In contrast with robust systems that resist noise or fragile systems that break with noise, antifragility is defined as a property of complex systems that benefit from noise or disorder. Here we define and test a simple measure of antifragility for complex dynamical systems. In this work we use our antifragility measure to analyze real data from return prices in the stock and cryptocurrency markets. Our definition of antifragility is the product of the return price and a perturbation. We explore different types of perturbations that typically arise from within the system. Our results suggest that for both the stock market and the cryptocurrency market, the tendency among the 'top performers' is to be robust rather than antifragile. It would be important to explore other possible definitions of antifragility to understand its role in financial markets and in complex dynamical systems in general.
In contrast with robust systems that resist noise or fragile systems that break with noise, antifragility is defined as a property of complex systems that benefit from noise or disorder. Here we define and test a simple measure of antifragility for complex dynamical systems. In this work we use our antifragility measure to analyze real data from return prices in the stock and cryptocurrency markets. Our definition of antifragility is the product of the return price and a perturbation. We explore different types of perturbations that typically arise from within the system. Our results suggest that for both the stock market and the cryptocurrency market, the tendency among the 'top performers' is to be robust rather than antifragile. It would be important to explore other possible definitions of antifragility to understand its role in financial markets and in complex dynamical systems in general.
Cryptocurrencies have become a prominent investment tool recently with increasing interest in them and their relationships with stock and foreign exchange markets. We analyze here the cross-correlations of price changes of different cryptocurrencies using Random Matrix Theory and extract community structures by constructing minimum spanning trees, finding their eigenvalues contrast sharply with universal predictions of Random Matrix Theory. We reveal distinct transient community structures among different groupings of cryptocurrencies. By studying the cross-correlation dynamics of sub-communities we find evidence of collective behaviour. Furthermore, we compare eigenvalue changes and find prominent groupings following a community trend, useful for creating cryptocurrency portfolios.
Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward task, particularly in the presence of heavy tails. We develop an empirical hypothesis test to identify whether a time series is likely to exhibit multifractal scaling in the presence of heavy tails. The test is constructed by comparing estimated scaling functions of financial time series to simulated scaling functions of both an iid Student t-distributed process and a Brownian Motion in Multifractal Time (BMMT), a multifractal processes constructed in Mandelbrot et al. (1997). Concavity measures of the respective scaling functions are estimated, and it is observed that the concavity measures form different distributions which allow us to construct a hypothesis test. We apply this method to test for multifractal scaling across several financial time series including Bitcoin. We observe that multifractal scaling cannot be ruled out for Bitcoin or the Nasdaq Composite Index, both technology driven assets.
Tobias Burggraf, Toan Luu Duc Huynh, Markus Rudolf, Mei Wang
Purpose This study examines the prediction power of investor sentiment on Bitcoin return. Design/methodology/approach We construct a Financial and Economic Attitudes Revealed by Search (FEARS) index using search volume from Google's search engine to reveal household-level (“bankruptcy”, “unemployment”, “job search”, etc.) and market-level sentiment (“bankruptcy”, “unemployment”, “job search”, etc.). Findings Using a variety of quantitative methodologies such as the transfer entropy model as well as threshold regression and OLS, GLS and 2SLS estimations, we find that (1) investor sentiment has strong predictive power on Bitcoin, (2) household-level sentiment has larger effects than market-level sentiment and (3) the impact of sentiment is greater in low sentiment regimes than in high sentiment regimes. Based on these information, we build a hypothetical trading strategy that outperforms a simple buy-and-hold strategy both on an absolute and risk-adjusted basis. The results are consistent across cryptocurrencies and regions. Research limitations/implications The findings contribute to the ongoing debate in the literature on the efficiency of cryptocurrency markets. The results reveal that the Bitcoin market is not efficient in the sense of the efficient market hypothesis – asset prices do not fully reflect all available information and we were able to “beat the market”. In addition, it sheds further light on the debate whether Bitcoin can be considered a medium of exchange, i.e. a currency or an investment product. Because investors are reallocating their Bitcoin holdings during times of increased market sentiment due to liquidity needs, they obviously consider bitcoin an investment product rather than a currency. Originality/value This study is the first to examine the impact of investor sentiment measured by FEARS on Bitcoin return.
