Macroeconomic fundamentals and cryptocurrency prices: A common trend approach

Based on asset pricing theory, we posit and find that equity markets and cryptocurrency markets share a common fundamental. Our cointegration tests show that the most important asset pricing primitive, consumption, can serve as the common fundamental. We further show that additional macroeconomic factors, as well as uncertainty and sentiment, all play a role in explaining the deviation from fundamentals. To understand the linkage between equity markets, cryptocurrency markets, and the macroeconomy, we suggest the following three channels: (i) portfolio allocation decisions, (ii) intermarket order flows, and (iii) technological adaption expectations.     

Volatility cascades in cryptocurrency trading

This paper studies volatility cascades across multiple trading horizons in cryptocurrency markets. Using one-minute data on Bitcoin, Ethereum and Ripple against the US dollar, we implement the wavelet Hidden Markov Tree model. This model allows us to estimate the transition probability of high or low volatility at one time scale (horizon) propagating to high or low volatility at the next time scale. We find that when moving from long to short horizons, volatility cascades tend to be symmetric: low volatility at long horizons is likely to be followed by low volatility at short horizons, and high volatility is likely to be followed by high volatility. In contrast, when moving from short to long horizons, volatility cascades are strongly asymmetric: high volatility at short horizons is now likely to be followed by low volatility at long horizons. These results are robust across time periods and cryptocurrencies.     

The entry and exit dynamics of the cryptocurrency market

This paper presents an analysis of the entry and exit dynamics of the cryptocurrency market that focuses on the growth of initial coin offerings during 2015–2020. We used two different datasets: one includes long-lived cryptocurrencies, while the other includes the whole cryptocurrency system at our disposal–that is, it considers the entering and exiting cryptocurrencies. Comparing the dynamics between both datasets with the index cohesive force approach, we assessed how the growth of the initial coin offerings and the exiting cryptocurrencies affected the connectedness of the market. Our results show that the expansion of the cryptocurrency system gave rise to a strong collective movement during 2018–2019. Afterwards, the group pressure, due to the bubble of the initial coin offerings, decreased in favour of the largest cryptocurrencies. Lastly, we observed changes in the hierarchical order of the most influential cryptocurrencies. In particular, Ethereum became the most influential cryptocurrency, at the detriment of Bitcoin.     

Performance and learning in an ambiguous environment: A study of cryptocurrency traders

We investigate the performance and learning ability of traders in an environment governed by ambiguity, such as the cryptocurrency market. Using a profit decomposition methodology, we find significant cross-sectional and temporal heterogeneity in performance. Traders do not learn to progressively increase the magnitude of returns; however, they are able to improve on their ability to realise profits as a mechanism of adaptation to survive through ambiguity. This adaptation increases as traders progress through their career. Moreover, we find evidence in support of the gambler’s fallacy. We argue that learning in ambiguous environments has limitations, allowing traders primarily to survive.     

Sustainable finance and blockchain: A systematic review and research agenda

Sustainable finance and blockchain studies have garnered considerable interest recently. but there has been no systematic analysis of blockchain in sustainable finance to far. To fill this gap, based on the theme structure of blockchain research in the field of renewable finance from November 1, 2008 to January 31, 2022, this paper proposes a multi-level and all-round comprehensive bibliometric method (Co-occurrence Analysis method, Natural Language Processing method, and Exploratory Factor Analysis method) to comprehend the mode, process, and mechanism of the integration of them. The findings indicate that: (1) Blockchain has been widely used in many industries involved in sustainable finance; (2) Blockchain will have a long-term impact on Sustainable Finance in the fields of smart city and sharing economy; (3) Blockchain can be deeply integrated with other technologies to promote the diversified development of sustainable finance. Additionally, we highlight trends and research directions regarding blockchain in sustainable finance research.     

A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction

In this study, a price prediction model for futures markets of crypto assets is presented. Random Forest was used to study three scenarios as a function of input variables: technical indicators, candlestick patterns and both simultaneously. In turn, the model parameters, the time intervals, and the most suitable investment horizons were studied. In addition to showing the results from the model, a one-year out-of-sample prediction was simulated. The entire year of 2020 was chosen because the three possible stock market scenarios occurred in this year: a sideways market, a bear market resulting from the global pandemic and an end-of-year bull market. Last, this out-of-sample simulation was analyzed as a real operation, that is, by retraining the model after each new collection of data, so that the model had the maximum information at all times. In conclusion, using candlestick patterns instead of technical indicators, improves the efficiency of the results.     

