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,Saed Mara'Beh,Víctor Leiva,Carlos Martin-Barreiro

doi:10.3390/s21186319

Abstract

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.

Highlights

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]
Our objective is to propose a new approach for predicting cryptocurrency returns by using the support vector classification (SVC) technique
Instead of using a traditional time series model to predict the return of the cryptocurrencies, we have proposed a new easy approach based on an advanced machine learning algorithm to achieve accurate forecasting results for the classification of the returns

Summary

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. This does not indicate that cryptocurrencies are a substitute for gold [2]. 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]. Investments in cryptocurrencies are, often, equated to investments in gold, where cryptocurrencies may benefit from higher allocations to gold and vice-versa [2]

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Conclusion