Candor Partners

Collaboration with Mike Seigne - Candor Partners Limited

I am not connected to Candor Partners Limited, but, from time to time, collaborate with Mike Seigne on certain research/ white papers related to Share Buybacks.

https://clsbluesky.law.columbia.edu/2023/12/11/boards-dilemma-the-compounding-problem-hidden-in-share-buyback-execution-products/

https://corpgov.law.harvard.edu/2023/10/27/implementation-of-share-buybacks-and-their-impact-on-corporate-governance/

Role Overview

I,from time to time, contribute to a (unconnected) research collaboration with Mike Seigne of Candor Partners, assisting on analyses and models for select papers on share buyback execution, market microstructure, and governance. Mike leads these efforts; my contributions support empirical testing, optimization modeling, and anomaly review.

Technical Insights from Key Papers

Temporal Optionality & Genetic Algorithms

“Temporal Optionality in Share Buyback Execution: An Empirical Anomaly and Value Optimization Approach”explores how brokers strategically modulate trading (waiting for price drops, delaying when prices rise) to increase payout. It applies Genetic Algorithms and Monte Carlo simulations to model optimal trading schedules (papers.ssrn.com).

“Temporal Optionality … Brokerage Outperformance”hypothesizes that brokers can almost always outperform VWAP benchmarks due to this timing flexibility (papers.ssrn.com).

Free-Lunch Phenomenon

“A Free Lunch for Share Buybacks”proposes that specific buyback product structures yield consistently positive fees across market conditions–essentially a “free lunch” for brokers (papers.ssrn.com).

Uncovering Anomalies & Cognitive Biases

“The Mysteries of Share Buyback Execution”identifies three anomalies: trading schedule anomalies, benchmark paradoxes, and psychological misconceptions, using GA simulations to uncover misalignments between execution behavior and shareholder interests (papers.ssrn.com).

Comprehensive Analysis of Execution Inefficiencies

“The Great Deception: A Comprehensive Study of Execution Strategies in Corporate Share Buy-Backs”documents broker-imposed inefficiencies resulting in an estimated $8 billion in excess fees over five years, and $276 billion in share price risk exposure (papers.ssrn.com).

Methodologies & Models

Genetic Algorithmsare used to reverse-engineer trading schedules that maximize broker compensation, replicating empirical patterns seen in real-world data (frontiersin.org).

Monte Carlo simulationsmodel stochastic share price paths to compute Value-at-Risk during buyback execution and stress-test broker-determined schedules (papers.ssrn.com).

Empirical anomaly detectionleverages trading volume and price timestamp data to confirm observed broker behaviors align with modeled strategies (researchgate.net).

Practical Implications

Execution Strategy

Corporate boards must re-evaluate broker engagement strategies, ensuring alignment with shareholder value rather than benchmark outperformance.

Regulatory Oversight

Findings support clearer disclosure standards and governance around execution benchmark mechanisms.

Model Development

Engines using genetic algorithms provide testable frameworks for optimizing buyback implementation in line with stated corporate objectives.

Summary

Benchmark analysiscritiques “Bogus VWAP”–the simple average of daily VWAP–highlighting how brokers exploit benchmark design to schedule trades advantageously (frontiersin.org).

In my collaboration with Mike Seigne, I support technical components, statistics, modeling, and simulations, within rigorous empirical research on share buyback execution. These works offer advanced insights via genetic algorithms, anomaly detection, and governance critiques, contributing original models and evidence to both academic and professional communities.