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Citation: Tran, K.L.; Le, H.A.;
Nguyen, T.H.; Nguyen, D.T.
Explainable Machine Learning for
Financial Distress Prediction:
Evidence from Vietnam. Data 2022,7,
160. https://doi.org/10.3390/
data7110160
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data
Article
Explainable Machine Learning for Financial Distress Prediction:
Evidence from Vietnam
Kim Long Tran 1, Hoang Anh Le 2,* , Thanh Hien Nguyen 3and Duc Trung Nguyen 1
1Faculty of Banking, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street,
Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam
2Institute for Research Science and Banking Technology, Ho Chi Minh University of Banking,
No. 36 Ton That Dam Street, Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam
3Department of Economic Mathematics, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street,
Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam
*Correspondence: anhlh_vnc@buh.edu.vn
Abstract:
The past decade has witnessed the rapid development of machine learning applied in
economics and finance. Recent evidence suggests that machine learning models have produced
superior results to traditional statistical models and have become the driving force for dramatic
improvement in the financial industry. However, a much-debated question is whether the prediction
results from black box machine learning models can be interpreted. In this study, we compared
the predictive power of machine learning algorithms and applied SHAP values to interpret the
prediction results on the dataset of listed companies in Vietnam from 2010 to 2021. The results
showed that the extreme gradient boosting and random forest models outperformed other models.
In addition, based on Shapley values, we also found that long-term debts to equity, enterprise value
to revenues, account payable to equity, and diluted EPS had greatly influenced the outputs. In terms
of practical contributions, the study helps credit rating companies have a new method for predicting
the possibility of default of bond issuers in the market. The study also provides an early warning tool
for policymakers about the risks of public companies in order to develop measures to protect retail
investors against the risk of bond default.
Keywords: explainable AI; financial distress; machine learning
1. Introduction
Financial distress refers to the situation in which a company fail to meet debt obliga-
tions to its creditors at maturity. The prolonged and severe financial distress can eventually
lead to bankruptcy. Traditionally, the assessment of the financial distress situation of com-
panies was mainly based on the subjective judgment of experts. However, this expert-based
approach exposes many drawbacks, including the results are inconsistent, cannot be vali-
dated and are highly dependent on expert competence. Therefore, other approaches have
been developed to improve consistency and accuracy.These classification techniques can be
categorized into statistical methods and machine learning methods. Statistical methods
include univariate analysis [
1
], multiple discriminant analysis [
2
], logistic regression [
3
],
and Cox survival model [4]. Statistical models are simple in structure, highly explanatory,
and take less time to train. However, statistical models require many strict assumptions
unavailable in real life, including linear relationships, homogeneity of variances and inde-
pendence assumptions. Violation of these assumptions can reduce the predictive power
of statistical methods. Then, the development of machine learning algorithms marked
a breakthrough in the science of prediction. The application of machine learning models,
such as support vector machine [
5
], decision tree [
6
], and artificial neural networks [
7
], have
enhanced the predictive power of traditional models. Recently, ensemble models such as
random forest [
8
], adaptive boosting [
9
], and extreme gradient boosting [
10
] have become
Data 2022,7, 160. https://doi.org/10.3390/data7110160 https://www.mdpi.com/journal/data
Data 2022,7, 160 2 of 12
significant drivers of developments in the economy and financial sectors, especially in risk
management. Although providing better forecasting results, machine learning methods
also have drawbacks, as these models are complex and unable to interpret. Meanwhile,
explaining and interpreting become extremely necessary for internal use, such as by man-
agers and programmers, and external stakeholders, such as creditors, shareholders, credit
rating agencies, and regulators.
Recently, studies have been conducted to enhance the explainability of the machine
learning models, but they mainly focused on P2P loans and the SME loans market. In this
study, we aim to apply machine learning and enhance the explainability of forecasting
results on the data of listed companies in Vietnam. Our study contributes to two new
points to the area of risk forecasting. Firstly, to our best knowledge, this is a pioneering
study in applying machine learning to predict financial distress in the dataset of companies
in Vietnam. Second, we also found important features that explain the forecast results
using the SHAP values. Based on the results, we also gained more valuable information to
improve the risk assessment process of debt issuers.
