CFA, PhD
Affiliated researcher @ IVADO, Multidisciplinary Institute for Cybersecurity and Cyber Resilience (IMC²)
Research interests
46 updates since 2025
Recent work and interviews. Coverage that cites my papers is listed under each paper in Research.
Who Wins and Who Loses In Prediction Markets? in the virtual market microstructure seminar series. The recording is on YouTube.
Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket.
The share of passive-voice sentences in earnings call Q&A predicts lower announcement-day returns after good news, a discount that reverses over the next quarter.
Trades, Quotes, and Price Discovery in Prediction Markets.
A decision framework for governing AI agents in asset management, out in the Emerging Risks 2026 issue.
Optimal transport separates what an earnings call repeats from the press release and what it adds. The novel content of the call carries information that the market appears to underreact to.
Liquidity and price discovery on Polymarket from on-chain trades and tick-level quotes. Most one-minute intervals see no trade, and quote updates drive several times more price discovery than trades.
CME and ICE are launching compute futures on rival GPU rental-rate indices. Whichever index settles the contract, one side of the trade is left with the basis.
The limit order book reconstruction framework is now peer reviewed and citable.
Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket.
Co-investigator with Tolga Cenesizoglu and David Ardia, and with Charles Martineau and Yoshio Nozawa.
1.4 million Polymarket accounts, 70 million trades, and a very concentrated set of winners.
A real-options model of frontier AI labs, written in four days with heavy AI assistance.
A research program on AI, prediction markets and cyber risk in financial markets.
Vincent Codes Finance
Vibe Research II: My Three Days with Fable 5
A personal account of using Fable 5 for academic research.
What is Parquet and Why You Should Use It
A short introduction to the Parquet file format, why it’s faster and lighter than CSV, and how to read and write it from Python and R.
Vibe Research, or How I Wrote an Academic Paper in Four Days
A personal account of using AI to write an academic research paper in four days.
25 papers and chapters
Working Papers
with Pat Akey, Nicolas Harvie, Charles Martineau
Abstract
We study liquidity and price discovery on Polymarket using complete on-chain trades and tick-level top-of-book quotes. Spreads are tightest and books deepest near $0 and $1, and widest and thinnest around $0.50. No trade occurs in 99% of one-minute intervals, yet they account for 76% of price variation. A vector autoregression attributes at most 4% of efficient-price innovation variance to trades, with quote updates driving several times more price discovery. Midquotes are accurate forecasts when spreads are tight and a recent trade is marginally more accurate, but most markets have little to no trading and trade's accuracy worsens in staleness.
Links
BibTeX
@unpublished{akey2026trades,
title={Trades, Quotes, and Price Discovery in Prediction Markets},
author={Akey, Pat and Gr{\'e}goire, Vincent and Harvie, Nicolas and Martineau, Charles},
note={Working Paper},
year={2026},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7547106}
}with Lynn Tao, Yuntao Wu, Charles Martineau, Andreas Veneris
Abstract
We study linguistic passiveness as the fraction of passive-voice sentences in earnings conference call Q&A sessions and show it is informative for equity markets. Using 105,642 U.S. calls from 2008 to 2024, passiveness is negatively associated with announcement-day returns among positive-surprise firms: returns fall from 4.38% to 2.74% across passiveness quintiles. This announcement-day discount fully reverses over the subsequent quarter, indicating a temporary mispricing when news is good. For negative-surprise firms, passiveness has no announcement-day effect but predicts negative long-run returns, consistent with investor under-attention when salient bad news dominates. Passiveness remains informative after controlling for sentiment and standard readability proxies, suggesting that linguistic passiveness captures a distinct yet important dimension of managerial communication.
