Repositório :: Percorrer por autor "Timotin, Vasile"

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A Leakage-Aware Benchmark of Hourly Bitcoin Return Forecasting:Naive, Statistical, and Machine Learning Models under Walk-Forward Validation
Publication . Timotin, Vasile
This dissertation evaluates whether machine-learning models reliably forecast hourly Bitcoin returns under a reproducible protocol that prevents future information from leaking into training. The data comprise hourly Bitcoin prices, trading volumes and technical indicators from the Binance exchange, from August 2017 to January 2025, together with external variables for the periods with sufficient coverage. Seven models were compared — two naive benchmarks, a linear regression, a random forest, two classical time-series models and a recurrent neural network — at horizons of 1, 6 and 24 hours, using walk forward validation with 24-hour purge gaps across 53 segments of the final year of the sample. No model outperformed a zero-return forecast in mean absolute error, and the gap to the closest challenger, the linear regression, was not statistically significant. The contribution is methodological: it proposes a reproducible evaluation protocol and shows that the predictive gains reported under permissive validation do not survive rigorous testing.
2026-07-30Dissertação de mestrado Acesso aberto Ver mais