Research Collaboration
Since mid‑2023
I am not connected to Cantor Partners Limited, but, from time to time, collaborate with Mike Seigne on certain research/ white papers related to Share Buybacks.
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.
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).
Genetic Algorithms are used to reverse-engineer trading schedules that maximize broker compensation, replicating empirical patterns seen in real-world data (frontiersin.org).
Monte Carlo simulations model stochastic share price paths to compute Value-at-Risk during buyback execution and stress-test broker-determined schedules (papers.ssrn.com).
Benchmark analysis critiques “Bogus VWAP”—the simple average of daily VWAP—highlighting how brokers exploit benchmark design to schedule trades advantageously (frontiersin.org).
Empirical anomaly detection leverages trading volume and price timestamp data to confirm observed broker behaviors align with modeled strategies (researchgate.net).
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.
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.
Joerg Osterrieder has engaged in a selective collaboration with Mike Seigne of Candor Partners on a series of advanced research papers addressing the execution of corporate share buyback programs. This collaboration spans multiple publications from 2023–2024, focusing on share buyback execution strategies, benchmark optimization, and corporate governance. The joint work investigates how buyback execution can be optimized and where it often goes wrong – offering technical insights into algorithmic trading schedules, execution-related anomalies, temporal optionality, and governance implications.
Collaborative Publications: Boards’ Dilemma: The Compounding Problem Hidden in Share Buyback Execution Products (2023), Share Buybacks: A Theoretical Exploration of Genetic Algorithms and Mathematical Optionality (2023), Implementation of Share Buybacks and Their Impact on Corporate Governance (2023), A Free Lunch Hypothesis for Share Buybacks (2023), Temporal Optionality in Share Buybacks: Hypothesizing an Inevitable Brokerage Outperformance (2023), Rethinking Share Buyback Execution: Insights into Temporal Optionality and Empirical Anomalies (2023–24), Temporal Optionality in Share Buyback Execution: An Empirical Anomaly and Value Optimization Approach (2023), The Great Deception: A Comprehensive Study of Execution Strategies in Corporate Share Buy-Backs (2023), and The Mysteries of Share Buyback Execution: Trading Anomalies, Benchmarks, and Psychological Misconceptions (2023). Each paper contributes a unique perspective, collectively shedding light on how execution methods and incentives affect the efficacy of buybacks.
Context: Buyback Prevalence and the Need for Execution Insight
Share repurchases have become a global corporate phenomenon, with companies returning massive capital to shareholders through buybacks. In the past five years, firms in the US, UK, and EU have repurchased roughly $5.6 trillion of stock, and in 2023 nearly 29% of all listed companies globally engaged in buybacks (corpgov.law.harvard.edu). Given this scale, even small inefficiencies in execution can translate to significant shareholder value gains or losses. The collaboration’s research highlights that how a buyback is executed is as critical as why it is initiated, especially from an industry practitioner’s standpoint.
Boards and executives often justify buybacks for three main reasons: (1) investing in undervalued shares (to capture perceived value), (2) adjusting capital structure (e.g. improving financial ratios), or (3) returning excess cash to shareholders as an alternative to dividends (papers.ssrn.com). Osterrieder and Seigne’s work emphasizes that regardless of the rationale, the implementation process must align with shareholder value creation, or else the intended benefits of the buyback can be eroded. In fact, their analysis suggests that flawed execution strategies and products – such as certain structured trading programs – have affected up to 40% of recent buyback programs across the US, UK, and EU, leading to significant lost value for shareholders (papers.ssrn.com). This startling statistic signals a pressing need for better execution practices and oversight in the industry.
Unveiling Execution Anomalies and Benchmark Pitfalls
One core theme in the collaboration is identifying hidden anomalies in buyback trading patterns and the pitfalls of common performance benchmarks. In “The Mysteries of Share Buyback Execution,” Osterrieder and Seigne unveil three key anomalies in how share repurchases are typically carried out:
Trading Schedule Anomaly: an atypical timing and sequencing pattern in buyback trades that deviates from what traditional finance would predict (papers.ssrn.com). Empirical evidence shows that buyback execution often follows non-linear patterns – not simply pro-rata or random – hinting at strategic behavior by execution agents (brokers) which may not always align with the company’s best interest.