Links
BibTeX
@unpublished{tao2026linguistic,
title={Linguistic Passiveness in Earnings Calls and Stock Returns},
author={Tao, Lynn and Wu, Yuntao and Martineau, Charles and Gr{\'e}goire, Vincent and Veneris, Andreas},
note={Working Paper},
year={2026},
eprint={2610.19087},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.19087}
}with Yuntao Wu, Lynn Tao, Charles Martineau, Andreas Veneris
Abstract
We introduce an optimal transport framework to decompose the textual content of earnings press releases and conference calls into aligned (shared) and unaligned (unique) components, and use these as predictors of stock returns around earnings announcements. Using over 105,000 document pairs from 2008 to 2023, we find that the aligned portions of both disclosures explain announcement-day returns with high statistical significance. The unaligned component of conference calls, which captures forward-looking content absent from press releases, provides substantial incremental explanatory power, shedding light on why conference calls are central to market reactions to earnings news. Extending to post-announcement returns, investors appear to overreact to shared press release content while underreacting to the unique information conveyed in conference calls.
Links
BibTeX
@unpublished{wu2026measuring,
title={Measuring What is Repeated and Novel in Earnings Disclosures Using Optimal Transport},
author={Wu, Yuntao and Tao, Lynn and Martineau, Charles and Gr{\'e}goire, Vincent and Veneris, Andreas},
note={Working Paper},
year={2026},
eprint={2610.04094},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.04094}
}Abstract
CME Group and Intercontinental Exchange have announced cash-settled compute futures on rival GPU rental-rate indices before any trading has revealed which benchmark hedgers will use. A locked seller earns one configuration's rental rate; a flexible buyer faces a basket of rates. That asymmetry limits any benchmark: an index built for one side leaves the other exposed. Using illustrative inputs, a benchmark aligned with the buyer's basket hedges 99.7% of that buyer's variance but 28.2% of seller variance. Two H100 indices from different publishers correlate too weakly to hedge each other, so which one settles is part of the contract.
Links
BibTeX
@unpublished{fernandez2026compute,
title={Compute Futures: Who Is Left Exposed?},
author={Fern{\'a}ndez-Fuertes, Rub{\'e}n and Gr{\'e}goire, Vincent},
note={Working Paper},
year={2026},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7224223}
}with Pat Akey, Nicolas Harvie, Charles Martineau
Abstract
We study trading gains and losses on Polymarket, the world's largest prediction market platform, using a comprehensive dataset of more than 1.4 million users from 2022 to 2025, totaling over $20 billion in volume across 70 million trades. We document a striking profit concentration: the top 1% of users capture 84% of all trading gains. Gains flow almost entirely to sophisticated traders who outperformed market-implied probabilities. Long-shot betting drives loss concentration, while market-making strongly predicts positive performance. Our results suggest that the informational benefits of prediction markets come at a cost to unsophisticated participants.
Media mentions
BibTeX
@unpublished{akey2026prediction,
title={Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket},
author={Akey, Pat and Gr{\'e}goire, Vincent and Harvie, Nicolas and Martineau, Charles},
note={Working Paper},
year={2026},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6443103}
}Abstract
Frontier AI laboratories racing toward Artificial General Intelligence (AGI) face uncertain timing of transformative demand and must decide how much irreversible capacity to build and how to split it between inference (current revenue) and training (future capability). I develop a real-options model with regime switching, duopoly competition, endogenous default risk, and diminishing returns calibrated to AI scaling laws. The model delivers analytical investment triggers and a duopoly preemption equilibrium in which the leader trains more and invests earlier than the follower. A "faith-based survival" mechanism emerges: training raises the expected post-AGI continuation value, lowering the default boundary-the hope of AGI keeps the firm alive. An asymmetric dilemma arises in which aggressive overinvestment carries higher downside risk than conservative underinvestment. Calibration to four AI lab archetypes illustrates how heterogeneity in beliefs about AI timelines drives cross-sectional variation in investment behavior.
Notes
Links
BibTeX
@unpublished{gregoire2026agi,
title={Investing in Artificial General Intelligence},
author={Gr{\'e}goire, Vincent},
note={Working Paper},
year={2026},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6305300}
}with Oliver Boguth, Adlai Fisher, Charles Martineau
Abstract
Extending methods from microstructure studies, we show that aggregate stock-market returns following FOMC announcements appear "noisy". Standard predictive regressions confirm significant reversal of event-window returns by announcement-cycle end. Consistent with theories of announcement information and price pressure, currently distinct branches of the literature, reversal predictors include VIX changes, abnormal volume, and ETF flows. We further document sustained post-announcement trade volume, and show persistent effects of monetary policy surprises on post-event price dynamics. FOMC announcements inform markets but also affect prices through heightened liquidity demands, highlighting the importance of connecting impactful public information to aggregate price pressure in future theories.