We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the outperformance of our model by comparing its outcomes with those obtained with different time series and machine learning methods. Finally, we discuss the predictions conditional to volume and we find that also in this case machine learning methods outperform econometric models.
María de la O González, Francisco Jareño, Frank S. Skinner
This article examines the connectedness between Bitcoin returns and returns of ten additional cryptocurrencies for several frequencies—daily, weekly, and monthly—over the period January 2015–March 2020 using a nonlinear autoregressive distributed lag (NARDL) approach. We find important and positive interdependencies among cryptocurrencies and significant long-run relationships among most of them. In addition, non-Bitcoin cryptocurrency returns seem to react in the same way to positive and negative changes in Bitcoin returns, obtaining strong evidence of asymmetry in the short run. Finally, our results show high persistence in the impact of both positive and negative changes in Bitcoin returns on most of the other cryptocurrency returns. Thus, our model explains about 50% of the other cryptocurrency returns with changes in Bitcoin returns.
Cryptocurrencies represent a new type of digital asset that cannot be linked to the framework of fundamental and systematic factors of existing financial instruments of the traditional capital market. Due to the lack of strictly defined fundamental indicators, supported by the results of research by the academic community, considering cryptocurrencies as investment opportunities can put investors in a subordinate position, a situation of complete uncertainty. Cryptocurrencies and their entire technical infrastructure are still a kind of unknown to the general public. Due to this, but also the lack of a regulatory framework, investors have to rely on sometimes uncertain information gathered through various media platforms. However, regardless of the type of assets and the mentioned shortcomings, when constructing a portfolio, investors should consider the dynamics of returns of potential components of the portfolio in order to identify and quantify the assumed investment risk and define the expected return. Cryptocurrencies are based on the idea of decentralization initially introduced by bitcoin blockchain technology and as such have their own historical sequence of origin. Since bitcoin is the first digital currency based on asymmetric cryptography, the change in its value can serve as a leading indicator of the movement of the cryptocurrency market as a whole. Accordingly, this paper will formally identify and describe the performance of the cryptocurrency portfolio with different optimization goals taking into account the assumption of a significant systematic impact of bitcoin cryptocurrency on the dynamics of the value of the aggregate secondary cryptocurrency market. For this purpose, six optimization targets will be formed: MinVar, MinCVaR, MaxSR, MaxSTARR, MaxUT and MaxMean. The results of the formed portfolios will be compared with the results of portfolios with the same allocation objectives, but which include a limitation on the impact of BTC as a systematic factor. The results suggest that by controlling the exposure by factor, better overall portfolio performance can be achieved through higher returns and Sharpe Ratio in four of the six implemented optimization strategies, while in terms of absolute risk measure five out of six portfolios achieved lower overall risk. Also, the obtained results confirm that the bitcoin transaction system plays a major role in defining the future movement of the value of the secondary cryptocurrency market.
The emergence of cryptocurrencies has been one of the most notable monetary phenomenon of the last decade. Many academics and analysts have found a clear precedent to this event in Friedrich Hayek's latest monetary work, Denationalization of money. The aim of this article is to analyze what we can learn about cryptocurrencies by re-reading this book. As will be proven, Hayek would surely have rejected the idea that Bitcoin and cryptocurrencies with similar characteristics could be accepted as money in the market. Furthermore, this paper will prove that a very close connection between Stablecoins and private money exists, following the Austrian economist’s predictions in a context of monetary competition.
Huaigang Long, Adam Zaremba, Ender Demir, Jan Jakub Szczygielski · 5 authors
This study presents the first attempt to examine the cross-sectional seasonality anomaly in cryptocurrency markets. To this end, we apply sorts and cross-sectional regressions to investigate daily returns on 151 cryptocurrencies for the years 2016 to 2019. We find a significant seasonal pattern: average past same-weekday returns positively predict future performance in the cross-section. Cryptocurrencies with high same-day returns in the past outperform cryptocurrencies with a low same-day return. This effect is not subsumed by other established return predictors such as momentum, size, beta, idiosyncratic risk, or liquidity.
