Paper: arXiv 2610.33453

Authors: Derun Zhong, Guolian Wang, Shiqing Wang, Hongjie Feng

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper demonstrates exceptional empirical rigor in its evaluation of deep learning models for convertible bond forecasting, employing strict temporal hygiene and robust statistical inference. While the mathematical complexity of the neural networks themselves is moderate, the rigorous econometric safeguards and detailed error decomposition contribute significantly to its overall strength and novelty in challenging existing assumptions.

Research Flowchart

  flowchart TD
    A[Research Goal: Next-Day Price Forecasting of Chinese CBs vs. Persistence] --> B(Methodology: Cross-Bond Panel Learning with Deep Neural Networks)
    B --> C{Data: Daily Panel of 100 A-share CBs (2022-2025, 73,806 obs)}
    C --> D[Computational Processes: Strict Temporal Split, Boundary Purging, Fold-Specific Preprocessing, Dependence-Aware Inference]
    D --> E(Evaluation: RMSE, Out-of-Sample R^2, HAC p-values, Block-Bootstrap Intervals)
    E --> F[Key Finding 1: MLPs Fail to Outperform Random Walk (RMSEs 2.36 vs 2.3577, neg R^2)]
    F --> G[Key Finding 2: Cross-Bond Pooling Reduces Individual Bond RMSE, but No Aggregate Advantage]
    G --> H[Key Finding 3: Failure Concentrated in 5 Volatile, Clause-Sensitive Bonds (74.4% of Excess Error)]
    H --> I(Conclusion: Deep Panel Architectures Lack Incremental Predictability Without External Info/Event Conditioning)