The use of recursive partitioning to build a financial distress prediction for JSE listed companies
type: CreativeWork
description: The financial crises of 2008 increased the focus around financial distress and even more so on predicting financially distressed companies prior to the fact. This research paper investigates using recursive partitioning to predict financially distressed companies on the Johannesburg Stock Exchange, taking different business cycle periods into account over the time period 1997-2014. The updated as well as longer time period over which the analysis is conducted distinguishes this research paper from prior research. This paper employs both the CART and CHAID algorithm and obtains financially distressed prediction models which have a higher correct classification rate than chance alone and prior literature in South Africa. This paper also makes use of a matched data sample approach and the manner in which missing data is addressed makes a valuable contribution to financial distress prediction research. Furthermore, support is found for prior literature in that financial variables are statistically significant in predicting financial distress.
abstract: The financial crises of 2008 increased the focus around financial distress and even more so on predicting financially distressed companies prior to the fact. This research paper investigates using recursive partitioning to predict financially distressed companies on the Johannesburg Stock Exchange, taking different business cycle periods into account over the time period 1997-2014. The updated as well as longer time period over which the analysis is conducted distinguishes this research paper from prior research. This paper employs both the CART and CHAID algorithm and obtains financially distressed prediction models which have a higher correct classification rate than chance alone and prior literature in South Africa. This paper also makes use of a matched data sample approach and the manner in which missing data is addressed makes a valuable contribution to financial distress prediction research. Furthermore, support is found for prior literature in that financial variables are statistically significant in predicting financial distress.
author: Smit, Candice
language: eng
date published: 2016
date modified: 2026-07-22T22:22:37Z
provider: University of Cape Town
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Department of Finance and Tax

PropertyValue
property ID: Handle
value: http://hdl.handle.net/11427/20633