The rest of this study is organized as follows. Section 2reviews the literature on
financial distress prediction, the introduction of explanatory techniques, and highlights the
contribution of previous research. Section 3presents the methodologies and techniques
used in this research. Section 4shows the results of the prediction and interpretation.
Section 5concludes with conclusions and limitations of this study.
2. Literature Review
2.1. Literature Review on Financial Distress Prediction
Default risk prediction models based on statistical techniques were built and devel-
oped in the late 1960s. Beaver [
1
] applied regression models to determine 30 financial ratios
that significantly impact the corporate default risk. Later, Altman [
2
] improved Beaver’s
work by developing a multiple discriminant analysis method to predict bankruptcy. He
built a Z-score model that employed a discriminant function to classify the observation.
However, discriminant model also has some disadvantages, such as (i) assuming a linear
relationship between the independent variable and the dependent variable; (ii) the results
are difficult to interpret and cannot quantify the level of risk between different groups. In
1980, Ohlson pioneered applied logistic regression models to predict the probability of
default of corporates. The advantage of this model is that the outputs are the borrower’s
probability of default, but the accuracy of the model is not always high [11].
Because the credit analysis process is similar to pattern recognition problems, machine
learning algorithms have been employed to classify borrowers’ creditworthiness [
11
].
Having less restrictive constraints than Altman and Olson’s model, support vector machines
(SVM) have been developed to solve the classification problem. [
12
]. Chen et al. [
13
] used
the SVM model to predict the bankruptcy risk of German firms. The study proved that
the SVM model produced better results than the traditional logit model. Moreover, the
authors found that the SVM model can better exploit the nonlinear relationships between
coefficients and default risk than traditional models such as discriminant or logit models.
Shin et al. [
14
] also applied SVM to predict bankruptcy for 2320 medium-sized enterprises
at the Korea Credit Guarantee Fund from 1996 to 1999. The results showed that SVM
brought better predictive results than other models, including artificial neural network
(ANN) models.
Zhao et al. [
15
] conducted a study to build a credit scoring system based on ANN on the
German credit data dataset. The results showed that ANN could predict credit scores more
accurately than traditional models, with an efficiency of 87%. Geng et al. [
16
] used machine
learning models to predict financial distress for firms listed on the Shanghai and Shenzhen
stock exchanges from 2001 to 2008. They found that the ANN model produced better results
than decision trees, SVM, and assembled models. Barboza et al. [
17
] used SVM, bagging,
boosting, and random forest methods to predict the bankruptcy of
10,000 companies
in the
North American market from 1985 to 2013 and compared them with traditional statistical
Data 2022,7, 160 3 of 12
models. The results showed that bagging, boosting, and random forest outperforms the
others. Specifically, machine learning models had an average accuracy of about 10% higher
than traditional models. In more detail, the random forest model had an accuracy of up
to 87%, while the traditional model had an accuracy of 50% to 69%. Chakraborty and
Joseph [
18
] constructed a predictive model of financial distress based on balance sheet
items. They found that the random forest model showed better results than 10 percent
measured by AUC ROC. Similarly, Fuster et al. [
19
] studied mortgage defaults in the US
and found that a random forest model had more accurate predictive results than the logistic
model. Based on recent research by Dubyna et al. [
20
], the use of technologies in the
provision of financial services is extremely important, influencing the transformation of
the financial behavior of customers and the business models of financial institutions. In
addition, research by Zhavoronok et al. [
21
] shows that innovation processes affect the
financial behavior patterns of households in innovative economies in different meanings
and forms.
Previous studies have proved that machine learning models yield better results than
traditional statistical models. However, the results are not consistent and depend on the
data set used in the study. In addition, machine learning models also have drawbacks
such as (i) they do not work well for unbalanced data because they tend to classify many
observations into classes with more larger data; (ii) model accuracy increases with a more
extensive training dataset, but validation is insufficient to meet a certain rate (iii) selecting
hidden later layers is problematic, resulting in a trade-off between computation time and
high prediction rate [15].
