Can cryptocurrencies price variations be explained by exogenous classical market prices? We evaluate this issue by using daily data on some of the most important asset prices and indexes in Thailand i.e. Gold, Oil, SET50 index, Tourism index, Mutual fund, and THB/USD exchange rate in comparison with digital asset prices i.e. Bitcoin, Ethereum, Litecoin, Ripple, DASH, and Stellar. By performing both direct and inverse relationships using correlation matrix to find distance relationship and using minimum spanning tree to find the closest path between assets, we found strong direct relationship among cryptocurrencies in digital market with SET50 index and oil price in classical markets. We also found that THB-USD exchange rate has inverse relationship with Bitcoin price, SET50 index and oil price. There is a link between cryptocurrencies asset price and some classical assets' market price.
Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is rare. There are many different predicting cryptocurrencies' price methods that cover various purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. Sometimes realizing the trend of a coin in a long run period is needed. In this paper, some machine learning algorithms are applied to find the best ones that can forecast Bitcoin price based on three other famous coins. Second, a new methodology is developed to predict Bitcoin's worth, this is also done by considering different cryptocurrencies prices (Ethereum, Zcash, and Litecoin). The results demonstrated that Zcash has the best performance in forecasting Bitcoin's price without any data on Bitcoin's fluctuations price among these three cryptocurrencies.
During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.
In this paper, we consider a variety of multi-state Hidden Markov models for predicting and explaining the Bitcoin, Ether and Ripple returns in the presence of state (regime) dynamics. In addition, we examine the effects of several financial, economic and cryptocurrency specific predictors on the cryptocurrency return series. Our results indicate that the Non-Homogeneous Hidden Markov (NHHM) model with four states has the best one-step-ahead forecasting performance among all competing models for all three series. The dominance of the predictive densities over the single regime random walk model relies on the fact that the states capture alternating periods with distinct return characteristics. In particular, the four state NHHM model distinguishes bull, bear and calm regimes for the Bitcoin series, and periods with different profit and risk magnitudes for the Ether and Ripple series. Also, conditionally on the hidden states, it identifies predictors with different linear and non-linear effects on the cryptocurrency returns. These empirical findings provide important insight for portfolio management and policy implementation.
Tahmin teknikleri ve modelleri, doğru karar alma ve yatırım aşamasında kişiler ve kuruluşlar için son derece önemlidir. Tahminin doğruluğu başarılı kararlar alınmasını sağlar ve yatırımcıların fayda maksimizasyonuna ulaşmasına imkân tanır. Bu çalışmada, kripto para türlerinden en yaygın olarak kullanılan Bitcoin fiyatlarının yapay sinir ağları yöntemi ile tahmin edilmesi amaçlanmıştır. Girdi değişkenler olarak; Dow-Jones, S&P500, Nasdaq100, Eurostoxx Endeksleri, İsviçre Frangı, İngiliz Sterlini, Euro, Altın, Gümüş yatırım araçları alınmıştır. 2013-2018 tarihleri arasında günlük kapanış fiyatları verileri kullanılmıştır. Çalışmada geri beslemeli yapay sinir ağı modeli kullanılmıştır. 2019 Ocak ayı tahmini yapılarak model test edilmiştir ve modelin tahmin doğruluğu R2 değeri %99 başarı ile gerçekleşmiştir.
Otabek Sattarov, Heung Seok Jeon, Ryum-Duck Oh, Jun Dong Lee
Bitcoin is one of the main phenomena in recent times together with other cryptocurrencies due to the redefinition of the money term and its price fluctuations. Moreover, scientists are increasingly recognizing Twitter's predictive power for a wide range of events, and particularly for financial markets. This article examines to what degree Bitcoin returns can be estimated using public opinion on Twitter. Using a sentiment analyzer on Bitcoin-related tweets and financial data, the Twitter sentiment was found to have predictive power for Bitcoin's results. Once again, our findings confirm the presence of a correlation between them. We observed 62.48% accuracy when making predictions based on bitcoin-related tweet sentiment and historical bitcoin price.
