Accelerated share repurchase and other buyback programs: what neural networks can bring

Olivier Guéant, Iuliia Manziuk and Jiang Pu

Quantitative Finance, 2020, vol. 20, issue 8, 1389-1404

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.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2020.1729397 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Accelerated Share Repurchase and other buyback programs: what neural networks can bring (2020)
Working Paper: Accelerated share repurchase and other buyback programs: what neural networks can bring (2020)
Working Paper: Accelerated share repurchase and other buyback programs: what neural networks can bring (2020)
Working Paper: Accelerated Share Repurchase and other buyback programs: what neural networks can bring (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:20:y:2020:i:8:p:1389-1404

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2020.1729397

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().