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American Journal of  Financial 
Technology and Innovation (AJFTI)

Financial Distress Multi-Classification Prediction: A Case Study in Vietnam
Tram Thi Hoai Vo1, Pai-Chou Wang2*

Volume 3 Issue 1, Year 2025
ISSN: 2996-0975 (Online)

DOI: https://doi.org/10.54536/ajfti.v3i1.4570
https://journals.e-palli.com/home/index.php/ajfti

Article Information ABSTRACT

Received: February 15, 2025

Accepted: March 19, 2025

Published: July 26, 2025

Stock investment remains one of  the most attractive and profitable activities in financial 
markets. However, assessing a company’s financial health is a complex task due to the vast 
amount of  financial data involved. This study classifies companies into three financial status 
categories of  safe, risky, and distressed by employing three key financial distress measures: 
Distance to Default (DD), Emerging Market Score (EMS), and Altman Z-score. A set of  
68 financial ratios is utilized to predict the financial status of  a company. We employ the 
Adaptive Synthetic Sampling (ADASYN) technique alongside advanced machine learning 
algorithms, including Random Forest, CatBoost, XGBoost, and Support Vector Machine 
to further improve model performance. Our results show that Random Forest yields highly 
accurate predictions in multi-class classification by integrating machine learning with the EMS 
method. The best-performing model achieves an exceptional ROC-AUC score of  99.26%. 
These findings provide a powerful decision-making tool for investors, traders, practitioners, 
and policymakers, enabling more precise assessments of  corporate financial stability.

Keywords
Adaptive Synthetic Sampling, 
Financial Distress, Multi-
Classification

INTRODUCTION
Vietnam’s stock market operates through two primary 
exchanges: the Hanoi Stock Exchange (HNX) and the 
Ho Chi Minh Stock Exchange (HOSE). While Vietnam 
remains a promising frontier market, it also presents 
significant risks. According to a report from Vietnam.
vn  (Triều, 2025), the number of  listed enterprises on 
HNX has been declining, with an increasing number of  
firms being delisted due to stricter regulatory oversight 
and sanctions against violations. In the first ten months 
of  2024 alone, 15 companies were delisted, and 22 
deregistered for trading. This trend raises concerns about 
transparency and financial stability, as companies often 
do not fully disclose the true state of  their financial health 
in financial statements such as balance sheets, income 
statements, and cash flow reports. Traditional financial 
metrics like Price-to-Earnings (P/E) ratio, Price-to-Book 
(P/B) ratio, and Earnings Per Share (EPS) are commonly 
used to evaluate company performance. However, these 
indicators may not provide a reliable assessment of  a 
company’s financial health, especially in a market where 
institutional trading volume significantly influences stock 
prices like Vietnam. During financial crises or major 
economic events, institutional investors can drive sharp 
price swings, making it even more challenging for retail 
investors to make informed decisions.
Several methodologies have been developed to assess a 
company’s financial distress, with Distance to Default 
(DD), Emerging Market Score (EMS), and Altman’s 
Z-score being among the most popular. However, 
these methods differ in their analytical approach and 
effectiveness across varying market conditions and time 
periods. EMS and Altman’s Z-score are accounting-
based models, relying on financial statements to assess a 

company’s solvency and financial stability. These methods 
evaluate distress risk based on historical financial data, 
providing insights into a firm’s liquidity, profitability, 
and leverage. In contrast, the Distance to Default (DD) 
is a measurement metric based on the stock market, 
incorporating real-time market variables such as stock 
prices and volatility to estimate the probability of  default. 
Unlike accounting-based models, which rely on past 
performance, Distance to Default (DD) reflects current 
market sentiment and forward-looking risk assessments, 
making it particularly valuable in rapidly changing financial 
environments. Each method focuses on different aspects 
of  financial performance, and their effectiveness varies 
depending on market conditions and time periods. 
This study aims to address this gap by analyzing the 
predictive power of  68 financial ratios derived from 
company financial statements. Instead of  relying on 
binary classification (distressed vs. non-distressed) based 
on interest coverage ratio (IC), as commonly seen in 
previous research, we introduce a more granular, multi-
class prediction framework that categorizes companies 
into three financial health statuses: Green (Stable), Grey 
(At-Risk), and Red (Distressed), which leverage from 
three approaches: Distance to Default (DD), Emerging 
Market Score (EMS), and Altman’s Z-score.
In addition, widely used machine learning models in 
classification tasks such as Random Forest, XGBoost, 
CatBoost and Support Vector Machine are applied in this 
study. Among the tested machine learning algorithms, 
Random Forest performed the best with an impressive 
ROC-AUC score of  99.26%. The EMS score emerged 
as a highly effective predictor of  financial distress when 
integrated with machine learning models, surpassing 
traditional methods and demonstrating the potential 

1 College of  Business, Southern Taiwan University of  Science and Technology, Tainan 710301, Taiwan ROC
2 Department of  Information Management, Southern Taiwan University of  Science and Technology, Tainan 710301, Taiwan ROC
* Corresponding author’s e-mail: pwang@stust.edu.tw



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of  machine learning in financial health prediction. This 
research makes a significant contribution by identifying 
an effective method for predicting financial distress in 
Vietnam and verifying the effectiveness of  the EMS 
score as an early warning indicator in the Vietnamese 
stock market. The results will provide useful tools for 
traders, investors, and policymakers, helping them make 
data-driven decisions. Moreover, our approach enhances 
existing distress prediction models by leveraging machine 
learning techniques, which have been underutilized in 
Vietnam’s financial market analysis.
For this study, we collected financial statement data from 
34 Vietnamese companies, spanning from their initial 
public offering (IPO) to 2023. The remaining of  this 
paper is organized is organized as follows:  The pertinent 
literature on predicting financial crisis in Vietnam is 
reviewed in Section 2.  In Section 3, the method is 
described.  The findings are covered in Section 4, and 
Section 5 provides the key findings, contributions, and 
further discussion of  this study.

