Multiple Discriminant Analysis Research Articles

Application of the Altman Model for the Prediction of Financial Distress in the Case of Slovenian Companies

Abstract Background/Purpose The aim of this paper is to verify the applicability and accuracy of the Altman model in the case of Slovenian companies. The use of the Altman model is hugely popular and widespread among financiers, analysts and other stakeholders who want to determine the creditworthiness of a company’s operations and the likelihood of it running into financial difficulties in the coming years. Methods The study was conducted on a sample of 66 Slovenian companies, which were divided into two equal groups: bankruptcy and non-bankruptcy companies. Based on accounting data for the last five years, the authors of this paper calculated the Z-Score, which is based on the Multiple Discriminant Analysis (MDA). By calculating the statistical error of the estimate (type I and II), the authors verified the extent (in percentage terms) to which the companies had been correctly classified by the model. The Mann-Whitney U test was used to check whether there was a difference in the average Z-Score between the two groups of companies. Results The authors determined that the reliability of the Altman model was 71.21% when tested at the upper bound (the threshold value of the Z-Score was 2.6) and 80.30% when tested at the lower bound (the threshold value of the Z-Score was 1.1). This is similar to other countries, where the reliability was found to be over 70% in most cases. Despite the lower reliability of the model, the Z-Score proved to be an important factor in differentiating between the two groups of companies, as bankruptcy companies had a lower value of this indicator than non-bankruptcy companies. Conclusion Based on the results of this study, as well as those of other studies, it can be summarized that the Altman model is a fairly good way for companies to determine the success of their business in a relatively simple and quick way and also to predict the potential risk of their operations in the future. However, since the reliability of the model is not 100%, it is important to be careful when making business predictions and carry out additional in-depth analyses or use other methods.

Examining leisure constraints and motivations within the psychological continuum model: a study among recreational video gamers

ABSTRACT This study investigates perceived leisure constraints and motivations among Iranian gamers, employing the Psychological Continuum Model (PCM). Targeting recreational gamers under 30 years old, dedicating 1 to 6 hours per week to gaming, we utilised (D. Funk et al. 2008) PCM stages questionnaire, Crawford’s et al. (1991) perceived hierarchical leisure constraints questionnaire, and Cianfrone et al. (2011) motivations questionnaire. Our analysis, featuring One-way analysis of variance (ANOVA) for constraints and motivations across PCM stages and Multiple Discriminant Analysis (MDA) for psychological communication, revealed compelling insights. Results indicate a noteworthy increase in perceived constraints with PCM progression (p < 0.05), highlighting significant differences in dimensions such as interest, time involvement, facilities, cost, and interpersonal aspects (p < 0.05). Concurrently, motivations progressively rise with PCM advancement, showcasing significant variations in competition, diversion, fantasy, and social interaction dimensions (p < 0.01). This study’s significant findings illuminate the evolving landscape of constraints and motivations across distinct PCM stages, offering valuable implications for comprehending gaming behaviours within this demographic.

Response of Chironomids (Diptera, Chironomidae) to Environmental Factors at Different Spatial Scales.

Factors responsible for species distribution of benthic macroinvertebrates, including responses at different spatial scales, have been previously investigated. The aim of the present research was to review the most relevant factors explaining chironomid species distribution focusing on factors operating at different spatial scales, such as latitude, longitude, altitude, substrate, salinity, water temperature, current velocity, conductivity, acidity, dissolved oxygen, nutrient content etc. acting at regional levels and at a large or small water basin level. Data including chironomid species abundances from different lentic and lotic waters in Italy and other surrounding countries were analyzed using partial canonical correspondence analysis (pCCA) and multiple discriminant analysis (DISCR). Spatial analyses, including univariate Moran's I correlograms, multivariate Mantel correlograms and Moran's eigenvector maps (MEMs), were thereafter carried out. The results showed that habitat type, including different types of lotic waters (i.e., kryal, crenal, rhithral, potamal) and different lake types (i.e., littoral, sublittoral, profundal zones), is the most significant factor separating chironomid assemblages, while spatial factors act only as indirect influencers.

Distinct profiles of mental health need and high need overall among New Zealand adolescents - Cluster analysis of population survey data.

