Esam Mahdi, Víctor Leiva, Saed Mara’Beh, Carlos Martín-Barreiro
In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.
In recent years, there has been an increase in demand for machine learning and AI-assisted trading. To extract abnormal profits from the bitcoin market, the machine learning and artificial intelligence (AI) assisted trading process has been used. Each day, the data gets saved for the specified amount of time. These approaches produce great results when integrated with cutting-edge algorithms. The results of algorithms and architectural structures drive the development of cryptocurrency market. The unprecedented increase in market capitalization has enabled the cryptocurrency to flourish in 2017. Currently, the market accommodates totally 1500 cryptocurrencies, all of which are actively trading. It is always possible to mine the cryptocurrency and use it to pay for online purchases. The proposed research study is more focused on leveraging the accurate forecast of bitcoin prices via the normalization of a particular dataset. With the use of LSTM machine learning, this dataset has been trained to deploy a more accurate forecast of the bitcoin price. Furthermore, this research work has evaluated different machine learning methods and found that the suggested work delivers better results. Based on the resultant findings, the accuracy, recall, precision, and sensitivity of the test has been calculated.
Sentiments are extracted from tweets with the hashtag of cryptocurrencies to predict the price and sentiment prediction model generates the parameters for optimization procedure to make decision and re-allocate the portfolio in the further step. Moreover, after the process of prediction, the evaluation, which is conducted with RMSE, MAE and R2, select the KNN and CART model for the prediction of Bitcoin and Ethereum respectively. During the process of portfolio optimization, this project is trying to use predictive prescription to robust the uncertainty and meanwhile take full advantages of auxiliary data such as sentiments. For the outcome of optimization, the portfolio allocation and returns fluctuate acutely as the illustration of figure.
Many traders believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of automated sentiment analysis for cryptocurrencies. For the study, we targeted one cryptocurrency (NEO) altcoin and collected related data. The data collection and cleaning were essential components of the study. First, the last five years of daily tweets with NEO hashtags were obtained from Twitter. The collected tweets were then filtered to contain or mention only NEO. We manually tagged a subset of the tweets with positive, negative, and neutral sentiment labels. We trained and tested a Random Forest classifier on the labeled data where the test set accuracy reached 77%. In the second phase of the study, we investigated whether the daily sentiment of the tweets was correlated with the NEO price. We found positive correlations between the number of tweets and the daily prices, and between the prices of different crypto coins. We share the data publicly.
Cryptocurrency or virtual currency is a form of investment that has developed since 2010. Today, there are more than 2,000 types of crypto currencies worldwide. Cryptocurrency research in Indonesia is still focused on the legal status and legal status of cryptocurrency investments. This quantitative descriptive study aims to describe the returns and risks of investing in crypto currencies. Descriptive analysis by calculating risk measures and using the heteroscedastic model GARCH (1,1) was carried out on the return data of 15 crypto currencies that had the greatest value. Information was obtained that investing in most crypto currencies resulted in higher returns than investing in foreign currencies or the stock market. On the other hand, Crypto currencies have a higher risk of loss and volatility clustering or heteroscedasticity. Further research is needed to uncover the characteristics of Crypto currency returns and their performance in the form of a portfolio.
This paper initially presents a nontechnical overview of cryptocurrency, its history, and the technicalities of its usage as a means of exchange. Bitcoin’s working methodology and mathematical baseline is further presented in more depth. For the remaining majority of the paper, recent cryptocurrency price data of Bitcoin, Ethereum, Tether, Dogecoin, and Binance coin was used to train a machine learning model of Feed Forward Neural Networks to predict future prices for each of the datasets. Further and in conclusion, the results are discussed, and the efficiency and accuracy of these models are evaluated.
Fahad Mostafa, Pritam Saha, Mohammad Rafiqul Islam, Nguyet Nguyen
Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.
Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins’ price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection do not find significative the explanatory power of NASDAQ and Tesla. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to self-regulate and does not need additional drivers to improve the accuracy of the price direction.