LITERATURE REVIEW
Literature includes a variety of  studies focused on 
company bankruptcy or financial distress, aimed at 
assisting academics, investors, and policymakers in 
evaluating the stock market. However, most of  these 
studies typically use binary target variables to provide 
simple “Yes” or “No” outcomes. A research on forecasting 
financial difficulties and insolvency among Vietnamese 
listed firms was carried out by Pham Vo Ninh et al. (2018).  
They used accounting, market, and macroeconomic 
aspects in their study, using a dataset of  800 Vietnamese 
companies from 2003 to 2016 with 6,736 observations.  
The effect of  market data models, financial statement 
data models, and external economic variables on the 
probability of  financial distress in Vietnamese enterprises 
was investigated using a logistic regression model.  The 
results showed that while the leverage ratio shows a 
positive link with financial difficulty, larger enterprises 
had a reduced likelihood of  default.  Furthermore, it was 
discovered that financial hardship positively correlated 
with both inflation and short-term Treasury bill interest 
rates. Tran et al. (2022) applied various machine learning 
algorithms, including artificial neural networks, support 
vector machines (SVM), logistic regression, decision 
trees, and random forests to forecast risky credit in 
public companies in Vietnam over the period from 
2010-2021. The dataset consisted of  3,277 observations 
in which 436 companies (13.3%) were identified as 
financially distressed, while 2,841 companies (86.7%) 
were categorized as non-distressed. The outcomes 
demonstrated that factors such as accounts payable 
to equity ratio, long-term debt to equity ratio, diluted 
earnings per share and enterprise value to revenue ratio 
significantly influenced the predictive outcomes and were 
largely aligned with established expert knowledge. Tran et 
al. (2023) conducted an examination of  financial distress 
within a cohort of  500 publicly traded firms in Vietnam 

from 2012 to 2021. The results revealed that merely 
four financial indicators - Total Equity/Total Liabilities, 
Total Liabilities/Total Assets, Net Income/Total Assets, 
EBIT/Total Assets and - were proficient in forecasting 
financial distress in the Vietnamese context. According 
to the analysis, Altman Z’-score model is only applicable 
in Vietnam when financial difficulties is represented by 
the interest coverage ratio. Nguyen et al. (2024) utilized 
seven distinct analytical approaches - logistic regression, 
linear discriminant analysis, neural networks, support 
vector machines, decision trees, random forests, and the 
Merton model - to assess financial distress among publicly 
listed enterprises in Vietnam during the period from 
2011 to 2021. The research incorporated five elements 
from Altman’s model and nine from Ohlson’s model. 
The variable deemed most critical in both Altman’s 
model and the integrated Altman-Ohlson model was 
“reat”, while “ltat” and “wcapat” emerged as the most 
significant variables in Ohlson’s model. The outcomes 
further indicated that these models typically exhibited 
superior performance in forecasting financial distress 
for larger companies in comparison to smaller entities 
and demonstrated greater accuracy during periods of  
economic expansion than during recessions. Dinh et al. 
(2021) executed a study focused on predicting financial 
distress across the six largest nations within the ASEAN 
Economic Community (AEC). They inferred that the 
Distance to Default (DD) methodology is appropriate as 
an early warning signal for impending financial distress in 
the subsequent year. 
Besides, the advanced machine learning models like 
Random Forest, XGBoost, CatBoost and Support Vector 
Machine are employed in much research of  financial 
distress predictions. Hou et al. (2024) employed financial 
indicators such as working capital, operational funds, 
cash ratio, quick ratio, current ratio, and borrowing ratio 
for a Random Forest model to predict financial distress 
in 12 companies. The study revealed a strong predictive 
capability, with the model achieving an R2 value of  0.77. 
This shows that 77% of  the variation in the data could be 
explained by the model, demonstrating its effectiveness 
in identifying financial distress. This result highlights the 
potential of  Random Forest as a robust tool for financial 
distress prediction, particularly when combined with 
key financial ratios. Lu and Hu (2024) utilized a set of  
15 financial indices including net profit, total liabilities, 
monetary capital, fixed assets, main business income, 
total assets, short-term borrowings, total operating 
income, net fixed assets, basic earnings per share, bonds 
payable, net assets per share, investment income, and 
long-term borrowings as input features for a CatBoost 
model. The model was optimized using the Particle 
Swarm Optimization (PSO) method.
The results demonstrated promising performance, with a 
Mean Squared Error of  0.032 and a Mean Absolute Error 
of  0.124, highlighting the model’s ability to effectively 
predict financial distress using these financial indicators. 
Carmona et al. (2022) analyzed 1,760 French firms to 



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identify key indicators of  business failure. Their findings 
highlighted the important indicators include equity per 
employee, solvency, current ratio, net profitability, and 
sustainable return on investment among 36 examined 
indicators. Adopting XGBoost as the primary model for 
failure prediction, the study achieved a high AUC score 
of  0.964, demonstrating the model’s strong predictive 
capability. Doğan et al. (2022) applied Support Vector 
Machine and Logistic Regression to predict financial 
distress among 172 companies listed on Borsa İstanbul. A 
total of  24 financial indicators were used as input features 
for the predictive models. Their findings revealed that the 
hybrid model combining Logistic Regression and Grid 
Search-optimized SVM achieved the highest accuracy 
of  93.75% on the testing set, proving its usefulness in 
forecasting financial turmoil.