2.2. Literature Review on Explanation
Interpretability is the ability to explain or present in terms that can be understood
by humans [
22
]. Miller [
23
] defined explainability as the degree to which people can
understand the causes of a decision. Thus, an interpretable system is a system that provides
knowledge that helps people understand how it works and can interpret the results of
a specific forecast.
Since 2019, studies have focused on the explanatory power of deep learning models
in predicting default. Bracke et al. [
24
] used the gradient tree boosting model to predict
default on mortgage loans in the UK. They introduced a new method named quantitative
input influence (QII) to evaluate the contribution of the input variables to the target variable
by calculating the Shapley values. The authors showed that this method could provide
a detailed explanation of the degree of impact of the variables on different customer groups.
Later, some studies used the Shapley values to measure the contribution of variables
in the model to the target variable. Babaei et al. [
25
] applied machine learning to predict
default for small and medium enterprises. The authors eliminated variables with low
explanatory Sharley values. The results showed that defaults and expected returns of these
companies are better forecasted with a smaller amount of input variables from the financial
statements. Bussmann et al. [
26
] applied XGB machine learning, correlation networks, and
Shapley computation on a sample of 15,000 SMEs in Southeast Europe. The results showed
that forecasting efficiency could be improved by understanding the factors that affect credit
risk in the model.
Additionally, some studies used Shapley Additive exPlanations (SHAP) and Local
Interpretable Model-Agnostic Explanations (LIME) methods to compare the explanatory
power of the variables in the model [
27
,
28
]. Ariza-Garzón et al. [
29
] compared the predictive
power of machine learning algorithms (decision trees, random forests, XGBoost) with
logistic regression models on personal loans from Lending Club company. Then, they
evaluated the contribution of variables in the model through SHAP and LIME methods. The
results showed that when applying SHAP to machine learning methods, the explanatory
power of these models was improved, even reflecting the nonlinear relationships better
than the traditional logistic regression model. A similar study was conducted by Hadji
Misheva et al. [
30
], and the authors also found the same results. They concluded that
Data 2022,7, 160 4 of 12
explanatory results were stable and consistent with the logical explanations in finance. This
study applied the SHAP method to interpret machine learning models’ results.
3. Methodology
3.1. Data and Data Processing
In this study, we used data extracted from the financial statements of Vietnamese
companies listed on the Ho Chi Minh Stock Exchange, Hanoi Stock Exchange, and UPCOM.
Data were collected from 2010 to 2021.
Financial distress companies are identified based on criteria such as negative equity,
EBITDA on interest being less than one for two consecutive years, and operational income
being negative for three consecutive years. In addition, we also consult the external
auditor’s conclusions in the financial statements and filter out companies suspected of not
being able to operate continuously. Finally, the selected insolvency companies meet the
above criteria and have sufficient financial data during the observation period to conduct
the research. Companies will be labeled one if they are in the financial distress group and
zero for the others.
Based on the study of Chakraborty and Joseph [
18
], and Standard & Poor’s evaluation
criteria, we used 25 financial ratios as input features for predictive machine learning models
(Appendix A). These ratios reflect essential aspects of companies, such as liquidity, financial
risk, business risk, and the market factor, that are expected to affect the debt repayment
capacity of companies.
The data were preprocessed for missing values and outliers. We also excluded fi-
nancial, insurance, accounting, and banking companies because of differences in financial
statements. The data had 3277 observations, of which 436 companies were in financial
distress (13.3%), and 2841 companies were in the group of non-financial distress (86.7%).
Because the data were unbalanced, we used SMOTE technique to handle the unbalance
data problem. Finally, the data were divided into training and validation sets, with 70%
and 30%, respectively.
