Cryptocurrency has been increasingly popular with investors. Using the 2018 National Financial Capability Study Investor survey, we examined the association between investment literacy and cryptocurrency investmentâabout 13% of investors invested in cryptocurrency directly or indirectly. Results from regression analyses show that objective investment literacy was negatively while sub- jective literacy was positively associated with holding cryptocurrency. Overconfident investors were more likely to invest in cryptocurrency, and results were robust across three overconfidence meas- ures. This study has implications for investment advice, financial education, and research.
Abstract We analyze cryptoasset markets (cryptocurrencies and stablecoins) at high frequency. We investigate intraday patterns. We show that Tether plays a crucial role as a safe haven and/or store of value facilitating trading in cryptocurrencies without going through traditional currencies. Markets centered on cryptocurrencies and stablecoins play a primary role aggregating preference/technology shocks and heterogeneous opinions, instead markets centered on the US dollar play a marginal role on price formation.
Rabaa Karaa, Skander Slim, John W. Goodell, Abhinav Goyal · 5 authors
We empirically examine whether feedback traders are active in the Bitcoin and the extent to which their presence is affected by a series of noise-related factors (sentiment; volume; liquidity) at three different frequencies (hourly; daily; weekly) for the April 2013âJuly 2019 period based on Bitstamp data. Our findings suggest that positive feedback trading grows stronger for higher (hourly; daily) frequencies, with its presence manifesting itself mainly during periods of high/improving sentiment and high/rising volume/liquidity. Additional tests reveal that the significance of hourly feedback trading is identified during hours corresponding to the trading hours of major European/North American markets. Overall, our results confirm extant literature evidence on the prevalence of noise trading in cryptocurrencies, while further showcasing that the factors motivating feedback trading in other asset classes (equities; ETFs; futures) exhibit similar effects over the presence of feedback traders in the cryptocurrency market.
Maurice OmaneâAdjepong, Paul Alagidede, Anna Gustav Lyimo, George Tweneboah
The letter examines herding in the most liquid cryptocurrency markets relative to traditional financial markets of 10 emerging economies within the G20. Our results reference significant symmetric crowd and imitation trading, which are dependent on time. Additionally, we report asymmetric herd behaviour in the cryptocurrency and stock markets, indicative that traders of these markets react collectively to extreme return movement with implied high risk and consequences for market informational efficiency.
Carol Alexander, Jun Deng, Jianfen Feng, Huning Wan
Bitcoin prices are driven by upward as well as downward jumps and so the bitcoin implied volatility surface behaves differently from those of established options markets. We analyze tick-level Deribit option price data, demonstrating increasing support for the limits-to-arbitrage hypothesis. Hence market makers are managing order imbalance and inventory more effectively as Deribit bitcoin options trading volumes increases. On the demand side, volatility traders drive both at-the-money and out-of-the-money option prices, the latter also being driven by directional traders. Directional effects were most pronounced during the price bubble of 2021. Further refinements of our tests assess time-to-maturity and time-of-day effects.
Uniswap is a decentralized exchange (DEX) and was first launched on November 2, 2018 on the Ethereum mainnet [1] and is part of an Ecosystem of products in Decentralized Finance (DeFi). It replaces a traditional order book type of trading common on centralized exchanges (CEX) with a deterministic model that swaps currencies (or tokens/assets) along a fixed price function determined by the amount of currencies supplied by the liquidity providers. Liquidity providers can be regarded as investors in the decentralized exchange and earn fixed commissions per trade. They lock up funds in liquidity pools for distinct pairs of currencies allowing market participants to swap them using the fixed price function. Liquidity providers take on market risk as a liquidity provider in exchange for earning commissions on each trade. Here we analyze the risk profile of a liquidity provider and the so called impermanent (unrealized) loss in particular. We provide an improved version of the commonly denoted impermanent loss function for Uniswap v2 on the semi-infinite domain. The differences between Uniswap v2 and v3 are also discussed.
We analyse high-frequency realised volatility dynamics and spillovers in the bitcoin market, focusing on two pairs: bitcoin against the US dollar (the main fiat-crypto pair) and trading bitcoin against tether (the main crypto-crypto pair). We find that the tether-margined perpetual contract on Binance is clearly the main source of volatility, continuously transmitting strong flows to all other instruments and receiving only a little volatility. Moreover, we find that (i) during US trading hours, traders pay more attention and are more reactive to prevailing market conditions when updating their expectations and (ii) the crypto market exhibits a higher interconnectedness when traditional Western stock markets are open. Our results highlight that regulators should not only consider spot exchanges offering bitcoin-fiat trading but also the tether-margined derivatives products available on most unregulated exchanges, most importantly Binance.
