Abstract Many corporate executives believe blockchain technology is broadly scalable and will achieve mainstream adoption, yet there is little evidence of significant shareholder value creation associated with corporate adoption of blockchain technology. We collect a broad sample of firms that invest in blockchain technology and examine the stock price reaction to the âfirstâ public revelation of this news. Initial reactions average close to +13% and are followed by reversals over the next 3 months. However, we report a striking difference based on the credibility of the investment. Blockchain investments that are at an advanced stage or are confirmed in subsequent financial statements are associated with higher initial reactions and little or no reversal. The results suggest that credible corporate strategies involving blockchain technology are viewed favorably by investors.
Alla Petukhina, Simon Trimborn, Wolfgang Karl Härdle, Hermann Elendner
Cryptocurrencies (CCs) have risen rapidly in market capitalization over the past years. Despite striking volatility, their high average returns and low correlations have established CCs as alternative investment assets for portfolio and risk management. We investigate the benefits of adding CCs to well-diversified portfolios of conventional financial assets for different types of investors, including risk-averse, return-maximizing and diversification-seeking investors who may trade at different frequencies, namely, daily, weekly or monthly. We calculate out-of-sample performance and diversification benefits for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. Our results demonstrate that CCs can improve the risk-return profile of portfolios, but their benefit depends on investor objectives. In particular, diversification strategies (maximizing the portfolio diversification index or equating risk contributions) draw appreciably on CCs and show, in line with spanning tests, CCs to be non-redundant extensions of the investment universe. However, when we introduce liquidity constraints via the LIBRO method to account for illiquidity of many CCs, out-of-sample performance drops considerably, while the diversification benefits persist. We conclude that the utility of CC investments strongly depends on investor characteristics.
This study employed an augmented AR-GJRGARCH model that incorporates an intraday-based buy-sell order size imbalance measure to explore how informed trading behaviour/activity impacted Bitcoin returns and volatility from January 2014 to February 2019. Our results show that the informed trading behaviour dominated by sell-order significantly led to decreased concurrent Bitcoin returns for alternative sample periods. However, the informed trading behaviour dominated by buy-order related positively to Bitcoin returns only for the full sample period. Moreover, the informed trading activity helped to reduce Bitcoinâs volatility, which is consistent with expectations based on dispersion of beliefs models. Finally, we uncovered a positive (inverted) asymmetric volatility effect for both the full and the rising sample periods, indicating the presence of the âfear of missing outâ psychological effect.
This paper investigates volatility spillovers among six competitor Cryptocurrencies from August 8, 2015 to September 01, 2019. A Generalized VAR framework is used to measure time varying spillovers index. Results provide evidence of (i) a rise in volatility spillovers transmitted among monitored Cryptocurrencies since the second quarter of 2017. (ii) Ethereum acts as the major contributor on spillovers index, contrary to Ripple that presents the main recipient of spillovers. (iii) the pairwise (Monero-Ripple) and (Bitcoin-Ethereum) present a low connectedness level driving consequently beneficial diversification opportunities for cryptocurrency investors.
Abstract This study investigates how twelve cryptocurrencies with large capitalization get influenced by the three cryptocurrencies with the largest market capitalization (Bitcoin, Ethereum, and Ripple). Twenty alternative specifications of ARCH, GARCH as well as DCC-GARCH are employed. Daily data covers the period from 1 January 1 2018 to 16 September 2018, representing the intense bearish cryptocurrency market. Empirical outcomes reveal that volatility among digital currencies is not best described by the same specification but varies according to the currency. It is evident that most cryptocurrencies have a positive relationship with Bitcoin, Ethereum and Ripple, therefore, there is no great possibility of hedging for crypto-currency portfolio managers and investors in distressed times.
The enormous rise of the cryptocurrencies over the last few years has created one of the largest unregulated markets in the world. In this study, we obtain millisecond data for the five major cryptocurrenciesâbitcoin, ethereum, ripple, litecoin and dashâand two cryptocurrency indicesâCrypto Index (CRIX) and CCI30 Crypto Currencies Indexâto investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during periods of extreme price movements (EPMs). We demonstrate that cryptocurrency traders (CTs) facilitate EPMs and demand liquidity even during the utmost EPMs. We observe the presence of herding behaviour during up markets across the entire dataset. Our robustness checks indicate that herding behaviour follows a dynamic pattern that varies over time with decreasing magnitude. We also provide novel evidence of CTsâ profitability after transaction costs, and demonstrate their strong profitability-generating record in the future.
