Blockchain Papers

Eunho Koo, Geonwoo Kim

No abstract is available for this record.

Erdinc Akyildirim, Oğuzhan Çepni, Shaen Corbet, Gazi Salah Uddin

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

Dennys Mallqui, Ricardo A. S. Fernandes

Cryptocurrencies are one of the most important financial and technological innovations of recent years. Currently, the interest in Bitcoin has grown, for traders and the general public. However, its high volatility represents a challenge in terms of prediction models for day-trade operations. In this way, recent studies have been proposed to predict the Bitcoin price direction for day-trade operations, but the maximum accuracy obtained was around 57.5%. In order to contribute and advance the state-of-the-art, this article experiences the impact of use Blockchain data, international economic indices, social trends information and technical indicators to overcome the predictions of the Bitcoin price direction. Thus, it is proposed a methodology based on data collection and processing, where weighted moving averages (for the Blockchain data, economic indices and social media trends) and technical indicators (considering the Bitcoin exchange rate data) were extracted/calculated from the original databases. The attributes were submitted to the Information Gain algorithm to select the most relevant ones. In the sequence, Support Vector Machines and Artificial Neural Networks models were used to predict the Bitcoin price direction for day-trade purposes. As a result, it was possible to obtain an average accuracy of 63.84% in a 1-year prediction period, overcoming other related studies.

Alvin Ho, Ramesh Vatambeti, Sathish Kumar Ravichandran

Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin. Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency. This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested. Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model. With the help of python libraries, the data filtration process was done. Python has provided with a best feature for data analysis and visualization. After the understanding of the data, we trim the data and use the features or attributes best suited for the model. Implementation of the model is done and the result is recorded. Finding: It was discovered that the linear regression model’s accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate. The LSTM model, on the other hand, shows a mini error rate of 0.08 percent. This, in turn, demonstrates that the neural network model is more optimized than the machine learning model. Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin. This paper compares the prediction outcomes of a machine learning model and an artificial neural network model. Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model. Keywords: Bitcoin; Block chain; Crypto currency; Machine Learning; Artificial Neural Network

Samuel Rikli, Nico, Bigler Daniel, Moritz Pfenninger, Joerg, Osterrieder

Modeling financial time series is challenging due to their high volatility and unexpected happenings on the market. Most financial models and algorithms trying to fill the lack of historical financial time series struggle to perform and are highly vulnerable to overfitting. As an alternative, we introduce in this paper a deep neural network called the WGAN-GP, a data-driven model that focuses on sample generation. The WGAN-GP consists of a generator and discriminator function which utilize an LSTM architecture. The WGAN-GP is supposed to learn the underlying structure of the input data, which in our case, is the Bitcoin. Bitcoin is unique in its behavior; the prices fluctuate what makes guessing the price trend hardly impossible. Through adversarial training, the WGAN-GP should learn the underlying structure of the bitcoin and generate very similar samples of the bitcoin distribution. The generated synthetic time series are visually indistinguishable from the real data. But the numerical results show that the generated data were close to the real data distribution but distinguishable. The model mainly shows a stable learning behavior. However, the model has space for optimization, which could be achieved by adjusting the hyperparameters.

José Benito Hernández C., Andrés García-Medina, Miguel Andrés Porro V.

We studied the effects of the recent financial turbulence of 2020 on the cryptocurrency market, taking into account both prices and volumes from December 2019 to July 2020. Time series were transformed into transaction matrices, and the Apriori algorithm was applied to find the association rules between different currencies, identifying whether the price or the volume of the currencies compose the rules. We divided the data set into two subsets and found that before the decline in cryptocurrency prices, the association rules were generally formed by these prices and that, then, the volumes of the transactions dominated to form the association rules.

