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Sensors 2021, 21, 6319 3 of 16 the SVC model was utilized to predict whether the price of three cryptocurrencies (BTC, ETH, and LTC) would increase or decrease in the next day. Note that traditional econometric and time-series models, such as the ARMA and GARCH models, might not provide helpful results in predicting cryptocurrency returns due to the impact of risk and uncertainty on return volatility. Although several SVM techniques have already been used in numerous applications, very few have been done in the cryptocurrency market. To the best of our knowledge, the SVM has never been employed to predict the daily classification of cryptocurrency returns according to the quantiles of the gold price the next day. Our objective is to propose a new approach for predicting cryptocurrency returns by using the SVC technique. Specifically, instead of forecasting the movement of the cryptocurrency return indicating whether it would go up or down, we forecast the type of this return reporting whether it would be in the first, second, third quartile, or any quantile of the gold price the next day. Therefore, our investigation fills this gap by proposing a new methodology for predicting the daily classification of cryptocurrency returns according to the quantiles of the gold price the next day. We classify the returns of the six major digital currencies (ADA, Binance Coin –BNB–, BTC, Dogecoin –DOGE–, ETH, and Ripple –XRP–) selected from the list of top ten cryptocurrencies into four categories according to the quartiles of the gold price values. In the current COVID-19 contingency scenario, data of cryptocurrency returns and COVID-19 are often collected through sensors [31–33]. The rest of this article is organized as follows. In Section 2, we propose an algorithm that uses a sensor in the database to predict the return of cryptocurrencies considering COVID-19 data and the price of gold. Section 3 defines the methodology based on the SVM models employed in this study. In Section 4, we present the data and results of our investigation, whereas in Section 5 our conclusions are reported. 2. Sensing Data of Cryptocurrencies, Gold, and COVID-19 To perform the empirical analysis proposed in this work (see Section 4), a software has been implemented with a local database that uses the SVM algorithm considering two aspects. In addition, we design an algorithm that allows us to sense changes in the data and show updated results. The database management system permits the configuration of a digital sensor. This sensor can detect if new data are entered into the database, if the data are modified, or if data are deleted. In this work, a sensor that detects the presence of new data is utilized. As a response to new data, a trigger is implemented, which enables instructions to be registered and executed when the new data arrive. This sensor permits the software that performs the calculations to be notified that new records were entered in the database and therefore that the computations must be updated. In summary, the local database is updated daily with external data (websites). A sensor in the local database sends a signal to the developed software to perform the calculations considering the new data. The software receives the signal and executes the corresponding processing. Lastly, the software delivers the results. Next, we describe Algorithm 1 that senses changes in the data and shows updated results. For the sake of better understanding, we relate some steps of this algorithm to our case study in Section 4.PDF Image | Predicting Cryptocurrency Returns Based on the Gold Prices
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