We implemented the models using Python 3.5 and other Python packages ori-
ented to data analysis, including Numpy1.19.3, Pandas 1.5.1, Scikit-learn 1.1.3, and
Seaborn 0.12.1 [31–34]
. For interpreting the results, we use Shap packages to compute
Shapley values and visualize the results [35].
3.2. Machine Learning Methods to Predict Financial Distress
In this research, we employed statistical methods and machine learning to predict the
distress of businesses, including logistic regression, support vector machine, decision tree,
random forest, artificial neural network, and extreme gradient boosting. The details of the
methods are presented as follows.
3.2.1. Logistic Regression
Logistic regression is a popular statistical technique for forecasting problems where
the dependent variable is binary, specifically, the financial distress status in this study. The
output of the model is the probability of financial distress
Pn
, corresponding to the input
features X. This probability is calculated as Equation (1).
Pn(y=1)=1
1+e−(β0+β1X1+...+βkXk)(1)
Logistic regression is often used as a benchmark in research to compare with other
forecasting methods. The advantage of logistic regression is that the results are easy to
interpret and understand for most users. In other words, this is one of the models with
high explanatory power, so it is often used in practice at financial institutions.
Data 2022,7, 160 5 of 12
3.2.2. Support Vector Machine
Support vector machines (SVMs) are based on the idea of defining hyperplanes that
decompose observations into high-dimensional feature spaces. Linear SVMs models focus
on maximizing the margin between positive and negative hyperplanes. The classification
process will take place according to Equation (2).
yi=+1 if b+αTx≥+1
−1 if b+αTx≤ −1(2)
where b is the bias.
For nonlinear cases, a kernel is used to project features into a high-dimensional space.
For example, a traditional Kernel function, a Gaussian radial basis, has the following
Equation (3).
K(x,xi)=exp−γ||x−y||2(3)
The strength of SVM is that it avoids overfitting with small samples and is less sensitive
to unbalanced distributions.
3.2.3. Decision Tree
Decision tree algorithms extract information from data to derive decision rules in the
form of a tree structure. More specifically, the decision tree algorithm determines the best
allocation to optimize each split with maximum purity based on a measure, such as the
Gini Index or Entropy Index. The root of a decision tree is called the root node, the most
distinguishable attribute. Leaf nodes represent classes, which are the following attributes.
The decision tree model has the advantage that model is intuitive and interpretable.
However, the drawback is that this model is more prone to overfitting during the feature
domain division or the branching process.
3.2.4. Random Forest
Breiman [
8
] developed the random forest technique based on the decision tree model.
In this method, many decision trees are constructed using subsets of randomly selected
features. The sample and feature subsets are randomly selected to ensure the diversity of
the classifiers. Then, the random forest is built for several subsets that generate the same
number of classification trees. The preferred class is defined by a majority of votes; thus,
the results are more precise and, most importantly, avoid model overfitting [8].
3.2.5. Extreme Gradient Boosting (XGB)
Gradient boosting is a machine-learning technique used in regression and classification
tasks. It gives a prediction model in the form of an ensemble of weak prediction models,
typically decision trees. Extreme gradient boosting constructs decision trees in parallel and
incorporates complexity control in the loss function to control overfitting and achieve better
performance results. The optimization function to minimize is as follows in Equation (4).
Lt=
n
∑
k=1
lyk,yt−1
k+φt(xk)+Ω(φt)(4)
where
l(.)
is a loss function.
Ω(φt)
is a regularization term that penalizes the complexity of
the model. The goal is to find the φtthat minimized the function Lt.
3.2.6. Artificial Neural Network
An artificial neural network (also known as a neural network) is a machine learning
algorithm designed based on the idea of how an organism’s brain works. The algo-
rithm solves complex problems by mimicking the brain’s structure and the connections
between neurons.
Data 2022,7, 160 6 of 12
An artificial neural network consists of connections between many layers of artificial
neurons. Each layer is divided into an input layer, an output layer, and a hidden layer.
These artificial neurons simulate the role of human neurons through mathematical models.
Each artificial neuron receives an input signal,
x1
,
x2
,
. . .
