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1 

 

FINANCE & BANKING REVIEW 
  ASFBR VOL 9 NO 1 (2025) P-ISSN 2576-1161   E-ISSN 2576-1188 

Journal homepage: https://www.cribfb.com/journal/index.php/asfbr 

Published by Asian Finance & Banking Society, USA 

THE ACCURACY ANALYSIS OF FINANCIAL DISTRESS MODEL 

A BENCHMARK OF OPERATIONAL PERFORMANCE AND 

FIRMS' INVESTMENT                 

 
 Syamsu Alang (a)1    

 

(a) Senior Lecturer, School of Economics, Bhakti Prasetya Karya Praja, Jakarta, Indonesia; E-mail: syamsualang@yahoo.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 4th March 2024 

Reviewed & Revised: 5th March  

to 4th August 2025 
Accepted: 4th August 2025 

Published: 5th August 2025 

 
Keywords: 

Financial Distress, the Zmijewski 

Prediction Model 

 
      JEL Classification Codes:  

 
      C52, G01, G32, L90, N25 

       

      Peer-Review Model:  

 

      External peer-review was done through  

      double-blind method. 

 

 
A B S T R A C T 

 
 

This study aims to find out the difference in the level of model accuracy among the Modified Altman 
Prediction (Z-Score), Springate (S-Score), and Zmijewski prediction models in predicting financial 

distress as a model of predicting operational Management and investment performance benchmarks in 

Transportation sub-sector firms in the Indonesian Stock Exchange (IDX) for the 4-period time. This study 

is a quantitative descriptive approach. The sampling technique is purposive sampling. This study utilizes 

sample data from the IDX, specifically www.idx.co.id, as well as the official websites of each firm. The 

results demonstrate that the Modification Altman Z-Score model can predict financial distress or 
potential bankruptcy by correctly assigning as many as 26 out of 48 samples, achieving an accuracy rate 

of 54.17%. The Springate S-Score model can predict financial distress or potential bankruptcy by 

assigning as many as 24 samples from 48 samples with an accuracy rate of 50%. The Zmijewski model 

was able to predict financial distress or potential bankruptcy with the highest accuracy level among the 

models used in this study, achieving an accuracy rate of 70.83% on 34 out of 48 samples. The conclusion 

from the three model bankruptcies is that the Zmijewski model is the most suitable for firms to use if they 

want to attract potential investors. It is used to predict financial distress and operational performance, 

as well as to inform firms' investment decisions. The findings of this study suggest that additional 
financial distress prediction models, such as Ohlson, Grover, and others, can be utilized to compare and 

contrast the yields of financial distress analysis. 

 
 

© 2025 by the authors. Licensee Asian Finance & Banking Society, USA. This article is an open-
access article distributed under the terms and conditions of the Creative Commons Attribution (CC 

BY) license (http://creativecommons.org/licenses/by/4.0/). 

                                                                  

 

INTRODUCTION 

Business potential increasingly attracts investment, especially in the development of the digital industry era 4.0, which is 

currently experiencing rapid growth (Botti et al., 2021). This map illustrates intense business competition among firms 

worldwide, particularly in Indonesia. All efforts are made to be the best. Effective management strategies and processes are 

key to success in today's industry, encompassing human resource management, marketing management, production 

management, and financial Management (APUS, 2024). Strategy and Management of operations and operational sectors 

are crucial management functions for an organization or firm. Operational Management is developing very rapidly. This is 

due to the emergence of many innovations in technology that are applied in business practices; therefore, firms in the world 

are starting to look at and try to make aspects of operational Management and operations a strategic tool to compete and 

outperform their competitors (Füller, Hutter, Wahl, Bilgram, & Tekic, 2022). 

In line with the firm's efforts to sharpen the service quality, especially in responding to the needs of effective 

operational Management of the firm in implementing programs and efficient in Management and productive in achievement, 

various potentials will be mobilized using limited resources owned by optimizing working capital and investment in capital 

goods according to the business competency entered (ICAI, 2021), considering that the success of the firm's operations will 

be tested by the final achievement of each financial report closing period. Transportation service firms are one type of 

business that is encouraged to continue investing in various promising areas, both domestically and internationally, including 

in Indonesia. 

