# Accelerated Share Repurchase and other buyback programs: what neural networks can bring

## TL;DR

This paper introduces a machine learning approach to optimize complex share repurchase contracts, overcoming limitations of traditional methods and enabling the management of higher-dimensional problems.

## Contribution

It presents a novel neural network-based method for managing buyback contracts, addressing high-dimensional challenges and extending beyond classical PDE and tree techniques.

## Key findings

- Neural network approach recovers strategies similar to traditional methods.
- Method handles higher-dimensional problems without curse of dimensionality.
- Enables management of complex buyback contracts previously intractable.

## Abstract

When firms want to buy back their own shares, they have a choice between several alternatives. If they often carry out open market repurchase, they also increasingly rely on banks through complex buyback contracts involving option components, e.g. accelerated share repurchase contracts, VWAP-minus profit-sharing contracts, etc. The entanglement between the execution problem and the option hedging problem makes the management of these contracts a difficult task that should not boil down to simple Greek-based risk hedging, contrary to what happens with classical books of options. In this paper, we propose a machine learning method to optimally manage several types of buyback contract. In particular, we recover strategies similar to those obtained in the literature with partial differential equation and recombinant tree methods and show that our new method, which does not suffer from the curse of dimensionality, enables to address types of contract that could not be addressed with grid or tree methods.