The cryptocurrency market has become increasingly accessible and significant to the financial markets. This is understood by not only major financial firms, governments, and investors, but also the individual market participants globally. We delve into the history of cryptocurrency to begin our examination of the Bitcoin, Ethereum and Litecoin. Understanding the circumstances of their humble beginning, the purpose it served, and the path of their evolution, helps us to create a fuller understanding of its functions, its limitations, and the drivers of its value. This enables us to identify key market factors and variables for deployment within a robust approach for pricing and product offerings associated with Bitcoin, Ethereum and Litecoin. In order to fully capture the volume, variety, and velocity of data associated with these cryptocurrencies, the use of machine learning can provide an advantageous approach to model development for cryptocurrency pricing. This paper provides the development of a promising initial prototype pricing model for Bitcoin, Ethereum and Litecoin. Our proposed pricing models resulted in an average 7% difference between actual and predicted price for Bitcoin and Ethereum, and a 4% difference for Litecoin along a timeline, through the use of machine learning and deep learning, artificial neural networks using the contributing factors of key variables and how they influence and capture pricing and investor behaviour. We also identify theinclusion of additional datasets, such as sentiment market data into the model, along with larger exploration of Blockchain and raw transaction mining to increase the accuracy and forecasting ability of the model.
Usman Amjad, Tahseen Ahmed, Humera Tariq, Amir Hussain
Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms.
Blockchain has been perceived by many professionals as the next revolution of humankind. Its application spreads across multiple industries and aspects of life, but the first impact was to be found in finance. In 2017, cryptocurrency became a new financial phenomenon around the globe when Bitcoin’s value skyrocketed to the peak of $19.535. Many investors, both professional and amateur, have taken part in this modern trend of trading. Unfortunately, a number of those experienced losses due to various reasons. Among which a prominent heuristic called “anchoring” might be one of the causes of incorrect assessment leading to potential damages. Several studies in the past have validated the existence of anchoring bias in conventional stock market. However, current literature failed to address similar effect in cryptocurrency market. This thesis examines the presence of Bitcoin price anchoring in trading decisions of investors. Order dataset, including bids and asks, were collected from Kraken exchange to serve the analysis purpose. The analysis has confirmed that investors’ trading decisions anchored to changes in Bitcoin market price. Furthermore, the result tells that anchoring bias influenced investors’ valuation of price differently when they placed bid or ask orders. Nonetheless, its impact does not vary between bull and bear market situations. In conclusion, investors should be well aware of anchoring bias when making trading decisions. The heuristic can lead to both negative and positive consequences, depending on investor’s perception toward it.
Cryptocurrencies are digital currencies that have garnered significant investor attention in the financial markets.The aim of this project is to predict the daily price, particularly the daily high and closing price, of the cryptocurrency Bitcoin.This plays a vital role in making trading decisions.There exist various factors which affect the price of Bitcoin, thereby making price prediction a complex and technically challenging task.To perform prediction, we trained temporal neural networks such as time-delay neural networks (TDNN) and recurrent neural networks (RNN) on historical time seriesthat is, past prices of Bitcoin over several years.Features such as the opening price, highest price, lowest price, closing price, and volume of a currency over several preceding quarters were taken into consideration so as to predict the highest and closing price of the next day.We designed and implemented TDNNs and RNNs using the NeuroSolutions artificial neural network (ANN) development environment to build predictive models and evaluated them by computing various measures such as the MSE (mean square error), NMSE (normalized mean square error), and r (Pearson's correlation coefficient) on a continuation of the training data from each time series, held out for validation.
In response to Molnár and Thies (2018) demonstrating that the price data of Bitcoin contained structural breaks, we identify the optimal number of states for a Markov regime-switching (MRS) model to capture the regime heteroskedasticity of Bitcoin. We determined that the restricted 5-state MRS model provided the best goodness-of-fit scores (-AIC, -BIC, -HQIC) for the fitted sample. In addition, we found evidence of stylised characteristics in the price data of Bitcoin, namely: volatility clustering; volatility jumps; asymmetric volatility transitions; and the persistence of shocks.
