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
Syed Abul Basher and Perry Sadorsky (2025). "How Important Are Climate Change Risks For Predicting Clean Energy Stock Prices? Evidence From Machine Learning Predictive Modeling And Interpretation", Journal of Climate Finance, 10, 100058.
Abstract
The clean energy equity sector plays an important role in the transition to a low-carbon economy. This paper explores the role of climate change risks in predicting the direction of clean energy stock prices (solar, wind, nuclear). We employ machine learning models, including random forests, boosting, extremely randomized trees, and support vector machines, to make our predictions. Variable importance is determined using Shapley/SHAP values. Notably, tree-based ensemble and boosting models show an accuracy exceeding 85 % for the 10 day to 20 day forecast period. For the stock prices of solar, wind, and nuclear energy, inflation expectations and technical indicators (which account for behavioral factors) such as on-balance volume and Williams’ accumulation/distribution are important features within this forecast range. For wind and solar energy stocks moving averages are also important additional features while for nuclear energy stocks economic policy uncertainty and stock market volatility are additional important features. In the five day to twenty day forecast horizon, climate change risks are not important features. These results align with a body of literature that raises concerns about equity prices not fully reflecting climate change risks. An equally weighted portfolio of wind, solar, and nuclear energy stock prices that used trading signals from an Extra Trees prediction model outperformed a buy and hold portfolio in terms of risk adjusted returns. These results are robust to trading costs and weekly or monthly portfolio rebalancing.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.