Bibliometric analysis of scientific production on international trade and cryptocurrency

There has been a remarkable increase in the number of publications on international trade and cryptocurrency in recent years. This paper aims to analyze the literature on international trade and cryptocurrency in the Web of Science database. This study uses the bibliometric method and mapping analysis. The cluster analysis is conducted based on the keyword analysis. These publications are reviewed from different aspects such as type of publication, language, and book title. This study found that 767 articles which are related to cryptocurrency and international trade. Among the countries in which these studies are conducted, China ranks the first, followed by the USA and UK, respectively. Various organizations in different countries support studies on this topic. In conclusion, cryptocurrency technologies draw the attention of academia, and the use of cryptocurrency in international trade will determine the future trade structure. The innovative features of cryptocurrency can develop new business models, which may be the reason for the academic interest in this matter. It will be useful for businesses and governments to follow this potential carefully to benefit from the advantages of innovative business models. © 2021 The Authors. Published by IASE. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis

The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon

Investment Decision on Cryptocurrency: Comparing Prediction Performance Using ARIMA and LSTM

The increasing popularity of cryptocurrencies as a means of financial inclusion for investment and trade has become a major concern for individuals seeking to benefit from the cryptocurrency market. This study aims to provide insights for cryptocurrency investors, financial sector professionals, and academics by utilizing machine learning techniques such as ARIMA and LSTM to compare the accuracy of modeling performance on datasets predicting the prices of five cryptocurrencies, namely Bitcoin, Ethereum, Binance Coin, Tether, and Cardano. Data was obtained by downloading from the Yahoo Finance website using Jupyter notebook. The LSTM method outperformed the ARIMA method, achieving a lower MAPE value of less than 10 percent and effectively capturing price movements, providing valuable information for decision-making

Підходи машинного навчання для прогнозування фінансових часових рядів

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.У цій роботі обговорюються проблеми короткострокового прогнозування фінансових часових рядів з використанням підходу під наглядом машинного навчання (ML). Для цієї мети ми застосували декілька найпотужніших методів, включаючи машину підтримки векторів (SVM), багатошаровий перцептрон (MLP), випадкові ліси (RF) та машину стохастичного градієнта (SGBM). В якості набору даних були обрані щоденні ціни закриття двох фондових індексів: SP 500 та NASDAQ, двох найбільш капіталізованих криптовалют: біткойна (BTC), ефіру (ETH) та курсу євро-долар США. В якості функцій ми використовували лише попередню інформацію про ціни. Для перевірки ефективності цих моделей ми зробили позавибірковий прогноз для вибраних часових рядів, використовуючи техніку на один крок вперед. Розраховували показники точності прогнозованих цін за допомогою моделей МЛ. Результати перевіряють придатність підходу до відмивання коштів для прогнозування фінансових часових рядів. Найкраща з вибіркової точності щоденна ціна закриття короткострокового прогнозування для обраних часових рядів, отримана за допомогою SGBM та MLP у перерахунку на середню абсолютну відсоткову помилку (MAPE), була в межах 0,46-3,71 %. Наші результати можна порівняти з точністю, отриманою підходами глибокого навчання

Feature Selection with Annealing for Forecasting Financial Time Series

Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy or hold strategies so that they may increase profitability. However, obtaining accurate and reliable predictions is challenging, noting that accuracy does not equate to reliability, especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies. To mitigate this complexity, this study provides a comprehensive method for forecasting financial time series based on tactical input output feature mapping techniques using machine learning (ML) models. During the prediction process, selecting the relevant indicators is vital to obtaining the desired results. In the financial field, limited attention has been paid to this problem with ML solutions. We investigate the use of feature selection with annealing (FSA) for the first time in this field, and we apply the least absolute shrinkage and selection operator (Lasso) method to select the features from more than 1,000 candidates obtained from 26 technical classifiers with different periods and lags. Boruta (BOR) feature selection, a wrapper method, is used as a baseline for comparison. Logistic regression (LR), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks. The dependent variables consisted of daily logarithmic returns and trends. The mean-squared error for regression, area under the receiver operating characteristic curve, and classification accuracy were used to evaluate model performance, and the statistical significance of the forecasting results was tested using paired t-tests. Experiments indicate that the FSA algorithm increased the performance of ML models, regardless of problem type.Comment: 37 pages, 1 figures and 12 table

Τεχνική ανάλυση και Νευρωνικά Δίκτυα στα Κρυπτονομίσματα

Η παρούσα μεταπτυχιακή εργασία παρουσιάζει ένα μοντέλο πρόβλεψης των μελλοντικών τιμών κλεισίματος ενός κρυπτονομίσματος (ΕΤΗ), το οποίο θα λαμβάνει σαν είσοδο ιστορικά δεδομένα κινήσεων των τιμών του. Το πολύ επίπεδο αυτό μοντέλο βασίζεται σε εκτιμήσεις τεχνικής ανάλυσης και νευρωνικών δικτύων, συνδυάζοντας έτσι την εμπειρική γνώση με την επιστημονική. Μέσω της εφαρμογής του μοντέλου αυτού σε ένα από τα ισχυρότερα κρυπτονομίσματα, αναδεικνύουμε τις δυνατότητες της μηχανικής μάθησης, των αλγοριθμικών συναλλαγών και την εφαρμοσιμότητα τους σε έναν από τους ταχύτερα ανερχόμενους χώρους σήμερα, αυτόν των κρυπτονομισμάτων. Η σύγκριση των αποτελεσμάτων με την σχετική βιβλιογραφία δείχνει καλή συσχέτιση, ενισχύοντας την αντίληψη ότι περαιτέρω παραμετροποίηση θα μπορούσε να μειώσει το σφάλμα υπολογισμών και να ενισχύσει σημαντικά τη διαδικασία λήψης αποφάσεων στις συναλλαγές αξιογράφων.The current thesis presents a model for predicting the future closing prices of a cryptocurrency (ETH), which receives as input historical data of its price movements. This multi-layer model is based on estimates of technical analysis and neural networks, thus combining empirical with scientific knowledge. Through the application of this model to one of the most powerful cryptocurrencies, we highlight the possibilities of machine learning, algorithmic trading and their applicability in one of the fastest rising markets today, that of cryptocurrencies. The comparison of the results with the relevant literature shows a good correlation, reinforcing the notion that further parameterization could reduce the calculation error and significantly enhance the decision-making process during trading