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Sensors 2021, 21, 6319 14 of 16 0.2 0.0 −0.2 −0.4 2019 2020 Date Q1 Q2 Q3 Q4 2021 1.0 0.5 0.0 −0.5 2019 2020 Date Q1 Q2 Q3 Q4 2021 Figure 7. Classifications of XRP (top) and DOGE (bottom) daily returns based on quartiles of daily gold prices predicted by SVM model. The vertical dotted line represents the threshold between the pre-COVID-19 period (left) and ongoing COVID-19 period (right) to visualize its impact on the cryptocurrency return. 5. Conclusions In this study, we have applied the support vector machine model to daily returns of six major cryptocurrencies: Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple, before and during the COVID-19 pandemic. The daily returns and volatility spillover substantially increased during the period of the pandemic, due to the high fluctuation in prices in comparison with the pre-pandemic period, making accurate forecasting a complex duty. 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. We have designed an algorithm that allows us to use sensors when submitting requests for data of cryptocurrency returns, gold prices, and COVID-19. After collecting the price series of the gold and a cryptocurrency, one can classify the returns of the cryptocurrency in the previous day based on the quartile of the gold price the next day. Also, we may employ the proposed model to predict the classification of the return, whether it will be in the first, second, third or fourth quartile of the gold price. We have shown that the SVM model can provide significant accurate forecasting, espe- cially during crises. Overall, our model is not only valuable for predicting the classification of the returns of the aforementioned cryptocurrencies, but it is also helpful to be used for other cryptocurrencies. Our findings support the claim that the SVM technique provides a robust algorithm for the predictability of cryptocurrencies under bad or good news. We believe that the findings of this research are not only helpful for exploring the relationship between the classification returns of the cryptocurrencies and the quantiles of the gold price, but they are also of great interest to macroeconomic policymakers, portfolio managers, and investors who are actively trading in the cryptocurrency market, especially under adverse market conditions and during times of uncertainty. In summary, our new approach can help DOGE log−returns classifications XRP log−returns classificationsPDF Image | Predicting Cryptocurrency Returns Based on the Gold Prices
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