This paper studies Granger Causality relations between Bitcoin and 5 stock market indexes which are Japan, Russia, South Korea, Sweden and the United States. The time-period examined is from 2013 to 2017 and all the tests are conducted based on daily data. We analyze this in three different periods, last 5 years (2013-2017), in 2017 and last 3 months of 2017. To estimate the relationship, we use unit root test and Augmented Dickey-Fuller, Lagrange Multiplier, Johansen Cointegration Test and finally Granger Causality Test. After the tests, countries have a same integrated order that exhibits a long-run relationship. In causality, except for Russia, each country has affected the Bitcoin prices and being affected in a different period, especially in the last 3 months of 2017, the impact and popularity of Bitcoin affect too much the stock market in the short-run. As a result, the causation between Bitcoin and stock market indexes shows impact statistically significant in the 2017 year. The importance of cryptocurrency and popularity not as much as hype like late 2017 in 2018, but we think that cryptocurrencies are one of the major currencies that affect economical world very deeply.
This study argues that the value of Bitcoin is dependent on the likelihood of its price volatility reducing in the future. This study attempted to shed light on whether increased speculation, in both spot and derivative market volumes, will eventually lead to a reduction in Bitcoin price volatility. The study investigates several factors that influence Bitcoin volatility and tests empirically whether trading volumes in the spot market and trading volumes in the new derivative markets have had an effect on the price volatility. The study used, among other tests, an ARCH(1) and Granger-causality test and found that spot trading volumes had a significant positive effect on price volatility in the study period. The study also found that, in the year of introduction of Bitmex derivative contracts, derivative trading volumes had a significant negative effect on Bitcoin price volatility. In the years thereafter though, the relationship was not sustained and therefore it is not definitive whether derivative contracts trading volume increases has led to reduced volatility in the Bitcoin price.
In this paper, we provided a general insight into the burgeoning cryptocurrency market. Having inspected the capabilities of cryptocurrencies as investment assets, we saw value in developing a price prediction model in this highly volatile market. Ether was chosen as the subject due to its unparalleled potential amongst its competitors. Driven by the similarities presented between cryptocurrency and stock, coupled with evidence of hidden states in the financial market, this paper conducted the first application of the Hidden Markov Model to the cryptocurrency space. Our main objectives were to first model the time series data of Ether since its establishment, then use the trained model to forecast future closing prices of Ether before finally devising an investment strategy. HMM was used to solve three fundamental problems namely the Evaluation Problem, Learning Problem and Decoding Problem. Given 936 observations of daily Ether prices obtained from Yahoo Finance, 80% of this data was used as a training set while the remaining 20% was used as the testing set. The Forward algorithm and Baum-Welch algorithm helped obtained the model parameters. Thereafter, Viterbi algorithm decoded the likely state sequence of the observations. Using Mean Absolute Percentage Error as the indication of forecasting power, the selected Hidden Markov Model (HMM) has 3 states and 3 mixtures of Gaussian distribution, with the lowest MAPE of 4.63568. Via Monte Carlo simulations, our HMM investment strategy produced a superior weekly return of 5.68% as compared to 3.94% for the “naïve” strategy. This indicated the successful adaptation of HMM in the cryptocurrency market despite limitations from the inherent assumption. Future extensions to the paper could include the use of more model inputs, account for transaction fees and consider the heavy correlation between cryptocurrencies. We also anticipate that our findings require re-validation over time given the rapidly evolving nature of the market.
The purpose of this study is to develop robust estimation of association between two types of crypto-currencies namely Bitcoin and Ethereum. Daily data of crypto-currencies are collected from https://coinmarketcap.com. The period for data analysis is started from January 2017 until October 2018. The value of mean return for Bitcoin is 13.18 %. Meanwhile, the value of mean return for Ethereum is 27.85 %. The standard deviation for Bitcoin is 30.27 % and Ethereum is 64.24 %. Then, this study performed Person product moment coefficient analysis to evaluate the correlation between these two crypto-currencies. Result indicates the association coefficient value is 0.50. The correlation shows there is strong positive correlation between Bitcoin return and Ethereum return. As conclusion, there is significant relationship between Bitcoin and Ethereum return data with strong positive correlation (r = 0.503, n = 21, p =0.020).The significant of this study is to help investors to make better decision in selecting appropriate investment portfolio for their investment fund that contributes better return and lower risk.
Abstract This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.
Predicting currency prices remains a difficult endeavour. Investors are continually seeking new ways to extract \nmeaningful information about the future direction of price changes. Recently, cryptocurrencies have attracted \nhuge attention due to their unique way of transferring value as well as its value as a hedge. A method proposed \nin this project involves using data mining techniques: mining text documents such as news articles and tweets \ntry to infer the relationship between information contained in such items and cryptocurrency price direction. \nThe Long Short-Term Memory Recurrent Neural Network (LSTM RNN) assists in creating a hybrid model \nwhich comprises of sentiment analysis techniques, as well as a predictive machine learning model. The success \nof the model was evaluated within the context of predicting the direction of Bitcoin price changes. Findings \nreported here reveal that our system yields more accurate and real-time predictions of Bitcoin price fluctuations \nwhen compared to other existing models in the market.
