Blockchain Papers

Werner Kristjanpoller, Marcel C. Minutolo

No abstract is available for this record.

Rohaifa Khaldi, Abdellatif El Afia, Raddouane Chiheb, Rdouan Faizi

The present study investigates the application of EEMD-ELMAN model to forecast the daily returns of the Bitcoin. More than seven years data were collected online from 18th July 2010 to 17th January 2018. Then the data signal was decomposed into several sub-signals using EEMD method. After, sub-signals were captured by different ELMAN models, and their output results were combined to generate the final forecast. Besides, the results of this study were compared against ELMAN and ARGARCH models. Hence, the statistical metrics revealed that the used model outperforms ELMAN network, and has approximately the same estimation error as ARGARCH, although the later model is prone to bad generalization due to the high gap between its approximation and generalization errors. Therefore, we can confirm that EEMD can be considered as a promising preprocessing technique, which enables to bring up the forecasting performance of ELMAN network with respect to highly volatile time series.

Tianyu Ray Li, Anup S. Chamrajnagar, Xander R. Fong, Nicholas R. Rizik · 5 authors

In this paper, we analyze Twitter signals as a medium for user sentiment to predict the price fluctuations of a small-cap alternative cryptocurrency called \emph{ZClassic}. We extracted tweets on an hourly basis for a period of 3.5 weeks, classifying each tweet as positive, neutral, or negative. We then compiled these tweets into an hourly sentiment index, creating an unweighted and weighted index, with the latter giving larger weight to retweets. These two indices, alongside the raw summations of positive, negative, and neutral sentiment were juxtaposed to $\sim 400$ data points of hourly pricing data to train an Extreme Gradient Boosting Regression Tree Model. Price predictions produced from this model were compared to historical price data, with the resulting predictions having a 0.81 correlation with the testing data. Our model'€™s predictive data yielded statistical significance at the $p < 0.0001$ level. Our model is the first academic proof of concept that social media platforms such as Twitter can serve as powerful social signals for predicting price movements in the highly speculative alternative cryptocurrency, or ``alt-coin'', market.

Ufuk Akoğuz, Taner Akkan

It is a very tiring process for people to watch the multiple parallel instant price changes in stock exchanges that are rapidly changing like the crypto money market. As a solution to this, a computer software that can make quick and objective decisions by constant observation can take the place of a person. In this study, an original decision algorithm that evaluates the instantaneous values of price change indicators and obtains relatively high earnings in a short period of time is examined. The Python programming language and Mathlib library have been used to construct this algorithm and to visualize the data, Moving Average Convergence Divergence (MACD) and Bollinger Bands have been used as a basic indicator. The result is an algorithm that requires less processing power and can operate continuously even on ARM-based mini-computers.

Kıvanç Ceyhan, Ekim Kurtulmaz, Onur Can Sert, Tansel Özyer

In the last few years, Bitcoin is one of the most discussed and popular topic in financial system. This article aims to predict Bitcoin movement by using Machine Learning and Text Mining models. Many models have been used to this end, including the most popular models in financial prediction; Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR). In addition to this, in order to examine the effect of daily news on Bitcoin movement, the text mining models are involved into the prediction system. This paper focuses on applying Machine Learning models on a integrated dataset, which contains both historical Bitcoin values and features from daily news text. Overall, our model can estimate the direction of Bitcoin with a high success.

Burcu Sakız, Esra Kutlugün

One of the most well-known and popular crypto money, that is also a digital currency enabled in 2009, is Bitcoin. Over time, many alternatives to Bitcoin have been developed. The blockchain system, which is a very important technology for crypto money transfer, provides many different possibilities besides providing transaction from one point to another without any intermediary. Block chaining is a distributed, shared form of recording that facilitates the recording of assets and transactions in a network. In this study, after explaining the basic concepts behind distributed architecture and blockchain technology behind crypto money, artificial intelligence algorithms were exploited, and based on last three years values of bitcoin forecasting was performed for Bitcoin which has a huge market share in since nine years.

