Blockchain Papers

N. M. Dhanya

No abstract is available for this record.

Česlovas Bartkus, Bilgehan Teki̇n

BITCOIN has a different criterion than traditional systems that pay in states’ currencies. This payment system is a complex scheme designed to facilitate the transfer of value between the parties. In this study, firstly, brief information about technical analysis, BITCOIN and behavioral finance is given. Then, in the literature part of the study, studies on BITCOIN prices in the context of behavioral finance and technical analysis are given. In this study, it is examined Relative Strength Index (RSI) availability in Bitcoin transactions and evaluate in the context of behavioral finance findings. For describing the risk of trading in Bitcoin, were chosen Value at Risk (VaR) ratio. In application part of the study it is supposed 1 Bitcoin “Buy” orders were opened when RSI was under 30 and closed when RSI was above 70. And also, 1 Bitcoin “Sell” orders were opened when RSI was above 70 and closed when it was under 30. All obtained data from trades was used for revealing results on accuracy, total profitability. Positive trades were divided by total trades and multiplied by 100 for calculation of accuracy. Period of research is 2015-01-01 till 2019-08-31. As a result of the study we see the effects of biases in Bitcoin transactions. It is observed the examples of conservatism, over and underreaction, status quo effect and loss aversion. And also it is determined in this study that RSI works better in stable market when traders play safer. In other words, RSI works better when conservatism wins over overreaction.

Xinyu Wu, NIU SHENGHAO, Xie Haibin

The results highlight the value of using price range and including a second component of the conditional range for forecasting the Bitcoin volatility.

Carlos Betancourt, Wenhui Chen

No abstract is available for this record.

Anirudhi Thanvi, Raghav Sharma, Bhanvi Menghani, Manish Kumar · 5 authors

No abstract is available for this record.

Boyu Yang, Yuying Sun, Shouyang Wang

No abstract is available for this record.

Mitul Verma, Pritish Sharma

Introduced in 2009, Bitcoin has demonstrated a huge potential as the world’s first digital currency and has been widely used as a financial investment. Our research aims to uncover the relationship between Bitcoin prices and people’s sentiments about Bitcoin on social media. Among various social media platforms, micro-blogging is one of the most popular. Millions of people use micro-blogging platforms to exchange ideas, broadcast views, and to provide opinions on different topics related to politics, culture, science, and technology. This makes them a potentially rich source of data for sentiment analysis. Therefore we chose one of the busiest micro-blogging platforms, Twitter, to perform sentiment analysis on Bitcoin. We used ELMo embedding model to convert Bitcoin-related tweets into a vector form and SVM classifier to divide the tweets into three sentiment categories - positive, negative, and neutral. We then used the sentiment data to find its relation with Bitcoin price fluctuation using the linear mixed model.

Mingxi Liu, Guowen Li, Jianping Li, Xiaoqian Zhu · 5 authors

No abstract is available for this record.

Chandrasekar Ravi

Prediction of stock market trends is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data and thus, the prediction becomes challenging among the investors to invest the money for making profits. Initially, the blockchain network is fed to the blockchain network bridge from which the bitcoin data is acquired that is followed with the bitcoin prediction. Bitcoin prediction is performed using the proposed FuzzyCSA-based Deep Long short-term memory (LSTM). At first, the flow strength indicators are extracted based on Double exponential moving average (DEMA), Rate of Change (ROCR), Average True Range (ATR), Simple Moving Average (SMA), and Moving Average Convergence Divergence (MACD) from the blockchain data. Based on the extracted features, the prediction is done using FuzzyCSA-based Deep LSTM, which is the combination of FuzzyCSA with Deep LSTM. Then, the CSA is modified using the fuzzy operator for determining the optimal weights in Deep LSTM. The experimentation of the proposed method is performed from the openly available dataset. The analysis of the method in terms of Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) reveals that the proposed FuzzyCSA-based Deep LSTM acquired a minimal MAE of 0.4811, and the minimal RMSE of 0.3905, respectively.

