Financial Distress Prediction Using Cutting-Edge Statistical Techniques: A Study of Australian Real Estate Sector

Sneha Raut, Milind Tiwari, Kuldeep Kumar

Research output: Contribution to journalArticleResearchpeer-review

Abstract

"Financial Distress" is a condition ·where a company fails to meet its financial obligations. Financial Distress Prediction (FDP) is a process of understanding if the company is leading to success or failure. FDP has gained tremendous interest after the failure of high-profile companies like Enron and WorldCom. The aim of this paper is to study various financial ratios of Australian Real Estate companies to design a model effective for predicting distress. The experiment data included 164 listed (success/11[) and 12 de listed (jailed) Australian Real Estate companies over hvo-year time frame from 2016-2018. Cutting-edge statistical techniques like Logistic Regression, Multivariate Discriminant Analysis, Artificial Neural Network, Hybrid Techniques, Decision Trees, Random Forest and Stochastic Gradient Boosting are used to construct various FDP models. The experiment results indicate that hybrid model combining Artificial Neural Network and Logistic Regression along with Stochastic Gradient Boosting had superior power at predicting if the company will be successful or a failure, These models can be effectively utilized by Australian Real Estate companies for distress prediction and by their investors to make correct investments decisions.
Original languageEnglish
Pages (from-to)2-32
Number of pages31
JournalShodh-Amrit: JKLU Journal of Engineering & Management
Volume1
Issue number2
Publication statusPublished - Dec 2018

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