The objective was to identify clinically meaningful groups of adolescents based on self-reported mental health and wellbeing data in a population sample of New Zealand secondary school students. We conducted a cluster analysis of six variables from the Youth19 Rangatahi Smart Survey (n = 7721, ages 13-18 years, 2019): wellbeing (World Health Organization Well-Being Index), possible anxiety symptoms (Generalized Anxiety Disorder 2-item, adapted), depression symptoms (short form of the Reynolds Adolescent Depression Scale) and past-year self-harm, suicide ideation and suicide attempt. Demographic, contextual and behavioural predictors of cluster membership were determined through multiple discriminant function analysis. We performed cross-validation analyses using holdout samples. We identified five clusters (n = 7083). The healthy cluster (n = 2855, 40.31%) reported positive mental health across indicators; the anxious cluster (n = 1994, 28.15%) reported high possible anxiety symptoms and otherwise generally positive results; the stressed and hurting cluster (n = 667, 9.42%) reported sub-clinical depression and possible anxiety symptoms and some self-harm; the distressed and ideating cluster (n = 1116, 15.76%) reported above-cutoff depression and possible anxiety symptoms and high suicide ideation; and the severe cluster (n = 451; 6.37%) reported the least positive mental health across indicators. Female, rainbow, Māori and Pacific students and those in higher deprivation areas were overrepresented in higher severity clusters. Factors including exposure to sexual harm and discrimination were associated with increasing cluster severity. We identified high prevalence of mental health challenges among adolescents, with distinct clusters of need. Youth mental health is not 'one size fits all'. Future research should explore youth behaviour and preferences in accessing support and consider how to best support the mental health of each cluster.

Ester-related volatile compounds reveal the diversity and commonalities of different types of late-ripening peaches.

To recognize the key ester-related volatile compounds, 5 types of peaches including 54 late-ripening peach materials were examined by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and E-nose. Here, a large number of esters were identified to be released by ripe peach fruits and were mainly characterized by fruity, green, and fatty notes. The variety and content of esters had greatly changed within or between cultivars, indicating that the fruit volatiles were highly differentiated depending on the specific genotypes and cultivation conditions. The ester types showed that fatty acid-derived C6 alcohols and methyl-/ethyl- short-chain alcohol were the main ester precursors, which were more likely to be utilized and well selected by alcohol acyltransferases, whereas the preference of acyl donors was not observed. The common peach type, which exhibited a unique volatile profile, displayed broader diversity and more abundant characteristics in ester-related volatiles than the other four types. A total of 19 key esters were identified as the main components and the content of most esters showed no significant difference among different peach types. Some key esters had even been enriched in nectarines. Moreover, the multiple discriminant analysis revealed a possible relationship between peach types and the domestication of the peach evolution. This study investigated ester-related volatiles released by different types of peach fruits and can be further used to evaluate the peach qualities, providing an important reference for peach breeding and processing.

The S-Score of Financial Sustainability for Professional Football Clubs

This article extends the extensive literature on business failure and develops the approach of Altman to provide quantified recommendations for the financial sustainability of football clubs. The article uniquely identifies four scenarios that have caused insolvency for football clubs in the English Football League (EFL) and uses multiple discriminant analysis to identify financial models that delineate between professional football clubs that experience insolvency events and those that do not. These models produce an S-score which is introduced to suggest minimum financial tolerances for accounting ratios that provide a reasonable level of resilience for the financial sustainability of clubs in each scenario. The results are relevant for academics and non-academics with an interest in the business of football, professional football clubs aiming to avoid financial distress, and specifically, the Regulator that the U.K. government has proposed for football in England.

Ensuring the longevity of WirelessHART devices in industrial automation and control systems using distinct native attribute fingerprinting

Distinct native attribute fingerprinting is considered as a means to ensure the longevity of WirelessHART communication devices used in industrial automation and control systems. The aim is for these devices to reach full life expectancy using a technical cradle-to-grave lifecycle protection strategy. The protection addressed here includes pre-deployment near-cradle counterfeit device detection using active fingerprinting and operational mid-life rogue device detection using passive fingerprinting. The counterfeit and rogue device detection rates are estimated for 56 five-class multiple discriminant analysis models. Detection demonstrations include using three non-modeled devices to complete a total of 3 × 5 × 56 = 840 individual rogue and counterfeit device identity (ID) verification demonstrations. The device ID verification process uses binary accept/reject decisions with false positive outcomes used to estimate rogue and counterfeit device detection rates. For device ID verification using active fingerprints, the demonstrated counterfeit detection rate approached 99 % using only 15-of-99 available features—an approximate 85 % dimensional-reduction. Device ID verification using passive fingerprints was more challenging and the rogue detection rate approached 94 % using 120-of-243 available features—an approximate 50 % dimensional-reduction.. Collectively, the dimensionally-reduced implementations support efficiency improvement objectives required for providing near-cradle counterfeit device and mid-life operational rogue device detection in critical industrial automation and control systems.