In the context of sustainability, the transportation sector is the business sector that experienced the deepest 

contraction out of 17 business sectors recorded by the Biro Pusat Statistik. It was recorded that, during the period from April 

to July 2020, the transportation and warehousing sector experienced a 30.84 percent decrease, and the growth rate of the 

                                                      
1Corresponding author: ORCID ID: 0009-0008-8974-1660 

© 2025 by the authors. Hosting by Asian Finance & Banking Society. Peer review under responsibility of Asian Finance & Banking Society, USA.  

https://doi.org/10.46281/asfbr.v9i1.2647 
 

To cite this article: Alang, S. (2025). THE ACCURACY ANALYSIS OF FINANCIAL DISTRESS MODEL A BENCHMARK OF OPERATIONAL 

PERFORMANCE AND FIRMS’ INVESTMENT. Asian Finance & Banking Review, 9(1), 1-8. https://doi.org/10.46281/asfbr.v9i1.2647 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/asfbr.v9i1.2647
https://orcid.org/0009-0008-8974-1660


Alang, Asian Finance & Banking Review 9(1) (2025), 1-8

  

2 
 

transportation sector is expected to remain slow until the end of this year (Ramli & Jatmiko, 2020). Furthermore, the 

transportation, warehousing, and logistics sectors in 2022 experienced the highest growth, reaching 19.87%. Especially after 

the COVID-19 pandemic, the transportation and logistics sector has been one of the businesses experiencing high growth 

in recent years (Supply Chain Indonesia, 2024). 

Among these potentials, it encourages every firm to create various innovations to attract the attention of consumers, 

which will ultimately increase the business profits. In the situation and conditions of the last few years because of implicated 

the Covid-19 Pandemic, the investment atmosphere felt by various firms is not the same including some enterprises that 

have decided to withdraw from the transportation sector competition considering that the firm's operational capabilities are 

not able to move up significantly amidst business competition, even quite a few of them have started to predict their financial 

condition by predicting bankruptcy (financial distress) as a method to ensure business capacity and optimize the business 

operational achievements in the future which of course is expected to attract investors to invest in this transportation sector. 

The calculation for the financial distress model analysis is classified into several models commonly used in previous 

research. For instance, Robiansyah et al. (2022) found that the Altman model is the most effective for predicting bankruptcy 

in manufacturing companies, outperforming the Springate, Zmijewski, and Grover models. In contrast, Meilawati et al. 

(2023) found that the Zmijewski model is the most suitable choice for predicting bankruptcy in the hotel, restaurant, and 

tourism subsectors, when compared to the Springate and Zmijewski models. For this reason, this study aims to find out the 

differences in the level of model accuracy among the Modified Altman Prediction (Z-Score), Springate (S-Score), and 

Zmijewski prediction models in predicting financial distress as a model of predicting operational Management and 

investment performance benchmarks in Transportation sub-sector firms in the Indonesian Stock Exchange (IDX). 

 

LITERATURE REVIEW 

Operational Management Concept 

Operational Management is needed in a business to control operational activities. A business requires supervision of several 

key elements that support its activities, including finance, marketing, and production, which are integral to operational 

activities (Porter, 2011). Broadly speaking, operational Management is a strategy used in the arrangement and administration 

of business operations practices to create maximum efficiency in a corporation (Kothari, 2025). Handoko (2019, hal. 3) 

argues that operations production management works to optimally carry out the utilization of resources (called production 

factors), such as raw materials, machines, labor, equipment, and so on, to transform materials and labor into diverse products 

or services. Initially, it involves the utilization of resources and then includes the processing phase til it creates a product, 

either of finished goods or services. To ensure that nothing hinders the operational process, a manager must be supervised 

and responsible for that division. A framework is needed that can categorize and formulate decisions in various operations. 

 

Financial Concepts and Bankruptcy Analysis (Financial Distress) 

Myers and Pogue (1974) stated that Management must ensure that corporate finances are used in accordance with the plan 

and are consistent with business goals. The most essential of the supply elements in operational Management is finance. 

