Optimal strategy and deep hedging for share repurchase programs

Battle Count: 69

Highly relevant to quantitative trading in the context of algorithmic execution, derivatives hedging, and risk management. The paper addresses practical challenges faced by investment banks executing ASR programs, including trading constraints, barrier-type payoff structures, and the infeasibility of traditional Greeks-based hedging. The deep hedging framework and joint optimization approach are directly applicable to quantitative trading desks managing structured products. However, the specific application to share repurchase programs narrows the immediate applicability compared to general derivatives hedging.

The implementation involves multiple neural networks (separate for repurchase policy and hedging policy), a relaxed optimal stopping framework with stochastic exercise probabilities, OCE risk measure computation, constraint handling (trading limits, notional bounds), and careful input scaling/normalization. The joint optimization of execution and hedging adds significant complexity. The paper provides architectural details but lacks specific training hyperparameters. The constraint capping mechanism and penalty terms add engineering complexity. The multi-stage validation (configurations A-D, sequential vs joint, constrained hedging) requires substantial computational resources.

The paper provides detailed mathematical formulations, network architectures (4 layers with 128 neurons for repurchase, 6 layers with 150 neurons for hedging), parameter specifications (Black-Scholes parameters, ASR term sheet, risk measure parameters), and training procedures. However, no code or data repository is mentioned. The use of synthetic Black-Scholes paths aids reproducibility, but specific training hyperparameters (learning rate, batch size, optimizer details) are not fully disclosed.