Mathias Hasler
I am a Visiting Assistant Professor of Finance at Boston College's Carroll School of Management. My research interests are in empirical asset pricing with a focus on stock return predictability, the value premium, statistical biases, and portfolio risk. I teach Fundamentals of Finance.
Publications
Is the Value Premium Smaller Than We Thought?
Critical Finance Review, forthcoming. Link to the paper on SSRN. I presented this paper at the AFFI 2021, EFMA 2021, and the FMA 2022. Tweeted by Wes Gray and covered by AlphaArchitect, AlphaArchitect, RationalReminder Podcast, RationalReminder Forum, FantasticAnachronism.
The construction of the original HML portfolio (Fama and French, 1993) includes six seemingly innocuous decisions that could easily have been replaced with alternatives that are just as reasonable. I propose such alternatives and construct HML portfolios. In sample, the average estimate of the value premium is smaller than the original estimate of the value premium. The difference is 0.09% per month and statistically significant. Out of sample, this difference is statistically insignificant. The results suggest that the original value premium estimate is upward biased due to a chance result in the original research decisions.
Working Papers
Looking Under the Hood of Data-Mining
Job Market Paper. Link to the paper on SSRN. I presented this paper at the Eastern Finance Association 2023, the Portuguese Finance Conference 2023, the China International Conference in Finance 2023, the Southern Finance Association 2023 (best paper award), and at the research seminars at Boston College and the University of Utah.
This paper re-evaluates academic research on 92 cross-sectional stock return predictors. Researchers studying return predictability must make decisions about portfolio construction; for example, whether to rebalance annually or monthly. In sample, the returns of portfolios constructed with the precise decisions made in the predictors’ papers are 0.23% per month larger than those of portfolios constructed with a random combination of decisions made in the literature. Out of sample, more than half of this difference disappears. Predictors published in top-ranked journals show a pronounced effect. The results are consistent with decision mining that produces biased return estimates.
Portfolio Evolution
with Jeffrey Pontiff and Daniel Bergstresser
Many investors hold equity portfolios with relatively small numbers of stocks. Advice about the appropriate number of stocks for a well-diversified equity portfolio is a standard component of financial advice for individual investors, but this advice has generally been static, ignoring rebalancing frequency. We document ‘untended’ portfolio drift from its initial characteristics. Un-rebalanced portfolios become increasingly concentrated over time. Yet, portfolio risk tends to decrease since high-return stocks tend to have lower risk. Thus, the motive for rebalancing is less compelling than previously thought.
Work-in-Progress
Anomalies and the Fed (with Michael Cooper and Matthew Ringgenberg)
Research Decisions and Stock Market Returns
Discussions
"Peer-Reviewed Theory Does Not Help Predict the Cross-section of Stock Returns" by Andrew Chen, Alejandro Lopez-Lira and Tom Zimmermann. Link to discussion slides.
“Risk-Return Tradeoff: Evidence from a Broad Sample of Developed Markets” by Aizhan Anarkulova, Eastern Finance Association, 2023. Link to discussion slides.
“Assaying Anomalies” by Robert Novy-Marx and Mihail Velikov, Portuguese Finance Research Network, 2023. Link to discussion slides.
Teaching
Fundamentals of Finance at Boston College
Spring 2023, 2 Sections, 4.55/5, Teaching Star Award
Fall 2022, 2 Sections, 4.72/5, Teaching Star Award
Spring 2022, 3 Sections, 4.54/5, Teaching Star Award
Fall 2021, 3 Sections, 4.55/5, Teaching Star Award
Investments at Boston College
Summer 2019, 1 Sections, 4.60/5
Awards
Best Paper Award of the Southern Finance Association 2023
Teaching Star Award of the Carroll School of Management
Other Information
Stata, MATLAB, R, Python, Machine Learning
Fluent in English and German, Basic in French
Swiss Citizen, Married, Sailing, Hiking