence of regulation-induced and voluntary bank mergers and acquisitions on banking stability in SSA. The study also controlled for bank-specific factors, market concentration and macroeconomic variables that affect banking stability. The study used three measures of bank stability: the Z-score, risk-adjusted return on assets and risk-adjusted bank capital.

Findings

The study results reveal that voluntary bank M&As, market concentration, net interest margin, bank capital, bank deposits and income diversification influence banking sector stability positively. However, the findings show that regulation-induced bank mergers and acquisitions impact banking stability negatively. Where bank M&As were a result of banking regulatory reforms, called regulation-induced mergers and acquisitions (RIM&As), banking stability suffered, but voluntary bank M&As improved banking stability. Again, the study supports the concentration–stability argument w?>rather than the competition–stability hypothesis. Therefore, more concentrated banking markets in SSA countries have more stable banks and fewer risks of system-wide bank failures. Other factors influencing banking stability in SSA are return on equity, bank efficiency (cost-to-income), bank size and deposits-to-assets ratio. However, their relationship is negative with the stability of the banking sector.

Practical implications

The findings imply that the regulatory authorities should encourage voluntary bank M&As and not regulation-induced bank M&As to improve the stability of the banking systems in SSA.

Originality/value

The study provides new evidence on the effects of regulation-induced bank M&As on the stability of banks.

Purpose

This study aims to test the contagion effect of the Tunisian revolution on the Egyptian stock market. Thus, the purpose of this research is to distinguish the contagion effect from the simple interdependence between these markets.

Design/methodology/approach

This paper examines the contagion hypothesis between Tunisia and Egypt during the Arab Spring, using a DCC-MGARCH model to capture time-varying contagion effects and dynamic linkages in stock markets. Therefore, to identify the contagion effect from the simple interdependence, the authors apply the pure contagion test developed by Forbes and Rigobon (2002).

Findings

The findings indicate a contagion effect, as the EGX 30 index exhibited similar changes, positive or negative, as the Tunindex index during the period of the Tunisian revolution. Moreover, the analysis demonstrates the presence of an interdependence between the Tunisian revolution and the Egyptian market, emphasizing the interconnections between these two economies.

Practical implications

The findings provide investors with a better understanding of financial market dynamics in times of major political unrest, notably on the Tunisian and Egyptian markets. By understanding the contagion effect of the Tunisian revolution on the Egyptian stock market, investors can further explore the complexities of these markets in times of financial crises, which can help mitigate losses and identify strategic investment opportunities.

Originality/value

This study makes two significant contributions to the field. First, it addresses the scarcity of research specifically focused on the contagion effect during the Arab Spring, aiming to fill this gap by testing the contagion effect of the Tunisian revolution on a nearby market. Second, it extends the contagion test of Forbes and Rigobon (2002), which associates “pure” contagion with a significantly higher correlation between markets during a crisis.

Purpose

This study aims to predict stock market crises in the Middle East North Africa (MENA) regions by leveraging the nonlinear autoregressive neural network with exogenous inputs (NARX) model with two measures of investor sentiment: the ARMS indicator and Google Trends' search volume of positive and negative words.

Design/methodology/approach

Employing a novel approach, this study utilizes the NARX model with ten neurons in the hidden layer and the Levenberg–Marquardt training algorithm. It evaluates model performance through learning, validation and test errors, as well as correlation analysis between predicted and actual crises.

Findings

The NARX model, incorporating investor sentiment, has proven to be a reliable tool for forecasting crises, helping market participants understand data complexity and avoid crisis consequences. The divergence in how investors interpret market news, with some focusing solely on negative developments and others valuing positive outcomes, highlights the predictive nature of the optimistic and pessimistic sentiments captured by the model.

Research limitations/implications

This study advocates for integrating behavioral approaches into stock market crisis prediction, highlighting the significance of investor sentiment and deep learning. It advances crisis mechanism understanding and opens avenues in behavioral finance. Integration of these findings into finance and economics education could enhance students' risk understanding and mitigation strategies.

Practical implications

The adoption of NARX models, incorporating investor sentiment, empowers market participants to proactively manage crises, adjust strategies, enhance asset protection and make informed decisions. These models enable them to minimize losses, maximize returns and diversify portfolios effectively in response to market fluctuations. These insights also guide policymakers such as governments, regulatory institutions and financial organizations in formulating crisis prevention and mitigation policies, bolstering economic and financial stability.

Social implications

This research reduces economic uncertainty, safeguards individuals' savings and investments and promotes a stable financial climate.

Originality/value

This study is one of the first attempts to demonstrate the detection and prediction of stock market crises, specifically in the MENA stock market, using the NARX model. It offers a robust forecasting model using machine learning and investor sentiment, providing decision-making support for investment strategies and policy development aimed at enhancing financial and economic stability.

Purpose

The purpose of this paper is to estimate the implications of illicit market use for the value of Bitcoin in an event studies framework.

Design/methodology/approach

This study uses a data set of 58 state-level marijuana decriminalisation and legalisation bills and referenda in the USA in 2010–2022.

Findings

Decriminalisation is associated with a strong and consistent positive Bitcoin price response around the event, recreational legalisation induces a more ambiguous reaction and medical legalisation is found to have a negative albeit small impact on Bitcoin value. This suggests decriminalisation enhances shadow economy use value of Bitcoin, whereas recreational and medical legalisation are not consistently reducing illicit drug cryptomarket activity. The effects are robust to various estimation windows, in subsamples, and also when outliers, heavy tails, conditional heteroskedasticity and state size are accounted for.

Originality/value

New to the literature, the choice of US marijuana bills, specifically as sample events, is based on both theoretical and empirical grounds.