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In Annals of operations research

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

Akyildirim Erdinc, Cepni Oguzhan, Corbet Shaen, Uddin Gazi Salah


Bitcoin futures, Covid-19, Cryptocurrency, Extreme gradient boosting, Logistic regression, Machine learning, Naive Bayes, Random forest, Support vector machine, k-Nearest neighbours