Comparing the resilience of Sharia and conventional banking to the financial crisis in the Association of Southeast Asian Nations

This study aims to analyze the comparison of the resilience of Islamic and conventional banking in the Association of Southeast Asian Nations (ASEAN) during the COVID-19 pandemic. Comparison of banking resilience was proxied by the Capital Adequacy Ratio (CAR) and Loan-to-Deposit Ratio (LDR) factors, Return on Assets (ROA) and Non-Performing Loans (NPL) with the Multiple Discriminant Analysis test. In this case, the emphasis is placed on the patterns by which Islamic and conventional banking in ASEAN weathered the recent financial crisis during the COVID-19 pandemic. The explanatory and quantitative analysis also used a purposive sample strategy and SPSS to obtain and analyze data from 120-unit analyses of Islamic and conventional banks, respectively. From 2020 to 2021, traditional banks in the ASEAN region, especially in Indonesia, Malaysia, and Brunei Darussalam, were emphasized. Moreover, some data were prioritized regarding the Comparison of Resilience of Sharia and Conventional Banking in ASEAN after the COVID-19 pandemic. The results showed that conventional and Islamic banks had different resilience due to the influence of Capital Adequacy Ratio (CAR) and Loan-to-Deposit Ratio (LDR) factors, but there was no significant difference in the Return on Assets (ROA) and Non-Performing Loans (NPL). Based on the accuracy of the average prediction of 80%, conventional and Islamic bank groups had classification values of 48% and 88%, respectively. This indicated that Islamic financial institutions were more successful than conventional groups in implementing banking resilience.

Accounting‐based variables as an early warning indicator of financial distress in crisis and non‐crisis periods

AbstractFinancial integration in the Association of Southeast Asian Nations (ASEAN) region is a key focus of the ASEAN Economic Community. Whereas many studies focus on modelling corporate default, this paper identifies early warning indicators of financial distress before a default, using multiple discriminant analysis (MDA) models with a sample of listed and delisted companies in the ASEAN region. The analysis examines 720 companies in 10 different industries across six ASEAN countries from 1997 to 2016. The study constructs individual models for each country as well as an overall model for the entire region, using both in‐sample and out‐of‐sample approaches. This overall model could be useful for an integrated banking system. To ensure robustness, the study also separately examines the predictive performance of the MDA models across different economic crises: the Asian financial crisis (AFC) from 1997 to 2000, the global financial crisis (GFC) from 2007 to 2009 and their pre‐ and post‐crisis periods. We find that profitability ratios are the best indicators of financial distress in the ASEAN region, followed by liquidity and leverage ratios. In addition, our findings reveal common indicators that can be used to predict financial distress across ASEAN countries. The single model performs reasonably well in predicting financial distress 1 year ahead. In addition, the model is extended to incorporate a market‐based indicator into the MDA models, the distance to default. However, the inclusion of this indicator does not significantly improve the accuracy of the models in predicting financial distress at listed firms in the ASEAN region.

River-influenced beluga (Delphinapterus leucas) summer habitat use in western Hudson Bay, Canada

River estuaries along western Hudson Bay, Canada, are important summer habitats for beluga whales ( Delphinapterus leucus (Pallas, 1776)) and subject to increasing industrial development activities including vessel traffic. The feasibility of establishing a National Marine Conservation Area (NMCA) in western Hudson Bay is under consideration, requiring baseline studies and habitat monitoring. In this study, beluga whale locations were identified using aerial photographs collected during summer 2018 of the Seal, Knife, Churchill, and Nelson River estuaries. Sentinel 2 wavelength bands were used to outline river plume boundaries for the Seal, Knife, and Churchill Rivers. Multiple discriminant analysis was used to differentiate between beluga habitat areas according to their environmental characteristics including concentration of total suspended sediments (TSS), and coloured dissolved organic matter (CDOM). The Seal River estuary, Knife River estuary, Churchill River outer estuary, Churchill River estuary, and Nelson River estuary were identified as distinct habitat areas. Resource selection functions and model selection were used to determine that habitat variables related to prey availability were important for beluga habitat selection, including TSS, CDOM, and the distance to river mouth or river plume. Identification of preferred habitat and habitat areas in this study are imperative for future management decisions including establishment of an NMCA.

