Bitcoin has attracted extensive attention from investors, researchers, regulators, and the media. A well-known and unusual feature is that Bitcoin’s price often fluctuates significantly, which has however received less attention. In this paper, we investigate the Bitcoin price fluctuation prediction problem, which can be described as whether Bitcoin price keeps or reversals after a large fluctuation. In this paper, three kinds of features are presented for the price fluctuation prediction, including basic features, traditional technical trading indicators, and features generated by a Denoising autoencoder. We evaluate these features using an Attentive LSTM network and an Embedding Network (ALEN). In particular, an attentive LSTM network can capture the time dependency representation of Bitcoin price and an embedding network can capture the hidden representations from related cryptocurrencies. Experimental results demonstrate that ALEN achieves superior state-of-the-art performance among all baselines. Furthermore, we investigate the impact of parameters on the Bitcoin price fluctuation prediction problem, which can be further used in a real trading environment by investors.
In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models.
Jonathan Gillett, Shahryar Rahnamayan, Masoud Makrehchi, Azam Asilian Bidgoli
Bitcoin is a novel digital currency that relies on cryptography instead of a central authority to verify transactions. Without a central authority, Bitcoin requires a complete list of all transactions to be made public so that they can be verified by all users. The major network influencers in a Bitcoin network are defined as users that accumulate a disproportionate amount of wealth compared to others. However, there are some defined metrics to identify major network influencers, considering multiple criteria can improve the detection task. In this paper, a multi-criteria metric is applied to identify the major network influencers based on the history of their activities recorded on the blockchain. The proposed metric is based on the Pareto front on multiple criteria, the maximum increase in wealth with the least amount of activity using non-dominated sorting inspired from multi-objective optimization. The provided descriptive statistics on extracted data demonstrates the efficiency of the proposed metric on identification of major influencers.
In this paper, we proposed to predict the Bitcoin price accurately taking into consideration various parameters that
affect the Bitcoin value. For the first moment of our paper we gathered the data set in Quandl site. Which come with some
pure dataset. The data set consist of various features related to the Bitcoin price and trading value. Then we have done the
training and testing for dataset. At the second stage of our paper, we have applied some prediction techniques as Linear
Regression model to perform the data set very accurately for getting the MAE, MSE and r-squared values. Then we move on
to the next stage as AI algorithm. In this paper, we have used the LASSO (Least Absolute Shrinkage Selection Operator)
machine learning algorithm. This helps to do the selection operator in large dataset and performing a fabulous for finding the
maximum accuracy. These two algorithms are very helpful for finding the Bitcoin price very high accuracy. In the Finally
stage of our investigation, using the available information we tested the results by real-time results. Then we get the
approximately accuracy then the Real-time accuracy. The sign of the daily price change with highest possible accuracy will
be predicted.
Block chain has an interesting support of bit coin, the digital crypto currency with an ever increasing sphere of users worldwide. But, block chain in itself is much more than just bit coin, it is the new generation security system encapsulating processes in series of blocks to provide a secure way of recording transactions and it is circulated among signatories, or any target group being the participants in the process. It draws its appeal out of the fact that it achieves this without the need of any central authority. Current banking architecture is largely centralized and therefore vulnerable to load defaults and frauds like the PNB scam, Videocon case, Kingfisher scam and many more. Banking all over the world has adopted block chain technologies and it is the need of the hour for regulation and avoidance of such scams. Thus, we are using block chain technology for the decentralized working of banks and the complete removal of authoritarian interception. The model which we are proposing includes block chain encapsulated in the process of NEFT (National Electronic Fund Transfer) using IFSC (Indian Financial System Code) incorporating the protocols set down by RBI for secure and decentralized fund transfer. Our blocks will consist of the process computed in java micro services. The ledger will be interconnected within themselves using consensus algorithms.
Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine (SVM) and Random Forest (RF) are proposed and analyzed for modelling and forecasting the Bitcoin price. While some of the proposed models are univariate, the other models are multivariate and as a result, the maximum, minimum and the opening daily price of Bitcoin are also used in these models. The proposed models are applied on the Bitcoin price from December 18, 2019 to March 1, 2020 and their performances are compared in terms of the performance measures of RMSE and MAPE by Diebold-Mariano statistical test. Based on RMSE and MAPE measures, the results show that SVM provides the best performance among all the models. In addition, ARIMA and Bayesian approaches outperform other univariate models where they provide smaller values for RMSE and MAPE.
Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Naylene Fraccanabbia, Viviana Cocco Mariani · 5 authors
Bitcoin is the leading currency in the cryptocurrency market capturing attention worldwide. Forecasting the Bitcoin price as accurate as possible is essential, but due to its high volatility this task is challenging. Many researchers try, through the years, to develop efficient models for predicting the Bitcoin price using several different data-driven approaches. The objective of this paper is to develop a novel decomposition-ensemble learning model that combines Variational Mode Decomposition (VMD) and Stacking-ensemble learning (STACK) with machine learning algorithms to forecast the Bitcoin price multi-step ahead. The algorithms are k-Nearest Neighbors, Support Vector Regression with Linear kernel, Feed-forward Artificial Neural Network with single-layer perceptron, Generalized Linear Model, and Cubist. Correlation matrix (CORR), principal component analysis (PCA), and Box-Cox transformation (BOXCOX) were used as data preprocessing techniques. Estimating the performance of the proposed models (namely VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) using relative root mean square error, symmetric mean absolute percentage error, and absolute percentage error measures, defined that for one-day-ahead forecast VMD-STAK-BOXCOX model presented the better performance, and for two and three-days-ahead forecast VMD-STACK-CORR model was chosen, compared to VMD, STACK, and machine learning algorithms models' performance. Diebold-Mariano statistical test was conducted to evaluate a reduction in forecasting errors. Therefore, the proposed models (VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) indeed forecast accurately Bitcoin price and outperformed the compared models (VMD, STACK, and machine learning models).
Nowadays, Bitcoin has become the most popular cryptocurrency, which gains the attention of investors and speculators alike. Asset pricing is a risky and challenging activity that enchants lots of shareholders. Indeed, the difficulty in making predictions lies in understanding the multiple factors that affect the Bitcoin price trend. Modeling the market behavior and thus, the sentiment in the Bitcoin ecosystem provides an insight into the predictions of the Bitcoin price. While there are significant studies that investigate the token economics based on the Bitcoin network, limited research has been performed to analyze the network sentiment on the overall Bitcoin price. In this paper, we investigate the predictive power of network sentiments and explore statistical and deep-learning methods to predict Bitcoin future price. In particular, we analyze financial and sentiment features extracted from economic and crowd-sourced data respectively, and we show how the sentiment is the most significant factor in predicting Bitcoin market stocks. Next, we compare two models used for Bitcoin time-series predictions: the Auto-Regressive Integrated Moving Average with eXogenous input (ARIMAX) and the Recurrent Neural Network (RNN). We demonstrate that both models achieve optimal results on new predictions, with a mean squared error lower than 0.14%, due to the inclusion of the studied sentiment feature. Besides, since the ARIMAX achieves better predictions than the RNN, we also prove that, with just a linear model, we may obtain outstanding market forecasts in the Bitcoin scenario.
Traditional stock exchange systems are centralized and have high transaction fees, third party brokerage and less secure environment vulnerable to attacks. Blockchain decentralizes the exchange and creates a fair trading environment for every individual. This paper aims to implement a decentralized stock exchange based on blockchain. The proposed system aims to bring down the transaction costs and reduce the time taken to settle orders and eliminate the need of third parties for executing trades. Three different contracts one for buyer, seller and exchange are used to separate stock exchange functionality and optimize transaction costs. The system is implemented using Ethereum. The system is evaluated for scalability of number of transactions and scalability of number of nodes using sampled trading data from NASDAQ. Transaction fees for the miner at 16.5 Gwei is 99.98% lesser than the brokerage fees of traditional traders for the same transactions. Experimental results of the proposed method show that it is possible to eliminate third party brokerages, reduce transaction fees to almost 0 and create a peer-to-peer trading environment that does not require a central exchange.
In this study, aiming at the problem that the price of Bitcoin varies greatly and is difficult to predict, a hybrid neural network model based on convolutional neural network (CNN) and long short‐term memory (LSTM) neural network is proposed. The transaction data of Bitcoin itself, as well as external information, such as macroeconomic variables and investor attention, are taken as input. Firstly, CNN is used for feature extraction. Then the feature vectors are input into LSTM for training and forecasting the short‐term price of Bitcoin. The result shows that the CNN‐LSTM hybrid neural network can effectively improve the accuracy of value prediction and direction prediction compared with the single structure neural network. The finding has important implications for researchers and investors in the digital currencies market.
ABSTRACT This research examines the dynamic linkage between four major cryptocurrencies—Bitcoin, Ethereum, Ripple, and Litecoin—and stock markets in ASEAN-5. The findings revealed that first, linkage testing, in the long run using Engle and Granger co-integration, provided evidence of a relationship among all cryptocurrencies with the stock markets in ASEAN-5, with the exception of Malaysia. Secondly, using the dynamic conditional correlation model, the results showed that time-varying patterns of short-run correlations were found in all relationships. Moreover, the Litecoin linkage with ASEAN-5 markets fluctuated significantly. Further, Bitcoin’s dynamic linkage with the stock markets showed a very high correlation from 2013 to 2015, and then became close to stable until January 2020. Finally, this paper tested the determinants of the linkage cryptocurrencies with financial market factors, consisting of GOLD, CRUDE, FX, and INT. The empirical results showed that GOLD and INT did not affect the degree of linkage with the stock market or cryptocurrency, although both CRUDE and FX impacted it. As for recommendations and policy implications, the cryptocurrencies demonstrated a dynamic linkage with stock markets and exhibited extreme volatility, and therefore the five countries should prepare a policy or regular information regarding cryptocurrencies for investors or policymakers. On the other hand, investors should focus on indicators such as foreign exchange rates and crude oil prices prior to trading.