Awards
Links
BibTeX
@unpublished{boguth2025noisy,
title={Noisy FOMC Returns? Information, Price Pressure, and Post-Announcement Reversals},
author={Boguth, Oliver and Fisher, Adlai and Gr{\'e}goire, Vincent and Martineau, Charles},
note={Working Paper},
year={2025},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4131740}
}with Juan Sotes-Paladino
Abstract
Using a unique international dataset of performance-fee mutual funds, we quantify incentives from relative performance evaluation (RPE) and their behavioral implications. We measure direct (short-term) incentives by the option delta embedded in performance fees and indirect (long-term) incentives via the value of future fees. RPE funds face stronger short-term and similar or weaker long-term incentives, yielding a more short-term compensation profile. Incentive sensitivity rises with benchmark risk, consistent with models of optimal contracting under learning. While stronger direct incentives increase active risk, long-horizon incentives attenuate this effect. However, performance effects are modest, and managerial skill is reflected mainly in base pay. Substitutability effects between contractual and market-based incentives highlight regulatory limits.
Links
BibTeX
@unpublished{gregoire2025rpe,
title={Relative Performance Evaluation for Asset Managers: A Quantitative Assessment},
author={Gr{\'e}goire, Vincent and Sotes-Paladino, Juan},
note={Working Paper},
year={2025},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5414638}
}with Nicolas Harvie, Anthony Sanford
Abstract
We evaluate the reliability of HAC estimators in typical asset pricing applications. Through simulations, we show that these estimators often produce inflated t-statistics and invalid inference when applied to return series with autocorrelation and heteroskedasticity—common features in anomaly returns. To address this, we introduce SHARFS, a simulation-based inference procedure that estimates p-values using empirically calibrated null data-generating processes. Unlike traditional methods, SHARFS provides valid finite-sample inference even under complex return dynamics. Applying our method to 212 documented anomalies, we find that standard estimators substantially overstate significance: many strategies deemed significant by HAC methods fail to pass our more robust test. Our results challenge the credibility of conventional inference in empirical finance and call for a shift toward simulation-based methods.
Links
BibTeX
@unpublished{gregoire2025size,
title={Size Distortions in Robust Estimators: Implications for Asset Pricing},
author={Gr{\'e}goire, Vincent and Harvie, Nicolas and Sanford, Anthony},
note={Working Paper},
year={2025},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4640678}
}Publications
Abstract
Financial institutions are deploying AI agents that plan and execute multi-step workflows across research, portfolio rebalancing, trading, and client service. Fully autonomous use remains rare, creating a window in which governance can precede scale. This paper gives portfolio managers, risk officers, boards, and supervisors a decision framework for that task. It classifies five emerging risks by evidence status and portfolio effect, including the possibility that common foundation models create behavior not visible in conventional exposures before stress. It compares the US, Canadian, and EU regulatory approaches and shows how divergence creates compliance, competitive, and cross-border risks. It then proposes four controls: autonomy-proportional governance, security by design, agent stress testing, and fiduciary controls. A stylized asset-manager case and an eight-question decision gate show how institutions can inventory agents, detect weak human review, constrain execution, test model updates and common-provider scenarios, and document client obligations before granting greater autonomy.
Links
BibTeX
@article{gregoire2026agentic,
title={Agentic AI in Finance: Emerging Risks and the Regulatory Gap},
author={Gr{\'e}goire, Vincent},
journal={The Journal of Portfolio Management},
volume={52},
number={11},
pages={278--291},
year={2026},
doi={10.3905/jpm.2026.054}
}with Charles Martineau
Abstract
MeatPy is a Python framework specifically developed for processing and analyzing financial market data, with a primary focus on reconstructing and examining limit order books. Limit order books are central to financial markets, recording all outstanding buy and sell orders for securities at different price levels. MeatPy offers robust support for high-frequency trading data formats, notably the Nasdaq ITCH standard. With an event-driven object-oriented architecture and strong type safety via modern Python typing, MeatPy provides a flexible environment for market data analysis.