Development of Discriminant Model for Classifying Sprinters and Throwers on the Basis of Anthropometric and Physical Fitness Variables

In the present study, 100 athletes who competed at the state level was constitute up the sample; only male athletes from Madhya Pradesh in the senior category was included. During the morning and evening sessions, the necessary information on the subject was gathered using the random sampling method. before the National camps and practicing at TT Nagar Stadium Bhopal. The data were collected on anthropometric variables i.e. height, weight, sitting height, (leg, arm, hand) length, shoulder width, (chest, hip, thigh, calf) circumference, (back, shoulder, grip and leg explosive) strength. The anthropometric variables were measured by using selected standardized instruments and measuring tapes. Data collected was analysed at 0.05 level of significance, descriptive statistics and multiple discriminant analysis was applied to classify and predict group membership of the throwers into the sprinters and Throwers. The results showed that 100.0% of original grouped cases were correctly classified.

Future preferred mode of learning of business undergraduates and its implications

e-Learning was abruptly adopted in many countries to mitigate the adverse consequences of the sudden closure of institutions of higher learning caused by the COVID-19 pandemic. Against this background, this study investigated how business undergraduates want to learn in the future and predictors of their future preferred mode of learning. 251 business undergraduates from a private university in Malaysia participated in an online survey conducted in July 2020, during the sudden closure of institutions of higher learning. Data collected were analysed using the multiple discriminant analysis to develop a characteristics profile of the three groups of business undergraduates (i.e., preferred fully conventional classroom learning, blended learning and fully e-learning) in terms of important predictors. Results revealed that the significant predictors of future preferred mode of learning of business undergraduates, in descending order, were disadvantages of e-learning, advantages of e-learning, self-regulated learning, learning outcomes, information and communications technology infrastructure and training, support and resources. This study concludes with some reflective thoughts about important lessons learned from this unprecedented pandemic pertaining to e-learning readiness to deal with future unexpected crises.

Bankruptcy Prediction Model Development and its Implications on Financial Performance in Slovakia

Abstract Research purpose. Financial distress being a global phenomenon makes it impact firms in all sectors of the economy and predicting corporate bankruptcy has become a crucial issue in economics. At the beginning of the last century, the first studies aimed to predict corporate bankruptcy were published. In Slovakia, however, several prediction models were developed with a significant delay. The main aim of this paper is to develop a model for predicting bankruptcy based on the financial information of 3,783 Slovak enterprises operating in the manufacturing and construction sectors in 2020 and 2021. Design / Methodology / Approach. A prediction model that uses the appropriate financial indicators as predictors may be developed using multiple discriminant analysis. Multiple discriminant analysis is currently used in prediction model development. In this case, financial health is assessed using several variables that are weighted in order to maximise the difference between the average value calculated in the group of prosperous and non-prosperous firms. When developing a bankruptcy prediction model based on multiple discriminant analysis, it is crucial to determine the independent variables used as primary financial health predictors. Findings. Due to the discriminant analysis results, the corporate debt level of the monitored firms may be regarded as appropriate. Despite the fact that the model identified 215 firms in financial distress due to an insufficient debt level, 3,568 out of 3,783 Slovak enterprises operating in the manufacturing and construction sectors did not have any problems with financing their debts. The self-financing ratio was identified in the developed model as the variable with the highest accuracy. Based on the results, the developed model has an overall discriminant ability of 93% since bankruptcy prediction models require strong discriminating abilities to be used in practice. Originality / Value / Practical implications. The principal contribution of the paper is its application of the latest available data, which could help in more accurate financial stability predictions for firms during the current difficult period. Additionally, this is a ground-breaking research study in Slovakia that models the financial health of enterprises in the post-pandemic period.

A Study on the Perception of Selected Ethnic Communities Toward Communication Media : An Empirical Study