MATERIALS AND METHODS
Figure 1 illustrates the methodology employed in this 
study. The process begins with the collection of  datasets 
which include financial ratios and the corresponding 
company status for each year. To address the issue of  class 
imbalance, the Adaptive Synthetic Sampling (ADASYN) 
technique is applied to ensure that all three classes in the 
dataset are balanced. Next, the dataset is divided into 
training (70%) and testing (30%) subsets. The training set 
is then used to train machine learning models including 
XGBoost, CatBoost, Random Forest, and Support Vector 
Machine (SVM) to predict company financial status. The 
trained models generate predictions for the testing set, 
which are subsequently evaluated against actual values 
using performance metrics such as AUC-ROC, F1-score, 
precision, accuracy, specificity and recall.

Figure 1: Process flow chart of this paper

Financial Distress Measurement
Financial distress prediction is a crucial area of  research 
in corporate finance which helps investors, regulators, 
and policymakers in assessing the likelihood of  firm 
bankruptcy. Among various financial distress prediction 
models, Altman’s Z-score remains one of  the most widely 
used methodologies. Developed by Altman (1968) in 
1968, the Altman Z-score is a model based on multiple 
discriminant analysis (MDA) that uses financial ratios to 
predict corporate bankruptcy. The model was initially 
designed for publicly traded manufacturing firms and 
demonstrated high predictive accuracy in distinguishing 

between businesses who are bankrupt and those that 
are not. Two additional variants were introduced: Z’ 
in 1983 (Altman, 1983) and Z’’ in 1995 (Altman et al., 
1995). These models are specifically designed for private 
companies and firms operating in emerging markets, with 
Z’’ particularly suited for non-manufacturing companies. 
In this study, the Z-score and Z’’-score are utilized to 
assess the financial status of  manufacturing and non-
manufacturing companies. The sample consists of  18 
manufacturing firms, representing approximately 53% of  
the total, and 16 non-manufacturing firms, making up the 
remaining 47% as shown in Table 1.

Table 1: Investigated companies in Vietnam
No Company Name Industry Catogories
1 Investment And Industrial Development 

Corporation (HOSE: BCM)
Real Estate Management & 
Development

Non-Manufacturing

2 Viettel Construction Joint Stock Corporation 
(HOSE: CTR)

Telecommunication Services Non-Manufacturing



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3 DHG Pharmaceutical Joint Stock Company 
(HOSE: DHG)

Pharmaceuticals, Biotechnology 
& Life Sciences

Manufacturing

4 FPT Corporation (HOSE: FPT) Software & Services Non-Manufacturing
5 PetroVietnam Gas Joint Stock Corporation Utilities Non-Manufacturing
6 Vietnam Rubber Group - Joint Stock Company 

(HOSE: GVR)
Materials Manufacturing

7 Hoa Phat Group Joint Stock Company (HOSE: HPG) Materials Manufacturing
8 Vietnam Airlines JSC (HOSE: HVN) Transportation Non-Manufacturing
9 IDICO Corporation - JSC (HNX: IDC) Real Estate Management & 

Development
Non-Manufacturing

10 Lam Dong Investment & Hydraulic 
Construction JSC (HNX: LHC)

Capital Goods Non-Manufacturing

11 Masan Group Corporation (HOSE: MSN) Food, Beverage & Tobacco Manufacturing
12 Mobile World Investment Corporation (HOSE: 

MWG)
Consumer Discretionary 
Distribution & Retail

Non-Manufacturing

13 Tien Phong Plastic Joint Stock Company (HNX: 
NTP)

Materials Manufacturing

14 Phuoc Hoa Rubber Joint Stock Company 
(HOSE: PHR)

Materials Manufacturing

15 Viet Nam National Petroleum Group (HOSE: 
PLX)

Consumer Discretionary 
Distribution & Retail

Non-Manufacturing

16 Phu Nhuan Jewelry Joint Stock Company 
(HOSE: PNJ)

Consumer Discretionary 
Distribution & Retail

Manufacturing

17 PetroVietnam Power Corporation (HOSE: POW) Utilities Non-Manufacturing
18 Saigon Beer - Alcohol - Beverage Corporation 

(HOSE: SAB)
Food, Beverage & Tobacco Manufacturing

19 Son La Sugar JSC (HNX: SLS) Food, Beverage & Tobacco Manufacturing
20 Thaiholdings Joint Stock Company (HNX: THD) Capital Goods Non-Manufacturing
21 TNG Investment and Trading JSC (HNX: 

TNG)
Consumer Durables & Apparel Manufacturing

22 Vicostone JSC (HNX: VCS) Materials Manufacturing
23 Vinhomes JSC (HOSE: VHM) Real Estate Management & 

Development
Non-Manufacturing

24 Vingroup Joint Stock Company (HOSE: VIC) Real Estate Management & 
Development

Non-Manufacturing

25 Vietjet Aviation Joint Stock Company (HOSE: 
VJC)

Transportation Non-Manufacturing

26 Viet Nam Dairy Products Joint Stock Company 
(HOSE: VNM)

Food, Beverage & Tobacco Manufacturing

27 Vincom Retail Joint Stock Company (HOSE: 
VRE)

Real Estate Management & 
Development

Non-Manufacturing

28 Hoang Kim Tay Nguyen Group JSC (HNX: CTC) Consumer Services Manufacturing
29 Thien Nam Trading Import Export JSC (HOSE: 

TNA)
Consumer Discretionary 
Distribution & Retail

Non-Manufacturing

30 Hoang Anh Gia Lai Agricultural JSC (HOSE: 
HNG)

Food, Beverage & Tobacco Manufacturing

31 Song Da 6 JSC (HNX: SD6) Capital Goods Non-Manufacturing
32 Dong A Plastic JSC (HOSE: DAG) Capital Goods Manufacturing
33 HTINVEST JSC (HNX: HTP) Commercial & Professional 

Services
Non-Manufacturing

34 Vietnam Electric Cable Corporation (HOSE: CAV) Capital Goods Manufacturing



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The original Altman Z-score consists of  five key financial 
ratios that capture a company’s profitability, liquidity, 
leverage, and efficiency:

• Liquidity A1 is assessed by working capital as a 
proportion of  total assets.

