深度学习与转债定价：转债量化定价2.0

Section 1: Investment Rating of the Reported Industry - The provided content does not mention the industry investment rating [1][2] Section 2: Core Views of the Report - Deep learning may be used for convertible bond pricing. Based on the Universal Approximation Theorem (UAT), if there is a reasonable analytical solution for convertible bond pricing, a neural network model can fit the result [2][5] - A Multilayer Perceptron (MLP) model is designed. It uses 11 factors, including core factors, convertible bond-specific factors, and market performance factors, to nonlinearly fit the pricing characteristics of convertible bonds [2][5] - The MLP model has good convergence and excellent extrapolation generalization ability. It can strongly explain out-of-sample data from 2024 to Q1 2025 [2][8] - After developing multiple models, including the MLP, MC, and traditional BS models, they can assist in investment activities in various scenarios such as new bond pricing, market interpretation, and clause pricing [2][13] - The neural network model indicates that the current market pricing of convertible bonds is overestimated, but not as much as expected. Convertible bond valuations are high but may still rise further [2][13] - In new bond pricing, the MLP and MC models form a "high-low combination." The MC model is better at pricing large-scale, high-rated convertible bonds, while the MLP is more effective for regular convertible bond listings [2][16] - The models also work well for pricing convertible bond downward revisions [2][19] Section 3: Summary by Relevant Catalog 1. Deep Learning Pricing Model's Concept and Design - The MLP model is based on the idea that if there is an analytical solution for convertible bond pricing, a neural network can fit it. It uses 11 factors for pricing [5] - The model has good convergence and generalization ability. After training with data from 2022 - 2023 and cleaning the dataset, it can effectively explain out-of-sample data from 2024 to Q1 2025 [8] - Compared with the BS and MC models, the MLP model has better pricing results for the overall market and individual convertible bonds. It has faster computation speed than the MC model and is more suitable for real - world scenarios than the BS model. However, it has limitations such as being a "black box" and requiring a large amount of historical data [10][11] 2. Convertible Bond Quantitative Pricing 2.0 - What Are the Model's Applications? - With multiple models (MLP, MC, and BS), they can assist in investment activities in various scenarios [13] - At the overall market pricing level, the neural network model shows that the current market pricing is overestimated, but not significantly. Convertible bond valuations are high but may still increase [13] - In new bond pricing, the MLP and MC models complement each other. The MC model is better for large - scale, high - rated convertible bonds, and the MLP is better for regular convertible bonds. Over 50% of convertible bond listing prices fall within the range defined by the two models, and over 80% are captured after the pricing repair in November 2024 [16] - For downward revision pricing, the MLP model can predict prices when the convertible bond is revised to the trigger threshold and to the lowest level. Most convertible bond prices on the second trading day after a downward revision proposal fall within or near this predicted range [19]