Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having\nbeen widely used as an exchange medium in areas such as financial transaction\nand asset transfer verification. However, there has been a lack of solutions\nthat can support real-time price prediction to cope with high currency\nvolatility, handle massive heterogeneous data volumes, including social media\nsentiments, while supporting fault tolerance and persistence in real time, and\nprovide real-time adaptation of learning algorithms to cope with new price and\nsentiment data. In this paper we introduce KryptoOracle, a novel real-time and\nadaptive cryptocurrency price prediction platform based on Twitter sentiments.\nThe integrative and modular platform is based on (i) a Spark-based architecture\nwhich handles the large volume of incoming data in a persistent and fault\ntolerant way; (ii) an approach that supports sentiment analysis which can\nrespond to large amounts of natural language processing queries in real time;\nand (iii) a predictive method grounded on online learning in which a model\nadapts its weights to cope with new prices and sentiments. Besides providing an\narchitectural design, the paper also describes the KryptoOracle platform\nimplementation and experimental evaluation. Overall, the proposed platform can\nhelp accelerate decision-making, uncover new opportunities and provide more\ntimely insights based on the available and ever-larger financial data volume\nand variety.\n
Cryptocurrencies that are based on Proof-of-Work (PoW) often rely on special purpose hardware to perform so-called mining operations that secure the system, with miners receiving freshly minted tokens as a reward for their work. A notable example of such a cryptocurrency is Bitcoin, which is primarily mined using application specific integrated circuit (ASIC) based machines. Due to the supposed profitability of cryptocurrency mining, such hardware has been in great demand in recent years, in-spite of high associated costs like electricity. In this work, we show that because mining rewards are given in the mined cryptocurrency, while expenses are usually paid in some fiat currency such as the United States Dollar (USD), cryptocurrency mining is in fact a bundle of financial options. When exercised, each option converts electricity to tokens. We provide a method of pricing mining hardware based on this insight, and prove that any other price creates arbitrage. Our method shows that contrary to the popular belief that mining hardware is worth less if the cryptocurrency is highly volatile, the opposite effect is true: volatility increases value. Thus, if a coin's volatility decreases, some miners may leave, affecting security. We compare the prices produced by our method to prices obtained from popular tools currently used by miners and show that the latter only consider the expected returns from mining, while neglecting to account for the inherent risk in mining, which is due to the high exchange-rate volatility of cryptocurrencies. Finally, we show that the returns made from mining can be imitated by trading in bonds and coins, and create such imitating investment portfolios. Historically, realized revenues of these portfolios have outperformed mining, showing that indeed hardware is mispriced.
Yunan Ye, Hengzhi Pei, Boxin Wang, Pin‐Yu Chen · 7 authors
Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines.
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a deep learning approach to predict the direction of the mid-price changes on the upcoming tick. We monitored live tick-level data from $8$ cryptocurrency pairs and applied both statistical and machine learning techniques to provide a live prediction. We reveal that promising results are possible for cryptocurrencies, and in particular, we achieve a consistent $78\%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.
Francisco López Herrera, División de Investigación, Facultad de Contaduría y Administración, Universidad Nacional Autónoma de México, Ciudad de México, México., Luis Guadalupe Macías-Trejo, Oscar V. De la Torre-Torres
Este artículo muestra los resultados de un análisis del desempeño de ocho de los criptoactivos más importantes entre la gran variedad que actualmente existe en el mercado. Se estudia su riesgo de mercado con base en métricas ampliamente utilizadas para activos financieros. El análisis se complementa con la evaluación de su desempeño dentro de portafolios formados con criterios convencionales. Se encuentra un comportamiento bastante heterogéneo entre los activos estudiados, sugiriendo que tal comportamiento obedece a las características específicas de cada uno de ellos, más que a las características comunes como una clase específica de activos.
In this article, we empirically examine the cryptocurrency market reaction to china prohibiting initial coin offerings, on the 4 September 2007 for the 100 largest cryptocurrencies. The announcement has a significant negative but temporary impact on cryptocurrency returns and liquidity.