Sashank Sridhar, Sowmya Sanagavarapu

Cryptocurrency market has witnessed a boom during the global pandemic and has proven as a strong investment with a wide institutional adoption. A time-series forecasting solution will play a vital role in analyzing the fluctuation of the bitcoin and altcoin markets. Dogecoin is one such altcoin that is a low-price, high-risk investment option garnering considerable interest this year. The variation of the price trend of this altcoin is studied using the multi-head attention mechanism implemented in a transformer, where the attention heads attend to the tokens that are relevant to each current token based on varying short-term and long-term dependencies. In this paper, a multi-head attention-based transformer encoder-decoder model is applied on the hourly data of the Dogecoin price for its prediction over time. The performance of the model has been evaluated using a number of evaluation metrics including MAE and predictive R-squared value. The model trained over the Dogecoin hourly price variation gave an impressive accuracy of 98.46% and R-squared value of 0.8616 comparable with the existing state-of-the-art cryptocurrency price forecasting models.

Mahir Iqbal, Muhammad Hammad Iqbal, Fawwad Hassan Jaskani, Khurum Iqbal · 5 authors

In the market of cryptocurrency the Bitcoins are the first currency which has gain the significant importance. To predict the market price and stability of Bitcoin in Crypto-market, a machine learning based time series analysis has been applied. Time-series analysis can predict the

Leandro Maciel, Rosângela Ballini, Fernando Gomide

No abstract is available for this record.

Bashar Yaser Almansour, Muneer M. Alshater, Ammar Yaser Almansour

The cryptocurrency market is highly volatile; this can be attributed to several factors such as being an emerging market that is purely digital and still evolving with many speculations taking place aligning with behavioural finance factors such as media and investors profile. This study aims to investigate the Autoregressive Conditional Heteroskedasticity (ARCH) and the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) in forecasting selected 9 cryptocurrencies that represent over 80% of the total market capitalization. This study carries a time-series of daily data ranges from 2010 to 2020 base on each cryptocurrency starting date. The results show that the ARCH and GARCH have a significant effect in forecasting cryptocurrency market volatility which means that the past volatility of cryptocurrencies affects the current volatility of it. It also shows that bad and good news can significantly affect the conditional volatility of all cryptocurrencies returns. This study contributes to the investors’ understanding of the dynamics of the cryptocurrency market which enhances the ability to make informed decisions based on a scientific approach.

Han-Min Kim, Gee‐Woo Bock, Gunwoong Lee

No abstract is available for this record.

Shreyas Pillai, Darshan Biyani, Ria Motghare, Deepak C. Karia

Cryptocurrencies are considered to be the next big thing in the financial sector and are the most emerging market in the current world. The amount of data available and the sophisticated architecture behind it make cryptocurrencies an excellent subject for research and thus an easy share to get deep insights of its value using machine learning for price prediction and sentiment analysis. While the previous works only used mathematical methods and various machine learning algorithms for predicting the price of cryptocurrency but forgot a vital and inseparable part that is the sentiments of the trading community which plays a vital and important role in determining and calculating the price of the share. This paper also included sentiment analysis of that share. The paper uses Long short term memory algorithm for predicting the price of the cryptocurrency and Vader sentiment analysis to predict the sentiment of the people by scrapping a news website. This paper also included a proposed methodology for creating a notification system using the dual moving cross-over technique. The result is an application which combines all three algorithms to create an efficient and accurate trading application.

Gausiya Momin, Trupti Ingle, Vaishnavi Mirajkar, Anand Magar

Bitcoin is the most profitable in the cryptocurrency market. However, the prices of Bitcoin have highly fluctuated which makes them very difficult to predict. This research aims to discover the most efficient accuracy model to predict Bitcoin prices from various machine learning algorithms. Using one-minute interval trading data on the exchange website name is bit stamp from January 1, 2012, to January 8, 2018, some different regression models with sci-kit- learn and Keras libraries had experimented. The best results showed that the Mean Squared Error (MSE) was as low as 0.00002 and the R-Square (R2) was as high as 99.2 Percentage.

Liping Yang

In recent years, Bitcoin price prediction has attracted the interest of researchers and investors. However, the accuracy of previous studies is not well enough. Machine learning and deep learning methods have been proved to have strong prediction ability in this area. This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast.

Zhuorui Zhang, Hong‐Ning Dai, Junhao Zhou, Subrota Kumar Mondal · 6 authors

No abstract is available for this record.