• Cumulative profitability A2 is measured by the ratio 
of  retained earnings to total assets 

• Operating efficiency A3 is computed by the ratio of  
EBIT to total assets.

• Leverage A4 is calculated by the ratio of  market value 
of  equity to total liabilities 

• Asset turnover A5 is determined by the ratio of  sales 
to total assets 
The model assigns a Z-score based on a weighted sum of  
these ratios where  Z > 2.99 indicates financial stability 
(Safe Zone). Scores between 1.81 and 2.99 suggest 
potential distress (Grey Zone). A Z < 1.81 signals a high 
risk of  bankruptcy (Distress Zone). The formula of  
Z-score is expressed in Equation (1).
Z=1.2A1+1.4A2+3.3A3+0.6A4+1.0A5              (1)
In 1995, the third variation of  the model, Z’’-Score, was 
developed to account for the economic conditions in 
emerging markets, where companies often experience higher 
financial volatility. The model excludes the Sales/Total Assets 
(X5) ratio, as emerging market firms often operate in diverse 
economic conditions with varying revenue structures. The 
formula is described in Equation (2).
Z’’=6.56A1+3.26A2+6.72A3+1.05A4              (2)
The classification thresholds are Z” score > 5.85 indicates 
financial stability (Safe Zone). Scores between 4.15 and 
5.85 suggest uncertainty (Grey Zone). A Z’’ score < 4.15 
signals high distress risk (Distress Zone).
Meanwhile, the Emerging Market Scoring (EMS) model 
(Altman, 2005) is founded on a fundamental financial 
analysis derived from a qualitative risk assessment 
framework. It functions as a refined rating system for 
evaluating specific credit risks. By building upon previous 
financial distress models, the EMS model integrates the 
strengths of  established approaches, such as the Z-score, 
Z’-score, and Z’’-score, while addressing their limitations. 
Notably, it incorporates an adjustment of  +3.25 to better 
suit emerging markets, including developing economies 
like Vietnam. This enhancement enables a more precise 
and comprehensive evaluation of  financial health in 
dynamic and volatile market conditions. The EMS 
model evaluates a company’s financial stability using the 
following Equation (3).
EMS=6.56A1+3.26A+6.72A3+1.05A’4+3.25                (3)
where A’4 is Book value of  equity to total liabilities, 
indicating financial leverage and solvency.
The EMS score categorizes companies into three risk 
levels:

• Safe Zone (EMS>5.85) – Firms in this category are 
financially stable, with minimal bankruptcy risk.

• Warning Zone (4.15≤EMS≤5.85) – These firms face 
financial uncertainty, with potential distress risks.

• Distress Zone (EMS<4.15) – Companies in this range 
have a high probability of  default or bankruptcy.
Unlike the accounting-based approach such as Altman 
Z-score or Emerging Market score (EMS), Distance to 
Default (DD) method is a structural credit risk model 
based on Merton’s model (1974) (Merton, 1974). It 
measures how close a company is to default based on 
the value of  its assets relative to its liabilities. The idea 
is that a company defaults when its asset value falls 
below its liabilities. However, the original model assumed 
constant debt which lacks empirical support. To address 
this limitation, the DD model was refined to incorporate 
default risk more effectively by including factors such 
as firm leverage ratio and equity volatility. By adopting 
a constant leverage ratio, the modified version of  the 
DD model is considered more realistic and dynamic 
compared to the traditional Merton model. As a result, the 
updated DD model provides a more accurate estimation 
of  default probability across various types of  firms. 
Notably, this enhanced version is particularly effective 
in measuring financial distress in emerging and volatile 
markets (Byström, 2006; Pham Vo Ninh et al., 2018).
The updated Distance-to-Default (DD) model is rebuilt 
using the Merton model as a basis (Pham Vo Ninh et 
al., 2018), where leverage ratio L=D/((E+D)) (E is the 
market value of  ewuity and D is the book value of  debt) 
and σE is represented for the volatility of  the firm’s equity. 
The formula of  DD score is illustrated in Equation (4).

A robust correlation is evident between the Distance-
to-Default (DD) metric and the likelihood of  default, 
commonly referred to as the Expected Default Frequency 
(EDF). The EDF is derived from the DD value through 
the application of  the cumulative normal distribution. 
Subsequently, this EDF outcome is aligned with the credit 
ratings provided by Standard & Poor’s (S&P). S&P rating 
categories divide companies into twenty-two different 
tiers, ranging from 0% to 20%, within the KMV-EDF 
paradigm. Through bivariate analysis, these classifications 
are segmented into three categories: Safe Zone (0.00 
– 0.52%], Grey Zone (0.52 – 6.94%], and Distress 
Zone (6.94 – 20.00%]. This categorization adheres to 
the guidelines established by Lopez (2004) in 2004. 
Consequently, the revised DD model is conceived to be 
more straightforward and structurally analogous to its 
predecessor. Nonetheless, it provides enhanced precision 
compared to the earlier iteration and is more appropriately 
tailored for emerging economies such as Vietnam.

