Forecasting and trading cryptocurrencies with machine learning under changing market conditions
Hélder Sebastião; Pedro Godinho · 2021 · Financial Innovation
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
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 periods. The classification and regression methods use attributes from trading and network activi
Abstract by Hélder Sebastião; Pedro Godinho, Financial Innovation (2021) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s40854-020-00217-x