Likelihood of financial distress in Canadian oil and gas market: An optimized hybrid forecasting approach

Mohammad Mahbobi, Rashmit Singh G. Sukhmani

Abstract


Forecasting models are built on either multivariate parametric or nonparametric methodologies. We attempt to optimize the accuracy of the forecasts combining these approaches to make a robust hybrid forecasting model in predicting the likelihood of financial distress for companies in the Canadian oil and gas market. The proposed approach combined the forecasts out of a multivariate logit model based on the conventional Altman’s Z-score with a nonparametric Artificial Neural Network (ANN) technique. The sample firms are publicly traded and listed on the Toronto Stock Exchange (TSX) and span over a period from first quarter of 1999 to the last quarter of 2014. The results of a proposed three-stage estimation process for the period of 2015-2020 indicated that besides the fact that Canadian energy sector will go through ups and downs regarding the likelihood of financial distress, this industry would face a hard time by late 2020. Results show that the forecasting accuracy out of the proposed three-stage forecasting technique is significantly superior to the outcomes of any individual forecasting techniques, i.e. ANN and logit models.

Keywords


Forecasting; Financial Distress; Corporate Failure; Canadian Energy Sector; Artificial Neural Network (ANN); Logit Model.

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References


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DOI: http://dx.doi.org/10.18533/jefs.v5i3.272

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