Benchmark Paradox: the widespread use of flawed execution benchmarks that are easy to beat but can misdirect incentives (papers.ssrn.com). In particular, many buyback programs measure success against Volume-Weighted Average Price (VWAP) or similar averages over the execution period. Brokers are often tasked with achieving an average purchase price below a preset benchmark (e.g. “VWAP minus 0.5%”). While beating such benchmarks is presented as outperformance, it can be deceptively simple to accomplish – and may come at the cost of higher overall prices paid or fees incurred by the company. This paradox is that a benchmark which is seemingly effortless to surpass can still lead to inflated costs and misaligned incentives (papers.ssrn.com).
Psychological Misconception: a cognitive bias trap where corporate decision-makers take comfort in the idea of beating the benchmark, even if that benchmark is not truly aligned with shareholder value (papers.ssrn.com). This anomaly highlights that boards and CFOs can be lulled into paying excessive fees or tolerating suboptimal execution as long as the reported performance “looks good” relative to a poor benchmark. The illusion of outperformance – essentially a psychological win – can cloud judgment, causing companies to accept unnecessary costs under the false impression of success (papers.ssrn.com).
These anomalies are not just theoretical. Through a combination of empirical investigation and simulations, the authors demonstrate that such patterns indeed occur in practice (papers.ssrn.com). For example, brokers executing buybacks have been observed to adopt an opportunistic trading schedule: slowing or pausing repurchases when the stock price spikes and accelerating purchases when the price dips. This behavior creates a distinct timing pattern (the Trading Schedule Anomaly) that enables the broker to consistently buy below the average market price over the program. In industry terms, the broker is optimizing execution to maximize their “outperformance” relative to the benchmark.
Critically, what does this mean for the company and its shareholders? Osterrieder and Seigne argue that if a broker can almost always ensure an average purchase price below the overall VWAP, then the conventional benchmark is essentially a “bogus benchmark” – one that almost never truly challenges the execution. In other words, the broker’s outperformance is practically guaranteed under normal market volatility conditions, unless stock price volatility dropped near zero (an extremely unlikely scenario). This guaranteed outperformance might sound beneficial, but the research exposes a hidden cost: the broker often earns a performance fee or reward for beating the benchmark, which is effectively paid out of shareholder value without necessarily improving the true economic outcome of the buyback.
To illustrate, consider a brokered buyback product that “guarantees a discount to VWAP” – commonly marketed under names like “VWAP-minus”, “Guaranteed VWAP”, or “Enhanced Agency” execution. The broker commits to executing the buyback at, say, 0.5% below the VWAP of the period. Osterrieder and Seigne’s case studies (detailed in “The Great Deception”) show that brokers achieve this by front-loading purchases when the price is low and drastically cutting back when the price is above a threshold (the implicit benchmark). This virtually ensures the final average price will come in below the overall VWAP – fulfilling the contract and triggering the broker’s performance fee. However, this strategy can inflate the total cost to the company in subtle ways. By slowing down purchases during price rallies, the broker may miss opportunities to buy shares earlier at lower prices (had the schedule been more evenly distributed or value-driven). In one real-world example, the broker’s tactic of throttling execution when the price was above the benchmark increased the company’s average purchase price by about 0.6%, even as the broker’s “outperformance” metric improved by 265%. In essence, the company paid more for its shares while the broker locked in a better score – a clear misalignment of incentives.
Moreover, the fee structure in such arrangements often exacerbates the problem. The performance fee paid to the broker typically scales with the size of the reported outperformance. As the collaboration’s “Boards’ Dilemma” article points out, this creates a compounding cost problem: the better the broker “performs” relative to the benchmark, the higher the fee extracted from the company’s funds. Ideally, if a broker truly adds value by substantially beating the market, one might expect the frictional costs (fees) per share to decrease in proportion – sharing the savings with the client. Instead, these structured products often do the opposite: the broker’s fee grows with the outperformance, directly eating away any added value. In some documented cases, a corporation effectively paid tens to a hundred times more in fees under such a structure than it would have under a plain agency execution, all for the comfort of a VWAP-minus guarantee. This is the titular “Great Deception”: the company is sold on a “win-win” execution product, but in reality only the broker wins – harvesting an almost risk-free gain (a free lunch at shareholders’ expense) by exploiting the benchmark paradox.