Then, used to produce quality goods and services (Akintoye, Hardcastle, Beck, Chinyio, & Asenova, 2003). Appropriate 

finances facilitate the process of creating optimal products and services. Financial management arrangements, especially 

cash management, are crucial in the context of inventory turnover; however, they do not eliminate the risk of running out 

of inventory, which can result in future losses for the firm (Nasution, 2023). In addition, with good financial Management, 

the firm's operations and investments will align with the planning that has been set, both in the short and long term. 

The operational movement of firm investments can be measured and detected through an appropriate and accurate 

financial analysis model that the firm can use, including bankruptcy analysis (Financial Distress). According to Platt and 

Platt (2002), financial distress is defined as a condition of decline in a firm's financial position before bankruptcy or 

liquidation. Lau (1987) revealed that the deterioration in financial conditions stems from the composition of the balance 

sheet, specifically when the total assets are smaller than the amount of the firm's debt, rendering the business unable to pay 

its obligations (Mentariningrum & Prasetiono, 2022). Elloumi and Gueyié (2001) stated that a firm's condition of financial 

distress is characterized by two consecutive years of negative Earnings Per Share (EPS). Therefore, firms must prevent or 

minimize the event of financial distress by reviewing financial reports and conducting a financial distress analysis. 

Financial distress indicators that firm Management (internal parties) must pay attention to are related to the 

efficiency and effectiveness of its operations, as stated by Hartanto (1984), are: (1) A decrease in sales volume because of 

consumer moves, demands, or tastes. (2) Production costs are worked up. (3) Competition in the business climate rises. (4) 

Business expansion of a failed. (5) The collecting receivables function is ineffective. (6) Lack of contributions or funding 

facilities (credit). (7) Dependence on corporate receivables is high. The indicators that external parties must pay attention 

to include: (1) a decrease in dividends distributed to shareholders. (2) Losses corporation, and a continuous decline in the 

point of lower. (3) One or more business units which has stopped their operational activities. (4) Layoffs. (5) Resignation 

of top executives. (6) Decrease in the capital market of stock prices (Teng, 2002). 

 

Financial Distress Analysis Model 

Elements to express bankruptcy prediction analysis can use a model. This model must ensure that the construction meets 

the structure, form, and content, and has meaning, with parameters limited to specific measures in decision-making (Husain, 

2019). Several financial distress analysis models that are pretty popular and used in this study are: 

 Altman Z-Score; i.e., with the name ‘Altman Model Analysis’ (1968); calculated with the equation formula adhered: 

𝑍 =  1.2𝑋1  +  1.4𝑋2  +  3.3𝑋3  +  0.6𝑋4  +  1.0𝑋5  ... (1) 



Alang, Asian Finance & Banking Review 9(1) (2025), 1-8

  

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Explanation: 

𝑋1  is Working Capital divided by Total Assets 

𝑋2 is Retained Earnings (RE) divided by Total Assets 

𝑋3  is Earnings before Interest and Taxes (EBIT) divided by Total Assets 

𝑋4 is the Book Value of Equity divided by the Book Value of Total Debt 

𝑋5 = Sales to Total Assets (TA) 

The assumptions: (i) If the Z index score is < 1.81, then the firm is experiencing financial distress; (ii) if the Z index 

score is > 2.99, then the firm is not experiencing financial distress; (iii). If the index score Z is between 1.81 and 2.99, 

then it falls within the grey area (the firm is experiencing minor financial problems and has a probability of experiencing 

financial distress); and (iv) the cut-off value for this index is 2.675. 

 Modified – III, Altman Model Analysis (Altman, Hartzell, & Peck, 1998); in this model, Altman eliminates the variable 

𝑋5 (sales/total assets) because this ratio varies significantly across industries with different asset sizes. The calculated 

equation formula is: 

𝑍" =  6.56𝑋1  +  3.26𝑋2  +  6.72𝑋3  +  1.05𝑋4  ... (2) 

 Altman (1968) is a model constructed by re-evaluating and redesigning the Altman Z-Score model. Altman (1968) 

utilized a sample based on the Altman Z-Score model in 1968 by adding thirteen financial ratios. The calculated 

equation formula is: 

𝐺 − 𝑆𝑐𝑜𝑟𝑒 =  1.650𝑋1  +  3.404𝑋2  +  0.016𝑅𝑂𝐴 +  0.057 ... (3) 

Explanation: 

𝑋3 = Return on Assets (ROA) 

The assumptions: (i) if a firm gets a G-Score <- -0.02, it is an enterprise that will or has experienced financial distress; 

and (ii) if a firm gets a G-Score ≥ 0.01, it is categorized as a firm that is not experiencing financial distress. 