İktisat bilimi, ekonomik aktörlerin tercihlerini ve bu tercihlerin ekonomik göstergelere olan etkisini incelemektedir. Özelde, davranışsal iktisat okulu bu tercihlerin zaman zaman rasyonaliteden ayrıldığını ve bu irrasyonalitenin yol açtığı ekonomik krizleri de modelleyen çıkarımlar yapmaktadır. 2008 yılının sonlarında kripto para olan Bitcoin deneysel olarak kullanılmış, bu tarihten sonra bu kripto finans aracı hızla işlem görmeye başlamıştır. Ancak daha sonraki yıllarda Bitcoin’nin değerindeki dalgalanmalar onun finansal bir araç olmaktan daha çok bir deney aracı olduğu yönünde eleştirilmesine neden olmuştur. Bu çalışmada etkin piyasa hipotezi ve davranışsal iktisat öğretilerinden yola çıkarak bitcoin piyasasının fiyat balonlarıyla spekülatif ve rassal hareketlere açık olup olmadığı (Supremum Augmented Dickey-Fuller ) SADF testi ile analiz edilmiştir. Ampirik bulgulara göre 2015-2019 dönemine ait Bitcoin fiyatlarında ortaya çıkan şokların etkisi kalıcıdır ve rassal yürüyüş hipotezi geçerlidir.
Muhammad Yasir, Muhammad Attique, Khalid Latif, Ghulam Mujtaba Chaudhary · 7 authors
Purpose Business Intelligence has gained a significant attraction in the recent past and facilitates managers for efficient business decision-making. Over the years, the attraction toward the cryptocurrency (CC) market has increased. Since the CC market is highly volatile, it is extremely sensitive to shocks and web data related to large events happening around the globe. Design/methodology/approach This research study provides a business intelligence model to predict five top-performing CCs. In this study, deep learning, linear regression and support vector regression (SVR) are used to predict CC prices. The sentiment of some mega-events is also used to enhance the performance of these models. Findings The results show that models of business intelligence such as deep learning and SVR provide better results. Moreover, the results show that the incorporation of social media sentiment data significantly improves the performance of the proposed models. The overall accuracy of the model improves approximately twofold when multiple event sentiments were incorporated. Originality/value The use of social media sentiment of global and local events for different countries along with deep learning for CC forecasting.
Ziyang Ji, Victor Chang, Hao Lan, Ching-Hsien Robert Hsu · 5 authors
As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an effect on stock returns, which is a hot topic in behavioral finance. Blockchain is an essential part of FinTech, and with the fast development of this technology, investors’ sentiment varies as well. The online information that directly reflects investors’ mood could be utilized for mining and quantifying to construct a sentiment index. For a better understanding of how well some factors adequately explain the return of stocks related to blockchain companies in the Chinese stock market, the Fama-French three-factor model (FFTFM) will be introduced in this paper. Furthermore, sentiment could be a new independent variable to enhance the explanatory power of the FFTFM. A comparison between those two models reveals that the sentiment factor could raise the explanatory power. The results also indicate that the Chinses blockchain industry does not own the size effect and book-to-market effect.