BibTeX
@article{gregoire2026meatpy,
title={MeatPy: A Python Framework for Limit Order Book Reconstruction and Analysis},
author={Gr{\'e}goire, Vincent and Martineau, Charles},
journal={Journal of Open Source Software},
volume={11},
number={122},
pages={10480},
year={2026},
doi={10.21105/joss.10480}
}with Pat Akey, Charles Martineau
Abstract
From 2010 to 2015, a group of traders illegally accessed earnings information before their public release by hacking several newswire services. We use this scheme as a natural experiment to investigate how informed investors select among private signals and how efficiently financial markets incorporate private information contained in trades into prices. We construct a measure of qualitative information using machine learning and find that the hackers traded on both qualitative and quantitative signals. The hackers' trading caused 15% more of the earnings news to be incorporated in prices before their public release. Liquidity providers responded to the hackers' trades by widening spreads.
Media mentions
BibTeX
@article{akey2022price,
title={Price revelation from insider trading: Evidence from hacked earnings news},
author={Akey, Pat and Gr{\'e}goire, Vincent and Martineau, Charles},
journal={Journal of Financial Economics},
volume={143},
number={3},
pages={1162--1184},
year={2022},
publisher={Elsevier}
}with Charles Martineau
Abstract
We examine the speed and mechanism of the price discovery process following earnings announcements in the after-hours market, a very illiquid trading environment. Prices reflect earnings surprises mostly through changes in quotes rather than through trades. Following positive announcement surprises, ask prices adjust quickly while bid prices are slower to adjust, and vice versa for negative surprises. Returns computed from trade prices underestimate the speed and magnitude of price reactions following announcements relative to returns computed from quotes. These findings emphasize the importance of using quotes and not trade prices when examining intraday price discovery. Because firm announcements such as earnings generally occur in the after-hours market, using quotes is crucial as trading is sparse. We further illustrate the importance of quotes when examining the price discovery process around analyst recommendation revisions.
BibTeX
@article{gregoire2022earnings,
title={How is earnings news transmitted to stock prices?},
author={Gr{\'e}goire, Vincent and Martineau, Charles},
journal={Journal of Accounting Research},
volume={60},
number={1},
pages={261--297},
year={2022},
publisher={Wiley}
}with Carole Comerton-Forde, Zhuo Zhong
Abstract
Stock exchanges compete for order flow through their fee models. A traditional model pays rebates to liquidity suppliers, and an inverted model pays rebates to liquidity demanders. Using a regulatory intervention to examine the interaction between tick size, restrictions on dark trading, and exchange fees, we show that traders use inverted venues to adjust for suboptimal tick sizes. Increased inverted venue activity improves pricing efficiency and liquidity, especially when the tick size is binding. We show that the sub-tick price improvement offered by inverted venues enhances competition for liquidity provision and increases information impounded into prices through nonmarketable limit orders.
Awards
BibTeX
@article{comerton2019inverted,
title={Inverted fee structures, tick size, and market quality},
author={Comerton-Forde, Carole and Gr{\'e}goire, Vincent and Zhong, Zhuo},
journal={Journal of Financial Economics},
volume={134},
number={1},
pages={141--164},
year={2019},
publisher={Elsevier}
}with Oliver Boguth, Charles Martineau
Abstract
In an effort to increase transparency, the chair of the Federal Reserve now holds a press conference (PC) following some, but not all, Federal Open Market Committee (FOMC) announcements. Evidence from financial markets shows that investors lower their expectations of important decisions on days without PCs and that these announcements convey less price-relevant information. Correspondingly, we show that investors pay more attention to upcoming announcements with PCs. This coordination of attention can reduce welfare in models of the social value of public information. Consistent with theories of investor attention, the market risk premium is larger on days with PCs.
