Optimal strategy and deep hedging for share repurchase programs

TL;DR

This paper develops a machine-learning framework for optimal execution and hedging of share repurchase programs, considering market constraints and trading capabilities to improve performance and realistic risk management.

Contribution

It introduces a unified approach combining execution and hedging strategies using machine learning, addressing practical trading constraints and risk measures.

Findings

Enhanced hedging performance through optimized policies

Feasible strategies respecting market and contractual constraints

Risk-based pricing via indifference pricing framework

Abstract

In recent decades, companies have frequently adopted share repurchase programs to return capital to shareholders or for other strategic purposes, instructing investment banks to rapidly buy back shares on their behalf. When the executing institution is allowed to hedge its exposure, it encounters several challenges due to the intrinsic features of the product. Moreover, contractual clauses or market regulations on trading activity may make it infeasible to rely on Greeks. In this work, we address the hedging of these products by developing a machine-learning framework that determines the optimal execution of the buyback while explicitly accounting for the bank's actual trading capabilities. This unified treatment of execution and hedging yields substantial performance improvements, resulting in an optimized policy that provides a feasible and realistic hedging approach. The pricing of these programs can be framed in terms of the discount that banks offer to the client on the price at which the shares are delivered. Since, in our framework, risk measures serve as objective functions, we exploit the concept of indifference pricing to compute this discount, thereby capturing the actual execution performance.