The contribution of USM company financial data to the study of traditional MDA predictive models

Inman, Mark Lee (1990) The contribution of USM company financial data to the study of traditional MDA predictive models. Masters thesis, Middlesex Polytechnic. [Thesis]

[img]
Preview
PDF
Download (6MB) | Preview

Abstract

This thesis presents a detailed investigation of predicting of corporate financial failure, using two traditional (Altman and Taffler) Multiple Discriminant Analysis models on companies whose equities are traded on the London Unlisted Securities Market (USM) and on those that have passed or graduated from the USM to a full listing. The primary objective was to see if the two models can be effectively used in the context of
the USM to either to predict or at least indicate symptoms of financial collapse. Secondly, ratios were taken for further discriminant analysis to see if better ratios can be identified and predictions developed from one or a group of ratios. In this study consideration also has been given to the limited
progress in developing the underlying theory of bankruptcy.

The Altman and Taffler MDA models were tested on the USM data. Their predictions compared favourably with those of a multi-discriminant model that was derived from the USM data. However, it was found that all three models gave mediocre and late predictions of individual company bankruptcy. The research found that MDA analysis of company failure had to be supplemented by a more behavioural and subjective approach to the question of company failure in order to be useful. Even so, the dissertation is able to end with some useful pointers for future research.

Item Type: Thesis (Masters)
Research Areas: B. > Theses
Item ID: 13460
Depositing User: Adam Miller
Date Deposited: 14 Jan 2015 16:38
Last Modified: 21 Jun 2021 16:46
URI: https://eprints.mdx.ac.uk/id/eprint/13460

Actions (login required)

View Item View Item

Statistics

Downloads
Activity Overview
189Downloads
406Hits

Additional statistics are available via IRStats2.