Suyuan Luo, Tsan‐Ming Choi

No abstract is available for this record.

Yu Cheng Chien, Zoe Wang, Alexander Fleiss

This article sets forth a framework for deep reinforcement learning as applied to trading cryptocurrencies. Specifically, the authors adopt Q-Learning, which is a model-free reinforcement learning algorithm, to implement a deep neural network to approximate the best possible states and actions to take in the cryptocurrency market. Bitcoin, Ethereum, and Litecoin were selected as representatives to test the model. The Deep Q trading agent generated an average portfolio return of 65.98%, although it showed extreme volatility over the 2,000 runs. Despite the high volatility of deep reinforcement learning, the experiment demonstrates that it has exceptionally high potential to be employed and provides a solid foundation on which to build further research. <b>TOPICS:</b>Currency, big data/machine learning, performance measurement <b>Key Findings</b> ▪ The authors use deep neural networks to create a Deep Q-Learning trading agent that approximates the best actions to take based on rewards to maximize returns from trading the three cryptocurrencies with the largest market capitalization. ▪ The Deep Q-Learning agent generates a return of 65.98% on average over the course of 2,000 episodes; however, the returns do exhibit a large standard deviation given the highly volatile nature of the cryptocurrencies. ▪ The authors introduce a framework on which future deep reinforcement learning and rewards-based trading agents can be built and improved.

Grace. LK. Joshila, P. Asha, D. Usha Nandini, G. Kalaiarasi

This work aims to enhance the existing analysis made on bitcoin and predict the price of a Bitcoin by taking some parameters into consideration. After a huge research taking all the parameters which affect the price of the bitcoin value and identified daily changes in the bitcoin market. In this work all the data consists of different features over the past few year's daily records. This work is started by gaining all the information that all are needed to predict the bitcoin price. All the information was collected from the past few years and implemented the data into this work. In this work Support Vector Machine (SVM) algorithm is used as it gives much more accuracy better than previous algorithms. This study predicts sign of change in the price of bitcoin to the investors so that they can invest in this easily and also for the newcomers to this market or business.

G. M. Siddesh, S. R. Mani Sekhar, S R Vighnesh, N. Jagadeesh Sai · 6 authors

Supply chain management is the broad range of activities required to plan, control, and execute the flow of a product. As a less corruptible and more automated alternative to traditional databases, blockchains are well suited to the complicated record-keeping. However distributed database management system is a centralized software system; the blockchain technology can overcome the problem of synchronization between multiple databases; it also ensures that integrity problems are solved. In the proposed model, Ethereum blockchain is used to solve a few major supply chain problems to manage a distributed database. The model has incorporated techniques to predict the rise and fall of the demand for the medicine in the market by using machine learning algorithms such as linear regression and LSTM; also, the trend predicted by both the models has been compared. The result shows that while using linear regression the predicted trend is not very accurate and cannot trace the actual trend closely whereas BLSTM has performed well in predicting the trends of time series data.

Mert Baran Tunçel, Yaşar ALPTÜRK, Mehmet Akif ALTUNAY, İ̇smail BEKCİ

Bu araştırmanın amacı, Bitcoin fiyatları ile BIST100 endeksi arasındaki nedensellik ilişkisini tespit etmeye çalışmaktır. Araştırmada19 Temmuz 2010 ile 10 Ocak 2020 arasındaki dönemleri kapsayan Bitcoin fiyatları ve BIST100 endeksi günlük verileri(2452 Gözlem) kullanılmıştır. Serilerin durağanlığını test etmek için yapısal kırılmaları göz ardı etmeyen Lee Strazicich birim kök testi kullanılmıştır. Daha sonra Toda-Yamamoto testi ile değişkenler arasında nedensellik olup olmadığı, nedensellik varsa nedenselliğin yönünün ne olduğu tespit edilmeye çalışılmıştır. Toda-Yamamoto(1995) nedensellik testi sonuçlarına göre, BIST100 endeksi değişkeninden Bitcoin fiyatları değişkenine doğru ve Bitcoin fiyatları değişkeninden BIST100 endeksi değişkenine doğru %5 anlamlılık seviyesinde nedensellik ilişkisine rastlanılmamıştır.

