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

with E. Pätäri, S. Ahmed (2023). "Combining Low Volatility and Mean Reversion: Better Together?", Algorithmic Finance , 10, 3-4, 26-50.

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Abstract This paper contributes to the existing stock market anomaly literature by being the first to analyze the benefits of combining two distinct anomalies; specifically, the low-volatility and mean-reversion anomalies. Our results show that on a long-only basis, these two time-varying anomalies could be combined into a double-sort investment strategy that includes some desirable characteristics from each of them, thereby making the portfolio return accumulation more stable over time. As the added-value of low-volatility investing stems mostly from the risk-reduction side, while contrarian stocks are generally highly volatile with remarkable upside potential, the use of the double-sort portfolio-formation in which the contrarian stocks are picked from the sub-set of below-median volatility stocks can shorten the below-market performance periods that have occasionally materialized for plain low-volatility or plain contrarian investors.

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.

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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. and Yeomans, J.S. (2018). "Anomaly Interactions and the Cross-Section of Stock Returns", Fuzzy Economic Review, 23(1), 33-61.

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Abstract This study provides new evidence on anomaly interactions, as well as on the cross-section of returns in all-but-microcap universe of U.S. stocks over the 42-year sample period from 1971 to 2013. The five anomalies being examined are size, value, profitability, investment/asset growth, and momentum. We form 5x5 conditional double-sort portfolios for each pair of anomaly variables, resulting in 20 different 5x5 sorts when using each variable in the first-stage sorting and the remaining four in the second-stage sorting. The interrelation between each pair of anomaly variables is evaluated on the basis of the monotonic relation (MR) test of Patton and Timmermann (2010) for portfolio raw returns, and in addition, by means of the Sharpe ratio comparisons. Moreover, we run Fama-MacBeth (1973) cross-sectional regressions to compare the relative explanatory power of each variable in the presence of the others. The results show that investment/asset growth and momentum dimensions capture the cross-sectional return patterns better than size, value, or profitability. The relative efficacy of momentum is higher in all-but-microcap universe than previously documented for the corresponding unlimited market-cap samples of U.S. stocks.