Zhenghui Li, Hao Dong, Zhehao Huang, Pierre Failler
The rapid development of VFAs allows investors to diversify their choices of investment products. In this paper, we measure the return risk of VFAs based on GARCH-type model. By establishing a Markov regime-switching Regression (MSR) Model, we explore the asymmetric effects of speculation, investor attention, and market interoperability on return risks in different risk regimes of VFAs. The results show that the influences of speculation and investor attention on the risks of VFAs are significantly positive at all regimes, while market interoperability only admits a positive impact on risk under high risk regime. All of the three factors exert asymmetric effects on risks in different regimes. Further study presents that the risk regime-switching also shows asymmetric characteristic but the medium risk regime is more stable than any others. Therefore, transactions of investors and arbitrageurs are monitored by certain policies, such as limiting the number of transactions or restricting the trading amount at high risk regime. However, when return risk is low, it will return to a medium level if we encourage investors to access.
We study investor sentiment on a non-classical asset such as cryptocurrency using machine learning methods. We account for context-specific information and word similarity by using efficient language modelling tools such as construction of featurized word representations (embeddings) and recursive neural networks (RNNs). We apply these tools for sentence-level sentiment classification and sentiment index construction. This analysis is performed on a novel dataset of 1220K messages related to 425 cryptocurrencies posted on a microblogging platform StockTwits during the period between March 2013 and May 2018. Both in- and out-of-sample predictive regressions are run to test significance of the constructed sentiment index variables. We find that the constructed sentiment indices are informative regarding returns' and volatility predictability of the cryptocurrency market index.
We report the results of investigation of the momentum and contrarian effects on cryptocurrency markets. The investigated investment strategies involve 100 (amongst over 1200 present as of date Nov 2017) cryptocurrencies with the largest market cap and average 14-day daily volume exceeding a given threshold value. Investment portfolios are constructed using different assumptions regarding the portfolio reallocation period, width of the ranking window, the number of cryptocurrencies in the portfolio, and the percent transaction costs. The performance is benchmarked against: (1) equally weighted and (2) market-cap weighted investments in all of the ranked assets, as well as against the buy and hold strategies based on (3) S&P500 index, and (4) Bitcoin price. Our results show a clear and significant dominance of the short-term contrarian effect over both momentum effect and the benchmark portfolios. The information ratio coefficient for the contrarian strategies often exceeds two-digit values depending on the assumed reallocation period and the width of the ranking window. Additionally, we observe a significant diversification potential for all cryptocurrency portfolios with relation to the S&P500 index.
Jethin Abraham, Daniel Higdon, John B. Nelson, Juan G. Ibarra
In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over \$160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced significant price swings on both daily and long term valuations. Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to a cryptocurrency user or a trader. By analyzing tweets, we found that tweet volume, rather than tweet sentiment (which is invariably overall positive regardless of price direction), is a predictor of price direction. By utilizing a linear model that takes as input tweets and Google Trends data, we were able to accurately predict the direction of price changes. By utilizing this model, a person is able to make better informed purchase and selling decisions related to Bitcoin and Ethereum.
Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin's supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.
Zehui Xiong, Yang Zhang, Dusit Niyato, Ping Wang · 5 authors
Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications (e.g., finance, healthcare, and logistics), its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data (i.e., a block) to the blockchain. Solving the proof of work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.
Cryptocurrency trade is now a popular type of investment. Cryptocurrency market has been treated similar to foreign exchange and stock market. However, because of its volatility, there's a need for a prediction tool for investors to help them consider investment decisions for cryptocurrency trade. Nowadays, Artificial Neural Network (ANN) computing based tools are commonly used in stock and foreign exchange market predictions. There has been much research about ANN predictor on stocks and foreign exchange as case studies but none on cryptocurrency. Therefore, this research studied variety of ANN method to predict the market value of one of the most used cryptocurrency, Bitcoin. The ANN methods will be used to develop model to predict the close value of Bitcoin in the next day (next day prediction). This study compares four ANN methods, namely backpropagation neural network (BPNN), genetic algorithm neural network (GANN), genetic algorithm backpropagation neural network (GABPNN), and neuro-evolution of augmenting topologies (NEAT). The methods are evaluated based on accuracy and complexity. The result of the experiment showed that BPNN is the best method with MAPE 1.998 ± 0.038 % and training time 347 ± 63 seconds.