Publications Database
Welcome to the new Schulich Peer-Reviewed Publication Database!
The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:
- Faculty Member’s Name;
- Area of Expertise;
- Whether the Publication is Open-Access (free for public download);
- Journal Name; and
- Date Range.
At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.
If you have any questions or input, please don’t hesitate to get in touch.
Search Results
Yeomans, Julian Scott and E. Pätäri, P. Luukka, and S. Ahmed (2023). "Can Monthly-Return Rank Order Reveal a Hidden Dimension of Momentum? The Post-Cost Evidence from the U.S. Stock Markets", North American Journal of Economics and Finance, 65, 101884.
Abstract
We introduce a new return-momentum indicator that is based on monotonicity of monthly-return rank order within a lookback period (henceforth abbreviated as MRRO). Based on an extensive post-cost performance comparison of long-only momentum portfolios formed on six stand-alone and 36 double-sort criteria across three holding period lengths in the non-microcap universe of U.S. stocks over the 55-year sample period, MRRO is particularly useful for annual holding periods, towards the end of whom the conventional return-momentum indicators tend to lose their prediction power. Based on the return-based style analysis, MRRO adds some favorable style-diversification characteristics into long-only momentum portfolio selection.Karell, V., Luukka, P., Pätäri, E. and Yeomans, J.S. (2018). "Comparison of the Multicriteria Decision-Making Methods for Equity Portfolio Selection: The U.S. Evidence", European Journal of Operational Research, 265(2), 655-672.
Abstract
This paper compares the efficacy of four multicriteria decision-making (MCDM) methods in identifying the future best-performing stocks in two comprehensive samples of U.S. stocks. This is the first time that median-scaling (MS), the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the Analytic Hierarchy Process (AHP), and the additive Data Envelopment Analysis (add.DEA) have been used to combine value and momentum indicators into a single efficiency score. The results show that the MCDM methods examined can successfully be applied to equity portfolio selection. As a robustness check, we repeat all the main sample tests for the sample of the largest-cap stocks included in the two biggest size quintiles (i.e., stocks above 40% NYSE market-cap breakpoint) and find that the overall results are surprisingly robust to size effect. However, the best-performing portfolios formed on the basis of different MCDM methods have remarkably different exposures to the style factors that are commonly used to explain the abnormal returns of active equity portfolios. As a practical implication of this study, investors following certain investing styles could take these different style exposures into account when choosing the MCDM criteria that best fit their portfolio-selection purposes.Karell, V. and Yeomans, J.S. (2018). "Anomaly Interactions and the Cross-Section of Stock Returns", Fuzzy Economic Review, 23(1), 33-61.