Purpose : Ethnographic marketing is an emerging area in the field of management. In a world marked by growing multiculturalism, media plays a significant role in the dissemination of information. Marketers are required to know the nature of the perception held by ethnic communities toward media. This is essential to provide tailored media content to respective targeted communities. Hence, the study was carried out to know the perception of ethnic communities toward communication media. Design/Methodology/Approach : The study was conducted using an exploratory research design on a sample of 1,200 units comprising 400 respondents from three ethnic communities selected, namely Bodo, Mishing, and Rabha. The study used a convenience sampling technique, and data were collected by a structured interview schedule. Attitudinal statements were used to record the responses. Multiple discriminant analysis was used for data processing. Findings : The study found that the Rabha community associated media with advertisement as a platform for choice alternatives of products and as an informer of offers. The Bodo community perceived media as the news channel through which information is circulated, but it is not always deemed to be authentic. Finally, the Mishing community perceived media as the educator of people, as the mobilizer of public opinion, and as the provider of information on the latest trends. Practical Implications : As ethnic communities form significant segments of target markets today, the findings are vital inputs for marketers to suitably use the right media for communication in the right perspective. It will consequently facilitate in providing tailored media content to respective communities being targeted by the stakeholders. Originality/Value : Although studies on consumers’ perception toward communication media are available, studies on the specific ethnic communities in the specific geographical region identified are rare. The research findings will go a long way in enlightening academicians as well as marketers who are related to this area and, thus, find it novel. Furthermore, people from the field of anthropology and mass communication may get vital inputs on the behavior of ethnic communities toward media..

A Discriminant Analysis of the Factors Affecting Abnormalities in Chest Computed Tomographies of 608-Group Patients with COVID-19 Pneumonia

Objective: To study factor correlation and classification affecting abnormalities in chest computed tomographies (CTs) of 608-group patients with coronavirus disease 2019 (COVID-19) pneumonia. Material and Methods: We retrospectively collected data of 608-group patients with COVID-19 pneumonia from medical records combined with data from chest CTs which were interpreted by a radiologist for CT abnormalities. The findings were analyzed by descriptive statistics, Fisher’s Exact Test and multiple discriminant analysis (MDA) by a stepwise method. Results: The majority of the 161 patients were female (55.9%), with an average age of 62.90 years (S.D. 16.68) and average weight of 63.07 kg (S.D. 16.18), non-smoking and non-alcohol drinking (71.4% and 61.5%, respectively) and with underlying respiratory diseases (28.6%). The important symptoms brought to a doctor were main symptoms including fever, chills, cough, nasal congestion, sore throat, difficult breathing, shortness of breath (74.5%). The average duration from onset of the symptoms to perform chest CTs was 11.18 days (S.D. 5.42). The abnormalities of CTs chest such as characteristics and locations were periphery (54.7%) with ground-glass opacity (44.7%). The CT severity score was level 2 (24.8%) from 5 levels. MDA revealed there were 5 factors affecting the abnormalities in the chest CTs of 608-group patients with COVID-19 pneumonia. CT severity score, peripheral location, body weight, age and location in the lower lungs. These factors accurately predicted abnormalities in chest CTs (60.2%). Conclusion: Abnormalities in chest CTs, and factor correlation and classification that affect abnormalities in chest CTs of 608-group patients with COVID-19 pneumonia will benefit the medical and multidisciplinary team in helping to determine treatment method, accurately prognosing severity and reducing mortality.

Discriminating WirelessHART Communication Devices Using Sub-Nyquist Stimulated Responses

Reliable detection of counterfeit electronic, electrical, and electromechanical devices within critical information and communications technology systems ensures that operational integrity and resiliency are maintained. Counterfeit detection extends the device’s service life that spans manufacture and pre-installation to removal and disposition activity. This is addressed here using Distinct Native Attribute (DNA) fingerprinting while considering the effects of sub-Nyquist sampling on DNA-based discrimination. The sub-Nyquist sampled signals were obtained using factor-of-205 decimation on Nyquist-compliant WirelessHART response signals. The DNA is extracted from actively stimulated responses of eight commercial WirelessHART adapters and metrics introduced to characterize classifier performance. Adverse effects of sub-Nyquist decimation on active DNA fingerprinting are first demonstrated using a Multiple Discriminant Analysis (MDA) classifier. Relative to Nyquist feature performance, MDA sub-Nyquist performance included decreases in classification of %CΔ ≈ 35.2% and counterfeit detection of %CDRΔ ≈ 36.9% at SNR = −9 dB. Benefits of Convolutional Neural Network (CNN) processing are demonstrated and include a majority of this degradation being recovered. This includes an increase of %CΔ ≈ 26.2% at SNR = −9 dB and average CNN counterfeit detection, precision, and recall rates all exceeding 90%.