Bitcoin is one of the cryptocurrencies that had a high rate of return since its appearance in 2009. However, the exchange rate of Bitcoin against any foreign currency is considered to have high volatility making it difficult to determine the real value of Bitcoin. The main purpose of this research is to find the value of Bitcoin, especially US Dollar and Rupiah currencies. The test is carried out using the weak market efficiency hypothesis and the semi-form market coefficient hypothesis. The data processing methods are used the stationary test (ADF, KPSS, and ERS) to test the efficiency of the weak form market and the cointegration test (Johansen Cointegration) with the VECM model to check the efficiency of the semi-strong market. The results show that the Bitcoin exchange rate does not have a unit root so it is inefficient in a weak form and has a negative effect on the USD / IDR exchange rate so that it is not efficient in semi-strong form as well as on the US Dollar and Rupiah exchange rates. This happens because Bitcoin transactions as a medium of exchange in Indonesia are still illegal. So that the Bitcoin exchange rate against the US Dollar and Rupiah exchange rates is biased because it does not reflect the available information, both historical information and public information. Keywords—Bitcoin Exchange Rate; Market Efficiency; Unit Root; Cointegration
Natalia A. Van Heerden, Juan Cabral, Nadia Luczywo
In recent years, cryptocurrencies have gone from an obscure niche to a prominent place, with investment in these assets becoming increasingly popular. However, cryptocurrencies carry a high risk due to their high volatility. In this paper, criteria based on historical cryptocurrency data are defined in order to characterize returns and risks in different ways, in short time windows (7 and 15 days); then, the importance of criteria is analyzed by various methods and their impact is evaluated. Finally, the future plan is projected to use the knowledge obtained for the selection of investment portfolios by applying multi-criteria methods.
Yuze Li, Shangrong Jiang, Yunjie Wei, Shouyang Wang
Abstract The emergence and growing popularity of Bitcoins have attracted the attention of the financial world. However, few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market. It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio. In this study, we propose a novel ensemble portfolio optimization (NEPO) framework utilized for broad commodity assets, which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation. Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors. In addition, it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies, commodity funds, and indices.
Cryptocurrency is an advanced digital currency that is gotten by cryptography, numerous digital currencies are decentralized organizations dependent on blockchain innovation an appropriated record authorized by a different organization of computers. And Many present-day technologies are driving the transformative impact in the global financial system, in that impact cryptocurrency stands on first position in the list. Cryptocurrency offer several potential benefits, including better speed and efficiency in processing payments and transfers notably across borders and ultimately boosting financial inclusion. The intension of this paper is to summaries the difference between the normal or traditional method of currency transaction and crypto currency transaction. And why all are providing more interest towards crypto methods nowadays and what different they feel while choosing a crypto method over a normal or traditional currency methods. And how crypto currency is dragging current world attention towards its pocket and why many are developing interest towards following the crypto trend.
This work presents an application of self-attention networks for cryptocurrency trading. Cryptocurrencies are extremely volatile and unpredictable. Thus, cryptocurrency trading is challenging and involves higher risks than trading traditional financial assets such as stocks. To overcome the aforementioned problems, we propose a deep reinforcement learning (DRL) approach for cryptocurrency trading. The proposed trading system contains a self-attention network trained using an actor-critic DRL algorithm. Cryptocurrency markets contain hundreds of assets, allowing greater investment diversification, which can be accomplished if all the assets are analyzed against one another. Self-attention networks are suitable for dealing with the problem because the attention mechanism can process long sequences of data and focus on the most relevant parts of the inputs. Transaction fees are also considered in formulating the studied problem. Systems that perform trades in high frequencies cannot overlook this issue, since, after many trades, small fees can add up to significant expenses. To validate the proposed approach, a DRL environment is built using data from an important cryptocurrency market. We test our method against a state-of-the-art baseline in two different experiments. The experimental results show the proposed approach can obtain higher daily profits and has several advantages over existing methods.
Chidi U. Okonkwo, Bright O. Osu, Farid Chighoub, Ben I. Oruh
This paper investigated the co-movement between the bitcoin (BTC) and the exchange rates of some African currencies to the USD (United States Dollars) using the continuous wavelet transform (CWT) and wavelet coherence (WTC). This was done for the noisy as well as the denoised series. The CWT for the noisy series suggests high volatility for those who hold the currencies for the short term and low volatility for those who hold the currencies for a long-term period. The CWT of the denoised series suggests that volatility at low frequency is driven by noise, while volatility at a higher frequency is driven by market forces. The wavelet coherence suggests that in the presence of noise, bitcoin will be a hedge for the currencies. However, in the absence of noise, bitcoin is a haven for the Egyptian EGP, followed by the Algerian DZD, then the Nigerian NGN, and may not be a haven for the South African ZAR.
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.
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.
Open access
2 source records
Stock Market Forecasting Methods
Complex Systems and Time Series Analysis
Generative Adversarial Networks and Image Synthesis
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.
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
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.
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.
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.