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sensors Article A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data Esam Mahdi 1,∗ , Víctor Leiva 2 , Saed Mara’Beh 1 , Carlos Martin-Barreiro 3,4 Citation: Mahdi,E.;Leiva,V.; Mara’Beh, S.; Martin-Barreiro, C. A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data. Sensors2021,21,6319. https:// doi.org/10.3390/s21186319 Academic Editor: Wai Lok Woo Received: 30 August 2021 Accepted: 17 September 2021 Published: 21 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 2 3 4 Abstract: Inareal-worldsituationproducedunderCOVID-19scenarios,predictingcryptocurrency 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. Keywords: artificial intelligence; data science; digital currency; gold; SARS-CoV-2; machine learning; sensing and data extraction 1. Introduction In the last two years, the impact of the 2019 Coronavirus Disease (COVID-19) on the trading strategies between different types of cryptocurrencies, gold, exchange market, portfolio diversification, and macroeconomic policy has attracted a great deal of interest in the financial market [1]. The unexpected recent exponential increase in cryptocurrency prices has exceeded the investors’ imaginations. However, this does not indicate that cryptocurrencies are a substitute for gold [2]. For more than 2000 years, gold has been known as a high-quality liquid asset that has served as a hedge and safe haven for ex- changing commodities. Several robust statistical models have been proposed to explore thepredictabilityofthegoldprice[2,3]. Cryptocurrencies are digital currencies that belong to a decentralized system still classified in the nascent asset class [4]. Their prices are very sensitive to good and bad news with significant instability leading to high volatility and making the predictions hard and challenging, especially under adverse market conditions [2,4]. Thus, investments in cryptocurrencies are, often, equated to investments in gold, where cryptocurrencies may benefit from higher allocations to gold and vice-versa [2]. Department of Mathematics, Statistics and Physics, Qatar University, Doha 2713, Qatar; smarabeh@qu.edu.qa School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile; victor.leiva@pucv.cl Faculty of Natural Sciences and Mathematics, Universidad Politécnica ESPOL, Guayaquil 090902, Ecuador; cmmartin@espol.edu.ec Faculty of Engineering, Universidad Espíritu Santo, Samborondón 0901952, Ecuador * Correspondence: emahdi@qu.edu.qa Sensors 2021, 21, 6319. https://doi.org/10.3390/s21186319 https://www.mdpi.com/journal/sensorsPDF Image | Predicting Cryptocurrency Returns Based on the Gold Prices
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