Blockchain Papers

Eduard Silantyev

No abstract is available for this record.

Hirofumi Yamamoto, Hiroki Sakaji, Hiroyasu Matsushima, Yuki Yamashita · 7 authors

No abstract is available for this record.

Ferdiansyah Ferdiansyah, Edi Surya Negara, Yeni Widyanti

Cryptocurrency trade is now a popular type of investment. Cryptocurrency market has been treated similar to foreign exchange and stock market. The Characteristics of Bitcoin have made Bitcoin keep rising In the last few years. Bitcoin exchange rate to American Dollar (USD) is $3990 USD on November 2018, with daily pice fluctuations could reach 4.55%2. It is important to able to predict value to ensure profitable investment. 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, computing based tools are commonly used in stock and foreign exchange market predictions. There has been much research about SVM prediction on stocks and foreign exchange as case studies but none on cryptocurrency. Therefore, this research studied method to predict the market value of one of the most used cryptocurrency, Bitcoin. The preditct methods will be used on this research is regime prediction to develop model to predict the close value of Bitcoin and use Support vector classifier algorithm to predict the current day’s trend at the opening of the market

Daniel Broby, Devraj Basu, Ashwin Arulselvan

This paper investigates the importance of "time of execution" and the relevance of "precision time" in order driven transactions done over distributed ledgers. We created a distributed marketplace using stock market price data from the Toronto Stock Exchange (TMX). We then proceeded to test and measure the impact of timing of orders at the nanosecond level. Whilst price discovery in order driven markets is done instantaneously, with distributed markets, it is necessary to know which order to process first to avoid "front-running". We argue that a protocol for the time of order of receipt and execution should be subject to nanosecond stacking. Our approach incorporates both transitory and permanent price discovery components. It allows for the efficient processing of transactions and the order that are received by a market clearing distributed ledger.

Kalpanasonika R, Sayasri S M, Vinothini A, Suga Priya H

The accusative of this paper is to predict the bitcoin price accurately by taking various parameters into consideration which affects the bitcoin value. Here multi-layer perceptron algorithms under deep learning are used to predict the price of crypto-currency. Many researchers have analysed the crypto-currency features in many ways such as, market price prediction, the impact of cryptocurrency in real life. It has the ability to make long-term prediction of the exchange price in crypto-currencies particularly in US dollar, based on historical trends. The bitcoin cost prediction is done based on the data set which consists of 13 features relating to the crypto-currency price recorded daily over the period of particular range.

Sakshi Tandon, Shreya Tripathi, Pragya Saraswat, Chetna Dabas

This research paper reports the proposed model for price prediction of the popular Bitcoin crypto currency while applying different neural network approaches namely Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) along with 10-fold cross validation. In this work, the analysis of various trends of Bitcoin market is carried out and learning of important features used for price prediction is done. Daily price change is estimated by the neural network models. New activation functions are utilized in this research paper for improving efficiency. Further, this research paper compares the proposed model with other existing models namely; RNN with LSTM, Linear Regression and Random Forest applied in the same domain. The dataset utilized in this work is taken from the website named coinmarket and live streaming data is considered for the experimental work. Keras, Tensorflow and Scikit Learn have been used for performing the experimental work of the proposed model. The performance analysis of the proposed model with the existing ones has been carried out in terms of the Mean Absolute Error (MAE). It is observed from the results retrieved as a part of this work that the MAE for the proposed model came out to be 0.0043s which was significantly less than its existing counterparts.

Wang Yiying, Zang Yeze

Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and mechant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try to understand the factors that infiuence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyse the price dynamics of Bitcoin, Etherum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilise useful information hidden in historical memory is stronger than ANN. However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This study provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model.

Tetsuya Takaishi, Takanori Adachi

This letter investigates the dynamic relationship between market efficiency, liquidity, and multifractality of Bitcoin. We find that before 2013 liquidity is low and the Hurst exponent is less than 0.5, indicating that the Bitcoin time series is anti-persistent. After 2013, as liquidity increased, the Hurst exponent rose to approximately 0.5, improving market efficiency. For several periods, however, the Hurst exponent was found to be significantly less than 0.5, making the time series anti-persistent during those periods. We also investigate the multifractal degree of the Bitcoin time series using the generalized Hurst exponent and find that the multifractal degree is related to market efficiency in a non-linear manner.

