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

Journal of Economic & Financial Studies

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Title Likelihood of financial distress in Canadian oil and gas market: An optimized hybrid forecasting approach
 
Creator Mahbobi, Mohammad
Sukhmani, Rashmit Singh G.
 
Subject —
Forecasting; Financial Distress; Corporate Failure; Canadian Energy Sector; Artificial Neural Network (ANN); Logit Model.
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Description 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.
 
Publisher LAR Center Press
 
Contributor —
 
Date 2017-05-03
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://journalofeconomics.org/index.php/site/article/view/272
10.18533/jefs.v5i3.272
 
Source Journal of Economic & Financial Studies; Vol 5, No 3 (2017): June; 12-25
2379-9471
2379-9463
 
Language eng
 
Relation http://journalofeconomics.org/index.php/site/article/view/272/319
 
Rights Copyright (c) 2017 Mohammad Mahbobi, Rashmit Singh G. Sukhmani
http://creativecommons.org/licenses/by/4.0