Blockchain Papers

Dhruba Banjade

No abstract is available for this record.

Devesh Chandra, Pranav Tyagi, Radhe Shyam Gupta, Aayush Mohan Saxena · 5 authors

  The application of machine learning algorithms in predicting cryptocurrency prices has gained significant attention in recent years. Researchers have explored various approaches such as recurrent neural networks, deep learning neural networks, Bayesian regression, k-nearest neighbor, support vector machine, and other algorithms to forecast the prices of cryptocurrencies like Bitcoin, Ethereum, Dogecoin and Litecoin. This paper will draw on established literature on price prediction using machine learning, including studies on NFT sales predictability, NFT sale price fluctuations prediction, gold price prediction, and silver price forecasting. The research paper has focused on utilizing high-dimensional features, time-series analysis, as well as the comparison of different statistical models and machine learning algorithms. Additionally, the prediction models have incorporated factors such as market liquidity, exchange market dynamics. While the literature acknowledges the potential of machine learning in cryptocurrency price prediction, gold, silver and NFT’s there is a recognized gap in the application of these techniques across a broader range of cryptocurrencies. The proposed methodology will integrate various machine learning models and statistical methods to predict the prices of cryptocurrencies, gold, silver, and NFTs, taking into account factors such as market trends, trade networks and visual features. Furthermore, the studies emphasize the importance of feature engineering, sample dimension engineering, and the use of various machine learning techniques to enhance the accuracy and stability of cryptocurrency price predictions. As the cryptocurrency market continues to expand, there is a need for further research to develop robust machine learning models that can effectively forecast the prices of diverse cryptocurrencies, contributing to the advancement of this field.

Emmanouil Christoforou, Ioannis Z. Emiris, Apostolos Florakis

No abstract is available for this record.

Kiki Ramadani, Dodi Devianto

Bitcoin is electronic money that can be used as an alternative for investment. Investors will get benefit buying bitcoin when the price of bitcoin is down and reselling it when bitcoin prices are increasing. The fluctuating bitcoin prices cause forecasting as a basis for investors to make decisions, where the time series method is used as a forecasting model, then a pattern can be found to predict future events. The classical time series methods are often violating the statistical assumptions. To face these problems, then it is used free assumptions methods, the method with the Fuzzy Time Series Markov Chain, the Chen Logical Method, and its segmented methods due to unbalancing forecasting results. This study is built the forecasting model of the price of bitcoin for the coming period based on the data from 2010 to 2020. The proposed methods have a better fit for bitcoin time series data prices. Besides, the Fuzzy Time Series Markov Chain method has the slightly smallest accuracy error based on Mean Absolute Percentage Error (MAPE) comparing to the Fuzzy Time Series Segmented Chen Logical Method and Fuzzy Time Series Chen Logical Method.

Chia-Yen Tan, You Beng Koh, Kok Haur Ng, Kooi Huat Ng · 6 authors

No abstract is available for this record.

Johannes Jakubik, Abdolreza Nazemi, Andreas Geyer-Schulz, Frank J. Fabozzi

In this paper, we investigate how a deep learning machine learning model can be applied to improve Bitcoin price forecasting and trading by incorporating unstructured information from financial news. The two-stage model we propose that includes financial news significantly outperforms machine learning models without financial news. In the first stage, we leverage long short-term memory (LSTM) networks to extract structured information from financial news. In the second stage, we apply machine learning models with structured input from financial news to the prediction of Bitcoin prices. In addition to the superior performance relative to machine learning models without input from financial news, we find that the out-of-time rate of return attained with the proposed forecasting system is substantially higher than for a buy-and-hold strategy. Our study highlights how combining deep learning and financial news offers investors and traders support for the monetization of unstructured data in finance.