Dataset Pre-processing

Table 2: Investigated Financial Ratios
No Financial Ratios No Financial Ratios
1 Trailing EPS 35 Number of  days of  payables



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In this study , the set of  data was collected from 
Vietstock.vn and comprises 318 observations, with 68 
independent variables as shown in Table 2 and one target 
variable, which was classified into three categories -safety, 
risky, and distress- based on three financial distress 

measurement methods: Distance to Default (DD), EMS, 
and Altman Z-score. However, as illustrated in Figure 2, 
the dataset exhibits a significant class imbalance:

• Distance to Default (DD) method: 13 distressed 
cases, 73 risky cases, 232 safety cases

• EMS method: 42 distressed cases, 86 risky cases, 190 
safety cases

• Z-score method: 74 distressed cases, 84 risky cases, 
160 safety cases
As illustrated in Figure 2, the number of  companies 
classified as distressed using the Z-score method is nearly 
twice that of  the EMS method and six times higher than 
the DD method. Meanwhile, the number of  companies 
categorized as risky remains relatively consistent across 
the three methods. However, the classification of  safety 
status varies significantly - the DD method identifies 
the highest number of  safe companies, while the count 

2 Book value per share (BVPS) 36 Fixed asset turnover
3 P/E 37 Total asset turnover
4 P/B 38 Equity turnover
5 P/S 39 Short-term liabilities to total liabilities
6 Dividend yield 40 Debt to assets
7 Beta 41 Liabilities to assets
8 EV/EBIT 42 Equity to assets
9 Gross profit margin 43 Short-term liabilities to equity
10 EBIT margin 44 Debt to equity
11 EBITDA/Net revenue 45 Liabilities to equity
12 Net profit margin 46 Accrual ratio CF
13 ROE 47 Cash to income
14 Return on capital employed (ROCE) 48 Net cash flows/Short -term liabilities
15 ROA 49 Cash return to assets
16 ROE Trailling 50 Cash return on equity
17 ROA Trailling 51 Cash to income
18 Net revenue 52 Debt coverage
19 Gross profit 53 Cash flow per share (CPS)
20 Profit before tax 54 Cost of  goods sold/Net revenue
21 Profit after tax for shareholders of  the parent company 55 Selling expenses/Net revenue
22 Total assets 56 General and Administrative expenses/Net revenue
23 Long-term liabilities 57 Interest expenses/Net revenue
24 Liabilities 58 Short-term assets/Total assets
25 Owner's equity 59 Cash/Short-term assets
26 Cash ratio 60 Short-term investments/Short-term assets
27 Quick ratio 61 Short-term receivables/Short-term assets
28 Short-term ratio 62 Inventory/Short-term assets
29 Interest coverage 63 Other Short-term assets/Short-term assets
30 Receivables turnover 64 Long-term assets/Total assets
31 Days of  sales outstanding 65 Fixed assets/Total assets
32 Inventory turnover 66 Tangible fixed assets/Fixed assets
33 Days of  inventory on hand 67 Intangible fixed assets/Fixed assets
34 Payables turnover 68 Construction in progress/Fixed assets

Figure 2: Dataset Description



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from ADASYN in handling imbalanced data. ADASYN 
is predicated on the principle of  adaptively producing 
synthetic data instances for minority classes in accordance 
with their respective distributions: a greater volume of  
synthetic data is generated for minority class instances 
that present greater challenges for learning in comparison 
to those minority instances that are less complex to learn. 
The ADASYN technique possesses the capacity to not 
only mitigate the learning bias engendered by the initial 
imbalanced data distribution, but it also has the ability to 
dynamically adjust the decision boundary to concentrate 
on those instances that are particularly challenging to 
learn (Haibo et al., 2008).

Classification Models
To compare the suitability of  Altman Z-score, EMS and 
DD method in evaluating the listed companies in Vietnam, 
we employed four widely used machine learning models: 
Random Forest, XGBoost, CatBoost and Support Vector 
Machine models.
The Support Vector Machine (SVM) represents a 
formidable supervised learning algorithm introduced in 
1995 by Cortes and Vapnik (1995), which is extensively 
employed for both classification and regression tasks. Its 
efficacy is particularly pronounced in high-dimensional 
spaces, and it is distinguished by its capability to manage 
intricate decision boundaries. Support Vector Machines 
(SVM) function by pinpointing an optimal hyperplane 
that maximizes the margin between divergent classes. This 
hyperplane acts as a decision boundary that delineates 
data points associated with separate categories. The data 
points that are in closest proximity to the hyperplane, 
termed support vectors, are pivotal in establishing the 
optimal decision boundary. SVM can utilize a variety 
of  kernel functions to transform data into elevated-
dimensional spaces, facilitating the separation of  data that 
is not linearly separable. Furthermore, regularization and 
slack variables are integral in addressing noisy data. The 
C parameter regulates the balance between maximizing 
the margin and minimizing misclassification, while slack 
variables permit SVM to accommodate some degree of  
misclassification, thereby enhancing its resilience to noise. 
A higher C value prioritizes correct classification over 
a large margin, while a lower C allows a larger margin 
but tolerates some misclassification. This combination 
enhances SVM’s ability to generalize, particularly in real-
world datasets where achieving perfect separation is 
challenging.
Random Forest model is introduced by Breiman (2001) 
in 2001. It is a widely used ensemble learning algorithm 
for both classification and regression tasks, as it can 
handle both continuous and categorical datasets. This 
study employs a Random Forest Classifier (RFC), 
which consists of  multiple decision trees that operate 
collectively. A bootstrap sample of  the dataset is used to 
train each tree in the forest, and features are randomly 
selected at each split to ensure diversity among trees. A 
classification tree serves the purpose of  forecasting a 

