Maximilian Voigt

Maximilian Voigt

Assistant Professor of Finance
HEC Montréal

About

I am an Assistant Professor of Finance at the HEC Montréal. I completed my Ph.D. in Financial Economics at the Frankfurt School in 2024. Prior to my doctoral studies, I gained industry experience and studied Financial Economics at the University of Oxford.

Working Papers

Investor Beliefs and Asset Prices Under Selective Memory [Paper]

  • Brattle Group PhD Candidate Award for Outstanding Research, 2024 WFA Conference
  • Conferences: NFA 2024, WFA 2024, FTG Summer School 2023
AbstractI present a consumption-based asset pricing model in which the representative agent selectively recalls past fundamentals that resemble current fundamentals and updates beliefs as if the recalled observations are all that occurred. This similarity-weighted selective memory jointly explains important facts about belief formation, survey data, and realized asset prices. Subjective expectations overreact and are procyclical, the subjective volatility is countercyclical, and the subjective risk premium has a low volatility. In contrast, realized returns are predictably countercyclical, highly volatile, and unrelated to variation of objective risk measures. My results suggest that human memory can simultaneously account for individual-level data and aggregate asset pricing facts.


Eliciting Stopping Times (with Sebastian Ebert) [Paper]

  • Conferences: RBFC 2024, 50th EGRIE Seminar, European Decision Sciences Day 2023, SEF 2023
AbstractWe propose an experimental method to elicit stopping times—each subject’s complete contingent plan for taking a risk for up to five times—to study repeated risk-taking under precommitment. In addition to time- and outcome-contingent risk-taking, we allow some subjects to use path-dependent or randomized stopping times. Our experimental design thus allows for hundreds of different risk-taking plans. Using an unsupervised machine-learning algorithm, we find that individuals’ risk-taking strategies map well to stop-loss, take-profit, or buy-and-hold strategies. Most strategies are of a continue-when-winning and stop-when-losing type, with a profit-trailing stopping barrier. Path-dependence and randomization are used extensively, even if they are costly. We further analyze dynamic consistency in a sequential risk-taking task and find that subjects largely follow the unconstrained plans that we elicited.


Learning and Strategic Trading in ETF Markets [Paper]

  • Conferences: 3rd FutInfo, 15th RGS Doctoral Conference
AbstractDesignated broker-dealers arbitrage away differences between the market price of an ETF and the net asset value of the underlying assets. Using a dynamic strategic trading model, I show that this arbitrage mechanism increases long-term price informativeness but reduces short-term price informativeness. The information contained in the ETF price leads to additional learning, which improves long-term price informativeness. However, traders informed about the value of an underlying asset use their informational advantage to forecast arbitrage-induced price changes of all other assets contained in the ETF. The predictability of future price changes induces speculative cross-asset trading, which reduces short-term price informativeness. Thus, regulation targeting ETFs must balance short- and long-term price informativeness.