Alexander Thoma

This paper investigates the risk and return properties of a trading strategy for the cryptocurrency market. The main predictive power for portfolio formation comes from a simple prospect theory model that only uses price information readily available. The dataset consists of a large body of cryptocurrencies from 2014 to 2020. I find a strong outperformance over the market, even after controlling for known predictors. Factor regressions with a cryptocurrency three-factor model further reveal significant alphas. Robustness test emphasize the legitimacy of the strategy. On average, cryptocurrencies with a high (low) prospect theory value earn low (high) subsequent returns. Interestingly, traders in the cryptocurrency market seem to assess the attractiveness of cryptocurrency in a way described by prospect theory. Mechanical tests of the model show that probability weighting is a main driver behind this assessment. Cryptocurrencies with a high prospect theory value tend to be highly positively skewed. This skewness could be the reason why the cryptocurrency seems attractive to traders, similar to lottery-like gambles.

Ioannis Chalkiadakis, Anna Zaremba, Gareth W. Peters, Michael J. Chantler

This paper establishes a new framework for assessing multimodal statistical causality between cryptocurrency market (cryptomarket) sentiment and cryptocurrency price processes. In order to achieve this, we present an efficient algorithm for multimodal statistical causality analysis based on Multiple-Output Gaussian Processes. Signals from different information sources (modalities) are jointly modelled as a Multiple-Output Gaussian Process, and then using a novel approach to statistical causality based on Gaussian Processes (GPs), we study linear and non-linear causal effects between the different modalities. We demonstrate the effectiveness of our approach in a machine learning application by studying the relationship between cryptocurrency spot price dynamics and sentiment time-series data specific to the crypto sector, which we conjecture influences retail investor behaviour. The investor sentiment is extracted from cryptomarket news data via methods developed in the area of statistical machine learning known as Natural Language Processing (NLP). To capture sentiment, we present a novel framework for text to time-series embedding, which we then use to construct a sentiment index from publicly available news articles. We conduct a statistical analysis of our sentiment statistical index model and compare it to alternative state-of-the-art sentiment models popular in the NLP literature. In regard to the multimodal causality, the investor sentiment is our primary modality of exploration, in addition to price and a blockchain technology-related indicator (hash rate). Analysis shows that our approach is effective in modelling causal structures of variable degree of complexity between heterogeneous data sources and illustrates the impact that certain modelling choices for the different modalities can have on detecting causality. A solid understanding of these factors is necessary to gauge cryptocurrency adoption by retail investors and provide sentiment- and technology-based insights about the cryptocurrency market dynamics.

Minjung Park, Sangmi Chai

With the increase in the attention to cryptocurrency, studies on the factors affecting the price fluctuation of cryptocurrency have been actively conducted. Prior researches suggested that policy announcements (i.e., public information) related to cryptocurrency have been found to affect the price volatility in the market in particular. Privileged information, which is hard to be observable unlike public information published by the government or corporations, is hardly homogenously distributed to individual investors. However, it inevitably affects the price in any market. Therefore, this study aims to identify the information asymmetry, which is mainly formed by privileged information, in the cryptocurrency market. Moreover, this study examines whether investment sentiment, which mainly influences transaction behaviors of uninformed traders, has a significant effect on the cryptocurrency market as well. The results contribute to the understanding of the cryptocurrency market in a basis of the existing market theories.

Jiahang Zhang, Chi Zhang

No abstract is available for this record.

Yu Wang, Runyu Chen

With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically. As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market. Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions. However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult. To this end, based on the transaction data of three different markets and the number and content of user comments and responses from online forums, this paper constructs a price prediction model of cryptocurrencies using a variety of machine learning and deep learning algorithms. It turns out that the trading price premium rate in different markets will affect the price to be predicted, and adding social media comment features can significantly improve the accuracy of the forecast. This article is conducive to investors who encrypt currencies to make more scientific decisions.