Temporal Optionality: The “Free Lunch” and Genetic Algorithm Optimization
A significant innovation of Osterrieder and Seigne’s research is the formalization and quantification of temporal optionality in buyback execution. Temporal optionality refers to the flexibility an executing party (e.g. the broker) has to choose when to buy shares within the authorized buyback period. This timing option has inherent value – much like a financial option – because stock prices fluctuate over time. If one can strategically time purchases (buy more on dips, less on peaks), one can systematically achieve a lower average price than a naïve static strategy. The collaboration hypothesized that this timing flexibility grants brokers an almost inevitable advantage in traditional buyback arrangements (papers.ssrn.com).
In the paper “Temporal Optionality in Share Buybacks: Hypothesizing an Inevitable Brokerage Outperformance,” Osterrieder and Seigne coin this as a “free lunch” hypothesis. They posit that due to temporal optionality, brokerage firms can consistently outperform simple benchmarks regardless of stock price trends (papers.ssrn.com). Crucially, if a corporation measures execution success by comparing to an average price benchmark like VWAP, the broker’s guaranteed ability to beat that benchmark constitutes a camouflaged cost to the firm (papers.ssrn.com). The broker is essentially extracting value by exercising the timing option – value which rightfully belongs to the shareholders if execution were done optimally. This concept reframes the execution question: instead of asking “Did the broker beat the VWAP?”, boards should ask “At what cost was that outperformance achieved, and could a better strategy capture that value for the company?”.
To explore the optimal capture of this value, the research collaboration employs computational techniques, notably Genetic Algorithms (GAs), to design and evaluate buyback execution strategies. In “Share Buybacks: A Theoretical Exploration of Genetic Algorithms and Mathematical Optionality,” Osterrieder establishes a framework for modeling the execution scheduling as a mathematical optimization problem (pmc.ncbi.nlm.nih.gov). The objective can be defined as minimizing the total cost (or equivalently maximizing the “savings” relative to a benchmark price), subject to constraints like daily volume limits, market impact, and regulatory rules. This is a complex, high-dimensional optimization – well-suited for metaheuristic algorithms like GAs that can search a vast solution space for near-optimal schedules (pmc.ncbi.nlm.nih.govpmc.ncbi.nlm.nih.gov). GAs simulate a process of “natural selection” among candidate trading schedules, iteratively improving the execution plan to better achieve the objective.
The findings from these optimization studies are illuminating. By applying GAs and Monte Carlo simulations to realistic buyback scenarios, Osterrieder and Seigne demonstrate that the optimal trading schedule adapts to price movements in a way very similar to what was observed empirically with brokers (papers.ssrn.compapers.ssrn.com). Specifically, the GA-based models learn to modulate daily trading volumes based on the stock’s price trajectory: they tend to hold back (wait-and-see) when prices are temporarily high, and accelerate purchases when prices dip (papers.ssrn.com). This dynamic strategy is exactly how a rational agent would exercise the timing option – buy more when it’s cheap, less when it’s expensive – and it confirms that there is tangible quantitative value in having this flexibility. In fact, the genetic algorithm’s optimized schedules closely mirror the patterns brokers were using, providing strong evidence that brokers are effectively pursuing an implicit optimization strategy of their ownpapers.ssrn.com. The difference, of course, is that the GA’s objective is aligned with the company’s interest (minimizing cost), whereas a broker’s objective may be aligned with hitting a benchmark and maximizing their fee.