,
xj
, consisting of the numbers
0 and 1, then it estimates the weighted sum of the signals it receives according to their
weights,
w1
,
w2
,
. . . wj
. A signal is only transmitted to the next artificial neuron when the
sum of the weights of the received signals exceeds a certain threshold. An artificial neuron
can be represented as the Equation (5).
yi=output =
0i f ∑
j
wjxj≤threshold
1i f ∑
j
wjxj>threshold
(5)
Based on historical data, neural network optimization is conducted by determining
weights and thresholds for activation.
3.3. Explainability Methods
SHApely Additive exPlanation (SHAP) is applied to meet interpretation requirements.
This algorithm aims to build a linear model explaining the feature importance for a given
prediction by computing Shapley sampling values. The SHAP values are calculated based
on cooperative game theory in order to explain the prediction through the marginal contri-
bution of each feature. The SHAP model can be represented as a linear combination of the
binary variables in the following Equation (6).
gz0=Φ0+
M
∑
i=1
Φiz0
i(6)
where
g
is an explanatory model,
z0e{0, 1}M
is the coalition vector, M is the maximum
number of features, the ith feature has a contribution (
z0=
1) or not
(z0=0)
.
Φi
is the
SHAP value of the ith feature, representing the contribution of the ith feature and can be
calculated according to the following Equation (7).
Φi(f,x)=∑
S⊆N
|S|!(M− |S| − 1)!
M
where N is the set of all features, |S| represents the number of features in feature subset
S
excluding the ith feature.
fx(S)
represents the result of the machine learning model f
training in feature subset S.
SHAP is an interpretation technique that works very well on structured data with
a limited number of features. SHAP can be interpreted at the global level and on a specific
data point. At the global level, feature importance is determined by the average absolute
values per feature. In this research, TreeSHAP was employed to compute SHAP values and
explain the output of the decision tree and XGBoost models. We chose TreeSHAP because
it is a fast and exact method to estimate SHAP values for tree models and ensembles
of trees [
35
]. Moreover, although the tree-based methods, e.g., XGBoost and random
forest have their permutation feature importance values, the SHAP values have significant
differences with such scales. Permutation feature importance is based on the decrease in
model performance. SHAP is based on the magnitude of feature attributions.
Data 2022,7, 160 7 of 12
3.4. Evaluation of Model Performance
To evaluate the model’s performance, we use the following performance metrics.
•Accuracy—The proportion of correct classification in the evaluation data
Accuracy =TP +TN
TP +T N +FP +FN (8)
•Precision—The proportion of true positives among the predicted positives
Precision =TP
TP +FP (9)
•Sensitivity (Recall)—The proportion of positives correctly predicted
Recall =TP
TP +F N (10)
•F1Score—The harmonic mean of precision and recall.
F1Score =2×Precision ×Recall
Precision +Recall (11)
•The ROC plots the true positive rate to the false positive rate.
•
Area under the receiver operating curve (AUC)—The receiver operating curve (ROC)
measures the model’s classification ability subject to varying decision boundary thresh-
olds. The area under the curve (AUC) aggregates the performance measures given
by the ROC curve. AUC also helps to provide criteria for evaluating and comparing
models: AUC had to be more than 0.5 for the model to be acceptable, and the close
to 1, the stronger its predictive power.
4. Results and Discussions
4.1. Prediction Results
Table 1presents the hyper-parameter settings and the evaluation of the models on
the model performance metrics. According to Abellán and Castellano [
36
], the accuracy
measure may not be accurate because it does not consider that false positives are more
important than false negatives. So, precision and recall are better measures of the model
performance, which is more sensitive to the imbalanced dataset. This research also uses the
balanced F-score (F1 score), the harmonic mean of precision and recall.
XGB and random forest also have higher recall and F1 scores than other models,
indicating that both models are good at predicting positive values. In contrast, logistic
regression, ANN, and SVM have relatively low sensitivity values, indicating that these
models have higher Type I errors. Interestingly, SVM has the highest value (0.9427),
showing the ability to predict the positive accuracy of the predicted values.