Minul Wimalagunaratne, Guhanathan Poravi

The realm of cryptocurrency has grown exponentially over the past decade, with the most rapid advances seen in the past few years as more and more parties around the world recognize the value of holding digital assets online. Statistics from Twitter support this statement where, approximately 1,500 Tweets about Bitcoin alone is recorded per hour. Consequently, many people are beginning to become more aware and accepting of the nature of digital currencies, and traders in particular seek to know how they can make profitable crypto-coin trades and investments. Although a number of research projects have been undertaken to develop systems that can effectively predict price movements in the cryptocurrency market, they display significant efficiency gaps, which this paper further explores. The authors then attempt to learn from past studies and construct a more holistic approach to a predictive price model for the cryptocurrency market. This focuses on assessing key factors that affect the volatility of the market - public perception, trading data, historic price data, and the interdependencies between Bitcoin and Altcoins - and how they can be best utilized from a technological aspect by applying sentiment analysis and machine learning techniques, to increase the efficiency of the process.

Seçkin Karasu, Aytaç Altan, Zehra Saraç, Rıfat Hacıoğlu

In this study, Bitcoin prediction is performed with Linear Regression (LR) and Support Vector Machine (SVM) from machine learning methods by using time series consisting of daily Bitcoin closing prices between 2012-2018. The prediction model with include the least error is obtained by testing with different parameter combinations such as SVM with including linear and polynomial kernel functions. Filters with different weight coefficients are used for different window lengths. For different window lengths, Bitcoin price prediction is made using filters with different weight coefficients. 10-fold cross-validation method in training phase is used in order to construct a model with high performance independent of the data set. The performance of the obtained model is measured by means of statistical indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson Correlation. It is seen that the price prediction performance of the proposed SVM model for Bitcoin data set is higher than that of the LR model.

Ruchi Mittal, M. P. S. Bhatia

In the modern era, researchers are predicting prices of various kinds of cryptocurrency to understand their trend in the sector of finance. In this paper, we focus on price prediction of cryptocurrencies based on a period, i.e., for the year 2013 to 2018. From our research, we have identified the highest prices for bitcoin for historical dates and trained Long Short-Term Memory Networks to learn and predict the highest rate for a future period. Thus, trend analysis of cryptocurrency prices has been done, and neural networks have been leveraged to determine from time series data and predict future values.

Ruchi Mittal, Rashmi Gehi, M. P. S. Bhatia

With the increase in popularity of cryptocurrencies, it is becoming extremely crucial to predict what the prices of the currencies are going to be in the future. This paper uses a dataset that consists of over 1500 cryptocurrencies with their prices starting from their initiation till May, 2018. A lot of the effort went into getting the data set ready before predicting the future prices of all the cryptocurrencies, i.e., making sure that the cryptocurrencies were stationary time-series. Beginning with learning about the ARIMA model and the conditions to run the model successfully, first validation of the model is done. An average accuracy of 86.424 is observed for 95% of the currencies are observed. After this validation, forecasting is performed on these cryptocurrencies and the percentage change of the price is calculated.

Mareena Fernandes, Saloni Khanna, Leandra Monteiro, Anu Thomas · 5 authors

Advancement in technological developments introduced virtual currency exchange methods viz Bitcoin, Litecoin, Ethereum and so on which are evolving rapidly. Cryptocurrencies were introduced to eliminate financial intermediaries leading to direct peer-to-peer transactions. With the spread of the global Coronavirus pandemic, the relationship between Bitcoin and the equity market has expanded. Cryptocurrencies are highly volatile but can also prove to be good investments. Cryptocurrency, being a novel technique for transaction systems, has led to a lot of confusion among investors and any rumours or news on social media has been claimed to significantly affect the prices of cryptocurrencies. The huge percentage increase/decrease in Bitcoin's price over a short period of time is an intriguing phenomenon that cannot be foreseen. For a long time, bitcoin price prediction has been a hot topic of study.In this paper, we discuss the implementation and results of the Deep Learning Bitcoin Price Prediction Model and prepare a strategy to maximize gains for investors. The paper covers to framework with a set of deep learning models, analysis methods with a fixed set of factors to predict daily Bitcoin prices and design-integration of price prediction of different cryptocurrencies using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory) and GRU (Gated recurrent units). The idea of incorporating Public Sentiment in the prediction of the hikes and falls of the Bitcoin market from Social Media platforms like Reddit and Twitter leading to meaningful predicted results. This prediction can bring confidence to the common man to invest with lesser risk and more profit. Also, this can enable the digital new-age currency to become a primary method of transaction.

