Trend-based forecast of cryptocurrency returns*

【Author】 Tan, Xilong; Tao, Yubo

【Source】ECONOMIC MODELLING

【影响因子】3.875

【Abstract】Cryptocurrencies are widely known for their limited publicly available information, making it challenging to predict market returns. Technical analysis has emerged as an essential tool in this context, but its effectiveness in the cryptocurrency market remains an open question. Using data from nearly 3,000 cryptocurrencies at daily, weekly, and monthly horizons from 2013 to 2022, we systematically re-examine the efficacy of trend -based technical indicators in predicting cryptocurrency market returns and find that price-based signals are more effective in predicting short-term horizons, while volume-based signals are more powerful in predicting long-term horizons. Further analysis shows that machine learning techniques can significantly improve the performance of technical indicators, and technical indicators based on different information respond differently to the COVID-19 outbreak. These results provide direct evidence that volume imparts information to technical analysis independently of price.

【Keywords】Cryptocurrency; Return predictability; Technical analysis; Investment horizon; Machine learning; COVID-19

【发表时间】2023 JUL

【收录时间】2023-06-05

【文献类型】实证数据

【主题类别】

区块链应用-虚拟经济-虚拟货币