Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called "altcoins" often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter.
RODOLFO ANGELO MAGTANGGOL III DE GUZMAN, Mike K. P. So
This paper proposes the use of threshold heteroskedastic models which integrate threshold nonlinearity [Tong, H (1978). On a Threshold Model, pp. 575–586. Netherlands: Sijthoff & Noordhoff; Tong, H and KS Lim (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 3, 245–292.] and GARCH-type conditional variance for modeling Bitcoin returns to provide an understanding on the huge volatility that Bitcoin has been famous for. Specifically, the model attempts to identify different regimes throughout the history of Bitcoin using the different available Bitcoin network characteristics, such as cost per transaction, number of transactions per block, number of active addresses and number of transactions. Estimation and diagnostic checks are performed using Markov chain Monte Carlo methods. In the empirical analysis, we show that our model is able to identify periods of crashes as one of these regimes, which is also a period of declining returns and declining number of active users. We also find that the number of users and the number of transactions determine the magnitude or persistence of a crash period.
This study investigates the asymmetric causal relations between Bitcoin and gold, Brent oil, US dollar, S&P 500 and BIST 100 Indexes for the weekly data of the period between November 2013 and July 2018 via by Hatemi-J (2012) test. The results indicate only a causal link going from the Bitcoin price to S&P 500 Index. Consequently, a change in Bitcoin prices appears to influence the investors’ decisions on the S&P 500 Index. Therefore, it can be said that the investors in S&P 500 Index have closely followed the new macro-financial developments in the market and have been active on the S&P 500 market. However, the presence of a causality relation between Bitcoin price and other variables cannot be determined. Thus, it is supposed that Bitcoin may exist in association with the commodity market and other global indicators in the future, along with the recognition of the Bitcoin currency by countries, its being accepted as a means of exchange and its increased reliability.
There has been much debate about whether returns on financial assets, such as stock returns or commodity returns, are predictable; however, few studies have investigated cryptocurrency return predictability. In this article we examine whether bitcoin returns are predictable by a large set of bitcoin price-based technical indicators. Specifically, we construct a classification tree-based model for return prediction using 124 technical indicators. We provide evidence that the proposed model has strong out-of-sample predictive power for narrow ranges of daily returns on bitcoin. This finding indicates that using big data and technical analysis can help predict bitcoin returns that are hardly driven by fundamentals.
This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the algorithm efficiently determines the optimal non-linear mapping and the corresponding lead–lag structure between two time series. Afterwards, this study explores the use of optimal causal paths as a means for identifying promising stock pairs and for generating buy and sell signals. For this purpose, the established trading strategy exploits information about the leading stock to predict future returns of the following stock. The value-add of the proposed framework is assessed by benchmarking it with variants relying on classic similarity measures and a buy-and-hold investment in the S&P 500 index. In the empirical back-testing study, the trading algorithm generates statistically and economically significant returns of 54.98% p.a. and an annualized Sharpe ratio of 3.57 after transaction costs. Returns are well superior to the benchmark approaches and do not load on any common sources of systematic risk. The strategy outperforms in the context of cryptocurrencies even in recent times due to the fact that stock returns contain substantial information about the future bitcoin returns.
Boris Radovanov, Aleksandra Marcikić, Nebojša Gvozdenović
Because of increasing interest in cryptocurrency investments, there is a need to quantify their variation over time. Therefore, in this paper we try to answer a few important questions related to a time series of cryptocurrencies. According to our goals and due to market capitalization, here we discuss the daily market price data of four major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Litecoin (LTC). In the first phase, we characterize the daily returns of exchange rates versus the U.S. Dollar by assessing the main statistical properties of them. In many ways, the interpretation of these results could be a crucial point in the investment decision making process. In the following phase, we apply an autocorrelation function in order to find repeating patterns or a random walk of daily returns. Also, the lack of literature on the comparison of cryptocurrency price movements refers to the correlation analysis between the aforementioned data series. These findings are an appropriate base for portfolio management. Finally, the paper conducts an analysis of volatility using dynamic volatility models such as GARCH, GJR and EGARCH. The results confirm that volatility is persistent over time and the asymmetry of volatility is small for daily returns.
Bitcoin is the world’s leading cryptocurrency, with a market capitalization briefly exceeding $300 billion. This hints at Bitcoin’s \namorphous nature: is this a monetary or a corporate measure? Hard values become explicit in the processing of transactions and \nthe digital mining of Bitcoins. Electricity is a primary input cost. Bitcoins earned are often used to circumvent local currency \ncontrols and acquire US dollars. For the period August 2010 to February 2018, we examine the components of Bitcoin mining \nrevenues, their statistical contribution to daily changes, and to its variance. We provide evidence that Bitcoin transaction processing \nis capacity constrained.