Ui-Jun Baek, Shin Mu-gon, Min-Seong Lee, Boseon Kim · 6 authors

Since the development of Bitcoin, the first blockchain-based cryptocurrency, many cryptocurrencies have formed and have traded in markets. The integrity and anonymity of cryptocurrency was enough to raise its value and its price gained worldwide attention. Therefore, many studies are being carried out to predict the price of cryptocurrency for make a profit. We cluster time series through K-Medoids algorithm and train and evaluate each cluster with predictive models. We also examine the predictive performance in Bitcoin price according to the various distance measurement of clustering.

Maher Alharby, Aad van Moorsel

To simulate blockchain systems as close to reality as possible, we need accurate estimates of the probability distribution of various variables. In this paper we obtain distributions for Ethereum smart contract transactions, with respect to Gas Limit, Used Gas, Gas Price and CPU Time. To determine these distributions we use publicly available Ethereum smart contract information, augmented with experimental data for over 300,000 smart contracts obtained on a test bed. We conclude that Gaussian Mixture Models are appropriate for distributions of smart contracts with respect to Used Gas and Gas Price, and use a uniform distribution for the distribution with respect to the Gas Limit. A correlation analysis shows that the CPU Time is strongly correlated with Used Gas and we therefore apply regression techniques to estimate the CPU Time conditioned on Used Gas. We experiment with three ensemble regression methods, namely Random Forest, Gradient Boosting Machine and Adaptive Boosting and conclude that Random Forest is both fast and accurate.

Ihyak Ulumuddin, Sunardi Sunardi, Abdul Fadlil

Price fluctuation is a necessity in every business, including the price of Bitcoin. Therefore humans need a device or application that can assist in making predictions quickly and accurately. Deep Learning can be used to make predictions which include setting the parameters used during training, choosing the best parameters for prediction, and choosing the prediction results with the smallest error level in the actual situation. This study performs a Bitcoin price prediction using Long Short Term Memory (LSTM). This study uses quantitative calculations on the prediction results. Measurement of accuracy is done by testing the previous price (back testing) and calculating the average error using RMSE and MAPE. The method for generating predictions is LSTM as a type of Recurrent Neural Network (RNN) which has the advantage of using long-term memory in handling time series data. LSTM implementation using Python is used for price forecasting with stages starting from data collection and normalization, input and output modeling. The result of this research is a prediction of Bitcoin price with an accuracy rate of 97.48% based on the best model with input layer 2, the number of epochs 100, the number of hidden layers 100, and activation using Softmax. These price predictions can be used as a reference and consideration for traders to create trading strategies and run them automatically on the digital currency market.

Canbaz, Ayşe Gül

This study aims to investigate if the investors’ sentiment expressed on contentspecific online forum affects the return of Bitcoin. We use a large dataset consisting of 2.8 million forum posts sourced from “Bitcointalk.org” for a period between Jan 2016 and May 2020. The sentiment is derived daily with the Hu Liu lexical model after a detailed investigation of different lexicons. Using time-series data, we test for the relationship between the investors’ sentiment and Bitcoin price along with other financial variables that may inform about the direction of Bitcoin price. Our results show that sentiment derived from online forums do not present an autoregressive relationship with return of Bitcoin

Mohil Maheshkumar Patel, Sudeep Tanwar, Rajesh Gupta, Neeraj Kumar

No abstract is available for this record.

T. Gopalakrishnan

Cryptocurrency such as Bitcoin, Ethereum etc. is increasingly well known nowadays among cryptocurrency miners and enthusiasts.Our goal is to estimate the various cryptocurrency accurately considering various parameters that impact the cost.We use various deep learning and machine learning algorithms to be able to recognize the value pattern on the closing value thus giving us the predicted price.The point of this is to determine the precision of cryptocurrency values utilizing various AI algorithms and predict their estimated prices.The dataset that we will be using contains consolidated financial information for the top 10 cryptocurrencies sorted by Market Cap which also have various attributes for each type of cryptocurrency such as open, close, high, low, value and date.Therefore, we are going to use Linear Regression, SGD Regression, Support Vector Regression, and LSTM algorithms to predict the various cryptocurrencies prices.