Sean Mcnally, Jason T. Roche, Simon Caton

The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index. The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. The LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7%.

Tian Guo, Albert Bifet, Nino Antulov-Fantulin

Bitcoin is one of the most prominent decentralized digital cryptocurrencies. Ability to understand which factors drive the fluctuations of the Bitcoin price and to what extent they are predictable is interesting both from the theoretical and practical perspective. In this paper, we study the problem of the Bitcoin short-term volatility forecasting based on volatility history and order book data. Order book, consisting of buy and sell orders over time, reflects the intention of the market and is closely related to the evolution of volatility. We propose temporal mixture models capable of adaptively exploiting both volatility history and order book features. By leveraging rolling and incremental learning and evaluation procedures, we demonstrate the prediction performance of our model as well as studying the robustness, in comparison to a variety of statistical and machine learning baselines. Meanwhile, our temporal mixture model enables to decipher the time-varying effect of order book features on volatility. It demonstrates the prospect of our temporal mixture model as an interpretable forecasting framework over heterogeneous Bitcoin data.

Tian Guo, Nino Antulov-Fantulin

In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market. We use the data of realized volatility collected from one of the largest Bitcoin digital trading offices in 2016 and 2017 as well as order information. Experiments are performed to evaluate a variety of statistical and machine learning approaches.

Li Guo, Wolfgang Karl Härdle, Yubo Tao

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.

M Vaidehi, Alivia Pandit, Bhaskar Jindal, Minu Kumari · 5 authors

After the boom and bust in cryptocurrencies’ prices in recent years, Bitcoin has been totally regarded as an investment asset. As it is highly volatile in nature, there has been a need for good predictions for carrying base investment decisions. Although current study has used machine learning for more accurate Bitcoin price prediction, some of them did focused on the feasibility of applying different modeling techniques to the samples that has different data structures and dimension features. To predict Bitcoin price on different frequencies after using machine learning techniques, firstly we have to classify the Bitcoin price with daily price and high-frequency price. Here, we attempt to predict Bitcoin price as accurately as possible by taking into consideration various protocols that affect the Bitcoin value. Using the provided data we would predict the sign of daily price change with highest possible accuracy. We have used Random Forest Classifier and compared with benchmark results as daily price prediction, we achieve a better performance, with the highest accuracies of the statistical methods and machine learning algorithms of 99%. my investigation in Bitcoin price prediction can be considered as a pilot study for the importance of the sample dimension in the machine learning techniques. Keywords Bitcoin, Crypto Currency, Machine Learning, Blockchain, Long Short Term Memory(LSTM), Recurrent Neural Network(RNN), Prediction

N.I. Indera, Ahmad Ihsan Mohd Yassin, Azlee Zabidi, Zairi Ismael Rizman

This paper presents a Multi-Layer Exogeneous Inputs (NARX) Bitcoin price forecasting model using the opening, closing, minimum and maximum past prices together with Moving Average (MA) technical indicators.

Feng Mai, Zhe Shan, Qing Bai, Xin Wang · 5 authors

Bitcoin’s emergence has the potential to pave the way for a technological revolution in financial markets. What determines its valuation is an important open question with far-reaching business and policy implications. Building on information systems and finance literature, we examine the dynamic interactions between social media and the monetary value of bitcoin using textual analysis and vector error correction models. We show that more bullish forum posts are associated with higher future bitcoin values. Interestingly, social media’s effects on bitcoin are driven primarily by the silent majority, the 95 percent of users who are less active and whose contributions amount to less than 40 percent of total messages. In addition, messages on an Internet forum, relative to tweets, have a stronger impact on future bitcoin value. Overall, our findings reveal that social media sentiment is an important predictor in determining bitcoin’s valuation, but not all social media messages are of equal impact. This study offers new insights into the digital currency market and the economic impact of social media.

