Cross-Bond Panel Learning for Next-Day Price Forecasting of Chinese Convertible Bonds: A Reality Check
Recent advances in financial machine learning have prompted growing interest in applying deep neural networks to convertible bond valuation and forecasting. However, existing studies often focus on in-sample valuation fit or cross-sectional return rankings, leaving out-of-sample next-day price forecasting insufficiently benchmarked against persistence. This paper provides a comprehensive empirical reality check on cross-bond panel learning for Chinese convertible bonds. Using a daily panel of 73,806 observations across 100 A-share convertible bonds from 2022 to 2025, we evaluate whether cross-bond neural architectures reliably outperform the random walk under strict temporal splitting, boundary purging, fold-specific preprocessing, and dependence-aware inference. On the 29,514-observation holdout, the random walk achieves an RMSE of 2.3577 CNY. Five-seed ensembles of small and large price-change MLPs yield RMSEs of 2.3619 and 2.3645 CNY, with negative out-of-sample R^2 values (-0.0035 and -0.0057) and target-date clustered HAC p-values of 0.453 and 0.248. Ten-day circular block-bootstrap intervals span zero, while direct price-level training degrades performance substantially. Although cross-bond pooling provides a variance-stabilizing shrinkage effect over single-bond estimation (lowering test RMSE for 79 of 90 individual bonds), it fails to produce an aggregate forecast advantage over persistence. Error decomposition reveals that failure is not uniform: five highly volatile, clause-sensitive bonds account for 74.4% of the model's total excess squared error. Without contemporaneous equity market information or explicit contractual event conditioning, deep panel architectures do not deliver incremental next-day predictability over persistence.