Based on AUC values, it can be seen that random forest has the highest AUC value
(0.9788), followed by extreme gradient boosting (0.9702), showing that these two models
have better classification ability than other models. These results are similar to the results
of Barboza et al. [17]; Chakraborty and Joseph [18]; Fuster et al. [19].
Figure 1shows that the ROC curve of random forest and XGB closer to the top left
corner indicate better performance than other models. It is noted that the ROC does not
depend on the class distribution, so it helps evaluate classifiers predicting rare events such
as default risk or financial distress risk.
Data 2022,7, 160 8 of 12
Table 1. The performance results of classifiers.
Algorithms Hyper-Parameter AUC Accuracy Precision Recall F1 Score
1
Extreme
Gradient
Boosting
booster = “gbtree”,
n_estimator = 100, max_depth = 1,
random_state = 42
0.9702 0.9566 0.8726 0.8354 0.8536
2Random
Forest
max_depth = 14,n_estimators = 100,
random_state = 42 0.9788 0.9529 0.8535 0.8272 0.8401
3Logistic
Regression random_state = 42 0.9303 0.8623 0.8854 0.5148 0.6511
4
Artificial
Neural
Network
n_hidden = 2, max_iter = 200,
activations = relu, Optimizer = adam
0.9034 0.9168 0.8025 0.6811 0.7368
5Decision
Trees
Criterion = “gini”, max_depth = 14,
random_state = 42 0.8848 0.9251 0.828 0.7065 0.7625
6
Support
Vector
Machine
Kernel = “rbf”, probability = True,
class_weight = “balanced”,
random_state = 42
0.7889 0.8789 0.9427 0.4022 0.5815
Source: author’s calculation.
Data 2022, 7, x FOR PEER REVIEW 8 of 12
6
Support Vector
Machine
Kernel = “rbf”, probabil-
ity = True, class_weight =
“balanced”, ran-
dom_state = 42
0.7889
0.8789
0.9427
0.4022
0.5815
Source: author’s calculation.
XGB and random forest also have higher recall and F1 scores than other models, in-
dicating that both models are good at predicting positive values. In contrast, logistic re-
gression, ANN, and SVM have relatively low sensitivity values, indicating that these mod-
els have higher Type I errors. Interestingly, SVM has the highest value (0.9427), showing
the ability to predict the positive accuracy of the predicted values.
Based on AUC values, it can be seen that random forest has the highest AUC value
(0.9788), followed by extreme gradient boosting (0.9702), showing that these two models
have better classification ability than other models. These results are similar to the results
of Barboza et al. [17]; Chakraborty and Joseph [18]; Fuster et al. [19].
Figure 1 shows that the ROC curve of random forest and XGB closer to the top left
corner indicate better performance than other models. It is noted that the ROC does not
depend on the class distribution, so it helps evaluate classifiers predicting rare events such
as default risk or financial distress risk.
Figure 1. ROC of classifiers. Source: author’s calculation.
4.2. Interpretation Results
We calculated SHAP values on two models with the best predictive results, random
forest and XGBoost. We calculated the average Shapley values across all observations to
obtain an “overall” or “global” explanation. This technique was used in the research of
Kim and Shin [5] and Bussmann et al. [26].
Figure 2 shows that four of the five important features are the same between the two
models. They are long-term debts to equity (X4), account payable to equity (X10), enter-
prise value to revenues (X22), and diluted EPS (X25). Thus, the important features deter-
mined based on Shapley values are relatively stable between XGB and random forest
models.
Figure 1. ROC of classifiers. Source: author ’s calculation.
4.2. Interpretation Results
We calculated SHAP values on two models with the best predictive results, random
forest and XGBoost. We calculated the average Shapley values across all observations to
obtain an “overall” or “global” explanation. This technique was used in the research of
Kim and Shin [5] and Bussmann et al. [26].
Figure 2shows that four of the five important features are the same between the
two models. They are long-term debts to equity (X4), account payable to equity (X10), en-
terprise value to revenues (X22), and diluted EPS (X25). Thus, the important features
determined based on Shapley values are relatively stable between XGB and random
forest models.