 Gordon L.V. Springate (1978) is a model that uses the same method as Altman. Springate offers four ratios that are 

guaranteed to differ between firms in distress and those that are not. The calculated equation formula is: 

𝑆 =  1.03𝐴 +  3.07𝐵 +  0.66𝐶 + 0.4𝐷   ... (4) 

A  is Working Capital divided by TA 

B  is EBIT divided by TA 

C  is Earnings before Taxes (EBT) to Current Liabilities (CL) 

D  is Sales to TA 

 The assumptions: (i) the cut-off value criteria for the Springate model is 0.861; if the score is < 0.861, it indicates that 

the corporation will experience financial distress; and (ii) if the score is > 0.861, it indicates that the corporation will not 

impress financial distress 

 Mark E. Zmijewski (1984) is a model that utilizes the liquidity ratio analysis, leverage, and measures a firm's 

performance. F-Test indicators against the ratio of group 39 rate of return, liquidity, turnover, fixed payment coverage, 

leverage, firm size, trends, and stock return volatility indicate significant differences among healthy and unhealthy firms. 

The calculated equation formula is: 

𝑍 =  −4.3 –  4.5𝑋1  +  5.7𝑋2 –  0.004𝑋3 ... (5) 

Explanation: 

𝑋1 is Earnings after Tax (EAT) to TA 

𝑋2 is Total Debt to TA 

𝑋3 is Current Assets to CL 

The assumptions: (i) the criteria in this model are that if the score obtained is ≥ 0 (zero), then the firm will indicate 

financial distress; and (ii) if the score obtained is ≤ 0 (zero), then the firm has no potential to indicate financial distress. 

 

Calculating Accuracy Level 

To evaluate which financial distress model is the best predictor among the models to be used, a comparison between 

predictions and sample categories is carried out on all existing samples after all samples have been calculated, the results of 

a summary of correct and incorrect predictions are obtained, and through the summary of forecasts, the accuracy level of 

each model can be determined. The accuracy rate indicates a high percentage of the model's predictions that are correct for 

all existing firms. The calculated equation, as an accuracy level formula, adheres: 

= 100 
∑ 𝑥𝑖𝑖𝑖

𝑛
  ... (6) 

(Galdi & Tagliaferri, 2019) 

In the calculation of each model, there is always a possibility of prediction error. Therefore, in calculating the 

accuracy level of each model, the error level or error rate of each model is also considered, with the equation: 

 Error Type I is an error that prevails if the model predicts that the company is experiencing distress when, in fact, the 

corporation is not experiencing distress. 

𝐸𝑟𝑟𝑜𝑟 𝑇𝑦𝑝𝑒 − I =
Number of 𝑒𝑟𝑟𝑜𝑟 𝑡𝑦𝑝𝑒−I

Number of samples
𝑥100% ... (7) 

 

 Error Type-II is an error that prevails if the model predicts that the company is not experiencing distress when, in fact, 

the corporation is experiencing distress. 



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𝐸𝑟𝑟𝑜𝑟 𝑇𝑦𝑝𝑒 − II =
Number of 𝑒𝑟𝑟𝑜𝑟 𝑡𝑦𝑝𝑒−II

Number of samples
𝑥100% ... (8) 

(Sun, Li, Huang, & He, 2014) 

 

Relevant Research 

Research conducted by Robiansyah et al. (2022) analyzed the comparison of the feasibility level of the bankruptcy prediction 

model when it is taken to foresee the company, with the represent yields that of the four models studied, to predict the 

bankruptcy of manufacturing firmscompanies registered on the Indonesia Stock Exchange the Springate model cannot be 

used, because it has a significance probability is 0.175 (greater than 0.05). Research conducted by Meilawati et al. (2023) 

analyzed the comparison of Altman, Springate, and Zmijewski Models in Predicting Bankruptcy in Hotel, Restaurant, and 

Tourism sub-sectors Companies for the Period 2017-2021, with the findings arguing that the Altman, Springate, and 

Zmijewski models are very different from each other in predicting bankruptcy. The Zmijewski model is the most accurate, 

with an accuracy rate of 94.5 percent. 

