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The purpose of this paper is to develop conceptual designs for blockchain implementations aimed at reducing corporate fraud. The proposed framework consists of different levels of implementation with specific examples for each level.

The paper uses a multi-level framework to highlight the properties of blockchain technology as suitable for reducing corporate fraud. The five levels of technological complexity designed for this research include information storage, information flow, information processing, information enhancement and information and financial integration. Specific cases of corporate fraud are discussed to complement the proposed methodology.

The potential ability to limit fraud and increase transparency could greatly improve faith in financial reporting. These benefits accrue to all capital market participants. The blockchain infrastructure can significantly improve the existing monitoring system and provide value added in detecting, deterring, and documenting possible fraud.

The paper contributes to the growing field on corporate fraud and blockchain technology. The paper is novel in the implementation of the nascent blockchain methods to detect and deter fraud at the organizational level. The proposed five conceptual levels provide practical use.

In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties.

This study employs the connectedness approach following the combination of Ando et al. (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method.

Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics.

In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks.

Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.

The purpose of this paper is to investigate the emergence of blockchain to determine its feasibility for electronic transfer payments. An integrated conceptual framework is developed for blockchain electronic transfer. The three “markers” of classic money (store of value, medium of exchange and unit of account and efficiency) are assessed in the case of blockchain technology. A survey is also conducted among the executives of financial vs manufacturing sectors with respect to five major variables for the emerging distributed financial data based on blockchain.

Conducting a literature review and using prior knowledge from the works of those well-versed and knowledgeable in the field. This research is also supported by collecting and analyzing the results of a behavioral survey of the executives in two different industries.

Blockchain technology has the potential to have widespread change in how firms operate and implement a new method for electronic payments transfer through cryptocurrencies. Although the community for blockchain technology is deregulated and has drawbacks, it is expected that the limitations will gradually be mitigated through its growth. The results of the behavioral survey show that there are significant differences between the expectations/perceptions of participants in the sectors of finance vs manufacturing. Namely, the knowledge/awareness of participants about blockchain, the value-added convenience for end users, and participants’ willingness to embracing and accepting new applications are significantly different between the two sectors.

The reach and applications for Blockchain are not limited to business or any particular sector. Blockchain technology may contribute to the operations of different types of organizations and industry sectors. Since the perception of participants about blockchain is different between selected industries, this research suggests the need for more education and building awareness among the participants in different sectors of the economy.

Bitcoin’s introduction as the first cryptoasset in 2009 ushered in a new era, representing a seismic shift in the financial markets. Since then, this evolving asset class has generated much interest, excitement, and growth. This chapter begins by providing a brief background of cryptoassets. It then discusses their main types (cryptocurrencies, security tokens, and utility tokens), users (individual investors, major financial institutions, endowments, and hedge funds), and benefits and drawbacks. Next, it sets forth the book’s purpose, distinguishing features, intended audience, and structure. The chapter provides a synopsis of each of the remaining 21 chapters. Although no single book can encompass all changes and iterations of these technologies as they emerge in the marketplace, this book brings together a broad collection of industry expertise and academic analysis to create a book helpful to researchers, academics, and practitioners.

Blockchain is an emerging technology that started in the cryptocurrency sphere with bitcoin but expanded to include numerous applications. This chapter provides an overview of the book. It begins by identifying the three main components of a blockchain. Next, it discusses the book's purpose, distinguishing features, and its intended audience. The chapter then outlines the book's structure, consisting of 22 chapters divided into four main parts. It offers a brief synopsis of each section and chapter. Finally, it ends with a summary and conclusions.

Neural networks and expert systems are two major branches of artificial intelligence (AI). Their emergence has created the potential for a new generation of computer‐based applications in the area of financial decision making. Both systems are used by financial institutions and corporations for a variety of new applications from credit scoring to bond rating to detection of credit card fraud. While both systems belong to the applied field of artificial intelligence, there are many differences between them which differentiate their potential capabilities in the field of business. Presents an analysis of both neural networks and expert systems applications in terms of their capabilities and weaknesses. Uses examples of financial applications of expert systems and neural networks to provide a unified context for the comparison.

The current security trade settlement life cycle presents several inefficiencies derived from intermediaries involved in the transaction between buyers and sellers. This chapter examines distributed ledger technology (DLT), the underlying technology of all blockchain applications, including trade settlements. It also reviews the implications of using blockchain in trade settlements for cryptoassets. Emerging blockchain technology provides investors, exchanges, regulators, and countless potential intermediaries with the most up-to-date technology with the highest efficiency, transparency, credibility, and automation enabled by smart contracts. Smart contracts allow an ecosystem to manage the process of trade settlements starting from execution to clearing and then settlement. These contracts reduce reconciliation and recordkeeping costs and streamline repetitive processes present in today’s trade settlement system. The chapter highlights the benefits of implementing DLT in financial markets globally in all trading aspects, including cryptoassets.