Yuanyuan Zhang, Stephen Chan, Jeffrey Chu, Hana Sulieman
The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear phases in high-frequency markets for the three largest cryptocurrencies of Bitcoin, Ethereum, and Litecoin. We also examine the market efficiency and liquidity of the selected cryptocurrencies during these periods using high-frequency data. Our findings show that the hourly returns of the three cryptocurrencies during a bull market indicate market efficiency when using the detrended-fluctuation-analysis (DFA) method to analyse the Hurst exponent with a rolling window. However, when conditions turn and there is a bear-market period, we see signs of a more inefficient market. Furthermore, our results indicated differences between the cryptocurrencies in terms of their liquidity during the two market states. Moving from a bull to a bear market, Ethereum and Litecoin appear to become more illiquid, as opposed to Bitcoin, which appears to become more liquid. The motivation to study the high-frequency cryptocurrency market came from the increasing availability of higher-frequency cryptocurrency-pricing data. However, it also comes from a movement towards higher-frequency trading of cryptocurrency. In addition, the efficiency of cryptocurrency markets relates not only to whether prices are predictable and arbitrage opportunities exist, but, more widely, to topics such as testing the profitability of trading strategies and determining the maturity of cryptocurrency markets.
This study proposes a method to enhance cryptocurrency portfolios constructed by forecast models. This study forecasts returns on four liquid cryptocurrencies (Bitcoin, Litecoin, Ripple, and Dash) and determines the weights on the cryptocurrencies based upon a dynamic allocation framework. We assess the performances of the portfolios using the performance fee measure. Our results present that the proposed portfolios outperform the benchmark portfolio with the conventional level of the risk aversion parameter. The economic gain for an investor is equivalent to 12% per week. The economic gain is sensitive to a change in the risk aversion parameter, which contrasts with the studies of exchange rates which is due to the high volatility on the cryptocurrencies. Our predictors are related to the price momentum effects and they outperform widely used network factors.
The purpose of this thesis is to study the predictability of cryptocurrency returns by investor attention, the interconnections of the cryptocurrency market, and what causes attention to cryptocurrencies. This is done by examining Bitcoin, Ethereum and Ripple which are the three biggest cryptocurrencies by market capitalization in January 2020. The dataset is constructed from weekly returns, weekly changes in investor attention measured by Google trend data and weekly changes in average weekly trading volume between years 2016 and 2019. The empirical analysis is conducted by performing OLS regressions, vector autoregressions and Granger causality tests. Additional robust tests are conducted by dividing the sample in pre-bubble and post-bubble samples adding all of the investor attention proxies to individual Cryptocurrency regressions. The results suggest that the market phase for a cryptocurrency affects the predictability of returns as the statistically significant positive relationship between investor attention disappears in the post-bubble sample for Bitcoin and Ethereum but endures for Ripple in both samples. This provides more evidence for the earlier findings that cryptocurrencies become more efficient as the market matures. The interconnections of the cryptocurrency market are shown to exist as the returns of Bitcoin drive investor attention to Ripple which is shown to be a significant predictor for all of the three cryptocurrencies in the whole sample. The spillover effect is shown to take time confirming earlier findings and unfolding the herding effect via investor attention in cryptocurrencies. Additionally, investor attention is shown to be caused by earlier returns for the cryptocurrency as well as the returns of Bitcoin. These results explain the interconnections of cryptocurrencies, the changing market dynamics in the cryptocurrency market, and the predictability of cryptocurrency returns by investor attention.
This paper investigates the relations between multiple measures of investor sentiment and the returns, volatility, trading volume, and liquidity. Using both data outside and inside market, we find that the Bullishness from socio-finance model are significant related to future realized volatility and trading volume, similar to Tweet, which is thought to capture information of well-informed investors in Bitcoin market
This paper analyzes the stability of stablecoins and proposes a framework to test for absolute and relative stability of stablecoins. Based on high-frequency data, we find strong evidence of excess price variations. While Bitcoin is a likely source of this excess volatility because stablecoin returns, volatility and volumes are highly correlated with corresponding Bitcoin time-series, we also demonstrate through a quasi-natural experiment that stablecoins increase the trading volume of Bitcoin. The findings suggest stablecoins play a key role in cryptocurrency markets.
The study measures the risk level linked with different portfolios of cryptocurrencies by using portfolio diversification techniques. Data on prices and trade volumes of cryptocurrencies were collected on a daily basis, from 2012 till 2018. Ten portfolios were constructed based on diverse types and numbers of cryptocurrencies. The results of the study confirm a negative relationship between the average number of cryptocurrencies and the average risk level of the portfolio. Involving more cryptocurrencies within the portfolio reduces the diversification risk of the portfolio. The average volatility and average correlation coefficient both drop when moving towards portfolios with more cryptocurrencies. Average returns stand against portfolio theories, where more risky portfolios offer less daily weighted average returns and the other way around. Outcomes of the study provide indications for the individual investors and financial institutions on the risk characteristic attached to the portfolio of cryptocurrencies.