The research brief “Rethinking Share Buyback Execution: Insights into Temporal Optionality and Empirical Anomalies” (Osterrieder & Seigne, 2023) further synthesizes these insights. Using a novel dataset of regulatory buyback disclosures, the authors confirm an empirical anomaly: actual buyback programs often exhibit front-loaded or back-loaded execution profiles that correlate with market price trends (papers.ssrn.com). For instance, if a stock’s price exhibits an upward drift over the buyback period, the optimal strategy (from a cost-minimization perspective) is to front-load the buyback – purchasing a larger proportion of shares early before prices rise. Conversely, if prices are drifting downward, a back-loaded strategy would yield a lower average price (waiting to buy more shares later at cheaper levels) (papers.ssrn.com). Their simulations show that incorporating temporal optionality – i.e. adjusting the trading schedule dynamically as these price movements unfold – can significantly improve the outcome compared to a static or evenly paced approachpapers.ssrn.com. In one study, the value-added by such adaptive execution was quantified, and the results underscore that dynamic execution policies consistently outperformed static benchmarks in terms of reducing the corporation’s cost of buybackpapers.ssrn.com.
These outcomes hold profound implications for industry practitioners: Rather than outsourcing execution to a broker with a simplistic benchmark contract, firms could employ advanced algorithms or data-driven strategies to retain the timing advantage for themselves. If the “free lunch” is an artifact of the execution benchmark and temporal optionality, the solution is to change the menu – use better benchmarks and leverage algorithmic execution that prioritizes absolute cost reduction over relative performance optics. In practical terms, this might mean structuring buyback mandates that reward brokers for actual savings achieved (versus a theoretical cost), or bringing execution in-house with quantitative trading teams leveraging algorithms informed by these studies. The collaboration’s work effectively calls for a paradigm shift in how corporate buybacks are executed: from static, benchmark-driven approaches to adaptive, optimization-driven approaches that truly serve the company’s and shareholders’ best interests.
Governance and Policy Implications for Institutional Practice
The research also delves into corporate governance implications of share buyback execution. If boards are responsible for stewardship of shareholder value, then execution strategy is very much a governance issue – not a mere technicality to be left entirely to brokers. In “Boards’ Dilemma: The Compounding Problem Hidden in Share Buyback Execution Products,” Seigne and Osterrieder highlight the fiduciary challenges boards face. They note that many boards authorize buybacks with the intent to create shareholder value, yet may inadvertently green-light execution arrangements that undermine that goal (papers.ssrn.com). A key governance message is that boards should scrutinize the execution plan and incentive structure just as closely as the decision to allocate capital to a buyback in the first place.
One concrete governance concern is the use of Accelerated Share Repurchases (ASRs) and other guaranteed execution products that lack proper safeguards. An ASR is a popular instrument where a company buys a large block of its shares upfront from an investment bank (often via a derivative structure), with the final price adjusted later. Osterrieder and Seigne cite that about 68% of ASR transactions have no cap or collar on the share price (corpgov.law.harvard.edu). This means the company is committing to buy shares regardless of how high the price might go during the execution period – a practice that blatantly conflicts with the rationale of “buying undervalued shares.” If the stock becomes overvalued, the company still keeps buying, effectively transferring value from remaining shareholders to selling shareholders. The authors question how a board’s governance process can reconcile an “undervalued” rationale for a buyback with an execution product that will purchase shares at any price (corpgov.law.harvard.educorpgov.law.harvard.edu). Good governance would demand setting a price limit or valuation-based cap on buybacks, yet the prevalence of uncapped ASRs suggests many boards either overlook this or rely on brokers without fully understanding the implications. The research implies that boards should insist on alignment between buyback rationale and execution method – for example, if the board believes the stock is undervalued up to $X per share, then the buyback should not pay above $X.