The salience theory of choice under risk shows that investor behavior drives cross-sectional cryptocurrency returns. Investors place too much weight on salient payouts, causing overvaluation of cryptocurrencies with upward salience returns and undervaluation of those with downward salience returns, leading to negative expected returns for the former and positive expected returns for the latter. The salience effect in the cryptocurrency market is more pronounced than in equity markets, making it a significant risk factor for explaining other cross-sectional returns in the cryptocurrency market. Unlike other documented return predictors, the salience theory uniquely contributes to understanding the cryptocurrency market. Video Abstract: https://youtu.be/F8BxhDWW7b4.
This paper examines the role of the information disclosed on blockchains in the cryptocurrency market. We find that blockchain disclosure on user adoption, measured as the number of new addresses, is highly value-relevant in the cryptocurrency market. Surprises in the disclosed number of new addresses explain 8% of the variation in cryptocurrency returns. The disclosed information is more value-relevant if the quality of the disclosure is higher and the cryptocurrency is larger in size. Unlike traditional markets, we do not find pre- or post-drift around the disclosure of new address information. The presence of strong market reactions at the disclosure and the absence of drifts around it highlight the distinct features of the information environment in this market, and provide a benchmark case where information is disclosed publicly and (almost) continuously. Lastly, we construct the price-to-new address ratios and find that they negatively predict future returnsâa cryptocurrency value effect.
The paper examines the influence of investor sentiment based on news headlines on the Cryptocurrency Market Index and ten individual cryptocurrency returns. We capture investorsâ sentiment from cryptocurrency-specific news headlines. We use a lexicon-based Natural Language Processing (NLP) technique to construct a unique sentiment indicator, and the sentiment scores are generated using two financial dictionaries: Henry(2008)(HE) and Loughran and Mcdonald(2011)(LM). The findings of the study show that news sentiment has a significant impact on cryptocurrency returns. When the investorsâ sentiment is optimistic or bullish, the cryptocurrency market experiences herding behaviour, leading to an increase in prices. The diverse and heterogeneous nature of the various cryptocurrencies causes each individual cryptocurrency to respond differently to sentiment. Further, we see that sentiment has a more pronounced impact on young, small, and volatile cryptocurrencies. Our study is among the few studies that use cryptocurrency-specific news headlines rather than news bodies to build a news sentiment indicator. JEL codes: E49, G14, G15
We analyze the market quality of centralized crypto exchanges (CEXs) and decentralized blockchain-based venues (DEXs) using a unique and comprehensive dataset. Focusing on two fundamental aspects, transaction costs and deviations from the no-arbitrage condition, we estimate the causal effect of ``gas fees'' on DEX market quality. We show that these fixed costs impose a significant burden on relatively small trades and cause persistent arbitrage deviations. Conversely, DEXs offer more competitive transaction costs for larger trades, offering a more favorable environment for institutional investors. Furthermore, we provide causal evidence that innovations aimed at enhancing the flexibility of liquidity provision in DEX markets lead to sizeable improvements in market quality.
Purpose The present study sets out to examine the empirical literature on the behavioural aspects of cryptocurrencies, showing the findings of related studies and discussing the various results. A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effects. Design/methodology/approach Systematic literature review. Findings A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment (investor's, market's) and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effect. Originality/value The authors' survey paper complements recent papers in the area by offering a systematic account on the influence of behavioural factors on cryptocurrencies. Further, this study's purpose is not just to index the relevant literature, but rather to showcase and pinpoint several research areas that have emerged in the field of behavioural cryptocurrency research. For all these reasons, a systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important.
In this paper, we investigate the herding behaviour of two types of cryptocurrencies, referred to as âblack/dirtyâ and âgreen/cleanâ based on their energy usage levels. Empirical results reveal that herding generally exists only in the dirty cryptocurrency market, and is more significant in down markets. Moreover, we find that clean cryptocurrencies do herd, but with dirty cryptocurrencies, when the two markets are both positive. Our findings are robust across value- and equal-weighted portfolios and provide valuable insights to investors and policy makers.
