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.

Nguyen, Phuong-Anh, Ambrus Kecskés, and Sattar Mansi (2020). "Does Corporate Social Responsibility Create Shareholder Value? The Importance of Long-term Investors", Journal of Banking and Finance, 112.

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Abstract We study the effect of corporate social responsibility (CSR) on shareholder value. We argue that long-term investors can ensure that managers choose the amount of CSR that maximizes shareholder value. We find that long-term investors do increase the value to shareholders of CSR activities, not through higher cash flow but rather through lower cash flow risk. Following prior work, we use indexing by investors and state laws on stakeholder orientation for identification. Our findings suggest that CSR activities can create shareholder value as long as managers are properly monitored by long-term investors.

Cao, M., Gold, N., Huang, H. and Wang, Q. (2017). "Liquidity and Volatility Commonality in the Canadian Stock Market", Mathematics-in-Industry Case Studies, 8(7), 1-20.

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Abstract This paper studies liquidity and volatility commonality in the Canadian stock market. We show that five various liquidity measures display strong evidence of commonality at both market-wide and industry specific levels. Our findings extend the results of previous studies in liquidity commonality, and show that even after controlling for individual determinants of liquidity such as price, volume, and volatility, liquidity commonality remains. In addition to demonstrating liquidity commonality, we also investigated the causal relationship between liquidity and volatility. Our evidence indicates that depth, proportional effective spread, and liquidity changes predict volatility changes for bid-ask spread, depth, and proportional effective spread.

Sadorsky, P. (2014). "Modeling Volatility And Correlations Between Emerging Market Stock Prices and the Prices Of Copper, Oil and Wheat", Energy Economics, 43, 72-81.

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Abstract Increased financial integration between countries and the financialization of commodity markets are providing investors with new ways to diversify their investment portfolios. This paper uses VARMA-AGARCH and DCC-AGARCH models to model volatilities and conditional correlations between emerging market stock prices, copper prices, oil prices and wheat prices. The dynamic conditional correlation model is found to fit the data the best and used to generate dynamic conditional correlations, hedge ratios and optimal portfolio weights. Emerging market stock prices and oil prices display leverage effects where negative residuals tend to increase the variance (conditional volatility) more than positive ones. Correlations between these assets increased considerably after 2008, and have yet to return to their pre 2008 values. On average, oil provides the cheapest hedge for emerging market stock prices while copper is the most expensive but given the variability in the hedge ratios, one should probably not put too much emphasis on average hedge ratios.

Sadorsky, P. (2014). "Modeling Volatility And Conditional Correlations Between Socially Responsible Investments, Gold and Oil", Economic Modelling, 38, 609-618.

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Abstract Socially responsible investing (SRI) is one of the fastest growing areas of investing. While there is a considerable literature comparing SRI to various benchmarks, very little is known about the volatility dynamics of socially responsible investing. In this paper, multivariate GARCH models are used to model volatilities and conditional correlations between a stock price index comprised of socially responsible companies, oil prices, and gold prices. The dynamic conditional correlation model is found to fit the data the best and used to generate dynamic conditional correlations, hedge ratios and optimal portfolio weights. From a risk management perspective, SRI offers very similar results in terms of dynamic conditional correlations, hedge ratios, and optimal portfolio weights as investing in the S&P 500. For example, SRI investors can expect to pay a similar amount to hedge their investment with oil or gold as investors in the S&P 500 would pay. These results can help investors and portfolio managers make more informed investment decisions.

Sadorsky, P. (2012). "Correlations And Volatility Spillovers Between Oil Prices and The Stock Prices of Clean Energy and Technology Companies", Energy Economics, 34(1), 248-255.

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Abstract In this paper, multivariate GARCH models are used to model conditional correlations and to analyze the volatility spillovers between oil prices and the stock prices of clean energy companies and technology companies. Four different multivariate GARCH models (BEKK, diagonal, constant conditional correlation, and dynamic conditional correlation) are compared and contrasted. The dynamic conditional correlation model is found to fit the data the best and generates results showing that the stock prices of clean energy companies correlate more highly with technology stock prices than with oil prices. On average, a $1 long position in clean energy companies can be hedged for 20 cents with a short position in the crude oil futures market.