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Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Financial Innovation 2021年第1期7卷 61-90页

作者： helder sebastiao Pedro Godinho Univ Coimbra CeBERFaculty of EconomicsAv.Dr.Dias da Silva1653004‑512 CoimbraPortugal

This study examines the predictability of three major cryptocurrencies—bitcoin,ethereum,and litecoin—and the profitability of trading strategies devised upon machine learning techniques(e.g.,linear models,random forests,and support vector machines).The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets,allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test *** classification and regression methods use attributes from trading and network activity for the period from August 15,2015 to March 03,2019,with the test sample beginning on April 13,*** the test period,five out of 18 individual models have success rates of less than 50%.The trading strategies are built on model *** ensemble assuming that five models produce identical signals(Ensemble 5)achieves the best performance for ethereum and litecoin,with annualized Sharpe ratios of 80.17%and 91.35%and annualized returns(after proportional round-trip trading costs of 0.5%)of 9.62%and 5.73%,*** positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets,even under adverse market conditions.

关键词： Bitcoin Ethereum Litecoin Machine learning Forecasting Trading

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Industry return lead‑lag relationships between the US and other major countries

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Financial Innovation 2023年第1期9卷 1055-1102页

作者： Ana Monteiro Nuno Silva helder sebastiao CeBER Faculty of EconomicsUniv CoimbraAv Dias da Silva 1653004‑512 CoimbraPortugal

In this study,we analyze the lead-lag relationships between the US industry index and those of six other major countries from January 1973 to May *** identify the leading role played by the US internationally by showing that the weekly returns of US industries,especially the US basic materials and energy industries,significantly Granger cause the returns of most other countries’industries,suggesting that non-US industries react with some delay to new *** delayed reaction is even more notice-able during periods of recession in the US when cross-country correlations are *** implies that the ability of the lagged returns of US industries to predict industries’returns from other countries is even more pronounced when the US experienced an economic recession.A similar asymmetric relationship is found between the volatil-ity of US industries and that of industries in other *** analysis of causality in the distribution of returns and volatility shows that causality runs mainly from the US to other countries,particularly in the presence of extreme negative ***,we demonstrate that our predictions are valuable to real-world ***-short strategies generate sizable and statistically significant alphas,and a constant relative risk-averse investor obtains certainty equivalent returns well above the risk-free rate.

关键词： International diversification Industry equity indexes Granger causality Causality in distribution

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