Elon Musk, one of the richest individuals in the world, is considered a technological visionary and has a social network of over 69 million followers on social media platform Twitter. He regularly uses his social media presence to communicate on various topics, one of which is cryptocurrency, such as Bitcoin or Dogecoin. Using an event study approach, we analyze to what extent Muskâs Twitter activity affects short-term cryptocurrency returns and volume. In other words, we investigate whether cryptocurrency markets exhibit a âMusk Effectâ. Based on a sample of 47 cryptocurrency-related Twitter events, we identify significant positive abnormal returns and trading volume following such events. However, we discover that on average, price effects are only significant for Dogecoin-related Tweets but not for Bitcoin. This is because regarding the latter, the significant price effects of positive and negative news cancel each other out, as further classification and analysis of Bitcoin-related tweets reveals. Our study shows the significant impact that the social media activity of influential individuals can have on cryptocurrencies. This suggests a conflict between the ideals of freedom of speech, morals and investor protection.
Examining the effect of behavioural factors, such as investor attention, on cryptocurrency markets is particularly important since, in contrast to traditional assets, they often have little intrinsic value, and so prices cannot be explained by fundamentals. This chapter presents several of the most commonly used proxies for investor attention, incorporating both indirect and direct measures. It then briefly introduces the research regarding the effects of investor attention in the context of stock markets. This provides a framework from which it is possible to understand the mechanism by which investor attention may influence cryptocurrencies. The chapter also discusses the emerging research that specifically relates to investor attention in cryptocurrency markets, including various measures of attention and the impact on returns, liquidity, volatility, and crash risk. The substantial price gains and extreme return volatility exhibited by cryptocurrencies has grabbed the attention of a range of investors, suggesting that investor attention is a particularly important behavioral factor to consider.
In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH), and the machine learning algorithm called the k-nearest neighbor (KNN) approach. We measure market liquidity as the log rates of bid-ask spreads in a sample of three cryptocurrencies (Bitcoin, Ethereum, and Ripple) and 16 major fiat currencies from 9 February 2018 to 8 February 2019. We find that the KNN approach is better suited for capturing the market liquidity in a cryptocurrency in the short-term than the ARMA and GARCH models maybe due to the complexity of the microstructure of the market. Considering traditional time series models, we find that ARMA models perform well when estimating the liquidity of fiat currencies in developed markets, whereas GARCH models do the same for fiat currencies in emerging markets. Nevertheless, our results show that the KNN approach can better predict the log rates of the bid-ask spreads of crypto and fiat currencies than ARMA and GARCH models.
Abstract. This research employs Capital Asset Pricing Model and foreign exchange exposure theory to explain how the value of financial stocks is affected by the home country cryptocurrency. Previous literature proposed that financial stocks were related to the economic or individual financial ratio, but rarely discussed the impact of a cryptocurrency variable in the digital economy. This paper presents specific findings to prove that cryptocurrency development causes structural change in the financial industry, by examining 67,166 panel data observations from China and Taiwan markets. We offer the following important conclusions: 1. Financial stocks in the China market suffer significantly higher impacts from home country cryptocurrency exposure than the Taiwan market. 2. Financial stocks in the China market are more greatly shocked by the CAPM three factors variables than the Taiwan market. 3. There are significant differences between the two financial markets. 4. The dynamics of the adjustment process of cryptocurrency evolution and the monetary system are key solutions for both markets. Keywords: cryptocurrency, Fin-Tech, Exchange rate Exposure. JEL Classification A14, D82, F65, G12, F3 Formulas: 2; fig.: 0; tabl.: 4; bibl. 31.
Ricardo de Souza Tavares, JoĂŁo F. Caldeira, Gerson de Souza Raimundo JĂșnior
This article analyzes whether cryptocurrenciesâ inclusion improves stock portfoliosâ performance and whether the application of portfolio selection methodologies would bring gains to investors in the digital currency market. Volatility Timing and Reward-to-Risk Timing methodologies were applied to a base containing only S&P 100 stocks, another containing only cryptocurrencies, and one mixing the previous two. The results suggest that the inclusion of cryptocurrencies has not brought performance gains to the stock portfolios and that the investor in the cryptocurrency market can benefit from the use of portfolio selection.