We reassess five economic claims about the cryptocurrency trend factor (CTREND): cross-sectional return predictability, high risk-adjusted performance, incremental information beyond market, size, and momentum, the ability to replace momentum in factor models, and robustness among large and liquid coins and after trading costs. Our reconstruction and code audit identify issues in information timing, technical indicators, forecast coverage, portfolio formation, and return accounting. We first correct future-information use and related settlement rules, then implement broader corrections to indicators, forecast availability, and portfolio accounting. Without additional coin exclusions, the first stage changes CTREND's mean weekly gross return from 4.07% to 4.25%, annualized Sharpe ratio from 2.08 to 1.48, and weekly alpha relative to published market, size, and momentum factors from 2.77% to 3.06%. After comprehensive correction, these become 0.05%, 0.01, and -1.38%; the alpha is no longer significant. Replacing momentum with CTREND yields an advantage in maximum squared Sharpe ratios of 0.058 in the original implementation, 0.024 after the first-stage correction, and -0.0185 after comprehensive correction, with a statistically significant overall decline. Mean weekly gross returns for the largest 100 coins change from 2.93% to 3.99% to 1.00%, while mean weekly net returns for the full long-short portfolio under the lowest assumed trading costs change from 2.93% to 3.35% to -0.87%. These findings show that reported performance and factor-substitution gains depend materially on implementation and sample composition. We distinguish predictive information, an independent return premium, and the incremental value of replacing an existing factor as separate economic claims.
Ni Zhang, Zhiqin Zhang, Ling Pan, Hoong Chuin Lau, Zhiguang Cao
Traditional neural solvers for solving vehicle routing problems (VRPs) often suffer from limited solution diversity, motivating the development of Generative Flow Network (GFlowNet)-based models. However, the effectiveness of these models is frequently constrained by insufficient flow expansion in high-reward regions, limiting their ability to distribute probability across promising solution routes. Deeper exploration could yield superior results. Diffusion models, in contrast, provide stronger structural guidance for exploration. These two paradigms are naturally complementary: GFlowNet can supply edge-level signals for diffusion to embed, while diffusion can guide broader exploration. Leveraging this synergy, we propose Diffusion-Enhanced GFlowNet (DEG), a novel framework that integrates GFlowNet with a diffusion model to encourage richer flow expansion toward high-reward regions and derive higher-quality solutions. Specifically, DEG exploits GFlowNet's inherent diversity to generate edge-specific backward signals, applies the stochastic noise schedule of diffusion to perturb these signals, and then denoises them within the GFlowNet paradigm. To further improve scalability, we introduce a specialized decoder capable of dynamically adapting to diverse problem scales. Extensive experimental evaluations on synthetic and real-world datasets, including instances with up to 10,000 nodes, demonstrate that DEG consistently achieves favorable performance compared to baseline methods.