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
Adam Diamant (2024). "Introducing Prescriptive and Predictive Analytics to MBA Students with Microsoft Excel", INFORMS Transactions on Education, 24(2), 152-174.
Abstract
Managers are increasingly being tasked with overseeing data-driven projects that incorporate prescriptive and predictive models. Furthermore, basic knowledge of the data analytics pipeline is a fundamental requirement in many modern organizations. Given the central importance of analytics in today’s business environment, there is a growing demand for educational pedagogies that give students the opportunity to learn the fundamentals while also familiarizing them with how such tools are applied. However, a tension exists between the introduction of real-world problems that students can analyze and extract insight from and the need for prerequisite knowledge of mathematical concepts and programming languages such as Python/R. As a consequence, this paper describes an application-focused course that uses Microsoft Excel and mathematical programming to introduce MBA students with nontechnical backgrounds to tools from both prescriptive and predictive analytics. While students’ gain proficiency in managing data and creating optimization and machine learning models, they are also exposed to broader business concepts. Teaching evaluations indicate that the course has helped students further develop their practical skills in Microsoft Excel, gain an appreciation of the real-world impact of data analytics, and has introduced them to a discipline they originally believed was best suited for more technically focused professionals.Irene Henriques, Perry Sadorsky (2023). "Forecasting Rare Earth Stock Prices With Machine Learning", Resources Policy, 86(A), 104248.
Abstract
Rare earth elements (REEs) are indispensable for producing green technologies and electronics. Demand for REEs in clean energy technologies in 2040 are projected to be three to seven times higher than today and will be critical to the clean technology transition needed to stave off catastrophic climate change. Forecasting rare earth stock prices is critical for making well informed investment decisions concerning this important asset class. Despite the latter, the literature on forecasting rare earth stock prices is scarce. We use machine learning techniques to forecast daily rare earth stock price direction. The analysis reveals that random forests, extremely randomized trees, RNN, and support vector machine have higher prediction accuracy than Lasso or Naïve Bayes. We find that the 10- to 20-day forecasts using random forests, extremely randomized trees, and support vector machine achieve prediction accuracies greater than 85% with some prediction accuracy reaching 90%. Lasso prediction accuracy is higher than Naïve Bayes but never greater than 67%. The MA200, MA50, on balance volume, VIX, and WAD are the most important predictive features of rare earth stock price direction. A switching portfolio that uses trading signals from an Extra Trees model impressively outperforms a buy and hold portfolio. Our results reveal the high prediction accuracy of using machine learning methods in forecasting rare earth stock price direction which should be useful to investors, policy makers and venture capitalists.Sadorsky, P. (2021). "A Random Forests Approach to Predicting Clean Energy Stock Prices", Journal of Risk and Financial Management, 14(2), 48.
Akbari, A., Ng, L. and Solnik, B. (2021). "Drivers of Global Market Integration: A Machine Learning Approach", 61, 82-102.
Abstract
We propose a new approach to identifying drivers of economic and financial integration, separately, and across emerging and developed countries. Our advanced machine learning technique allows for nonlinear relationships, corrects for over-fitting, and is less prone to noise. It also can tackle a large number of highly correlated explanatory variables and controls for multicollinearity. Results suggest that general economic growth, increasing international trade, and contained population growth have helped emerging countries catch up to the level of the economic integration of developed countries. However, slow financial development and a high level of investment riskiness have hindered the speed of emerging countries’ financial integration. Furthermore, the results suggest that integration is a gradual process and is not driven by cyclical or transitory events.Babier, A., Chan, T., Diamant, A., Mahmood, R. and McNiven, A. (2020). "The Importance of Evaluating the Complete Knowledge-Based Automated Planning Pipeline", European Journal of Medical Physics, 72, 73-79 .
Keywords
Abstract
We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans. We trained two dose prediction methods, a generative adversarial network (GAN) and a random forest (RF) with the same 130 treatment plans. The models were applied to 87 out-of-sample patients to create two sets of predicted dose distributions that were used as input to two optimization models. The first optimization model, inverse planning (IP), estimates weights for dose-objectives from a predicted dose distribution and generates new plans using conventional inverse planning. The second optimization model, dose mimicking (DM), minimizes the sum of one-sided quadratic penalties between the predictions and the generated plans using several dose-objectives. Altogether, four KBP pipelines (GAN-IP, GAN-DM, RF-IP, and RF-DM) were constructed and benchmarked against the corresponding clinical plans using clinical criteria; the error of both prediction methods was also evaluated. The best performing plans were GAN-IP plans, which satisfied the same criteria as their corresponding clinical plans (78%) more often than any other KBP pipeline. However, GAN did not necessarily provide the best prediction for the second-stage optimization models. Specifically, both the RF-IP and RF-DM plans satisfied the same criteria as the clinical plans 25% and 15% more often than GAN-DM plans (the worst performing plans), respectively. GAN predictions also had a higher mean absolute error (3.9 Gy) than those from RF (3.6 Gy). We find that state-of-the-art prediction methods when paired with different optimization algorithms, produce treatment plans with considerable variation in quality.Chan, T., Diamant, A. and Mahmood, R. (2020). "Sampling from the Complement of a Polyhedron: An MCMC Algorithm for Data Augmentation", Operations Research Letters, 48(6), 744-751.
Abstract
We present an MCMC algorithm for sampling from the complement of a polyhedron. Our approach is based on the Shake-and-bake algorithm for sampling from the boundary of a set and provably covers the complement. We use this algorithm for data augmentation in a machine learning task of classifying a hidden feasible set in a data-driven optimization pipeline. Numerical results on simulated and MIPLIB instances demonstrate that our algorithm, along with a supervised learning technique, outperforms conventional unsupervised baselines.Bai, X., Ge, Y., Li, Z. and Peng, Z. (Forthcoming). "What Will Be Popular Next? Predicting Hotspots inTwo-mode Social Networks", MIS Quarterly.
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