decreases when using the EMS method and further 
declines under the Z-score method. This discrepancy 
highlights the differing sensitivity of  each method in 
assessing financial distress. In this study, approximately 
53% of  the firms investigated are non-manufacturing 
companies, which significantly influences the results of  
different financial distress measurement methods. The 
DD method relies on stock prices to estimate distress, 
making it more sensitive to market fluctuations and 
investor sentiment. Non-manufacturing firms often have 
more stable stock prices because they are less exposed 
to factors like raw material costs, production delays, 
and supply chain disruptions. Since DD uses stock 
price volatility in its calculations, lower volatility leads to 
fewer distress classifications, explaining why the number 
of  distressed firms is significantly lower under the DD 
method compared to Z-score and EMS. The imbalances 
indicate that the dataset is skewed toward the majority 
class, which can lead to biased model predictions. To 
address this issue, the Adaptive Synthetic Sampling 
(ADASYN) is applied to balance the dataset, which 
guarantees a better representative distribution of  all 
classes for machine learning models. This step is crucial 
to improve model performance and ensuring more 
reliable financial distress predictions.
Most machine learning algorithms perform optimally 
when class distributions are relatively balanced; however, 
imbalanced data often lead to the majority class 
dominating the learning process, resulting in biased 
models and unreliable predictions. Adaptive Synthetic 
Sampling (ADASYN) is an oversampling technique used 
to handle imbalanced datasets especially in classification 
problems where certain classes are underrepresented. It 
improves upon the Synthetic Minority Over-sampling 
Technique (SMOTE) by focusing more on generating 
synthetic samples for harder-to-classify minority class 
instances. SMOTE is another oversampling method 
that generates synthetic samples for the minority class 
to address class imbalance in classification problems. 
It achieves this by selecting samples from the minority 
class, identifying their nearest neighbors, and generating 
new samples that interpolate between these points in 
the feature space (Hanafy & Ming, 2021). SMOTE 
interpolates instances of  the minority class to create 
synthetic samples rather than duplicating them; therefore, 
it does not change the expected value of  the minority 
class but decreases its variability. Similarity to strategy 
of  SMOTE, ADASYN (Adaptive Synthetic Sampling) 
addresses class imbalance by generating synthetic 
samples, focusing more on difficult-to-classify minority 
instances. It first identifies the imbalance, determines 
the number of  new samples needed, and assigns higher 
weights to minority instances that are harder to classify. 
Using nearest neighbors, ADASYN generates new data 
points in these challenging regions, ensuring a more 
balanced and representative dataset that improves model 
performance while reducing bias. To adopt ADASYN 
over SMOTE, we consider the outstanding attributes 



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categorical response as opposed to a numerical one. A 
classification tree asserts that each instance is allocated 
to the predominant category of  training instances within 
its respective domain. A majority voting mechanism is 
implemented for the classification process. Compared to 
a single decision tree, RFC offers a significant advantage: 
rather of  depending on a single model, it works as a group 
of  professionals, where each tree contributes to the final 
decision. This collective approach enhances the model’s 
accuracy, robustness, and generalization ability.
Extreme Gradient Boosting (XGBoost) constitutes a 
potent machine learning algorithm that builds upon the 
foundational concepts of  gradient boosting, which was 
advanced in 2016 by Chen and Guestrin (Chen & Guestrin, 
2016). Analogous to gradient boosting, XGBoost 
amalgamates the predictive capabilities of  multiple 
learners into a singular model through an iterative process. 
XGBoost adheres to the principle of  boosting, wherein 
weak learners (specifically decision trees) are sequentially 
trained to diminish the residual errors generated by 
preceding trees. It has gained widespread popularity due 
to its efficiency, scalability, and superior performance in 
classification and regression tasks. XGBoost is designed 
to handle missing data, feature sparsity, and large 
datasets while maintaining high predictive accuracy. The 
primary reasons for its effectiveness include advanced 
regularization techniques, parallelization, and efficient 
handling of  missing values. XGBoost incorporates 
L1 (Lasso) and L2 (Ridge) regularization to prevent 
overfitting, making it more robust than traditional 
gradient boosting models. Additionally, while gradient 
boosting minimizes overall model error by optimizing the 
loss function of  its base models, XGBoost enhances this 
process by incorporating both first- and second-order 
partial derivative approximations, known as the gradient 
and Hessian. This approach provides more precise 
information about the gradient direction, leading to faster 
convergence and more efficient loss minimization.
CatBoost (Categorical Boosting) is a gradient boosting 
algorithm developed in 2018 by Yandex, a Russian 
multinational technology company, that is optimized 
for both categorical and numerical datasets (Dorogush 

et al., 2018; Dorogush et al., 2017). CatBoost enhances 
gradient boosting through ordered boosting, which 
reduces prediction shift by using properly permuted 
datasets to prevent target leakage, leading to more 
stable predictions. It also employs symmetric trees, 
ensuring balanced splits at each depth, which improves 
training efficiency and reduces overfitting compared 
to traditional asymmetric trees. Additionally, CatBoost 
efficiently handles pure numerical features without 
requiring extensive normalization or scaling, allowing it 
to learn directly from raw data. Its optimized handling 
of  categorical features eliminates the need for one-hot 
encoding or complex preprocessing, making it a powerful 
choice for datasets with mixed data types. According to 
Kaggle’s “State of  Data Science and Machine Learning” 
surveys, CatBoost has been recognized among the most 
frequently used machine learning frameworks globally. In 
the 2020 survey, it ranked within the top 8, and in the 
2021 survey, it moved up to the top 7 (Mooney, 2020). 
While CatBoost is popular, other frameworks like Scikit-
learn, TensorFlow, and Keras have higher usage rates 
among data scientists. For instance, in the 2021 survey, 
over 80% of  respondents reported using Scikit-learn, 
making it the most widely adopted framework (Đạt, 
2021). This highlights CatBoost’s significant role in the 
machine learning community.
To tune the hyperparameter for these models, 
Randomized Search Cross-Validation technique is applied 
to find the appropriate hyperparameter. Randomized 
Search Cross-Validation represents a sophisticated 
optimization methodology employed for the fine-tuning 
of  hyperparameters within machine learning frameworks. 
This technique involves the stochastic selection of  
hyperparameter configurations from a specified 
distribution and subsequently assesses the efficacy of  
the model through the application of  cross-validation. 
Unlike Grid Search, which exhaustively searches all 
possible parameter combinations, Random Search selects 
a limited number of  random combinations, making it 
more computationally efficient while still providing good 
results (Alibrahim & Ludwig, 2021). The search spaces 
for predictive models are presented in Table 3.