Angelos Kalaitzis

In this thesis, an analysis of Bitcoin, Monero price and volatility is conducted with respect to S&amp;P500 and the VIX index. Moreover using Python, we computed correlation coefficients of nine cryptocurrencies with two different approaches: Pearson and Spearman from July 2016 -July 2018. Moreover the Pearson correlation coefficient was computed for each year from July2016 - July 2017 - July 2018. It has been concluded that in 2016 the correlation between the selected cryptocurrencies was very weak - almost none, but in 2017 the correlation increased and became moderate positive. In 2018, almost all of the cryptocurrencies were highly correlated. For example, from January until July of 2018, the Bitcoin - Monero correlation was 0.86 and Bitcoin - Ethereum was 0.82.

Filip Filipović, Joakim Nilgard

Bitcoin is a phenomenon that is new and there is little information on how and why it behaves as volatile as it does. This thesis uses existing data on Bitcoin’s exchange rate to estimate a model that describes the pattern and use it in a financial risk analysis. We also aim to contribute as a foundation for further studies in this field.\nThe statistical properties of the log-return of the exchange rate are analysed and it is deemed to be iid. From the eleven distributional candidates we study is the fitted skew generalised t distribution proven to represent the data best after evaluation by criteria and statistics. The estimated VaR and ES show that the rate is volatile and that the risk from investments is still high.\nThe findings show that it is necessary to describe the exchange rate with complex and flexible distributions, and even if the data shows more stability today than earlier is it important to show caution in interpretations and evaluations on the topic.\nKeywords: Bitcoin, cryptocurrencies, statistical distributions, statistical analysis, exchange rate, modelling

Yasser Mustafa

No abstract is available for this record.

Suni Ajay Kumar

The aim of the paper is to fit a regression model which can be used commonly for the four important crypto currencies: Bitcoin, Litecoin, Ethereum and Ripple to predict the prices. The data has information over the past six years regarding price, transaction volume, transaction count, exchange volume, generated coins etc of these currencies. Understanding the dynamics of crypto currency market can help to a certain extent to take wise investment decisions. Among the variables under consideration the study revealed that transaction volume can be used as an influencing variable to fit a quadratic regression model and predict the prices of the crypto currencies.

Anna NAZARUK

This research examines existence of Bitcoin bubble in the year of 2017 by studying time series data within four main paradigms of the modern bubble theory. All four models suggest that Bitcoin closing price was overstated during the period. The analysis also detects Bitcoin features that could lead to the behavioral bias on the cryptocurrency market. The study suggests that investors’ behavior on the crypto market should be investigated more within behavioral finance.

Afees A. Salisu, Kazeem O. Isah, Lateef O. Akanni

This paper attempts to establish that some inherent features of the Bitcoin price can be exploited to produce better forecast results for stock prices. It does so by constructing predictive models for stock prices of G7 countries with symmetric and asymmetric prices of Bitcoin. The underlying statistical properties of Bitcoin prices such as persistence and conditional heteroscedasticity are captured in the estimation process using the Westerlund and Narayan (2015) estimator that allows for such effects in forecasting. There are two striking findings from the analysis. First, the results suggest that accounting for asymmetries is more likely to enhance the predictive power of Bitcoin in forecasting stock prices regardless of the data sample and forecast horizon. Secondly, the Bitcoin-based predictive model for stock prices, particularly the asymmetric variant, outperforms the Fractionally Integrated Autoregressive Moving Average (ARFIMA) model. While there are concerns as to whether the cryptocurrencies are veritable substitutes to the conventional financial assets, their close link with the developed stock exchanges such as those in the G7 countries suggests that they share some common characteristics such as news effects [asymmetries] which can be exploited when forecasting the behaviour of stock prices.