Rethinking Share Buyback Execution: Insights into Temporal Optionality and Empirical Anomalies

Abstract

This research brief delves into the intricate domain of share buyback execution, particularly focusing on the temporal optionality aspect, which has been overlooked in traditional execution practices. Using a novel dataset sourced from regulatory filings on share buybacks, we employ Monte Carlo simulations and genetic algorithms to unearth an empirical anomaly and propose a value optimization approach. Our findings reveal that the optimal execution schedules for share buybacks are influenced by the market's price drift, leading to front-loaded or back-loaded trading schedules. Furthermore, we demonstrate the value-added through the incorporation of optionality in execution schedules. The study concludes with implications for brokers, firms, and financial markets, offering recommendations that emphasize dynamic execution, strategic collaboration, and continual learning. This research brief contributes to the broader discourse on share buyback execution and paves the way for future investigations in this domain.

Keywords: Share Buyback Execution, Temporal Optionality, Empirical Anomaly, Value Optimization, Monte Carlo Simulation, Genetic Algorithm, Front-loading, Back-loading, Brokerage Services, Financial Markets

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