This research aims to identify how Bitcoin-related news publications and\nonline discourse are expressed in Bitcoin exchange movements of price and\nvolume. Being inherently digital, all Bitcoin-related fundamental data (from\nexchanges, as well as transactional data directly from the blockchain) is\navailable online, something that is not true for traditional businesses or\ncurrencies traded on exchanges. This makes Bitcoin an interesting subject for\nsuch research, as it enables the mapping of sentiment to fundamental events\nthat might otherwise be inaccessible. Furthermore, Bitcoin discussion largely\ntakes place on online forums and chat channels. In stock trading, the value of\nsentiment data in trading decisions has been demonstrated numerous times [1]\n[2] [3], and this research aims to determine whether there is value in such\ndata for Bitcoin trading models. To achieve this, data over the year 2015 has\nbeen collected from Bitcointalk.org, (the biggest Bitcoin forum in post\nvolume), established news sources such as Bloomberg and the Wall Street\nJournal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and\nbitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we\nfind weak to moderate correlations between forum, news, and Reddit sentiment\nand movements in price and volume from 1 to 5 days after the sentiment was\nexpressed. A Granger causality test confirms the predictive causality of the\nsentiment on the daily percentage price and volume movements, and at the same\ntime underscores the predictive causality of market movements on sentiment\nexpressions in online communities\n
This paper is deeply motivated by the need to explore the impressive Bitcoin price development by addressing Bitcoin as money in its essential attribute as a medium of exchange. We adopt a monetary economics viewpoint and resort to a representative agent modelling strategy within a money-in-the-utility function (MIUF) framework. First, we show that the impressive Bitcoin price development observed since its inception can be interpreted as a hyperdeflation when we focus on Bitcoin role as a medium of exchange. Second, we show that specific monetary features of Bitcoin, its asymptotical fixed nominal stock and divisibility down to eight decimal places, account for a strong possibility of speculative hyperdeflationary paths. It is shown that those paths are fully consistent with the medium of exchange monetary role of Bitcoin and the representative agent optimizing behavior.
We propose in this article to study the behavior of investors in the bitcoin market in order to test whether investors' overconfidence is a driver of excess volatility, often associated with the aforementioned market. This paper presents an attempt to deepen the previously published studies by adopting a new ARMA(p,q)-FIEGARCH(1,d,k,1) parametrization capable of capturing the overconfidence element as well as simultaneously accounting for possible long memory effect. The data used in this study consists of daily closing prices along with daily exchange volume of Bitcoin, spanning the period ranging from 01/01/2012 up to 31/05/2018. The results and conclusions drafted in this research paper could help to understand the formation of volatility in the Bitcoin market. Therefore, this kind of studies will enable investors to better predict bubbles and irrational exuberances. The main contribution of the present article is drawn from the broadening of previous studies by adopting a newly constructed model, which combines capturing asymmetric response, long memory along with the overconfidence element.
In this article, we attempt to delineate the relationship between bitcoin prices and global factors such as stock index, economic policy uncertainty, gold spot prices, implied volatility and crude oil prices in a time-frequency domain. We resort to wavelet-based analysis to capture the multiscale interactive behavior of bitcoin with global factors. We primarily show that bitcoin is insulated from global factors in the short run. However, the existence of a significant relationship of bitcoin with global factors cannot be denied in the medium to long run, which could be attributed to the endogenous and intertwined economic system. Among the global factors considered in the study, we find the impact of economic policy uncertainty and crude oil prices to be more prominent on bitcoin. Our study offers some interesting insights on multiscale sensitivity of bitcoin to global factors, which may be useful for investors for taking informed decisions
We review the literature and examine the effects of shocks on bitcoin returns. We assess the effects of factors such as stock market returns, exchange rates, gold and oil returns, FED's and ECB's rates and internet trends on bitcoin returns. Alternative VAR and FAVAR models are employed and generalized as well as local impulse response functions are produced. Our results reveal (i) a significant interaction between bitcoin and traditional stock markets, (ii) a weaker interaction with FX markets and the macroeconomy and (iii) an anemic importance of popularity measures. Lastly, we reveal the increased impact of Asian markets on bitcoin compared to other geographically-defined markets, which however appears to have waned in the last two years after the Chinese regulatory interventions and the sudden contraction of CNY's share in bitcoin trading volume.
Chih‐Hung Wu, Chih-Chiang Lu, Yu-Feng Ma, Ruei-Shan Lu
Long short-term memory (LSTM) networks are a state-of-the-art sequence learning in deep learning for time series forecasting. However, less study applied to financial time series forecasting especially in cryptocurrency prediction. Therefore, we propose a new forecasting framework with LSTM model to forecasting bitcoin daily price with two various LSTM models (conventional LSTM model and LSTM with AR(2) model). The performance of the proposed models are evaluated using daily bitcoin price data during 2018/1/1 to 2018/7/28 in total 208 records. The results confirmed the excellent forecasting accuracy of the proposed model with AR(2). The test mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) for bitcoin price prediction, respectively. The our proposed LSTM with AR(2) model outperformed than conventional LSTM model. The contribution of this study is providing a new forecasting framework for bitcoin price prediction can overcome and improve the problem of input variables selection in LSTM without strict assumptions of data assumption. The results revealed its possible applicability in various cryptocurrencies prediction, industry instances such as medical data or financial time-series data.
In the present paper, we investigate connectedness within cryptocurrency markets as well as across the Bitcoin index (hereafter, BPI) and widely traded asset classes such as traditional currencies, stock market indices and commodities, such as gold and Brent oil. A spill over index approach with the spectral representation of variance decomposition networks, is employed to measure connectedness. Results show no significant spillover effects between the nascent market of cryptocurrencies and other financial markets. We suggest that cryptocurrencies are real independent financial instruments that pose no danger to financial system stability. Concerning the connectedness within the cryptocurrency markets, we report a time–frequency–dynamics connectedness nature. Moreover, the decomposition of the total spill over index is mostly dominated by a short frequency component (2–4 days) leading to the conclusion that this nascent market is highly speculative at present. These findings provide insights for regulators and potential international investors.