Huy Nghiem, Goran Murić, Fred Morstatter, Emilio Ferrara

No abstract is available for this record.

Burak KÖKSAL, Gözde ERDEM, Cansu TÜRKELİ, Zehra Kamışlı Öztürk

Bütün sektörler dahilinde finans sektöründe de müşterilere ait fikir ve düşüncelerinin belirlenmesi, firma ve kurumların ileriki dönemler için sunacağı hizmetleri etkilemektedir. Kripto para birimlerinin (Bitcoin, Ethereum, Ripple vb.) ekonomik ve sosyal etkileri hızla artmaya devam ettikçe, ilgili haber makalelerinin ve sosyal medya yayınlarının, özellikle de tweetlerin yaygınlığı da artmaktadır. Bu çalışmada, Twitter kullanıcılarının finans sektörü konularından biri olan Bitcoin ile ilgili yorumları derlenerek bir duygu analizi çalışması yapılmıştır. Kullanıcı yorumları, Twitter’ın sunmuş olduğu API hizmeti vasıtasıyla Python Programlama Dili kullanılarak alınmış; yorumlar olumlu, nötr ve olumsuz etiketler ile ayrıştırılmış, etiket bulutunda toplanmıştır. Naïve Bayes ve Lojistik Regresyon algoritmaları kullanılarak oluşturulan modellerde başarı oranları karşılaştırılmıştır. Naïve Bayes uygulamasının tweetlerin duygularını tahmin etmedeki başarı oranı %72,19 olurken, Lojistik Regresyon uygulamasında bu oran %75,53 olmuştur. Çalışmanın ikinci aşamasında ise, duygu analizinden sonra “Bitcoin” anahtar kelimesi içeren günlük pozitif tweet oranı ile Bitcoin günlük açılış değeri beraber kullanılarak Bitcoin kapanış değeri tahminlemesi yapılmıştır. Finans verileri Yahoo Finance web sitesi üzerinden alınmış; Doğrusal Regresyon ve Rastgele Orman Regresyon yöntemleri ile modeller oluşturulmuştur. Doğrusal Regresyon için r² değeri %88,97 çıkarken, Rastgele Orman Regresyonu için ise %94,16 olmuştur.Anahtar Kelimeler: Duygu analizi, Twitter, Bitcoin, Makine öğrenmesi, Veri madenciliği, Finans

Christoph J. Börner, Ingo Hoffmann, Jonas Krettek, Tim Schmitz

Abstract Cryptocurrencies (CCs) have become increasingly interesting for institutional investors’ strategic asset allocation and will therefore be a fixed component of professional portfolios in the future. However, this asset class differs from established assets primarily in that it has a higher standard deviation and tail risk. The question then arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, with the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components based on modern methods from image/pattern recognition.

Naratorn Boonpeam, Warodom Werapun, Tanakorn Karode

Cryptocurrencies are in great demand in society. The number of beginners on the cryptocurrency market increases every day. Most of them only focus on a high return on the cryptocurrency investment. However, they do not aware of the high risk from its volatility. Trading is the most popular way to invest with digital currencies due to its potential returns. The cryptocurrency exchange requires a high learning curve for beginners. There exists a strategy to minimize risk in the cryptocurrency investment called "arbitrage". It exploits the market inefficiency to discover a profitable method. This strategy has been investigated in traditional markets like stock markets. Furthermore, some researchers studied arbitrage on general exchange platforms that are operated by companies. However, to the best of our knowledge, there is no research works on arbitrage in the Decentralized Exchange (DEX). The DEX recently emerged with a huge amount of trading volume and profits. The high profit is along with the high risk. Thus, risk-reducing in the cryptocurrency investment is a crucial topic. We demonstrate arbitrage strategy on DEX. The strategy used in this work is to invest an amount of Ether and receive a higher return in each row. The market inefficiency on the current DEX platforms is searched by using an automatic method. We further investigate important factors, which should be considered for profit-maximizing in DEX arbitrage.