Xiao Li, Weili Wu

Bitcoin, as one of the most popular cryptocurrency, is recently attracting\nmuch attention of investors. Bitcoin price prediction task is consequently a\nrising academic topic for providing valuable insights and suggestions. Existing\nbitcoin prediction works mostly base on trivial feature engineering, that\nmanually designs features or factors from multiple areas, including Bticoin\nBlockchain information, finance and social media sentiments. The feature\nengineering not only requires much human effort, but the effectiveness of the\nintuitively designed features can not be guaranteed. In this paper, we aim to\nmining the abundant patterns encoded in bitcoin transactions, and propose\nk-order transaction graph to reveal patterns under different scope. We propose\nthe transaction graph based feature to automatically encode the patterns. A\nnovel prediction method is proposed to accept the features and make price\nprediction, which can take advantage from particular patterns from different\nhistory period. The results of comparison experiments demonstrate that the\nproposed method outperforms the most recent state-of-art methods.\n

Ahmed Ibrahim, Rasha Kashef, Menglu Li, Esteban Valencia · 5 authors

The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC is determined by the number of miners and available BTC and by scripting algorithms for blockchain hashing, while both speculators and investors determine demand. One major question then is to understand how BTC is valued and how different factors influence it. In this paper, the BTC market mechanics are broken down using vector autoregression (VAR) and Bayesian vector autoregression (BVAR) prediction models. The models proved to be very useful in simulating past BTC prices using a feature set of exogenous variables. The VAR model allows the analysis of individual factors of influence. This analysis contributes to an in-depth understanding of what drives BTC, and it can be useful to numerous stakeholders. This paper’s primary motivation is to capitalize on market movement and identify the significant price drivers, including stakeholders impacted, effects of time, as well as supply, demand, and other characteristics. The two VAR and BVAR models are compared with some state-of-the-art forecasting models over two time periods. Experimental results show that the vector-autoregression-based models achieved better performance compared to the traditional autoregression models and the Bayesian regression models.

Thanasis Zoumpekas, Elias N. Houstis, Manolis Vavalis

No abstract is available for this record.

Mayank Puri, Aman Garg, Lekha Rani

Education on cryptocurrency is essential for individuals to make informed decisions regarding foreign investment in digital assets. In recent years there is an exponential increase in the price of cryptocurrency due to its easy trading especially in developing countries so the trend of financial institutions buying cryptocurrency into their portfolios has grown in the past decade which results in economic growth. The first completely digital assets that asset managers have included are cryptocurrencies. Traders have a unique opportunity to forecast price swings due to social media’s impact on cryptocurrency prices. Trading using Al and Machine Learning has drawn more attention in recent years. One could investigate the above hypothesis to determine if it is feasible to capitalize on the Bitcoin market’s inefficiency for the purpose of generating unusually high profits. The advanced machine-learning techniques enable straightforward trading strategies to exceed conventional benchmarks. The findings demonstrate how basic computational processes might assist predict the near-term development of the bitcoin market. Further, there are prediction and comparison prices using SVM and Random Forest algorithms on the basics of efficiency while changing the number of days.

Siti Saadah, A.A Ahmad Whafa

Financial stability is the problem that correlated with many aspects. In this digital era, virtual currency has emerged as one of financial assets. Fluctuation from cryptocurrencies price made an impact into financial stability indirectly. It has been proved by the market capitalization of publicly traded cryptocurrencies in 2019 reach USD 240 billion. The percentages reach 66% for the market capitalization. Three highest cryptocurrencies uphold are Bitcoin, Ethereum and XRP. This condition made these three cryptocurrencies become the important investment products. However, cryptocurrency is the product with high volatility. Because of that, this study aims to monitor financial stability from cryptocurrencies prediction using artificial intelligent algorithm. This research copes the problem by predicting bitcoin, Ethereum and XRP using three different intelligent algorithms, which are K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). By this prediction had been figured out about up and down value of cryptocurrencies using Root Mean Square Error (RMSE) to evaluate stabilization of finance. Accuration system with LSTM shown that the price of cryptocurrency will fit with the data actual using LSTM, in which the accuracy around 80%. This condition meant that LSTM had been succeeded to proposed as algorithm that could fit the cryptocurrencies value. It could strengthen usability to indicate financial stability refer into cryptocurrencies price.

