Sofiati, Evi (2023) Financial Distress Predictive Analytics n the Basic Materials & Chemical Industry with a Hybrid Clustering and Multinomial Logistic Regression Model. Tesis (S2) - thesis, UNIVERSITAS BAKRIE.
Preview |
Text (Cover)
00. Cover.pdf - Submitted Version Download (400kB) | Preview |
![[thumbnail of BAB I- III]](http://repository.bakrie.ac.id/style/images/fileicons/text.png) |
Text (BAB I- III)
01. BAB I - III.pdf - Submitted Version Restricted to Registered users only Download (469kB) |
|
Text (BAB IV)
02. BAB IV.pdf - Submitted Version Restricted to Registered users only Download (1MB) |
|
Text (BAB V)
03.BAB V.pdf - Submitted Version Restricted to Registered users only Download (139kB) |
Preview |
Text (BAB Daftar Pustaka)
04. BAB Daftar Pustaka.pdf - Submitted Version Download (36kB) | Preview |
Abstract
This thesis aimed to examine the relationship between financial ratios performance of the companies listed in the Indonesia Stock Exchange Basic Materials and Chemical industry and their likelihood to face financial distress condition using a combined approach of K-Mean Clustering and Multinomial Logistic Regression. Using companies’ 2020’s financial performance as reflected in 5 (five) financial ratios, namely, Return on Assets (ROA), Return on Equity (ROE), Debt to Equity Ratio (DER), Price to Book Value Ratio (P/BV) and Net Profit Margin (NPM), the study investigated the impact of the pandemic on the companies towards the financial distress likelihood in 2021. The predictive analytics is important to assist investors in their investment decision making process to minimize their risks. The K-Mean Clustering Analysis was first conducted and successfully identified 6 (six) ranked-clusters. Then, predictive modelling using Multinomial Logisitic Regression was conducted to compute the Y Values associated with each cluster. The results showed 2 (two) lowest ranked clusters potentially associated closely with financial distress, i.e., Cluster 4 (potentially negatively impacted) and Cluster 0 (potentially highly negatively impacted) contained 2 (two) companies, namely, Tridomain Performance Materials Tbk and Charoen Pokphand Indonesia Tbk. From Multinomial Logistic Regression calculation, Y values of those 2 bad clusters were 0.02 and 0.04 which are far from 1, meaning that those 2 (two) companies should not belong to the clusters. Thus, their financial distress likelihoods in 2021 should be low. Hence, the hybrid model shows its efficacy in ascertaining the financial distress likelihood in which the clustering results are redeliberated by the multinomial logistic regression.
| Item Type: | Thesis (Tesis (S2) - ) |
|---|---|
| Uncontrolled Keywords: | Hybrid Model; K-Mean Clustering Analysis; Multinomial Logistic Regression; Financial Ratios; Basic Materials and Chemicals; Indonesia Stock Exchange. |
| Subjects: | Finance > Finance Management > Corporation Finance Finance Finance > Finance Management Thesis > Thesis (S2) |
| Divisions: | Fakultas Ekonomi dan Ilmu Sosial > Program Studi Magister Management |
| Depositing User: | Evi Sofiati |
| Date Deposited: | 04 Apr 2023 07:40 |
| Last Modified: | 04 Apr 2023 07:40 |
| URI: | http://repository.bakrie.ac.id/id/eprint/7702 |
Actions (login required)
 |
View Item |