 

MATERIALS AND METHODS 
Based on the research purpose, this study is a quantitative descriptive approach, i.n, research conducted to determine the 

value of a variable (Sugiyono, 2023, hal. 11). This study was conducted by accessing data on the Indonesia Stock Exchange 

(IDX), i.e., www.idx.co.id and the official website of each corporation. The subject research is firms in the Transportation 

sub-sector registered at IDX for utilization (4 a four-period time. Based on the data type, this study is categorized as 

secondary, specifically research that utilizes numerical data (quantitative). By its form, quantitative data can be processed 

using mathematical calculations and/or statistical analysis (Abdullah et al., 2022). The sampling method is purposive.  

 

Table 1. Determination of Population and Sample of Transportation Sub-Sector Firms 

 
Total Population 45 

C
r
it

e
ri

a
 Transportation sub-sector firms that do not publish complete 

financial reports for four periods 

(20) 

Transportation sub-sector firms listed on the Indonesia Stock 

Exchange (IDX) that publish Financial Reports in USD currency 

(13) 

Number of Firm Samples 12 

C
r
it

e
ri

a
 Sample Used 12 x 4 (Period) 48 

Category 0 (Distress) of Firms* 19 

Category 1 (Non Distress) of Firms* 29 

*)Firm Observation Data Details in Appendix 
Source: Author’s Ellaborated (2025) 

 

To establish that researchers can analyze easily, researchers use the adhere phases: 

Calculate and Analyze of:

Modified Altman Z-Score,

Springate,

Zmijewski Models

Data Entry of Model 

Analysis Financial Distress

Prepare to

Data

Processing

START

Analyzing and Concluding 

and the Accuracy Level

FINISH

 
 

Figure 1. Flowchart of Financial Distress Prediction Model 

 

This research is comparative because the researcher aims to identify the differences in model accuracy levels among 

the Modified Altman (Z-Score), Springate (S-Score), and Zmijewski prediction models in predicting financial distress. The 

http://www.idx.co.id/


Alang, Asian Finance & Banking Review 9(1) (2025), 1-8

  

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goal is to establish benchmarks for assessing the quality of a firm's operations and investments. 

 

RESULTS AND DISCUSSIONS 

After determining the predetermined sample, transportation sub-sector companies were identified that met the research 

criteria. The following is a list of firms that were used as samples in this study: 

 

Table 2. Sample Data Category 0 (Distress) and 1 (Non-Distress) 

 

DISTRESS 

Category 

Data 

Observation 

Issuer 

Code 

Period 

of 

NON-

DISTRESS 

Category 

Data 

Observation 

Issuer 

Code 

Period 

of 

 Issuer 

Code 

Period of 

1 
CMPP 

y 1 

AKSI 

y 20 
SAFE 

y 

2 y-1 2 y-1 21 y-3 

3 

LRNA 

y-1 3 y-2 22 SDMU y-3 

4 y-2 4 y-3 23 

TMAS 

y 

5 y-3 5 

ASSA 

y 24 y-1 

6 

MIRA 

y 6 y-1 25 y-2 

7 y-1 7 y-2 26 y-3 

8 y-2 8 y-3 27 

WEHA 

y 

9 y-3 9 

BIRD 

y 28 y-1 

10 

SAFE 

y-1 10 y-1 29 y-2 

11 y-2 11 y-2 y = current period 
y-1 = period of 1 year ago  

12 

SDMU 

y 12 y-3 y-2 = period of 2 years ago 

13 y-1 13 
CMPP 

y-2 y-3 = period of 3 years ago 

14 y-2 14 y-3    

15 

TAXI 

y 15 LRNA Y    

16 y-1 16 

NELY 

y    

17 y-2 17 y-1    

18 y-3 18 y-2    

19 WEHA y-3 19 y-3    

Source: Author's elaboration from IDX Data (2025) 

 

Descriptive data is used to describe or explain a summary of research variables without connecting or comparing them with 

other variables. 