H Kavitha, Uttam Kumar Sinha, Surbhi Jain

Bitcoin is one of the most valuable cryptocurrency in the world with the prices as high as 19,783 United States Dollar(USD) in December of 2017. It made Bitcoin a very profitable market for investment but Bitcoin saw many ups and down as well. Today’s Bitcoin price is 3913 USD but that doesn’t mean that the prices always keep falling. The price of Bitcoin vary over time and is governed by various factors, like the market it is being traded in, scarcity, supply and demand. What has made Bitcoin valuable is that it can be used as a currency, we can pay a part or a fraction of Bitcoin to a person in exchange for something and the part is easily verifiable by blockchain. The small number of Bitcoin, roughly 16 million Bitcoins for the entire world has made it scare and above that its high utility makes it more prestigious.Trading of Bitcoin has proved to be very profitable to many people but the risk in trading is huge as the market of Bitcoin is very volatile. To decrease the risks, this project has been carried out to predict the price of Bitcoin using Recurrent Neural Network(RNN), Long Short Term Memory (LSTM) and Linear Regression(LR) to predict the price of Bitcoin. Evaluation of these algorithms is carried out to determine which among the two is better for the prediction of Bitcoin prices. The dataset used contains minute by minute prices of Bitcoin of over 5 years and contains almost 30,00,000 entries. Since, the dataset used is a big data, evaluating the performance of algorithms over a large dataset will give accurate results.

Maria Čuljak, Bojan Tomić, Saša Žiković

No abstract is available for this record.

Akanksha Jalan, Roman Matkovskyy, Saqib Aziz

This paper offers the first-ever look at Bitcoin options by investigating the wedge between optimum Bitcoin option prices based on classical option valuation models (Black-Scholes-Merton and the Heston-Nandi GARCH (1, 1)) and actual premiums at which these options are trading. For this purpose, we use near-the-money call and put options traded on Deribit platform as on 27.01.2020, with the maturities ranging from January 31 to 25 September 2020. In addition, we analyse the risk inherent in Bitcoin options by calculating their Greeks and comparing them to those of traditional commodity options. Pricing results suggest slight overpricing and underpricing for Bitcoin call options with the strike $8, 000 maturing on 30.01.2020 and 28.02.2020, respectively. We also find that the Bitcoin options provide much stable deltas over time compared to the other commodity options. This result implies higher insulation from undue price rises with the passage of time for investors in Bitcoin options. Our results are useful to regulators, investors and market managers in better understanding the nuances of the Bitcoin options market in addition to making more informed investment choices.

Yuze Li, Shangrong Jiang, Xuerong Li, Shouyang Wang

Abstract In recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers. However, empirical works in the Bitcoin forecasting and trading support systems are at an early stage. To fill this void, this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two primary steps are involved in our methodology framework, namely, data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price. Results demonstrate that the proposed model outperforms other benchmark models, including econometric models, machine-learning models, and deep-learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals.

Xiangxi Jiang

Bitcoin is a current popular cryptocurrency with a promising future. It’s like a stock market with time series, the series of indexed data points. We looked at different deep learning networks and methods of improving the accuracy, including min-max normalization, Adam optimizer and windows min-max normalization. We gathered data on the Bitcoin price per minute, and we rearranged them to reflect Bitcoin price in hours, a total of 56,832 points. We took 24 hours of data as input and output the Bitcoin price of the next hour. We compared the different models and found that the lack of memory means that Multi-Layer Perceptron (MLP) is ill-suited for the case of predicting price based on current trend. Long Short-Term Memory (LSTM) provides relatively the best prediction when past memory and Gated Recurrent Network (GRU) is included in the model.

Yiqing Hua

The goal of this paper is to compare the accuracy of bitcoin price in USD prediction based on two different model, Long Short term Memory (LSTM) network and ARIMA model. Real-time price data is collected by Pycurl from Bitfine. LSTM model is implemented by Keras and TensorFlow. ARIMA model used in this paper is mainly to present a classical comparison of time series forecasting, as expected, it could make efficient prediction limited in short-time interval, and the outcome depends on the time period. The LSTM could reach a better performance, with extra, indispensable time for model training, especially via CPU.

Peterson Owusu, Anokye M. Adam, George Tweneboah

This paper explores the symmetric and asymmetric dependency structure of decomposed return series of Gold and eight cryptocurrencies to establish the hedging and diversification potentials of these asset classes. Daily data spanning 30 April 2013 to 18 April 2019 are employed within the Ensemble Empirical Mode Decomposition and Quantile-in-Quantile regression techniques. Our empirical results provide evidence that cryptocurrencies and Gold can both hedge and diversify for each other at different conditional distributions of their returns. We also find that cryptocurrencies are not purely speculative but can be driven by medium- and long-term fundamentals. In addition, both Gold and cryptocurrencies can be hedge and diversifiers for other traditional asset classes such as crude oil, fiat currencies, and other commodities.