Awards
Notes
Media mentions
BibTeX
@article{boguth2019shaping,
title={Shaping expectations and coordinating attention: The unintended consequences of FOMC press conferences},
author={Boguth, Oliver and Gr{\'e}goire, Vincent and Martineau, Charles},
journal={Journal of Financial and Quantitative Analysis},
volume={54},
number={6},
pages={2327--2353},
year={2019},
publisher={Cambridge University Press}
}Abstract
I introduce a general equilibrium model with active investors and indexers. Indexing causes market segmentation, and the degree of segmentation is a function of the relative wealth of indexers in the economy. Shocks to this relative wealth induce correlated shocks to discount rates of index stocks. The wealthier indexers are, the greater the resulting comovement is. I confirm empirically that S&P 500 stocks comove more with other index stocks and less with non-index stocks, and that changes in passive holdings of S&P 500 stocks predict changes in comovement of index stocks.
BibTeX
@article{gregoire2020rise,
title={The rise of passive investing and index-linked comovement},
author={Gr{\'e}goire, Vincent},
journal={North American Journal of Economics and Finance},
volume={51},
pages={101059},
year={2020},
publisher={Elsevier}
}Conference Proceedings
with Yuntao Wu, Ege Mert Akin, Charles Martineau, Andreas Veneris
Abstract
We examine how textual features in earnings press releases predict stock returns on earnings announcement days. Using over 138,000 press releases from 2005 to 2023, we compare traditional bag-of-words and BERT-based embeddings. We find that press release content (soft information) is as informative as earnings surprise (hard information), with FinBERT yielding the highest predictive power. Combining models enhances the explanatory strength and interpretability of the content of press releases. Stock prices fully reflect the content of press releases at market open. If press releases are leaked, it offers predictive advantage. Topic analysis reveals self-serving bias in managerial narratives. Our framework supports real-time return prediction through the integration of online learning, provides interpretability and reveals the nuanced role of language in price formation.
Links
BibTeX
@inproceedings{wu2025extracting,
title={Extracting the Structure of Press Releases for Predicting Earnings Announcement Returns},
author={Wu, Yuntao and Akin, Ege Mert and Gr{\'e}goire, Vincent and Martineau, Charles and Veneris, Andreas},
booktitle={Proceedings of the 6th ACM International Conference on AI in Finance},
year={2025},
publisher={ACM}
}with Frederic Schlackl, Alina Dulipovici
Abstract
Ransomware attacks on financial institutions can have effects beyond the attacked institution itself. When the attacked institution is forced offline, other financial market participants are left unable to complete transactions or obtain information, disturbing normal operations of financial markets. In this paper, we argue that such spillover effects are an important aspect of ransomware attacks and that cybersecurity management should be concerned with them and their mitigation. We illustrate this using three recent cases of ransomware attacks by the LockBit group on financial institutions that had spillover effects on the wider financial market. We identify four lessons learned for market participants facing such spillovers: ensuring the quick substitution of blocked resources, preparing to execute automated processes manually, networking with industry associations, and actively involving regulators. Overall, we hope to raise awareness of spillover effects of ransomware attacks for cybersecurity research and practice.
Links
BibTeX
@inproceedings{gregoire2025ransomware,
title={Mitigating Spillover Effects of Ransomware in Financial Markets: Lessons from the LockBit Attacks},
author={Gr{\'e}goire, Vincent and Schlackl, Frederic and Dulipovici, Alina},
booktitle={International Symposium on Foundations and Practice of Security},
year={2025},
publisher={Springer}
}Supervised Student Work
with Kevin Guay
Notes
Links
BibTeX
@incollection{guay2023circular,
title={Circular Economy: A Fintech Driven Solution for Sustainable Practices},
author={Guay, Kevin and Gr{\'e}goire, Vincent},
booktitle={Fintech and Sustainability: How Financial Technologies Can Help Address Today's Environmental and Societal Challenges},
pages={149--168},
year={2023},
publisher={Palgrave Macmillan}
}with Javad Yaali, Thomas Hurtut
Abstract
High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.