Kevin E. Martin, Izzat Alsmadi, Mohamed Rahouti, Moussa Ayyash
Blockchain is an emerging technology that enables a vital framework for various cryptocurrency operations such as bitcoin. Notably, without any involvement from third party authorities, blockchain offers a decentralized consensus scheme to process user transactions, fund transfer, and various data records in a secure and reliable way. Furthermore, bitcoin price forecasting has been a vital research trend, where machine learning techniques play a substantial role. A sophisticated and appropriately trained model can be useless if the features being tested are unreliable. Independently, one of the most desirable aspects of a system that utilizes the blockchain is the concrete objectiveness by which each entry is cataloged. Any data collected and reported on the blockchain is unambiguous, and therefore, extremely suitable for a machine learning algorithm. To efficiently forecast bitcoin price movements, in this work, we propose and examine various lenses by which to view this union, each with varying degrees of success.
Maria Letizia Guerra, Laerte Sorini, Luciano Stefanini
Sentiment analysis to characterize the properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy Transform (F-transform for short) to capture stylized facts and mutual connections between time series with different natures. The recently proposed Lp-norm F-transform is a powerful and flexible methodology for data analysis, non-parametric smoothing and for fitting and forecasting. Its capabilities are illustrated by empirical analyses concerning Bitcoin prices and Google Trend scores (six years of daily data): we apply the (inverse) F-transform to both time series and, using clustering techniques, we identify stylized facts for Bitcoin prices, based on (local) smoothing and fitting F-transform, and we study their time evolution in terms of a transition matrix. Finally, we examine the dependence of Bitcoin prices on Google Trend scores and we estimate short-term forecasting models; the Diebold–Mariano (DM) test statistics, applied for their significance, shows that sentiment analysis is useful in short-term forecasting of Bitcoin cryptocurrency.
In the finance sector, in general, a single VaR method is used for one single portfolio or for all similar portfolios and it hampers the opportunity for comparison. Such shortcoming deriving from trusting one single VaR method results in very incoherent results for the analysis as well as in untrustable transactions based upon those risk estimations. In order to overcome that, similar investments tools/portfolios should be analysed simultaneously by different VaR methods for comparison. Considering such overcome, this study is aimed to compare the VaR (value at risk) estimation methodologies for all 5 separated portfolios (which are similar considering their liquidity and investment process) holding USD, EUR, GOLD, BIST100 Index (Istanbul Stock Exchange Index) and BITCOIN considering their daily return on TRL (Turkish Lira). For performance measurement of different methodologies listed namely as extreme value VaR (GRPD-gnadenko theorem), ewma based volatility filtered historical simulation, historical simulation, delta normal, and bootstrapping; the 3 backtesting procedures and the related statistics are used.
Abstract The purpose of this research is to identify how effective the determinants of the improved price changes in cryptocurrencies are and if they are predictable. The study addresses several independent variables that are in our consideration which may impact the prices the most. To obtain the results, panel data has been used to run fixed effects models. Then we treated them as time series data to run dynamic trend indicator and first-differencing volatility regression model. Important political shocks and instabilities have been analyzed and interpreted in this paper. In the light of our findings we were able to comment on the complex relation between cryptocurrency prices and socio-political situations throughout the time range. The results address that cryptocurrency price changes are predictable. It is easy to say that major stakeholders (Apple, Amazon, Facebook, Google, Tesla) affect the most prices. Internet search trends seem to have an impact but at the end it has been found that the correlation is strong. We have evaluated all the major cryptocurrency prices with exact accuracy of 95.38% using the volatility regression model effectively. All the cryptocurrencies are evaluated against US dollars in regard of different cryptocurrency like Bitcoin, Ethereum, Litecoin and Ripple digital currency. Cryptocurrencies shouldn’t be seen as a gambling medium and should be taken more seriously like an investment medium. In some specific occasions investing in cryptocurrencies may lead lucrative income.