Avinash Barnwal, Hari Pad Bharti, Aasim Ali, Vishal Krishna Singh

Predicting the direction of assets have been an active area of study and a difficult task. Machine learning models have been used to build robust models to model the above task. Ensemble methods is one of them showing results better than a single supervised method. In this paper, we have used generative and discriminative classifiers to create the stack, particularly 3 generative and 6 discriminative classifiers and optimized over one-layer Neural Network to model the direction of price cryptocurrencies. Features used are technical indicators used are not limited to trend, momentum, volume, volatility indicators, and sentiment analysis has also been used to gain useful insight combined with the above features. For Cross-validation, Purged Walk forward cross-validation has been used. In terms of accuracy, we have done a comparative analysis of the performance of Ensemble method with Stacking and Ensemble method with blending. We have also developed a methodology for combined features importance for the stacked model. Important indicators are also identified based on feature importance.

Thomas Fischer, Christopher Krauß, Alexander Deinert

Machine learning research has gained momentum—also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., Takeuchi and Lee (2013); Moritz and Zimmermann (2014); Krauss et al. (2017). With our paper, we pose the question how such a statistical arbitrage approach would fare in the cryptocurrency space on minute-binned data. Specifically, we train a random forest on lagged returns of 40 cryptocurrency coins, with the objective to predict whether a coin outperforms the cross-sectional median of all 40 coins over the subsequent 120 min. We buy the coins with the top-3 predictions and short-sell the coins with the flop-3 predictions, only to reverse the positions after 120 min. During the out-of-sample period of our backtest, ranging from 18 June 2018 to 17 September 2018, and after more than 100,000 trades, we find statistically and economically significant returns of 7.1 bps per day, after transaction costs of 15 bps per half-turn. While this finding poses a challenge to the semi-strong from of market efficiency, we critically discuss it in light of limits to arbitrage, focusing on total volume constraints of the presented intraday-strategy.

Matheus José Silva de Souza, Fahad Almudhaf, Bruno Miranda Henrique, Ana Beatriz Silveira Negredo · 7 authors

This paper aims to investigate how Machine Learning (ML) techniques perform in the prediction of cryptocurrency prices. We answer if Support Vector Machines (SVM) and Artificial Neural Networks (ANN) based strategies can generate abnormal risk-adjusted returns when applied to Bitcoin, the largest decentralized digital currency in terms of market capitalization. Findings indicate that traders are able to earn conservative returns on the risk adjusted basis, even accounting for transaction costs, when using SVM. Furthermore, the study suggests that ANN can explore short run informational inefficiencies to generate abnormal profits, being able to beat even buy-and-hold during strong bull trends.

Jorge Salmerón, Departamento de Economía, División de Ciencias Sociales y Humanidades, Universidad Autónoma Metropolitana -Unidad Azcapotzalco

En el siguiente trabajo se realiza un análisis sobre el comportamiento de los precios de seis criptomonedas: el Bitcoin, el Etherum, el Dash, el Ripple, el Litecoin y el Zcash, haciendo uso de vectores autorregresivos, funciones de impulso-respuesta, pruebas de causalidad de Granger y la prueba de cointegración de Johansen. El objetivo de la investigación es mostrar si existe relación de corto y/o largo plazo entre las variaciones en los precios de las criptomonedas, de tal forma que cuando los precios de alguna de éstas varían, las otras también fluctúan en relación a la variación de los precios de la criptomoneda en cuestión, y por tanto, intentar anticipar las fluctuaciones, en los precios de una criptomoneda, a partir de los movimientos de las demás.

Nico Smuts

The Google Trends 1 search analysis service and the Telegram 2 messaging platform are investigated to determine their respective relationships to cryptocurrency price behaviour. It is shown that, in contrast to earlier findings, the relationship between cryptocurrency price movements and internet search volumes obtained from Google Trends is no longer consistently positive, with strong negative correlations detected for Bitcoin and Ethereum during June 2018. Sentiment extracted from cryptocurrency investment groups on Telegram is found to be positively correlated to Bitcoin and Ethereum price movements, particularly during periods of elevated volatility. The number of messages posted on a Bitcoin-themed Telegram group is found to be an indicator of Bitcoin price action in the subsequent week. A long shortterm memory (LSTM) recurrent neural network is developed to predict the direction of cryptocurrency prices using data obtained from Google Trends and Telegram. It is shown that Telegram data is a better predictor of the direction of the Bitcoin market than Google Trends. The converse is true for Ethereum. The LSTM model produces the most accurate results when predicting price movements over a one-week period.

Takuya Shintate, Lukáš Pichl

We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the prediction of unstable Bitcoin prices in the OkCoin market and show that the baseline approaches are easily biased by class imbalance, whereas our model mitigates this problem. We also show that the classification performance of our method expressed as the F-measure substantially exceeds the odds of a uniform random process with three outcomes, proving that extraction of deterministic patterns for trend classification, and hence market prediction, is possible to some degree. The profit rates based on RSM outperformed those based on LSTM, although they did not exceed those of the buy-and-hold strategy within the testing data period, and thus do not provide a basis for algorithmic trading.