On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach

The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions.     

Cryptocurrency,confirmatory bias and news readability – evidence from the largest Chinese cryptocurrency exchange

We investigate whether Chinese cryptocurrency investors show confirmatory bias when processing authority‐related news. Authority‐related news is defined as news that is related to government authority (including central bank) policies or talk. By using data from the largest cryptocurrency exchange in China, we find that investors’ response to authority‐related news is negative and significant in general. Moreover, we find that the abnormal trading volume and standard deviation of abnormal trading volume are significantly higher for authority‐related news with higher readability, suggesting investors respond to the more readable authority‐related news with more trading behaviour.     

Estimating the reaction of Bitcoin prices to the uncertainty of fiat currency

Recent studies have found that investors move from fiat currencies to Bitcoin cryptocurrency in environments with low trust and high uncertainty. This paper investigates the reaction of Bitcoin prices to uncertainty concerning fiat currencies by introducing a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-based event analysis approach. The 2013 Cyprus bailout is used as an event over the uncertainty of fiat currencies. With the proposed approach, the original Bitcoin price series is decomposed into high-frequency, low-frequency, and trend components, thus disentangling the short-, medium-, and long-term effects of the events on Bitcoin prices, respectively. We find that the low-frequency component is dominant and increased because of the event. In addition, the announcement significantly increased the intensity of short-term fluctuations in Bitcoin prices. However, there was no structural change in Bitcoin prices in the long-term trend. This paper provides a way to show the reaction of Bitcoin prices to the uncertainty of fiat currencies at different time scales and suggests that the reaction is mainly captured by the medium-term trend.     

Taxing bitcoin: Incentivizing the difficulty adjustment mechanism to reduce electricity usage

This paper develops a model of the bitcoin market that views the bitcoin as a tradeable commodity whose supply is managed by the Bitcoin protocol. Miners utilize equipment and electricity to solve complex computational problems and the first miner to solve a problem is rewarded with bitcoins. The protocol adjusts the difficulty of the problem to target a constant growth rate in the supply of bitcoins over time. The model demonstrates that an increase (decrease) in the difficulty works in effect like a government’s placing an ad valorem tax (subsidy) on the price of a commodity. The rents that would have arisen from limiting supply, however, are wasted as electricity costs. It is shown that an actual tax on the price of the bitcoin can be used to displace the electricity costs. Using data from March 2014 to January 2019, it is estimated that the difficulty adjustment mechanism resulted in net welfare losses to the miners and buyers of bitcoins of 373.8 million USD. Average initial tax rates of 35% and 347.5% would have fully displaced the electricity costs and maximized their reduction, respectively.     

Emerging new themes in environmental,social and governance investing: a systematic literature review

Environmental, social and governance (ESG) investing is a large and influential industry. Its activities could be illuminated, appraised and improved by academic research. Unfortunately, research to date has focused on measuring return performance. A wider range of topics is necessary before research can properly inform investors, companies and regulators. This paper contributes to the challenge by systematically exploring the literature to reveal a richer array of topics: the heterogeneous nature of ESG investing, its costs and motivations, and its management literature origins. In addition to these established topics, five emerging themes are identified: the human element, climate change, fund flows, fixed income and the rise of non-Western players.     

A bibliometric review of dividend policy literature

This study systematically reviews the dividend policy literature, combining quantitative and qualitative techniques. We screened a sample of 270 articles retrieved from the Scopus database from 1981 to 2022. We contribute to the literature by identifying six research streams based on bibliometric co-citation analysis: (1) Dividend payment practices, (2) Price–dividend relationship, (3) Capital market valuation, and dividend policy, (4) Risk governance and dividend policy, (5) Taxes and dividend policy, and (6) The dividend disconnects and catering incentives. For each of these streams, the central research theme is outlined, allowing us to recommend potential directions for further investigation. We provide influential journals, authors, topics, articles, and institutions from our analyses. We also contribute 77 research questions that can be explored in future research to develop the field of dividend policy. Our findings should be of value to academics, financial executives, policymakers, investors, and other practitioners.     