Another governance aspect is transparency and equitable treatment of shareholders. A long-running critique of buybacks is the potential for value transfer: if a company repurchases shares when the price is overvalued, it hurts long-term shareholders while benefitting those who sell. To mitigate this, some argue for continuous or regular buybacks (like a steady return-of-capital policy) so that timing is less of an issue. However, Osterrieder and Seigne point out a practical hurdle: shareholders who wish to benefit by selling a pro-rata portion during a buyback (to mimic a “synthetic dividend”) face information asymmetry and timing challengescorpgov.law.harvard.educorpgov.law.harvard.edu. In the Harvard Law School Forum piece “Implementation of Share Buybacks and Their Impact on Corporate Governance,” they explain that companies typically announce only the total intended buyback size (e.g. “up to $1 billion over the next 6 months”), but shareholders have no visibility into the day-to-day progress of the buyback until periodic disclosurescorpgov.law.harvard.educorpgov.law.harvard.edu. In the U.S., new SEC rules will mandate more frequent buyback disclosures, but still often with a lag (e.g. quarterly or monthly reports). This delay means shareholders cannot precisely calculate how many shares to sell into the market to maintain their percentage stake (and thus “harvest” the cash equivalent of the buyback)corpgov.law.harvard.educorpgov.law.harvard.edu. The governance implication is that boards and regulators should be mindful of fair disclosure – ensuring that the mechanics of execution do not disadvantage certain shareholders. The new SEC share repurchase disclosure modernization (to which the authors have also responded in their research) is a step toward greater transparency, but it also puts more onus on boards to justify and explain their execution choices in hindsight.
Finally, the collaboration’s findings carry implications for market microstructure and execution policy at an institutional level. Large open-market buyback programs inevitably interact with the trading environment, and how they are executed can influence market liquidity and price dynamics. Executives and traders designing a buyback must balance executing efficiently without “flooding” the market. As noted in a literature review by Osterrieder, a too-condensed buyback program (e.g. trying to buy back the entire authorized amount in a very short window) can drive up the stock price artificially due to heavy demand pressurefrontiersin.org. On the other hand, an overly prolonged program might send mixed signals or even reduce the intended positive impact on market perception (investors may question the company’s conviction or perceive the slow buyback as ineffective)frontiersin.org. The optimal execution policy must calibrate daily volume and program duration to market conditionsfrontiersin.org. For example, firms should consider average trading volume (to avoid exceeding a sensible percentage of daily volume), volatility, and liquidity when setting the pace of repurchasesfrontiersin.org. The research by Osterrieder and Seigne, with its emphasis on dynamic strategies, suggests that adaptive execution – adjusting the pace in response to real-time liquidity and price changes – could achieve a better balance of minimizing market impact while capturing value. Importantly, the authors advocate that institutional execution policies incorporate these insights: rather than using blunt rules (like “buy X shares per day”), policies could set algorithmic guidelines (e.g. “buy more when price ≤ Y, hold fire when price ≥ Z, within safe harbor limits”) to systematically improve outcomes.
The market microstructure discussion also touches on fairness and manipulation concerns. Most buybacks operate under legal safe harbors (such as Rule 10b-18 in the U.S.) which require trading to be done in the open market and limit the portion of daily volume a firm can buy. The collaborative research raises the point that when brokers are incentivized to beat a benchmark, they will concentrate trades in certain patterns (as evidenced by the Trading Schedule Anomaly). These patterns could potentially be anticipated by savvy market participants, leading to information leakage or front-running risks in the market microstructure. While the research does not accuse brokers of market manipulation, it does question whether current safe harbor rules and common benchmarks inadvertently encourage a form of predictable trading behavior. For practitioners, this means that buyback execution strategies must be handled with discretion, and innovative techniques (like randomization or algorithmic execution that disguises intent) might be necessary to avoid impacting the market or being gamed by others.
Conclusion: Toward Better Buyback Execution and Governance
The collaborative body of work between Joerg Osterrieder and Mike Seigne provides a comprehensive, quantitative examination of share buyback execution that is directly relevant to finance industry practitioners and corporate boards. It challenges the status quo of execution-by-convention and urges a rethinking in several areas:
Benchmark Optimization: Companies should critically assess the benchmarks used in buyback execution agreements. A benchmark like full-period VWAP is easy to beat and can misalign incentivespapers.ssrn.com. More robust metrics (or a focus on absolute results) are needed to ensure brokers deliver genuine value, not just an illusion of outperformance.
Adaptive Execution Strategies: The use of genetic algorithms and simulations demonstrates that dynamic trading strategies can materially improve execution outcomespapers.ssrn.compapers.ssrn.com. Firms should consider leveraging algorithmic tools either in-house or via brokers to incorporate temporal optionality – capturing the timing advantage for shareholders rather than giving it away as a “free lunch” to intermediaries.