Uğur Kaya, Fırat Akba, İ̇hsan Tolga Medeni, Tunç D. Medeni

Son zamanlarda kullanımı oldukça yaygınlaşan blokzinciri teknolojisinin, İnternet teknolojisi ile beraber adı sıkça anılır olmaya başlamıştır. Blokzinciri teknolojisiyle geliştirilen Bitcoin, sanal para birimleri arasında en çok piyasa hacmini elinde bulunduran sanal para birimidir. Sanal para piyasalarının kontrolünü elinde bulunduran bir merkezi otoritenin olmaması sebebiyle fiyat manipülasyonlarına ve dışarıdan müdahalelere açık olan bu pazarda, en uçtaki yatırımcının yatırım yapabilmesi açısından yol gösterimine ihtiyaç duyulmaktadır. Son zamanlarda bu ihtiyacı karşılamak amacıyla birtakım yöntemler kullanılmaya başlanmıştır. Bu çalışmada makine öğrenmesi, zaman serileri analizi ve derin öğrenme yöntemleri kullanılarak Bitcoin fiyatlarındaki dalgalanma hakkında çeşitli tahminleme ve sınıflama yöntemleri beraber olarak değerlendirilmiştir. Bu bağlamda, koronavirüs pandemisi öncesi ve sonrasındaki Bitcoin kapanış fiyatları ve düşüş-yükseliş eğilimleri baz alınarak iki ayrı veri kümesi oluşturulmuştur. Bu veri kümeleri üzerinde tahmin ve sınıflama yöntemleri değerlendirilerek, başarıları karşılaştırılmıştır. Karşılaştırmalar sonucunda, pandemi öncesi verilerle yapılan çalışmada Destek Vektör Makineleri, pandemi sonrası verilerle yapılan çalışmada ise ARIMA en başarılı sonuçları vermiştir.

Gil Cohen

This research has examined the ability of two forecasting methods to forecast Bitcoin's price trends. The research is based on Bitcoin-USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin's price changes do not follow the "Random Walk" efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin's price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.

Prosper Lamothe-Fernández, David Alaminos, Prosper Lamothe-López, Manuel Á. Fernández-Gámez

A precise prediction of Bitcoin price is an important aspect of digital financial markets because it improves the valuation of an asset belonging to a decentralized control market. Numerous studies have studied the accuracy of models from a set of factors. Hence, previous literature shows how models for the prediction of Bitcoin suffer from poor performance capacity and, therefore, more progress is needed on predictive models, and they do not select the most significant variables. This paper presents a comparison of deep learning methodologies for forecasting Bitcoin price and, therefore, a new prediction model with the ability to estimate accurately. A sample of 29 initial factors was used, which has made possible the application of explanatory factors of different aspects related to the formation of the price of Bitcoin. To the sample under study, different methods have been applied to achieve a robust model, namely, deep recurrent convolutional neural networks, which have shown the importance of transaction costs and difficulty in Bitcoin price, among others. Our results have a great potential impact on the adequacy of asset pricing against the uncertainties derived from digital currencies, providing tools that help to achieve stability in cryptocurrency markets. Our models offer high and stable success results for a future prediction horizon, something useful for asset valuation of cryptocurrencies like Bitcoin.

Alla A. Petukhina, Raphael C. G. Reule, Wolfgang Karl Härdle

This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns related to algorithmic trading and its impact on the European cryptocurrency market. We study trading quantitatives such as returns, traded volumes, volatility periodicity, and provide summary statistics of return correlations to CRIX (CRyptocurrency IndeX), as well as respective overall high-frequency based market statistics with respect to temporal aspects. Our results provide mandatory insight into a market, where the grand scale employment of automated trading algorithms and the extremely rapid execution of trades might seem to be a standard based on media reports. Our findings on intraday momentum of trading patterns lead to a new quantitative view on approaching the predictability of economic value in this new digital market.