 Calculation of Accuracy and Error Modified Altman Z-Score 

 

Table 3. Modified Altman Z-Score Model (Error Type) 

 

Recapitulation Identification 
Prediction 

Total 
Distress Non Distress 

Fact 
Distress 12 7 19 

Non Distress 15 14 29 

  Total 27 21 48 

Accuracy Level Test Results  

Accuracy Scores 

Accuration 54.17%     

Error Type-I 14.58%     

Error Type-II 31.25%     

Source: Author’s Calculated (2025) 

 

Table 3 shows that, out of a total of 19 samples in category 0 (distress), the Modified Altman Z-Score model 

predicts 12 samples experiencing financial distress. In comparison, seven other samples do not exhibit financial distress. 

Therefore, there is an error in this model that predicts that seven samples do not experience financial distress. Meanwhile, 

from the 29 samples in category 1 (non-distress), the Modified Altman Z-Score model predicts that 14 samples do not 

experience financial distress, while 15 other samples indicate financial distress. Therefore, there is an error in this model, 

which predicts that 15 samples will experience financial distress when, in reality, this is not the case. Therefore, it can be 

inferred that the Modified Altman Z-Score model yields correct predictions for 26 samples and incorrect predictions for 22 

samples. The accuracy level of the Modified Altman Z-Score model is 54.17%. The error rate of this model for error type I 

is 14.58%, while the error rate for error type II is 31.25%. 

 Calculation of Accuracy and Error Type of Springate S-Score Model 

 

Table 4. Springate S-Score Model Error Type 

 

Recapitulation Identification 
Prediction 

Total 
Distress Non Distress 

Fact 
Distress 19 - 19 

Non Distress 24 5 29 

  Total 43 5 48 

Accuracy Level Test Results  

Accuracy Scores 
Accuration 50.00%     

Error Type-I 0.00%     



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Recapitulation Identification 
Prediction 

Total 
Distress Non Distress 

Error Type-II 50.00%     

Source: Author’s Calculated (2025) 

 

Table 4 shows that, out of a total of 19 samples in category 0 (distress), the Springate S-Score model predicts 19 

samples experiencing financial distress, and no samples are not experiencing financial distress. Therefore, there is no error 

in this model in predicting samples in category 0 (distress). Meanwhile, from the 29 samples in category 1 (non-distress), 

the Springate S-Score model predicts that five samples do not experience financial distress, while 24 other samples indicate 

financial distress. Therefore, there is an error in this model, as it predicts that 24 samples will experience financial distress; 

in reality, this is not the case. Therefore, it can be inferred that the Springate S-Score model yields correct predictions for 

26 samples and incorrect predictions for 22 samples. The accuracy level of the Springate S-Score model is 50%. There are 

no errors in this model for error type I, while error type II occurs at a rate of 50%. 

 Calculation of Accuracy and Error Type of the Zmijewski Model 

 

Table 5. Zmijewski Model Error Type 

 

Recapitulation Identification 
Prediction 

Total 
Distress Non Distress 

Fact 
Distress 9 10 19 

Non Distress 4 25 29 

  Total 13 35 48 

Accuracy Level Test Results  

Accuracy Scores 

Accuration 70.83%     

Error Type-I 20.83%     

Error Type-II 8.33%     

Source: Author’s Calculated (2025) 

 

Table 5 shows that, out of a total of 19 samples in category 0 (distress), the Zmijewski model predicts that nine 

samples will experience financial distress. In comparison, the remaining 10 samples do not exhibit financial distress. 