Ama -Son on ylda finans alannda dijital inovasyonlar zellikle Blockchain teknolojisine bal olarak ortaya kmaktadr. Blockchain teknolojisinin tm dnyada en yaygn olarak kullanld rn ise kripto para birimleridir. Kripto para birimleri ierisinde Bitcoin gerek piyasa kapitalizasyonu gerekse ilem hacmi ile dikkat ekmektedir
Bitcoin is the first digital decentralized cryptocurrency that has shown a\nsignificant increase in market capitalization in recent years. The objective of\nthis paper is to determine the predictable price direction of Bitcoin in USD by\nmachine learning techniques and sentiment analysis. Twitter and Reddit have\nattracted a great deal of attention from researchers to study public sentiment.\nWe have applied sentiment analysis and supervised machine learning principles\nto the extracted tweets from Twitter and Reddit posts, and we analyze the\ncorrelation between bitcoin price movements and sentiments in tweets. We\nexplored several algorithms of machine learning using supervised learning to\ndevelop a prediction model and provide informative analysis of future market\nprices. Due to the difficulty of evaluating the exact nature of a Time\nSeries(ARIMA) model, it is often very difficult to produce appropriate\nforecasts. Then we continue to implement Recurrent Neural Networks (RNN) with\nlong short-term memory cells (LSTM). Thus, we analyzed the time series model\nprediction of bitcoin prices with greater efficiency using long short-term\nmemory (LSTM) techniques and compared the predictability of bitcoin price and\nsentiment analysis of bitcoin tweets to the standard method (ARIMA). The RMSE\n(Root-mean-square error) of LSTM are 198.448 (single feature) and 197.515\n(multi-feature) whereas the ARIMA model RMSE is 209.263 which shows that LSTM\nwith multi feature shows the more accurate result.\n
The Theta method has attracted academic attention lately due to its simplicity and superior performance. This paper proposes a new hybrid forecasting approach based on combining the Theta decomposition method and support vector regression (SVR) for forecasting highly volatile and noisy Bitcoin price time series. Using Theta decomposition with coefficients ranging from 0 to 2 with 0.1 steps, we extracted 20 Theta lines from the original time series. Each of these 20 lines is used for a univariate regression. Then the results of each forecasts aggregated to construct the final predicted values. Moreover, we used the Theta lines to construct a predictor space for multivariate regression using SVR. However, due to poor performance of the multivariate regression and to further enhance its performance, we eliminated inefficient Theta lines from the predictor space. Enhanced MASE by 10.45% and 5.68% in comparison to the Theta-SES (classic Theta) and SVR, the results indicate the superiority of the proposed hybrid Theta-SVR.
Bitcoin is a digital currency and investment tool that has received worldwide attention recently. However, the fluctuation of Bitcoin price has been a concern to the users and investors. Forecasting the Bitcoin price can serve as a guideline for investor and user to make effective strategy in their investment or usage. With the rapid development of the Internet, online data including news article can facilitate forecasting Bitcoin price. This research aims to study the effect of news article sentiment towards Bitcoin price with study period from September 2017 to August 2019. Accordingly, this study introduces sentiment analysis to understand the relevant information of online news articles and use it as an input feature for Bitcoin price prediction. Two main phases are included in the study, which is sentiment analysis and price prediction. In sentiment analysis, the sentiment is extracted based on a lexicon-based approach to capture the relevant news articles information regarding cryptocurrency markets. In price prediction, the sentiment is used as an input feature and Long Short-Term Memory (LSTM) model is used in price prediction phase. With Bitcoin market data and news articles as samples, the empirical results show that news articles sentiment reduced the overall error in Bitcoin price predictions.
Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM deep learning model. By applying Pearson's correlation, we found that the sentiment of the day (d) had a positive effect on the future Bitcoin returns on the next day (d+1). The prediction accuracy of the linear regression model for the next day's revenue was 78%.
Reshma Sundari Gadey, Nikita Thakur, Naveen Charan, R. Obulakonda Reddy
With the development Machine learning and AI-assisted trading has gained interest in the past few years. To bring out the abnormal profits from the cryptocurrency market, we use this machine learning and AIassisted trading. We store the daily data for a certain period. With the strategies assisted by state-of-the-art algorithms we obtain great outcomes. With the help of simple algorithms and architecture, the outcomes made the growth in the cryptocurrency market. The cryptocurrency has become popular in 2017 because of the growth in market capitalization. More than 1500 crypto currencies are actively trading in today's scenario. The crypto currency can be generated and be used for online transactions. Bitcoin is a cryptocurrency technology. The value of Bitcoin keeps varying every second. Therefore, to predict the value of bitcoin price here, we use the LSTM Architecture. With the help of this architecture, we are trying to prove this LSTM architecture provides more accurate results than any other machine learning algorithms and architecture.
‘‘Cryptocurrency trading was one of the most exciting jobs of 2017’’. ‘‘Bit- coin’’,‘‘Blockchain’’, ‘‘Bitcoin Trading’’ were the most searched words in Google during 2017. High return on investment has attracted many people towards this crypto market. Existing research has shown that the trading price is completely based on speculation, and its trading volume is highly impacted by news media. This paper discusses the existing work to evaluate the sentiment and price of the cryptocurrency, the issues with the current trading models. It builds possible solutions to understand better the semantic orientation of text by comparing different machine learning techniques and predicts Bitcoin trading price based on Twitter feed sentiment and additional Bitcoin metrics. We observe that the statistical machine learning model was able to better predict the sentiment of Twitter tweet feed compared to the advanced BERT model. Using Twitter feed sentiment and additional Bitcoin metrics, we were able to improve the prediction of bitcoin price compared to only using bitcoin’s previous day closing pricing.