Ioannis E. Livieris, Emmanuel Pintelas, Stavros Stavroyiannis, Panagiotis Pintelas
Nowadays, cryptocurrency has infiltrated almost all financial transactions; thus, it is generally recognized as an alternative method for paying and exchanging currency. Cryptocurrency trade constitutes a constantly increasing financial market and a promising type of profitable investment; however, it is characterized by high volatility and strong fluctuations of prices over time. Therefore, the development of an intelligent forecasting model is considered essential for portfolio optimization and decision making. The main contribution of this research is the combination of three of the most widely employed ensemble learning strategies: ensemble-averaging, bagging and stacking with advanced deep learning models for forecasting major cryptocurrency hourly prices. The proposed ensemble models were evaluated utilizing state-of-the-art deep learning models as component learners, which were comprised by combinations of long short-term memory (LSTM), Bi-directional LSTM and convolutional layers. The ensemble models were evaluated on prediction of the cryptocurrency price on the following hour (regression) and also on the prediction if the price on the following hour will increase or decrease with respect to the current price (classification). Additionally, the reliability of each forecasting model and the efficiency of its predictions is evaluated by examining for autocorrelation of the errors. Our detailed experimental analysis indicates that ensemble learning and deep learning can be efficiently beneficial to each other, for developing strong, stable, and reliable forecasting models.
Marina Resta, Paolo Pagnottoni, Maria Elena De Giuli
In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during the period 1 January 2012 to 20 August 2019. Our findings suggest that, overall, trading on daily data is more profitable than going intraday. Furthermore, we concluded that the Buy and Hold strategy outperforms the examined alternatives on an intraday basis, while Simple Moving Averages yield the best performances when dealing with daily data.
Philip Daian, Steven Goldfeder, Tyler Kell, Yunqi Li · 8 authors
Blockchains, and specifically smart contracts, have promised to create fair and transparent trading ecosystems.Unfortunately, we show that this promise has not been met. We document and quantify the widespread and rising deployment of arbitrage bots in blockchain systems, specifically in decentralized exchanges (or "DEXes"). Like high-frequency traders on Wall Street, these bots exploit inefficiencies in DEXes, paying high transaction fees and optimizing network latency to frontrun, i.e., anticipate and exploit, ordinary users' DEX trades.We study the breadth of DEX arbitrage bots in a subset of transactions that yield quantifiable revenue to these bots. We also study bots' profit-making strategies, with a focus on blockchain-specific elements. We observe bots engage in what we call priority gas auctions (PGAs), competitively bidding up transaction fees in order to obtain priority ordering, i.e., early block position and execution, for their transactions. PGAs present an interesting and complex new continuous-time, partial-information, game-theoretic model that we formalize and study. We release an interactive web portal, frontrun.me, to provide the community with real-time data on PGAs. We additionally show that high fees paid for priority transaction ordering poses a systemic risk to consensus-layer security. We explain that such fees are just one form of a general phenomenon in DEXes and beyond-what we call miner extractable value (MEV)-that poses concrete, measurable, consensus-layer security risks. We show empirically that MEV poses a realistic threat to Ethereum today. Our work highlights the large, complex risks created by transaction-ordering dependencies in smart contracts and the ways in which traditional forms of financial-market exploitation are adapting to and penetrating blockchain economies.
This paper sets out to explore whether Bitcoin can be considered as a globally accepted asset that has a resemblance to gold, which is widely considered to be the safest choice. An integrated overview of the empirical findings generated by the nascent but increasingly proliferating literature concerning the nexus between Bitcoin and gold is provided. The majority of evidence reveals that Bitcoin has a long way to go before it acquires the same characteristics as the safe-haven asset of gold. Overall, Bitcoin is found to be an efficient hedge against oil and stock market indices, but to a lesser extent than gold. Bitcoin presents low or negative correlations or an asymmetric non-linear linkage with gold. Despite sharing some common features with traditional assets, Bitcoin is found to be a good hedging asset in portfolios with gold. Moreover, evidence reveals that gold is a better and more stable safe-haven investment than Bitcoin.