Table 3: Hyperparameter Search Spaces for Predictive Models
Hyperparameters Search Spaces Hyperparameters Search Spaces
XGBoost Random Forest
Learning rate (0.01, 0.3) n_estimators 50, 500
n_estimators (100, 500) Max depth (3, 20)
Max depth (3, 15) Min samples split (2, 10)
Min child weight (1, 6) Min samples leaf (1, 5)
gamma (0, 1) Max features sqrt, log2
subsample (0.5, 0.5) bootstrap True, False
Colsample bytree (0.5, 0.5)
Reg alpha (0.0001, 10 ) (logarithmic scale)
Reg lambda (0.0001, 10 ) (logarithmic scale)



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Performance Evaluation
This segment delineates the evaluative metrics employed 
to gauge the efficacy of  each model, specifically Precision, 
Recall, Balanced Accuracy, F1 Score, and Specificity. 
Their corresponding formulations are articulated in 
Equations (5)-(9). An elevation in these metrics signifies 
an enhancement in model performance. True Positive 
(TP) denotes the scenario in which the model accurately 
discerns a positive class, while a False Positive (FP) 
transpires when the model mistakenly classifies a negative 
instance as positive. In a similar vein, True Negative (TN) 
signifies that the model correctly identifies a negative 
class, whereas False Negative (FN) emerges when the 
model neglects to acknowledge a positive instance. The 
term β2 denotes a variable incorporated within the F-beta 
score, which represents a weighted harmonic mean that 
integrates both Precision and Recall.

For multiclass classification problems, the one-vs-all 
methodology is applied to calculate individual ROC-AUC 
scores for each class, treating each class as the positive 
class while combining all other classes as negative. The 
final weighted ROC-AUC score is then computed, 
which provides a more comprehensive evaluation of  
the model’s overall performance across all classes. A 
model with a ROC-AUC scores close to 1 is considered 
optimal, demonstrating strong classification power. We 
employed this one-vs-all approach to obtain the weighted 
ROC-AUC score for our models, ensuring a robust 
and accurate assessment of  performance. The detailed 
performance analysis of  each method will be presented 
in the following section.

RESULTS AND DISCUSSION
The performance of  four machine learning models—
XGBoost, CatBoost, SVM, and Random Forest - using 
different target variables from DD score, Altman 
Z-score, and EMS score is presented in Table 4, Table 5 
and Table 6, respectively. The best-performing machine 
learning model in this study was Random Forest, 
followed by XGBoost, then CatBoost, with SVM ranking 
last. The underperformance of  SVM can be attributed to 
several factors. Unlike decision tree-based models, which 
inherently perform feature selection by identifying the 
most relevant variables during training, SVM lacks built-
in feature selection. Furthermore, financial ratios often 
exhibit complex, non-linear relationships with financial 
distress, making tree-based models more effective at 
capturing these interactions compared to SVM. The 
best performance in predicting financial distress for 
Vietnamese companies is achieved when using the EMS 
score as the target variable, with a ROC-AUC value of  
99.26%. The Z-score follows in ranking, while the DD 
score performs the worst in predicting financial distress. 
In Vietnam, stock markets may be less developed, with 
lower liquidity, information asymmetry, and possible 
government influence on firms, leading to less reliable 
market-based distress signals, reducing the effectiveness 
of  the DD score. Although the results section does not 
provide a detailed comparison between the SMOTE 
and ADASYN techniques, our experiments during the 
research showed that ADASYN outperforms SMOTE 
in improving the accuracy of  predictive models. The 
specicific results of  the findings are described in the 
following parts.

CatBoost SVM
iterations (100, 1000) C (0.1, 10)
depth (3, 12) kernel linear, poly, rbf, sigmoid
Learning rate (0.01, 0.3) gamma scale, auto
l2 leaf  reg (1, 10)
Border count (32, 255)
Random strength (1, 10)
Bagging temperature (0, 1)

Besides, we utilized confusion matrix to plot the detail 
results for the best methods to see clearly the values in 
each part of  confusion matrix. Confusion Matrix with 
(n*n) table (n is the number of  classes) is created to 
measure the performance of  our classification models. 
In multiclass classification, the positive class refers to the 
specific label being evaluated, while the negative class 
encompasses all remaining labels. 
The model’s capacity to differentiate between classes is 
gauged by the Receiver Operating Characteristic-Area 
Under the Curve (ROC-AUC). The ROC-AUC score 
ranges from 0 to 1, where a score closer to 1 indicates a 
highly effective model in distinguishing between classes. 



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Table 4 presents XGBoost and Random Forest are the 
best-performing models, with the highest accuracy 
(76.04%) and balanced precision, recall, and F1-scores. 

CatBoost is slightly worse but still performs well, especially 
for class 2. SVM is the weakest model, performing poorly 
for class 1 and class 2, with the lowest accuracy (57.29%).

Table 4: Prediction accuracy of each model when utilizing DD score
DD SCORE XGBOOST RANDOM FOREST CATBOOST SVM 
Accuracy 0.7604 0.7604 0.7396 0.5729
Precision (WEIGHTED) 0.7257 0.7189 0.6837 0.6016
F1 Score (WEIGHTED) 0.7279 0.7238 0.6901 0.5849
Recall (WEIGHTED) 0.7604 0.7604 0.7396 0.5729
Specificity (CLASS 0, 1, 2) [0.9783,  

0.9296,  
0.4483]

[0.9891, 
0.9296, 
0.4138]

[0.9783, 
0.9296,
0.4483]

[0.8913, 
0.7887, 
0.4483]

ROC-AUC Score (WEIGHTED) 0.8437 0.8594 0.8333 0.7188

Table 5: Prediction accuracy of each model when utilizing Z- score
Z-SCORE XGBOOST RANDOM FOREST CATBOOST SVM 
Accuracy 0.7812 0.7812 0.7812 0.6562
Precision (WEIGHTED) 0.7964 0.7971 0.7961 0.7282
F1 Score (WEIGHTED) 0.7821 0.7633 0.7739 0.6704
Recall (WEIGHTED) 0.7812 0.7812 0.7812 0.6562
Specificity (CLASS 0, 1, 2) [0.8590, 

