This paper tests the random walk hypothesis in the cryptocurrency market. Based on the well-known MeeseโRogoff puzzle, we evaluate whether cryptocurrency returns are predictable or not. For this purpose, we conduct in-sample and out-of-sample analyses to examine the forecasting power of our model built with autoregressive components and lagged returns of BITCOIN, compared with the random walk benchmark. To this end, we considered the 13 major cryptocurrencies between 2018 and 2022. Our results indicate that our models significantly outperform the random walk benchmark. In particular, cryptocurrencies tend to be far more persistent than regular exchange rates, and BITCOIN (BTC) seems to improve the predictive accuracy of our models for some cryptocurrencies. Furthermore, while the predictive performance is time varying, we find predictive ability in different regimes before and during the pandemic crisis. We think that these results are helpful to policymakers and investors because they open a new perspective on cryptocurrency investing strategies and regulations to improve financial stability.
Isabela Ruiz Roque da Silva, Eli Hadad, Pedro Paulo Balbi
Abstract This study conducts a bibliometric analysis and systematic review of cryptocurrency trading algorithms to identify existing gaps in the area. From our standpoint, this is the first study to carry out a deep analysis of price forecasts and portfolio management in cryptocurrencies in addition to analyzing the most relevant studies and authors, trend topics of the area, and identifying countries with the most published studies. During our research, we identified some gaps that can be used for further research. Currently, there are approximately 16,000 cryptocurrencies; however, in majority of the papers, the authors have only used the top 10 ranking market capitalization cryptocurrencies, leaving aside potential minor cryptocurrencies. Thus, trading strategies using Big Data can be a potential research topic, considering the greater number of emerging cryptocurrencies.
The possibility of including financial instruments, such as equity, debt, derivative and market-based funds, in a portfolio varies with their market sensitivity. Cryptocurrency (crypto) has been of recent origin and interest to investors and policymakers. The study has attempted to explore opportunities for Indian and international investors in equity and crypto markets. Bivariate analysis between the crypto index and Indian market indices revealed few causal linkages between crypto and other indices. Standard VAR and Granger causality have been used for exploring the association between the variables. DCC-GARCH has been applied for checking further on volatility spillover and the relationship between indices. Granger results indicate the presence of linkages between crypto and energy, media, and oil & gas indices. However, spillover results have shown an absence of such linkages in the short run but a significant presence in the long run except for a few indices.
H. M. Tenkam, Jules C. Mba, Sutene Mwambetania Mwambi
This paper focuses on the selection and optimisation of a cryptoasset portfolio, using the K-means clustering algorithm and GARCH C-Vine copula model combined with the differential evolution algorithm. This integrated approach allows the construction of a diversified portfolio of eight cryptocurrencies and determines an optimal allocation strategy making it possible to minimize the conditional value-at-risk of the portfolio and maximise the return. Our results show that stablecoins such as True-USD are negatively correlated to the other cryptoassets in the portfolio and could therefore be a safe haven for crypto-investors during market turmoil. Our findings are in line with previous studies exhibiting stablecoins as potential diversifiers.
In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars? We designed Machine Learning (ML) systems that can trade four major cryptocurrencies: Bitcoin, Ethereum, BNB, and Solana. We found that more indicators do not necessarily mean better trading performance. Swing traders that use daily bars should trade Bitcoin and Solana using Ichimoku Cloud (IC) plus Moving Average Convergence Divergence (MACD), Ethereum with IC plus Chaikin Money Flow (CMF), and BNB with IC alone. With regard to intraday trading, we documented that different cryptocurrencies should be trading using different time frames. These results emphasize that the optimal number of indicators that are used to trade daily bars is one or, at maximum, two. The Multi-Layer (MUL) system that consists of all three examined technical indicators failed to improve the trading results for both days (swing) and intraday trades. The main implication of this study for traders is that more indicators does not necessarily improve trades performances.
John W. Goodell, Ilan Alon, Laura Chiaramonte, Alberto Dreassi ยท 6 authors
We investigate the impact of BRICS regulatory announcements on cryptocurrency volatilities and returns. Results evidence risk substitutions after announcements moving from ETH, XRP and LTC to BTC and vice versa, with BTC having volatility reactions to regulatory announcements that differ from those of other cryptocurrencies. Bootstrap quantile regression indicates a stronger detrimental impact of announcements when BTC is currently manifesting lower volatility and higher daily returns. Robustness checks confirm our findings, as well as evidence that the cryptocurrencies in our sample are considerably more reactive to BRICS announcements than US Fed announcements, suggesting important linkages between emerging markets and cryptocurrencies.
In this paper, we take a forecasting perspective and compare the information content of a set of market risk factors, cryptocurrency-specific predictors, and sentiment variables for the returns of cryptocurrencies vs traditional asset classes. To this aim, we rely on a flexible dynamic econometric model that not only features time-varying coefficients, but also allows for the entire forecasting model to change over time to capture the time variation in the exposures of major digital currencies to the predictive variables. Besides, we investigate whether the inclusion of cryptocurrencies in an already diversified portfolio leads to additional economic gains. The main empirical results suggest that cryptocurrencies are not systematically predicted by stock market factors, precious metal commodities or supply factors. On the contrary, they display a time-varying but significant exposure to investors' attention. In addition, also because of a lack of predictability compared to traditional asset classes, cryptocurrencies lead to realized expected utility gains for a power utility investor.