Abstract Research Summary How emotions impact firm valuation is empirically understudied because affective traits are difficult to quantify. However, using artificial emotional intelligence, positive and negative affects can be identified from facial muscle contractionârelaxation patterns obtained from public CEO photos during initial coin offerings, that is, blockchainâbased issuances of cryptocurrency tokens to raise growth capital. The results suggest that CEO affects impact firm valuation in two ways. First, CEOs' own firm valuations conform more to those of industry peers if negative affects are pronounced ( conformity mechanism ). Second, investors use CEO affects as signals about firm value and discount when negative affects are salient ( signaling mechanism ). Both mechanisms are stronger in the presence of asymmetric information. Managerial Summary The purpose of this paper is to advance our understanding of how CEOs' affective traits influence firm valuation by both, CEOs themselves and investors. The effect of CEO emotions is plausibly particularly pronounced for startâup firms, whose success prospects critically depend on their leaders. My results suggest that CEO emotions impact underpricing in initial coin offerings twofold. First, negative emotions are associated with CEOs choosing an underpricing level that closely conforms to their peer firms' average. Second, investors react to negative CEO emotions by demanding higher discounts on firm value. These effects are more pronounced when there is relatively little public information about the ICO firm. My paper is accompanied by artificial emotional intelligence software for implementation in practice and future research.
We empirically examine the initial returns of Initial Coin Offerings (ICOs) and show that ICO underpricing is enormous, which implies that cryptocurrency markets are inefficient. Moreover, we find that having a short offering phase, not holding a presale, a precisely written whitepaper, and the creation of an independent blockchain all have a positive impact on ICOsâ initial returns. Our results also suggest that the driving factor behind initial returns is the movement of the cryptocurrency markets, measured by both Bitcoin and Ethereum returns. In addition, whether or not the jurisdiction has cryptocurrency regulations is an influential indicator. ICOs that belong to the high-tech services and platform industries have higher initial returns. Conventional financial assets, such as the stock market and gold, have a positive influence on ICOsâ initial returns.
Abstract Higher media coverage and stronger investor interest in cryptocurrency market may create closer linkages with traditional assets, leading to deteriorated diversification benefits. Cryptocurrencies have recently emerged as an alternative digital asset class; however, very little is known about their portfolio performances. In this study, we investigate the timeâvarying investment benefits of cryptocurrencies for stock portfolios using a correlationâbased conditional diversification benefits (CDB) measure. We construct six portfolios consisting of cryptocurrencies, developed and emerging equity markets and find that the timeâvarying correlations between cryptocurrencies and stock markets are generally low. However, the level of correlations significantly increases in turbulent periods, such as Brexit referendum and Coincheck hack. The dynamic CDB measures suggest that adding cryptocurrencies to equity market portfolios enhances portfolio diversification; however, the benefits of diversification have diminished after late 2017. Our results offer significant insights and potential implications for market participants.
Education on cryptocurrency is essential for individuals to make informed decisions regarding foreign investment in digital assets. In recent years there is an exponential increase in the price of cryptocurrency due to its easy trading especially in developing countries so the trend of financial institutions buying cryptocurrency into their portfolios has grown in the past decade which results in economic growth. The first completely digital assets that asset managers have included are cryptocurrencies. Traders have a unique opportunity to forecast price swings due to social mediaâs impact on cryptocurrency prices. Trading using Al and Machine Learning has drawn more attention in recent years. One could investigate the above hypothesis to determine if it is feasible to capitalize on the Bitcoin marketâs inefficiency for the purpose of generating unusually high profits. The advanced machine-learning techniques enable straightforward trading strategies to exceed conventional benchmarks. The findings demonstrate how basic computational processes might assist predict the near-term development of the bitcoin market. Further, there are prediction and comparison prices using SVM and Random Forest algorithms on the basics of efficiency while changing the number of days.
Mubbashar Altaf Khan, Mohsin M. Jamali, Taras Maksymyuk, Juraj Gazda
Cognitive radio (CR) technology offers the possibility of an increase in spectrum utilization efficiency to resolve the prevalent spectrum scarcity problem. The economic survival of secondary spectrum markets (SSMs) is heavily dependent on the sharing of both the licensed spectrum and spectrum infrastructure by primary licensed operators (PLOs). In this research, an automated pricing model using a blockchain token called the spectrum dollar has been implemented for secondary radio spectrum trade. The use of spectrum dollars enables noncash-based secondary spectrum trade among PLOs based on a floor-and-trade rule. The pricing of spectrum dollars and the associated revenue shares are based on the underlying secondary spectrum trading behaviours of PLOs. PLOs that do not contribute enough secondary spectra to the SSM (to satisfy demand) suffer a loss proportional to the difference between their earned revenues and the specified floor value in the SSM. The secondary spectrum trade is assumed to be centrally managed by a spectrum broker, which announces the floor value for each bidding period while ensuring nonnegative revenue for the market itself. The use of the spectrum dollar along with the floor-and-trade methodology eliminates the possibilities for economic malpractice by PLOs that could increase spectrum reuse costs. In addition, the floor value provides automatic regulatory control to ensure the economic viability and prevent the technological hijacking of future SSMs.