Abstract From the past two years with increasing geopolitical and economic issues, global currency values have been falling and stock markets have been having a poor run & investors losing wealth. This has led to a renewal of interest in digital currencies. Cryptocurrency one of the most prominent digital currency has found itself in spotlight with investors wanting a piece of it and business establishments accepting it as a source of payment due to its stable performance in the last few years. This research has been done on predicting cryptocurrency prices using machine learning based neural network which has a lowest the model loss over 100 epochs during training and Technical Trade Indicators (TTI) graphs depicts a real BTC value 5 to 10 times in 300-days of current fiscal year has further supported this increasing trader confidence and a shift in global cryptocurrency graph by predicted BTC values. On the same lines, we are analyzing bitcoin prices using Machine Learning and Sentiment Analysis. We also study stock market trends in order to better predict bitcoin prices quantitively. In this work we analyze the impact of global currencies like US Dollar, foreign exchanges on Bitcoin prices and whether Bitcoin has the stability to dethrone global currencies and become the single medium of transaction. This work is adequate enough to aid in predicting price and with results obtained from predicting Bitcoin prices using machine learning based neural network achieving an accuracy of 94.89% under all circumstances of technical trade indication thereby bringing down its price prediction by over 13.7% in April 2020 itself during evaluation.
Abstract Electronic payments is something that is currently in high demand by investors today, but transactions are often constrained due to various problems, especially from third parties. For this reason, cryptocurrency emerged, which is one of the solutions for conducting electronic payment transactions. Some types of cryptocurrency that are most in demand by investors are bitcoin, ethereum, and ripple. The fluctuation value of cryptocurrency is very difficult to predict so that investors often experience losses when making transactions. This study aims to predict cryptocurrency prices such as bitcoin, ethereum and ripple using data mining algorithms. The data mining algorithm used in this prediction process is K-NN, Neural Network, SVM, Linear Regression, Random Forest and Decision Tree. Data mining modeling is done by dividing the dataset into each type of commodity and then analyzed using each algorithm. The results of this study indicate that the accuracy value obtained from some data mining algorithms is good enough to predict cryptocurrency prices
This paper is discusses the problems of the short-term forecasting of financial time series using supervised machine learning (ML) approach. For this goal, we applied several the most powerful methods including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). As dataset were selected the daily close prices of two stock index: SP 500 and NASDAQ, two the most capitalized cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and exchange rate of EUR-USD. As features we used only the past price information. To check the efficiency of these models we made out-of-sample forecast for selected time series by using one step ahead technique. The accuracy rates of the forecasted prices by using ML models were calculated. The results verify the applicability of the ML approach for the forecasting of financial time series. The best out of sample accuracy of short-term prediction daily close prices for selected time series obtained by SGBM and MLP in terms of Mean Absolute Percentage Error (MAPE) was within 0.46-3.71 %. Our results are comparable with accuracy obtained by Deep learning approaches.
Rational investors look into maximizing returns with minimal risk. Since this is highly unlikely, optimizing risk and return is a practical solution. Bitcoin is a new financial product that can be included in an investment portfolio. This paper looks at Bitcoins as a separate asset class and attempts to capture the volatility using the Exponential GARCH (E‐GARCH) as well as to check if Bitcoins can be used as an optimal tool to hedge using the Dynamic Conditional Correlation GARCH against four traditional asset classes in the U.S. economy which includes the stock market (S&P 500 index), Bonds (U.S. Aggregate Bond Index), Gold and Crude Oil. The period of study is a little over 7 years. The results suggest that Bitcoin stands as a highly speculative class of asset with extremely high volatility and with respect to hedging, Bitcoin stands as a possible tool of hedge with the U.S. Aggregate Bond index and to a certain extent against Gold but fails to be an optimal hedge against the S&P 500 and Crude Oil in the U.S. economy between April 29, 2013 and October 31, 2019 due to its highly volatile nature.
In recent years, bitcoin has become a very attractive investment in financial industry, which is not controlled by governments, but is based on trust between transfers under the technology of block chain. Hence, forecasting future bitcoin cryptocurrency values is a problem that has attracted the attention of many researchers in the field, while proving to be a very challenging problem. This work presents an experimental analysis using LSTM and GRUs for forecasting bitcoin values in a minute-granulated time for the entire next day. To this end we also present our methodology for conducting the experiments. The final goal is to create the core of a financial prediction tool around the RNNs. In our experiments, we achieved interesting results such as a SMAPE of 0.0002, a RMSE of US$ 3.844 and a rRMSE of 0.0028 in a day where bitcoin rates vary from US$ 13.2K and US$ 14.6K, surpassing the results of SMAPE found in the literature and proposed limit of SMAPE smaller than 0.007 for forecasts.