Links
BibTeX
@article{yaali2022hftviz,
title={HFTViz: Visualization for the exploration of high frequency trading data},
author={Yaali, Javad and Gr{\'e}goire, Vincent and Hurtut, Thomas},
journal={Information Visualization},
volume={21},
number={2},
pages={169--186},
year={2022},
publisher={SAGE}
}with Noah Jepson
Notes
Links
BibTeX
@incollection{jepson2022alternative,
title={Alternative Data},
author={Jepson, Noah and Gr{\'e}goire, Vincent},
booktitle={Big Data in Finance: Opportunities and Challenges of Financial Digitalization},
pages={13--33},
year={2022},
publisher={Palgrave Macmillan}
}Other Research Contributions
Main authors: Albert J. Menkveld, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Kirchler, Michael Razen, Utz Weitzel
Abstract
In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
Notes
BibTeX
@article{menkveld2024nonstandard,
title={Nonstandard Errors},
author={Menkveld, Albert J and Dreber, Anna and Holzmeister, Felix and Huber, Juergen and Johannesson, Magnus and Kirchler, Michael and Razen, Michael and Weitzel, Utz and others},
journal={The Journal of Finance},
volume={79},
number={3},
pages={2339--2390},
year={2024},
publisher={Wiley}
}Forever Working Papers
Pre-PhD Publications
with Christian Genest, Michel Gendron
Abstract
Vincent Grégoire, Christian Genest and Michel Gendron show how forecasts for crude oil and natural gas prices can be improved by modelling the dependence between them. Movements in individual returns are described by time-series models, their dependence is captured by a copula, and Monte Carlo simulations are used to forecast prices.
Links
BibTeX
@article{genest2008copulas,
title={Using copulas to model price dependence in energy markets},
author={Genest, Christian and Gendron, Michel and Gr{\'e}goire, Vincent},
journal={Energy risk},
volume={5},
number={5},
pages={58--64},
year={2008}
}with Amar El-Maadi, Louis St-Laurent, Hélène Torresan, Benoit Turgeon, Donald Prévost, Patrick Hébert, Denis Laurendeau, Benoit Ricard, Xavier Maldague
Abstract
In this text, we summarize recent works done in the use of visible and infrared imagery for surveillance applications. Moreover, we also present latest developments that have occurred in three partner institutions of the Québec, Canada area in this field. Our focus is on both hardware and software. Hardware here concerns channel registration and innovative optical systems while software is related to high level information extraction. Extensive literature review is provided.
Links
BibTeX
@article{elmaadi2007visible,
title={Visible and infrared imagery for surveillance applications: software and hardware considerations},
author={El-Maadi, Amar and St-Laurent, Louis and Torresan, H{\'e}l{\`e}ne and Turgeon, Benoit and Pr{\'e}vost, Donald and H{\'e}bert, Patrick and Laurendeau, Denis and Gr{\'e}goire, Vincent and Ricard, Benoit and Maldague, Xavier},
journal={Quantitative InfraRed Thermography Journal},
volume={4},
number={1},
pages={25--40},
year={2007},
publisher={Taylor \& Francis}
}9 repositories
My publicly available code is distributed through GitHub. You can also find some of my tutorials on my blog, Vincent Codes Finance.
Select Projects
Code from Papers
Journal of Financial Economics, 143(3)
Journal of Accounting Research, 60(1)
Journal of Finance, 79(3)
1 released dataset
Datasets released alongside my research papers. Citations and reuse instructions are in each dataset's guide.
Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket
Working Paper
10 current, 33 graduated
PhD
I will not be taking on new PhD students for Fall 2027.
MSc
I only consider supervision requests from students already admitted to HEC Montréal. If you are not yet admitted, please see the MSc program page.
If you are interested in working under my supervision for your MSc thesis or supervised project, please contact me by email. The initial email requesting supervision for a supervised project or thesis should include the following elements:
Note that I am often overbooked for supervision. I will either try to book a meeting with you to discuss your project further or recommend other professors in line with your topics of interest.
Current students
PhD
MSc
MSc - HEC Montréal
Honours - University of Melbourne