Anti-money laundering and financial fraud detection: A systematic literature review

Money laundering has affected the global economy for many years, and there are several methods of solving it presented in the literature. However, when tackling money laundering and financial fraud together there are few methods for solving them. Thus, this study aims to identify methods for anti-money laundering (AML) and financial fraud detection (FFD). A systematic literature review was performed for analysis and research of the methods used, utilizing the SCOPUS and Web of Science databases. Of the 48 articles that aligned with the research theme, 20 used quantitative methods for AML and FFD solution, 13 were literature reviews, 7 used qualitative methods, and 8 used mixed methods. This study contributes by presenting a systematic literature review that fills two research gaps: lack of studies on AML and FFD, and the methods used to solve them. This will assist researchers in identifying gaps and related research.     

The determinants of secondary market prices for developing country loans: the impact of country risk

With better-defined variables based on Euromoney country risk data as explanatory variables, the determinants of the prices of the debts of less-developed countries (LDCs) in the secondary market are estimated. With the use of cross-sectional data on 27 countries for the years 1992, 1993, and 1994, the regression results indicate that sovereign credit ratings constitute the most important variable influencing prices; other significant variables include the level of external indebtedness and the amount of debt in default. Separate results have been obtained for each of the two categories of countries grouped according to the level of economic development. These results are more meaningful than those of previous studies because the model includes, in addition to debt-servicing capacity, other variables that best explain the prices of LDCs' debt within the context of a risky debt instrument.     

Evidence on the existence of speculative bubbles in farmland prices

We conduct tests for the contribution of speculative bubbles to farmland prices. These tests are carried out under the hypothesis that farmland investors rationally form expectations. The outcome of tests reported here allows us to infer whether farmland prices are determined by market fundamentals-discounted returns from the highest economic land use-or whether rumors about farmland price movements are self-fulfilling. The tests are stationarity and cointegration tests relating farmland prices to rents. The tests are carried out using data from three farm production regions-the Corn Belt, the Northern Plains, and the Lake States. In each region, we find little evidence to reject the hypothesis that market fundamentals determine farmland prices.     

On the topology of cryptocurrency markets

Cryptocurrency markets are characterised by high volatility, high returns and comparative immaturity relative to equity and commodity markets. Topological Data Analysis (TDA) persistence norms are effective tools for the analysis of noisy dynamical systems like the cryptocurrency markets. We show how information from the shape of daily return data adds additional inference on activity within the cryptocurrency markets. TDA persistence norms embed volatility and connectedness between coins as well as incorporating information from uncertainty indexes, financial market performance and commodity returns. Our TDA measures are robust to noise and are consistent across a raft of alternative coin selections. Further, we exposit how persistence norms peak to forewarn of crashes and stay low as markets face exogenous shocks. We demonstrate the clear advantages of TDA for the study of cryptocurrency markets and develop the next steps for exploiting the potential of TDA for application to cryptocurrency markets.     

Central bank digital currency: A systematic literature review using text mining approach

Central bank digital currency (CBDC) is seen as a possible next step in the evolution of money, offering a more stable unit of account, a more efficient medium of exchange, and a safer way to store value. However, since it began to get significant attention from academics and practitioners a few years ago, many concerns about how a central bank may build an efficient CBDC and how it would impact a country's current financial system still remain unanswered satisfactorily. Based on the combination of text mining and systematic review methods, this work presents a thorough literature assessment of 191 academic papers on CBDC in order to identify major research issues and knowledge gaps that may be addressed in the future. We find seven primary research themes linked to CBDC including (1) Central bank, (2) CBDC and other digital currency, (3) CBDC and money markets, (4) CBDC and monetary policy, (5) CBDC design and technologies, (6) CBDC and payment system, and (7) CBDC and financial stability and regulatory. The finding helps provide both overall and in-depth views of the current state of research in digital fiat currency topics, as well as drawing some important implications and suggestions on directions for the future research.     

Facilitating co-production in public services: management implications from a systematic literature review

Drawing on the results of a systematic literature review of empirical studies, this paper sheds light on six broad factors that facilitate the initiation and implementation of co-production in public services. The factors are classified into two overarching categories: organizational factors, including organizational arrangements, professional roles, and managerial tools; and procedural factors, including participant recruitment, participant preparation, and process design. For each set of factors, the paper provides a series of management implications. It concludes with additional observations for practice.