Nicola Uras, Lodovica Marchesi, Michele Marchesi, Roberto Tonelli

In this article we forecast daily closing price series of Bitcoin, Litecoin and Ethereum cryptocurrencies, using data on prices and volumes of prior days. Cryptocurrencies price behaviour is still largely unexplored, presenting new opportunities for researchers and economists to highlight similarities and differences with standard financial prices. We compared our results with various benchmarks: one recent work on Bitcoin prices forecasting that follows different approaches, a well-known paper that uses Intel, National Bank shares and Microsoft daily NASDAQ closing prices spanning a 3-year interval and another, more recent paper which gives quantitative results on stock market index predictions. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms: the Simple Linear Regression (SLR) model for uni-variate series forecast using only closing prices, and the Multiple Linear Regression (MLR) model for multivariate series using both price and volume data. We used two artificial neural networks as well: Multilayer Perceptron (MLP) and Long short-term memory (LSTM). While the entire time series resulted to be indistinguishable from a random walk, the partitioning of datasets into shorter sequences, representing different price "regimes", allows to obtain precise forecast as evaluated in terms of Mean Absolute Percentage Error(MAPE) and relative Root Mean Square Error (relativeRMSE). In this case the best results are obtained using more than one previous price, thus confirming the existence of time regimes different from random walks. Our models perform well also in terms of time complexity, and provide overall results better than those obtained in the benchmark studies, improving the state-of-the-art.

Hakan Pabuçcu, Serdar Ongan, Ayşe Ongan

Cryptocurrencies, such as Bitcoin, are one of the most controversial and complex technological innovations in today's financial system. This study aims to forecast the movements of Bitcoin prices at a high degree of accuracy. To this aim, four different Machine Learning (ML) algorithms are applied, namely, the Support Vector Machines (<i>SVM</i>), the Artificial Neural Network (<i>ANN</i>), the Naï ve Bayes (<i>NB)</i> and the Random Forest (<i>RF</i>) besides the logistic regression (LR) as a benchmark model. In order to test these algorithms, besides existing continuous dataset, discrete dataset was also created and used. For the evaluations of algorithm performances, the <i>F</i> statistic, accuracy statistic, the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Root Absolute Error (RAE) metrics were used. The <i>t</i> test was used to compare the performances of the SVM, ANN, NB and RF with the performance of the LR. Empirical findings reveal that, while the <i>RF</i> has the highest forecasting performance in the continuous dataset, the <i>NB</i> has the lowest. On the other hand, while the <i>ANN</i> has the highest and the <i>NB</i> the lowest performance in the discrete dataset. Furthermore, the discrete dataset improves the overall forecasting performance in all algorithms (models) estimated.

Emmanuel Pintelas, Ioannis E. Livieris, Stavros Stavroyiannis, Theodore Kotsilieris · 5 authors

No abstract is available for this record.

Vasily Derbentsev, Andriy Matviychuk, Vladimir Soloviev

No abstract is available for this record.

Farida Sabry, Wadha Labda, Aiman Erbad, Qutaibah Malluhi

Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin was introduced as the first cryptocurrency to allow direct online payments without relying on centralized financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the cryptocurrencies' market. With plenty of cryptocurrencies being used as financial assets and with millions of trades being executed through different exchange services, cryptocurrencies are susceptible to trading problems and challenges similar to those traditionally encountered in the financial domain. Price and trend prediction, volatility prediction, portfolio construction and fraud detection are some examples related to trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper discusses the recent research work done in this emerging area and compares them in terms of used techniques and datasets. It also highlights possible research gaps and some potential areas for improvement.

Constantin Gurdgiev, Daniel O’Loughlin

No abstract is available for this record.