Purpose Cryptocurrencies lack fundamental values and are often subject to behavioral bias leading to market bubbles. This study aims to investigate the contribution of the coronavirus pandemic to the creation of market bubbles. Design/methodology/approach This study identifies four major cryptocurrency market bubbles by using the Phillips et al. (2016) (hereafter PSY) test. Subsequently, the co-movements of the coronavirus proxies with PSY measurement using the wavelet approach were studied. Findings Short-lived bubbles are detected at the beginning of the studied period, and more extended bubble periods are identified at the end. Besides, the empirical results show evidence of significant negative co-movement between each pandemic proxy and each cryptocurrency bubble measurement. Research limitations/implications Given the complex financial dynamics of the cryptocurrency markets due to some behavioral biases in some circumstances, investors can benefit from the date stamping of the bubbles bursting to make the best trading positions. In the same way, governments could support the healthy development of cryptocurrencies by preventing bubbles during such pandemics. Originality/value The financial bubble is commonly attributed to a change in investor behavior. Because traders and investors think they can resell the asset at a higher price in the future. This study explored the contribution of the COVID-19 pandemic in the creation of these bubbles by date stamping their occurrence and explosive periods. To the best of the authorsโ knowledge, this study is the first attempt that explores the contribution of the COVID-19 pandemic to the creation of bubbles caused by a change in the investorsโ behavior.
Purpose Bitcoin has emerged as a phenomenal asset earning abnormal profits. However, the factors with predictability power over its price are not widely studied. Therefore, this study aims to explore the factors that determine bitcoin prices. The analysis explores the determinants belonging to four categories โ macro economic, financial, technical and fundamental factors. Design/methodology/approach The study employs random effects regression on the panel data of five countries. Then Granger causality test is applied on the time series of all the variables. Lastly, diagnostic tests are conducted to confirm the findings to be robust and reliable. Findings The findings suggest that oil price, bitcoin supply, trading volume and market capitalization significantly impact the price of bitcoin in the long run. In short run, bitcoin returns are only caused by oil price and market capitalization. Interestingly, bitcoin returns influence its attractiveness to investors, market capitalization, S&P 500 returns and trading volume, in the short run. Practical implications The technical analysis is found to be redundant in the short run. In the long run, technical as well as fundamental analysis are useful. The bitcoin is found to be a good diversification tool as it has no linkages with the stock markets and gold market. It is also an inflationary hedger owing its limited supply. Originality/value The studies on cryptocurrency market have not conducted the analysis across countries. This study captures the cross-sectional effects along with time effects. The study also includes 17 variables belonging to four categories.
A. Thavaneswaran, You Liang, Sulalitha Bowala, Alex Paseka ยท 5 authors
Recently there has been a growing interest in applying neural network modelling from natural language processing to financial time series prediction problems in computational finance. Cryptocurrency price prediction is a challenging problem with non-stationary market price and volatility clustering. Cryp-tocurrency data tends to be non-stationary, which means that predictive information extracted using deep learning techniques on observed data can not be used with future data. Moreover, there is a very little signal in cryptocurrency data to indicate the future direction of the market. This paper proposes a sensible way to frame the prediction problem as a dynamic regression problem by defining the features in the feedforward neural networks and the target as an appropriate average of the historical data. The novelty of this paper is to use deep learning algorithms and statistical bootstrapping to obtain cryptocurrency price prediction and the corresponding prediction intervals. It is shown that neural networks are capable of modelling nonlinearity directly for nonlinear time series models. The proposed hybrid approach is evaluated using simulated and cryptocurrency data through numerical experiments. Moreover, Gaussian and boot-strap prediction intervals for the price and the volatility of the prediction errors, are also discussed in some detail.
You Liang, A. Thavaneswaran, Alex Paseka, Wei Qiao ยท 6 authors
Pairs trading strategies are constructed based on exploiting mean reversion in security prices, which have been demonstrated to perform well for stocks. However, their performance is not widely studied for cryptocurrencies, which are usually discerned as inefficient and unpredictable. One significant advantage of pairs trading is that potential profits can be generated regardless of the overall market movement. The pairs trading has the potential to be profitable for cryptocurrencies in bear markets and with intraday data. Kalman filter (KF) algorithms are popular for pairs trading to update the hedge ratio dynamically. They reduce the arbitrariness in parameter optimization by putting constraints on the parameter space. However, a major drawback is that the innovation volatility estimate calculated by using a KF algorithm is always affected by the initial values and outliers. An effective resilient filtering approach to estimate the innovation volatility is presented in this paper for cryptocurrencies. This paper presents rolling regression pairs trading strategies, traditional KF pairs trading strategies and resilient filter pairs trading strategies. The proposed trading strategies have been evaluated through some experiments on hourly Bitcoin USD and Ethereum USD prices and it is shown that the proposed resilient filter trading strategy is much more stable to initial values than the traditional KF trading strategy.
In this paper, we give a simple but very general definition of 'price stability' for a class of markets. This class of markets includes the popular constant function market makers (CFMMs) such as Uniswap, Curve, and Balancer, used extensively in decentralized finance (DeFi), which now have daily trading volumes in the billions of dollars. We show that our definition of price stability is deeply connected to the curvature of the trading function used in the CFMM, making the folk intuition that "flatter CFMMs are more price stable" more concrete. We also show that this definition gives sufficient conditions for the profitability of liquidity providers, and, similar to the classical market microstructure literature, gives bounds on the edge of informed traders and bounds on the losses of liquidity providers. We also show how these bounds help explain some of the behaviors observed in decentralized finance in the second half of 2020, including the rise of 'yield farming ' and 'vampire attacks.'
Bitcoin market had a significant momentum phenomenon before the launch of Futures, and then it turned into an insignificant reversal effect. After Covid-19 appeared, the momentum effect and reversal effect disappeared. The advent of bitcoin futures has increased how investors respond to information. With the outbreak of COVID-19, investor interest in Bitcoin as a safe-haven asset has increased the effectiveness of the price. We estimate the speed of signal diffusion in the bitcoin market, and the results support that effective response to information is the essential mechanism for the disappearance of momentum effect.