This study analyzes the efficacy and risks of various share buyback strategies, with a particular focus on the fixed participation rate approach. This strategy aligns buyback volumes with a fixed percentage of daily trading volume, aiming to minimize market impact, mitigate financial risk, and optimize shareholder value. Using Value at Risk (VaR) for risk assessment and Long Short-Term Memory (LSTM) models for trading volume prediction, we compare the fixed participation rate strategy against traditional methods. Our findings emphasize the need for a shift from strategies focused on short-term gains to those prioritizing market stability and long-term shareholder value creation. This research offers insights for corporate decision-making, policy development, and the advancement of sustainable financial management practices.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Chiocchio, Robert

Subject

• Infrastructure
• Computing

Publisher

• Worcester Polytechnic Institute

Identifier

• 119078
• E-project-032124-140706

Keyword

• Buyback
• Machine Learning
• Repurchase
• Corporate Finance
• LSTM
• RNN

Advisor

• Sturm, Stephan
• Hansen, Peter H.
• Blais, Marcel Y.
• Paffenroth, Randy Clinton

Year

• 2024

Center

• Switzerland Project Center - MQP

Sponsor

• Joerg Osterrieder (Berner Fachhochschule, University of Twente)

Date created

• 2024-03-21

Resource type

• Major Qualifying Project

Major

• Computer Science

Source

• E-project-032124-140706

Rights statement

• In Copyright