0.9014, 
0.9302]

[0.8462, 
0.95772, 
0.8605]

[0.8590, 
0.9014, 
0.9302]

[0.7564, 
0.8451, 
0.9302]

ROC-AUC Score (WEIGHTED) 0.9028 0.8989 0.8912 0.6737

Table 5 describes XGBoost performing the best, achieving 
the highest F1-score (0.7821) and ROC-AUC (0.9028). 
Random Forest & CatBoost are close contenders, with 
Random Forest showing the highest precision (0.7971) 

and specificity for Class 1 (0.9577). SVM performs the 
worst, with significantly lower accuracy (65.62%), recall, 
and ROC-AUC (0.6737), indicating poor predictive 
power.

Table 6: Prediction accuracy of each model when utilizing EMS score
EMS SCORE XGBOOST RANDOM FOREST CATBOOST SVM 
Accuracy 0.9271 0.9375 0.9167 0.8333
Precision (WEIGHTED) 0.9276 0.9407 0.9162 0.8438
F1 Score (WEIGHTED) 0.9270 0.9376 0.9158 0.8354
Recall (WEIGHTED) 0.9271 0.9375 0.9167 0.8333
Specificity (CLASS 0, 1, 2) [0.9884, 

0.9444, 
0.9412]

[1.0, 
0.9444, 
0.9412]

[0.9884, 
0.94444, 
0.9412]

[0.9186, 
0.9306, 
0.8824]

ROC-AUC Score (WEIGHTED) 0.9881 0.9926 0.9908 0.9055

Table 6 points out that the highest accuracy is achieved 
by Random Forest (0.9375), followed closely by XGBoost 
(0.9271) and CatBoost (0.9167) when using Altman 
Z-score as target classification. The SVM model performs 
the worst (0.8333). SVM performs the worst across all 
metrics, making more false positives and false negatives, 
and having the lowest ROC-AUC score. Since Random 
Forest (0.9926), CatBoost (0.9908), and XGBoost (0.9881) 
are close to 1 in ROC-AUC score, all three models are 
highly effective in distinguishing different classes. 

To illustrate the detailed performance of  the best 
method, we plot confusion matrix as  Figure 3 . It 
shows that all models performed well in Class 2, with 
60 correctly classified instances. CatBoost performed 
slightly better in Class 0 but worse in Class 1 compared 
to XGBoost and RF. Random Forest had the best balance 
for Class 1, with the highest correct classifications (22). 
XGBoost and RF had similar performance, but RF had 
fewer misclassifications in Class 1. SVM has the worst 
performance among investigated models.



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Random Forest excels in handling multiple classes 
effectively by constructing multiple decision trees and 
aggregating their predictions. This approach makes the 
multiclass decision-making process straightforward and 
efficient. One of  the key strengths of  Random Forest is 
its resilience to noisy data. Compared to other models 
like XGBoost, CatBoost, and SVM, Random Forest is 
better equipped to manage noisy datasets. By combining 
the results of  several trees, it minimizes the impact of  
individual errors or outliers, leading to a more stable and 
reliable model, particularly in real-world scenarios such 
as financial data, where noise and outliers are common. 
The best performance with 99.26% at ROC-AUC value 
achieves from models using EMS score as the target 
variable to predict. This implies that the Vietnamese 
stock market can be explained by EMS score. The 
Emerging Market Score is a composite measure that 
accounts for various economic and financial factors 
specific to emerging markets. In the case of  Vietnam, 
which is considered an emerging market, the EMS score 
captures key macroeconomic indicators, financial health, 

and market performance indicators that are critical in 
understanding the stability and risk of  companies within 
that market. Given the unique economic conditions, 
volatility, and risks associated with emerging markets like 
Vietnam, the EMS score serves as an effective predictor 
of  financial distress and stability for companies listed in 
such markets. By using this score as the target variable 
in the model, the machine learning algorithms can 
leverage the specific characteristics and risk factors of  the 
Vietnamese market, leading to higher prediction accuracy. 
The ROC-AUC score of  99.26% indicates that the EMS 
score is highly informative and predictive in this context.

CONCLUSION 
In this study, we aimed to predict the financial health 
of  listed companies in Vietnam using a combination of  
financial ratios and advanced machine learning techniques. 
By leveraging methods such as Distance to Default (DD), 
Emerging Market Score (EMS), and Altman Z-score, 
we developed models capable of  assessing the financial 
status of  companies. Among the machine learning 

Figure 3: Confusion Matrix of predictive model performance when using EMS scores



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algorithms tested, Random Forest demonstrated superior 
performance, achieving an impressive ROC-AUC score of  
99.26%. The EMS score, when combined with machine 
learning models, proved to be an effective predictor of  
financial distress, outperforming traditional methods and 
highlighting the potential for applying machine learning 
in the context of  financial health prediction.
Our study also emphasized the importance of  data 
balancing techniques, specifically the Adaptive Synthetic 
Sampling (ADASYN) method, which significantly 
enhanced the performance of  the models in predicting 
financial distress. This underscores the critical role of  data 
preprocessing in improving model accuracy, especially in 
cases involving imbalanced datasets common in financial 
markets.
While Random Forest excelled in this study due to 
its resistance to noise and ability to handle multi-
class classification, XGBoost and CatBoost remain 
powerful alternatives. The integration of  the EMS score 
demonstrated its value in financial distress prediction, 
especially in emerging markets like Vietnam. Future 
research could explore expanding these models to other 
markets or incorporating additional economic indicators 
to improve predictions. This study provides valuable 
insights for financial analysts and policymakers in 
assessing corporate health with higher accuracy. 

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