The Capacity to Forecast Enterprise Insolvency on the Polish Market Using the Precursory Altman Z-Score Model (1968)

Theoretical background: Many years have passed since the publication of the first multidimensional model (Z-score) of early bankruptcy warning by E.I. Altman in 1968. New models have since emerged in different countries. In this research stream, the share of econometric modelling based on the assumptions of discriminant analysis, i.e. MDA – Multiple Discriminant Analysis – one of the multidimensional classification methods categorised as empirical-induction methods, plays a particularly important role. Purpose of the article: The article assesses the capacity of the Altman Z-Score to forecast (1 year ahead) insolvency of enterprises on the Polish market. Research methods: Due to the sample size (i.e. over 2,700 bankrupt entities in Poland), the study constitutes one of the broadest studies carried out so far on the assessment of the predictive capacity of early warning models in Polish economy. Main findings: The authors, conducting research in this area, put forward a hypothesis that the variables that were well identified and introduced to discriminatory models in one economy work equally well in other countries. Despite the economic differences in these countries and the phases of the business cycle, the financial-analysis indicators that allow effective forecasting of enterprise bankruptcy are valid for different economies. The discriminant-function variability ranges that are characteristic of failing and healthy economic entities change but the variables themselves are good predictors.

Driving success: A segmentation of customer admiration in automotive industry

To stay relevant with the ever-changing customers, companies have to create value by perpetually evolving the relationship between their brand and customers. The enduring success from the world’s most well-known automotive brands such as BMW, Mercedes-Benz, and Harley-Davidson can be attributed because they are admired. However, to build customer’s admiration, a brand has to understand who those customers are, what motivates them, why they will admire a certain brand, and how they will admire it. This study formulates customer segmentation in the context of their admiration towards car brands in Indonesia. The variables used to segment the customers are demographics, motivation, reference group, current car usage and information channel they use. Analysis methods used in this quantitative research are cluster analysis, cross-tabulation, and multiple discriminant analysis to process the research data collected from 501 respondents via an online questionnaire. The result generates three customer segments: the Dreamer who dreams high but never bought a car before, the Working for Success who currently have an entry-level car looking for an upgrade. Lastly, the Family Oriented who have the biggest disposable income and are influenced by their family when admiring a car brand. For industry players, this research is expected to provide insight into the characteristics of each customer segment as a guide in implementing relevant strategies for each segment. While for the academic aspect, this study contributes new information in the area of brand management.

Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries

Research background: Effective monitoring of financial health is essential in the financial management of enterprises. Early studies to predict corporate bankruptcy were published at the beginning of the last century. The prediction models were developed with a significant delay even among the Visegrad group countries. Purpose of the article: The primary aim of this study is to create a model for predicting bankruptcy based on the financial information of 20,693 enterprises of all sectors that operated in the Visegrad group countries during the post-pandemic period (2020?2021) and identify significant predictors of bankruptcy. To reduce potential losses to shareholders, investors, and business partners brought on by the financial distress of enterprises, it is possible to use multiple discriminant analysis to build individual prediction models for each Visegrad group country and a complex model for the entire Visegrad group. Methods: A bankruptcy prediction model is developed using multiple discriminant analysis. Based on this model, prosperity is assessed using selected corporate financial indicators, which are assigned weights such that the difference between the average value calculated in the group of prosperous and non-prosperous enterprises is as large as possible. Findings &amp; value added: The created models based on 6?14 financial indicators were developed using different predictor combinations and coefficients. For all Visegrad group countries, the best variable with the best discriminating power was the total indebtedness ratio, which was included in each developed model. These findings can be used also in other Central European countries where the economic development is similar to the analyzed countries. However, sufficient discriminant ability is required for the model to be used in practice, especially in the post-pandemic period, when the financial health and stability of enterprises is threatened by macroeconomic development and the performance and prediction ability of current bankruptcy prediction models may have decreased. Based on the results, the developed models have an overall discriminant ability greater than 88%, which may be relevant for academicians to conduct further empirical studies in this field.

“Tiga Serangkai” untuk Memprediksi Perusahaan dengan Kesulitan Keuangan Sebelum Masa Pandemi

This research aims to foresee financial difficulties in the agricultural industry using three prominent variables: profitability, operating leverage, and liquidity insolvency, before the pandemic. The research employed the documentation technique as the main data collection method. The purposive sampling technique was conducted to obtain the distressed and non-distressed companies during the 2017-2019 period. Multiple discriminant analysis was employed as the data analysis method. Our study reveals that the “big three” factors of profitability, operating leverage, and liquidity insolvency are all capable of differentiating between businesses that are in financial crisis and those that are not. With a model accuracy of 85.700 percent, the “big three” factors can also serve as the determining variables in the discriminant function with profitability as the most influential variable.&#x0D; Keyword: Multiple Discriminant Analysis; Financial Distress; Profitability; Operating Leverage; Liquidity Insolvency