Edwards School of Business
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Item Balance sheet strength in the oil and gas industry: Saving for a rainy day or making hay while the sun shines(Advances in Accounting, 2025-06) Anderson, Mark; Ma, Yan; Park, Han-UpWe examine how a strategic emphasis on balance sheet strength relates to investment decisions and performance over time for firms operating in a cyclical environment. From a series of discussions with industry insiders and readings of disclosures for prominent oil and gas (O&G) companies in Canada, we identify two groups of upstream O&G firms based on how they match their resources and capabilities with the uncertainties posed by industry economic cycles. One group of firms borrows and invests aggressively when oil prices are strong and funds are available – “making hay while the sun shines”, while the other group grows conservatively to build and maintain balance sheet strength – “saving for a rainy day”. We use average cash flows to debt for each firm over time to measure emphasis on balance sheet strength and separate firms into rainy day and making hay companies. We leverage two steep price declines to observe the behavior of firms over industry cycles: one triggered by the widespread 2008 financial crisis and the other by a distinct and prolonged O&G industry downturn in 2014. While investment declined generally in both cases, we find that the decline in investment was significantly less for rainy day companies than making hay firms after the 2014 downturn. Across time, we find that rainy day companies make shrewder acquisitions and operate more efficiently than making hay companies. Nonetheless, the capital market rewards making hay companies with higher market valuation, but this is reduced in downturns.Item Modeling a realistic integrated energy hub with growing demand for electric vehicles: The case of the province of Ontario, Canada(Elsevier, 2025-01-28) Siroos, Ahmad; Samarghandi, HamedEnergy hubs are multi-carrier energy management systems that efficiently distribute various forms of energy, reducing losses and environmental pollution. This paper examines Ontario, Canada, as a major energy hub, using a typical fall day pattern for energy demand. The model includes all power generation sources in Ontario: photovoltaic (PV), wind turbine (WT), nuclear, hydro, biofuel, and natural gas power plants. It also integrates the charging and discharging of electric vehicles (EVs) within the energy distribution framework. Managing the intrinsic uncertainty of the parameters is crucial for efficient operation. This study employs probabilistic functions to account for the arrival and departure hours of EVs, controlled using the Conditional Value at Risk (CVaR) method. Three methods, Information Gap Decision Theory (IGDT) with risk-seeking and risk-averse behaviors, and robust optimization, address uncertainties such as wind and solar electricity production, energy prices, and electrical, heating, and cooling demands. We compare simulation results of three scheduling scenarios for optimal energy production and dispatch. The RS-IGDT method can lead to significant losses during peak hours due to fluctuations. The robust method incurs higher costs by planning for large deviations. The RA-IGDT method balances deviations without the pessimism of the robust method, making it the recommended approach.