Title

Forward-looking: A machine learning approach in predicting corporate delisting in the Philippine Stock Exchange

Date of Publication

2017

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Management of Financial Institutions

Subject Categories

Finance and Financial Management

College

Ramon V. Del Rosario College of Business

Department/Unit

Financial Management Department

Thesis Adviser

Mar Andriel Umali

Defense Panel Chair

Tyrone Panzer Chan Pao

Defense Panel Member

Dioscoro Baylon

Steven S. Lim

Patiu, Liberty, Dr., Thesis coordinator

Abstract/Summary

Delisting is the removal of a listed security from the exchange where it is traded. Its drawbacks extend to the liquidity status of the shareholder, access to funding sources, and immediate assessment of the enterprise, which could possibly result in financial loss and consumer confidence reduction.

With this, the study explores the predicting capabilities of Altman Z-score model, logistic regression analysis and artificial neural network on the probability of corporate delisting in the Philippine Stock Exchange (PSE). With a sample of twenty-six (26) delisted corporations and seventy-eight (78) publicly-listed corporations from 1995-2016, the researchers employed a machine learning approach to identify the most effective predictive model. Using T-test and chi-squared test, results showed that quick ratio (QUICK), total debt to equity ratio (DEBTEQ), and degree of financial leverage (DFL) were statistically significant and listing status was only statistically dependent to minimum public ownership compliance (MPO). Empirical results showed that artificial neural network was the most effective model with 77.42% accuracy and 67.00% precision using whole data set one (1) prior to delisting and 70.97% accuracy and 56.67% precision using whole data set two (2) years prior to delisting.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU19979

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

vi, 190 : illustrations ; 28 cm. + 1 computer disc ; 4 3/4 in.

Keywords

Business enterprises--Philippines--Finance; Business enterprises--Philippines

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