Examining Share Repurchase Executions: Insights and Synthesis from the Existing Literature

Abstract

This literature review thoroughly examines the prevailing research on share repurchase executions, with a particular emphasis on identifying and elucidating the substantial knowledge gap present in this field. With companies repurchasing trillions of dollars worth of their own shares, understanding the mechanisms and impact of these transactions is of paramount importance for the global economy. The paper provides a comprehensive discussion of share repurchase mechanisms and motivations, laying bare the complexity and varied impacts of these financial actions. It delves into open market repurchases and accelerated share repurchase contracts, spotlighting the critical need for deeper comprehension of the execution phase, which remains largely uncharted. Despite notable exceptions, such as the work by Guéant (2015, 2017, 2020), vital questions about trading schedules, implications, costs, broker and corporate performance, and the psychological effects of beating a buyback benchmark, among others, remain unanswered. The review further identifies significant limitations in current methodological approaches, including a heavy reliance on partial differential equations and tree methods, and champions the broader application and development of more advanced tools and methodologies, like machine learning and artificial intelligence. Finally, the paper suggests potential areas for future research, such as the role of technology in share repurchase execution, the psychological factors influencing corporate buybacks, and the development of performance metrics for brokers and corporations. This review not only brings to light the existing literature gap, but also paves the way for further exploration and analysis that could fundamentally enhance our understanding of share repurchase executions.

Keywords: Share repurchase, Buyback contracts, Machine learning, Neural networks, Accelerated share repurchase, Corporate governance, Algorithmic bias, Data privacy, Finance technology, Policy implications

JEL Classification: G1, G12, G14, G02, G4, G00