Temporal Optionality in Share Buyback Execution: An Empirical Anomaly and Value Optimization Approach

Abstract

This paper presents a robust analysis of temporal optionality in share buyback executions, shedding light on a significant empirical anomaly and providing a novel value optimization approach. Through empirical studies, we discern an intriguing pattern in trading schedules that aligns with the optimization of the difference between the buyback benchmark and the actual price paid for the shares. Specifically, brokers strategically modulate their trading volume in response to stock price movements, adopting a "wait-and-see" approach when prices are high and accelerating trading when prices drop. This strategic behavior seems designed to maximize their payout, effectively exploiting market volatility. We employ Genetic Algorithms (GAs) and Monte-Carlo Simulations to simulate these trading schedules, and the GA-replicated schedules corroborate our empirical observations, underlining the brokers' underlying optimization strategy. These findings have profound implications for brokerage services, corporate finance strategies, and financial market dynamics. They highlight the potential for temporal optionality to optimize value in share buyback executions.

Keywords: Share Buybacks, Temporal Optionality, Genetic Algorithms, Brokerage Services, Empirical Anomaly, Trading Schedules, Optimization, Market Volatility, Corporate Finance, Financial Markets

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