Therefore, there is an error in 10 samples of this model in predicting samples in category 0 (distress). Meanwhile, from 29 

samples in category 1 (non-distress), the Zmijewski model predicts that 25 samples do not indicate financial distress, and 

four other samples experience financial distress. Therefore, there is an error in this model, as it predicts that four samples 

will experience financial distress, whereas in reality, this is not the case. The Zmijewski model yields correct predictions 

for 34 samples and incorrect predictions for 14 samples. The accuracy level of the Zmijewski model is 70.83%. The error 

rate of this model for error type I is 20.83%, while the error rate for error type II is 8.33%. 

 Summary of Accuracy and Error Level Test Yields 

 

Table 6. Accuracy and Error Level Test Yields (Recapitulation) 

 

Test Result Scoring 
Model Analysis 

 Altman Modified  Springate  Zmijewski 

Accuracy Level 54.17% 50.00% 70.83% 

Error Type-I 14.58% 0.00% 20.83% 

Error Type-II 31.25% 50.00% 8.33% 

Source: Author’s Calculated (2025) 

 

The yields of the study conducted on transportation sub-sector firms registered on the Indonesia Stock Exchange 

over four periods indicate that the Altman Modified Z-Score prediction method ranks second as a suitable method for 

measuring financial distress in transportation sub-sector firms, with an accuracy rate of 54.17% and an error rate below 

35%. In contrast, the highest accuracy rate is obtained from the Zmijewski prediction method calculation, while the 

Springate method yields the lowest accuracy rate among the three. These findings are consistent with those of Robiansyah 

et al. (2022), who concluded that the Springate model is not suitable for predicting corporate bankruptcy, as it has no 

statistically significant effect on manufacturing companies. Additionally, it states that the Zmijewski model is the most 

accurate, producing a high level of accuracy of 94.5% in hotels, restaurants, and tourism sub-sector companies (Meilawati, 

Damayanti, & Ilhami, 2023). The Zmijewski model is widely exploited to measure firm performance and investments 

utilizing company financial ratio analysis. 

 

CONCLUSIONS  

This study investigates to wants to find out the differences in the level of model accuracy among the Modified Altman 

Prediction (Z-Score), Springate (S-Score), and Zmijewski prediction models in predicting financial distress as a model of 

predicting operational Management and investment performance benchmarks from Transportation sub-sectors firms 

registered on the Indonesia Stock Exchange (IDX) for the 4-periods, it can be drawn: (1) From the assessment of the Altman 

Modified Z-Score model analysis, it accurately predicts 26 samples out of 48 existing samples so that it has an accuracy 

level of 54.17%. (2) From the assessment of the Springate S-Score model analysis, it accurately predicts 24 samples out of 

48 existing samples, so that it has an accuracy level of 50%. (3) While the assessment of the Zmijewski model analysis 



Alang, Asian Finance & Banking Review 9(1) (2025), 1-8

  

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accurately predicts 34 samples out of 48 existing samples, it has an accuracy level of 70.83%. The conclusion from three 

model bankruptcies is that the Zmijewski model is the most appropriate for firms to use if they want to attract potential 

investors to invest, and is used to determine the prediction of financial distress as a method for predicting the benchmark 

for the quality of operational and investment companies in the Transportation sub-sectors firms registered on the IDX for 

the 4-periods. This study has the drawback of producing the lowest model accuracy percentage compared to the previous 

studies referred to, and also, the total number of samples observed to predict the three models tested was small. This study 

suggests that additional financial distress prediction models, such as Ohlson, Grover, and others, can be used to compare 

and contrast the results of financial distress analyses. The sample should be expanded in future studies. 

 
 

Author Contributions: Conceptualization, S.A.; Methodology, S.A.; Software, S.A.; Validation, S.A.; Formal Analysis, S.A.; Investigation, S.A.; 

Resources, S.A.; Data Curation, S.A.; Writing – Original Draft Preparation, S.A.; Writing – Review & Editing, S.A.; Visualization, S.A.; Supervision, 
S.A.; Project Administration, S.A.; Funding Acquisition, S.A. Authors have read and agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups 

or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgements: We acknowledge the Indonesian Stock Exchange (IDX), particularly the Transportation sub-sector firms, for providing the financial 

statements of the relevant firms on their official websites, which helped in calculating and conducting this analysis. 
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 
Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

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