Algorithmic Complexity vs. Market Efficiency: Evaluating Wavelet–Transformer Architectures for Cryptocurrency Price Forecasting
Abstract
We investigate whether sophisticated deep learning architectures justify their computational cost for short-term cryptocurrency price forecasting. Our study evaluates a 2.1M-parameter (M represents millions (e.g., 2.1M = 2,100,000 parameters), with all RMSE values reported in USD) wavelet-enhanced transformer that decomposes the Fear and Greed Index (FGI) into multiple timescales before integrating these signals with technical indicators. Using Diebold–Mariano tests with HAC-corrected variance, we find that all models—including our wavelet–transformer, ARIMA, XGBoost, LSTM, and vanilla Transformer—fail to significantly outperform the O(1) naive persistence baseline at the 1-day horizon (DM statistic = +19.13, p
Fields of Study & Subjects
Blockchain Technology Applications and Security
Stock Market Forecasting Methods
Adversarial Robustness in Machine Learning
Publication details
- Journal / source
- Algorithms
- Publisher
- MDPI AG
- Volume
- 19
- Issue
- 2
- Pages
- 101–101
- Publication date
- 27 January 2026
- Language
- EN