Governance Oversight: Boards must expand their oversight to include how buybacks are implemented, not just whether they should be done. This includes setting clear guidelines on price limits (ensuring alignment with any undervaluation claims)corpgov.law.harvard.edu, understanding fee structures, and possibly seeking independent execution advice to avoid the compounding cost traps hidden in complex buyback productspapers.ssrn.compapers.ssrn.com.
Policy and Disclosure: Regulators and industry groups, informed by findings like these, may push for greater transparency around buyback execution (e.g. detailed post-mortems of cost vs. alternative execution scenarios) to hold management accountable. Enhanced disclosure, as well as internal policies that prioritize shareholder value in execution, will mitigate governance risks and strengthen investor trust.
Market Microstructure Considerations: Execution tactics should be designed to minimize undue price impact and avoid signaling issuesfrontiersin.org. This might involve using multiple brokers, varying trading schedules unpredictably, or utilizing off-exchange venues where permissible, all while staying within regulatory safe harbors.
In summary, the selective collaboration with Mike Seigne has enabled the identification of crucial anomalies and inefficiencies in share buyback execution, and more importantly, has pointed the way toward optimized strategies and better governance practices. By combining academic rigor with practical insights, the series of papers serves as a valuable resource for financial executives, traders, and board members. It encourages industry professionals to adopt a more data-driven, strategic approach to share repurchases – one that safeguards shareholder value by optimizing execution, aligning incentives, and enforcing strong governance over these increasingly prevalent capital return programs. The hope is that such research-driven changes will transform buybacks from contentious or misconceived maneuvers into well-executed strategies that genuinely enhance long-term shareholder wealth.
Sources:
Osterrieder, J., & Seigne, M. (2023). Boards’ Dilemma: The Compounding Problem Hidden in Share Buyback Execution Products. Columbia Law School Blue Sky Blog papers.ssrn.compapers.ssrn.com.
Osterrieder, J., & Seigne, M. (2023). The Mysteries of Share Buyback Execution: Trading Anomalies, Benchmarks, and Psychological Misconceptions. SSRN Working Paper papers.ssrn.compapers.ssrn.com.
Osterrieder, J., & Seigne, M. (2023). Temporal Optionality in Share Buybacks: Hypothesizing an Inevitable Brokerage Outperformance. SSRN Working Paper papers.ssrn.com.
Osterrieder, J., & Seigne, M. (2023). Temporal Optionality in Share Buyback Execution: An Empirical Anomaly and Value Optimization Approach. SSRN Electronic Journal papers.ssrn.compapers.ssrn.com.
Osterrieder, J., & Seigne, M. (2023). Rethinking Share Buyback Execution: Insights into Temporal Optionality and Empirical Anomalies. SSRN Research Brief papers.ssrn.com.
Osterrieder, J. (2023). Share Buybacks: A Theoretical Exploration of Genetic Algorithms and Mathematical Optionality. SSRN Working Paper pmc.ncbi.nlm.nih.gov.
Seigne, M., & Osterrieder, J. (2023). The Great Deception: A Comprehensive Study of Execution Strategies in Corporate Share Buy-Backs. Unpublished Manuscript.
Seigne, M., & Osterrieder, J. (2023). Implementation of Share Buybacks and Their Impact on Corporate Governance. Harvard Law School Forum on Corporate Governance corpgov.law.harvard.edufrontiersin.org.
Frontiers in Applied Mathematics & Statistics (2023). Examining Share Repurchase Executions: Insights and Synthesis from the Existing Literature (Literature Review) frontiersin.org.
Boards’ Dilemma: The Compounding Problem Hidden in Share Buyback Execution Products
Share buybacks: a theoretical exploration of genetic algorithms and mathematical optionality
Implementation of Share Buybacks and Their Impact on Corporate Governance
A Free Lunch Hypothesis for Share Buybacks
Temporal Optionality in Share Buybacks: Hypothesizing an Inevitable Brokerage Outperformance
Rethinking Share Buyback Execution: Insights into Temporal Optionality and Empirical Anomalies
The Great Deception: A Comprehensive Study of Execution Strategies in Corporate Share Buy-Backs