Kais Tissaoui, Taha Zaghdoudi, Khaled issa Alfreahat
This paper examines two competing hypotheses, that is, mixture of distribution hypothesis (MDH) and sequential information arrival hypothesis (SIAH) in the cryptocurrency market using high-frequency data. Specifically, we attempt to test the explanatory power of intraday public information arrival for Bitcoin returns and volatility over the period from January 1, 2019 to May 16, 2019. Based on AR (2)-PGARCH (1.1. ĂĂ´), the empirical results reveal the following: First, we find more evidence to support the MDH than the SIAH since the current trading volume participates to absorb the persistence of Bitcoin volatility stronger than the lagged trading volume. Second, solid evidence of the instantaneous effect of intraday trading volume on intraday Bitcoin returns is verified more than the lagged effect, which supports the MDH rather than the SIAH.
This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.
We assess the qualification of Crypto Currency as a new emerging financial asset class using Bitcoin as a sample study. As a financial asset class, its value should be derived from business prospects, uncertainty, and opportunity cost of money (riskless rate). We model the asset value relationship in form of an error correction model in regard of possible nonstationary data properties. We use GSCI commodity index, S&P 500 Index, Economic Policy Uncertainty Index and Yield of 5-year US Treasury Bonds as proxies of explanatory variables. Our findings show that the notion of crypto currency as a financial asset might be spurious. Common stochastic trend is the source of apparent correlation between Bitcoin and the regressors. This lack of fundamental linkage opens a way to improve cryptocurrency business model for greater global acceptance.
This article empirically investigates some of the key features of cryptocurrency returns and volatilities, such as their relationship with traditional asset classes, as well as the main driving factors behind market activity. The main empirical results suggest that while there is a mild relationship between returns on cryptocurrencies and commodities, and precious metals in particular, the relationship does not translate into volatility spillover effects. Consistent with existing theoretical models in which trading activity is primarily driven by investor sentiment, we show that trading volume is driven by past returns. On the other hand, macroeconomic factors do not seem to affect market activity in either the short term or the long term. <b>TOPICS:</b>Currency, exchanges/markets/clearinghouses <b>Key Findings</b> ⢠There is only a mild, and not significant, correlation between returns on cryptocurrencies and returns on traditional asset classes on a daily basis. ⢠Past returns significantly drive trading volume, consistent with the idea that short-term market activity is primarily driven by sentiment. ⢠Macroeconomic factors such as the term structure of interest rates and inflation expectations do not seem to affect market activity in either the short or the long term.
Taking the unique advantage of the cryptocurrency market setting, this paper examines the relationships between blockchain participation and returns, trading volume and realized volatility of main cryptocurrencies (i.e., Bitcoin, Ethereum and Litecoin). Dissimilar to previous theoretical studies that model the influencing factors on participation, we employ the number of unique from addresses 1 as the proxy for cryptocurrency investorsâ blockchain participation and further explore the impact of such participation. By using vector autoregressive (VAR) model, we find that the blockchain participation has a significant and positive impact on the next dayâs trading volume and realized volatility for the main cryptocurrencies. Our results are robust to the Granger causality test and alternative measure for blockchain participation.
Alla A. Petukhina, Raphael C. G. Reule, Wolfgang Karl Härdle
This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns related to algorithmic trading and its impact on the European cryptocurrency market. We study trading quantitatives such as returns, traded volumes, volatility periodicity, and provide summary statistics of return correlations to CRIX (CRyptocurrency IndeX), as well as respective overall high-frequency based market statistics with respect to temporal aspects. Our results provide mandatory insight into a market, where the grand scale employment of automated trading algorithms and the extremely rapid execution of trades might seem to be a standard based on media reports. Our findings on intraday momentum of trading patterns lead to a new quantitative view on approaching the predictability of economic value in this new digital market.
ABSTRACT: We study the ability of hedge funds to restructure target firms. A purchase of at least 3% of a target firmâs stake is subject to a 13D SEC Filing in the US. We use these filings to investigate the impact of such transactions in the period 2009â2020. Our method of choice is the event study approach. We set the event on the date of the transaction and compute cumulative abnormal returns (CARs) within a specified event window. Based on accounting metrics, such as return on equity and return on assets, we study how restructuring impacts target companyâs capital structure. Based on SEC Section 13G filings, we are further able to distinguish between acquisitions with active and passive aims. We find that firms targeted for active purposes achieve higher abnormal returns and overall higher performance. We further look on the impact of the overall stock-market cycle on abnormal returns. We find that the level of abnormal returns for actively targeted companies remains higher with no regard to the market cycle. Based on these findings, we draw conclusions on the overall impact of hedge fund activism. KEY WORDS: Hedge funds, Shareholder Activism, Abnormal Returns, Event study, Restructuring
We examine diversification when cryptocurrencies are included in investment portfolios, around China prohibiting initial coin offerings on 4 September 2017. We discover, once we account for liquidity, that all portfolio diversification benefits of cryptocurrencies are eliminated.