This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.
Trading cryptocurrencies (digital currencies) are currently performed by applying methods similar to what is applied to the stock market or commodities; however, these algorithms are not necessarily well-suited for predicting cryptocurrency prices. Unlike stock exchanges, which shut down for several hours or days at a time, digital currency prediction and trading seem to be of a more consistent and predictable nature. In this work, we benefit from sentiment analysis of tweets using both an existing sentiment analysis package and a manually tailored “objective analysis,” to calculate one impact value for each analysis every 15[Formula: see text]min. We then select the most appropriate training method by applying evolutionary techniques and discover the best subset of the generated features to include, as well as other parameters. One of the unique contributions of this work is the analysis of both English and Japanese tweets with a tailored “objective analysis” tool. This resulted in implementation of predictors which yielded 28% to 122% profit in a four-week simulation, much more than simply holding a digital currency for the same period of time.
Waddah Waheeb, Habib Shah, Mohammed Jabreel, Domènec Puig
This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped methods, exponential smoothing (ETS), bagged ETS, Theta, multilayer perceptron, and extreme learning machines (ELM) were used to forecast the closing prices for the next 14 days. The findings of this study are three folds. First, there are seven forecasting methods outperformed the naive method namely MLP, ELM, damped exponential smoothing, simple exponential smoothing, Theta, ETS, and ARIMA. Second, MLP and ELM showed better forecasting accuracy on both validation and out-of-sample data among the forecasting methods used in this study. Third, the size of the training data is essential factor that should be considered when training forecasting methods.
This paper is devoted to the problems of the short-term forecasting cryptocurrencies time series using machine learning approach. We applied two the most powerful ensembles methods: Random Forests (RF) and Gradient Boosting Machine (GBM). For testing models we used the daily close prices of three the most capitalized coins: Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP), and as a features were selected the past price information and technical indicators (moving average). To check the efficiency of these models we made out-of-sample forecast for three cryptocurrencies by using one step ahead technique. As the accuracy rate for our models we were selected Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. According to comparative analysis of the predictive ability of the RF and GBM both models showed the same order of accuracy for the out-of-sample dataset prediction, although boosting also was somewhat more accurate. Computer experiments have confirmed the feasibility of using the machine learning ensembles approaches considered for the short-term forecasting of cryptocurrencies time series. Built models and their ensembles can be used as the basis algorithms for automated Internet trading systems.
In this study, a web model which provides price estimation in terms of Turkish Lira for Bitcoin, Ethereum and Ripple which are popular cryptocurrencies, is developed. Using the relevant model, the price estimation of these three crypto currencies between 21.09.2019 and 20.11.2019 is carried out on the web with dynamic data. Artificial intelligence methods such as adaptive neural fuzzy inference system, artificial neural networks, polynomial curve fitting and long short-term memory are used for price estimation. The aim of this study is to provide periodic forecasts to individuals or institutions interested in cryptocurrencies and to test the success of the exemplary model of the use of artificial intelligence in finance. When the forecastings that haven't yet been realized at the relevant dates and the actual values are compared, the successful results show that the model is well established.
This study investigates the connectedness between Bitcoin prices and major stock indices in the Asia-Pacific region from February 2012 to August 2019. Based on the wavelet transform framework, we find evidence of significant unidirectional association from Bitcoin to the selected markets in the short, medium, and long-run in the Asia-Pacific region. Overall, Asia-Pacific equity markets and Bitcoin cryptocurrency are weakly correlated at higher frequencies throughout the sample period, but the dependence of Bitcoin on the equity markets steadily increases at lower frequencies. Further, we construct the wavelet-based Granger causality test at different time scales to provide additional support to our connectedness results. Our findings provide important implications for policymakers, portfolio managers, and investors who are invited to take into account the dynamic linkages between Bitcoin and equity markets.