Hossein Hassani, Mohammad Reza Yeganegi

No abstract is available for this record.

Higor Y. D. Sigaki, Matjaž Perc, Haroldo V. Ribeiro

The efficient market hypothesis has far-reaching implications for financial trading and market stability. Whether or not cryptocurrencies are informationally efficient has therefore been the subject of intense recent investigation. Here, we use permutation entropy and statistical complexity over sliding time-windows of price log returns to quantify the dynamic efficiency of more than four hundred cryptocurrencies. We consider that a cryptocurrency is efficient within a time-window when these two complexity measures are statistically indistinguishable from their values obtained on randomly shuffled data. We find that 37% of the cryptocurrencies in our study stay efficient over 80% of the time, whereas 20% are informationally efficient in less than 20% of the time. Our results also show that the efficiency is not correlated with the market capitalization of the cryptocurrencies. A dynamic analysis of informational efficiency over time reveals clustering patterns in which different cryptocurrencies with similar temporal patterns form four clusters, and moreover, younger currencies in each group appear poised to follow the trend of their 'elders'. The cryptocurrency market thus already shows notable adherence to the efficient market hypothesis, although data also reveals that the coming-of-age of digital currencies is in this regard still very much underway.

Piyush Keshari, Santanu Koley, Kunal Kumar Mandal, Pradeep Kumar Singh

No abstract is available for this record.

Ng, Joash Jun Sen

With the introduction of Bitcoin back in 2008, the cryptocurrency market has evolved erratically and at unprecedented speed. There are approximately 2054 mineable cryptocurrencies as of October 2019 and the cryptocurrencies industry is estimated with a total market capitalization approximately about $219 billion USD in September 2019. Due to the volatility of the cryptocurrency market, it comes with uncertainty for the people who are trying to invest or using them as actual currency. Some of the factors affecting the value of cryptocurrency include the supply and demand of the currency, popularity of the cryptocurrency, political adaptation and restrictions, and speculations of investors.
\nThe primary objective of this project is to perform sentiment analysis on the news to determine whether the sentiment of news and events will affect the price of the cryptocurrencies. The aim of this project is to create a system is developed that performs the three tasks: the collection of price time series for multiple cryptocurrencies and information about events and news related to cryptocurrencies, execution of sentiment analysis on the news to determine if the news is positive or negative with respect to the price action of cryptocurrency, last but not least, analysis of sentiment to predict future price projections.
\nThe project findings show that there exist certain forms of correlation between news and events and the price of cryptocurrencies. It also shows that extremely positive or negative news could be used to project a potential surge or sudden drop in price. 
\nRecommendations for future enhancement work or potential research would be to improve the accuracy of the sentiment analysis value by enhancing the classifier.

David Suda, Luke Spiteri

No abstract is available for this record.

Kiril Desev, Stanimir Kabaivanov, Desislav Desevn, Kiril Desev · 6 authors

This paper analyses the efficiency of cryptocurrency markets by applying econometric models to different short-term investment horizons. A number of experiments are carried out to demonstrate that small training sets can still be used to build efficient and useful forecasts, which in turn can be transformed into straight-forward investment strategies. It also compares the application of selected models on cryptocurrency and mature stock markets. The forecasting accuracy of the models is explored using different error metrics and different horizons. The results suggest that the variation of the error estimates doesn’t appear to be tightly related to the maturity of the markets, but rather depends on the intrinsic characteristics of the analyzed time series.

Masafumi Nakano, Akihiko Takahashi

No abstract is available for this record.

Phan Duy Hung, Tran Quang Thinh

No abstract is available for this record.

Kuselo Ntsika Ntsaluba

In this study, a methodology is presented where a hybrid system combining an evolutionary algorithm with artificial neural networks (ANNs) is designed to make weekly directional change forecasts on the USD by inferring a prediction using closing spot rates of three currency pairs: EUR/USD, GBP/USD and CHF/USD. The forecasts made by the genetically trained ANN are compared to those made by a new variation of the simple moving average (MA) trading strategy, tailored to the methodology, as well as a random model. The same process is then repeated for the three major cryptocurrencies namely: BTC/USD, ETH/USD and XRP/USD. The overall prediction accuracy, uptrend and downtrend prediction accuracy is analyzed for all three methods within the fiat currency as well as the cryptocurrency contexts. The best models are then evaluated in terms of their ability to convert predictive accuracy to a profitable investment given an initial investment. The best model was found to be the hybrid model on the basis of overall prediction accuracy and accrued returns.

Ze Shen, Qing Wan, David J. Leatham, Shen, Ze · 6 authors

Agribusiness