Data 2022,7, 160 9 of 12
Data 2022, 7, x FOR PEER REVIEW 9 of 12
SHAP on XGB
SHAP on Random Forest
Figure 2. The feature importance of XGB and random forest. Source: author’s calculation.
Figure 3a illustrates the influence of long-term debts to equity (X4) on the prediction
results. X4 reflects the leverage risk of the company in the long term. If the leverage is
high, the company is under tremendous pressure to repay debts and is prone to liquidity
risk when the economy is in recession. Figure 3a shows the positive relationship between
the X4 and the SHAP values. When the X4 increases, the SHAP values increases, indicat-
ing that the probability of financial distress also increases. This phenomenon is in line with
the knowledge of financial experts.
(a) Depence plot for X4
(b) Depence plot for X10
(c) Depence plot for X22
(d) Depence plot for X25
Figure 3. The SHAP dependence plot of single feature. Source: author’s calculation.
Figure 3b displays the dependence plot for account payable to equity (X10). X10 re-
flects the default risk in the short term. If X10 is high, the company is under pressure to
pay short-term debt obligations, which may lead to liquidity risk. When X10 is under 2.5,
Figure 2. The feature importance of XGB and random forest. Source: author’s calculation.
Figure 3a illustrates the influence of long-term debts to equity (X4) on the prediction
results. X4 reflects the leverage risk of the company in the long term. If the leverage is high,
the company is under tremendous pressure to repay debts and is prone to liquidity risk
when the economy is in recession. Figure 3a shows the positive relationship between the
X4 and the SHAP values. When the X4 increases, the SHAP values increases, indicating
that the probability of financial distress also increases. This phenomenon is in line with the
knowledge of financial experts.
Data 2022, 7, x FOR PEER REVIEW 9 of 12
SHAP on XGB
SHAP on Random Forest
Figure 2. The feature importance of XGB and random forest. Source: author’s calculation.
Figure 3a illustrates the influence of long-term debts to equity (X4) on the prediction
results. X4 reflects the leverage risk of the company in the long term. If the leverage is
high, the company is under tremendous pressure to repay debts and is prone to liquidity
risk when the economy is in recession. Figure 3a shows the positive relationship between
the X4 and the SHAP values. When the X4 increases, the SHAP values increases, indicat-
ing that the probability of financial distress also increases. This phenomenon is in line with
the knowledge of financial experts.
(a) Depence plot for X4
(b) Depence plot for X10
(c) Depence plot for X22
(d) Depence plot for X25
Figure 3. The SHAP dependence plot of single feature. Source: author’s calculation.
Figure 3b displays the dependence plot for account payable to equity (X10). X10 re-
flects the default risk in the short term. If X10 is high, the company is under pressure to
pay short-term debt obligations, which may lead to liquidity risk. When X10 is under 2.5,
Figure 3. The SHAP dependence plot of single feature. Source: author ’s calculation.
Data 2022,7, 160 10 of 12
Figure 3b displays the dependence plot for account payable to equity (X10). X10
reflects the default risk in the short term. If X10 is high, the company is under pressure
to pay short-term debt obligations, which may lead to liquidity risk. When X10 is under
2.5, the SHAP values increase, implying that the likelihood of financial distress increases.
However, when the X10 is over 2.5, the SHAP values tend to be stable. This is an interesting
phenomenon from observable data, different from experts’ expectations.
Figure 3c shows the influence of enterprise value on revenues (X22) on the prediction
results. This ratio measures how much it would cost to purchase a company’s value relative
to its revenues. If EV/R increases, the company is overvalued. When the value of X22 is
close to 0, the variation of the SHAP value is high. As X22 increases, the SHAP value also
gradually increases. However, after X22 is greater than 0.6, the SHAP values tend to be flat.
Thus, increasing of X22 will influence SHAP values when X22 is low, but this effect will
decrease when X22 is high.
Diluted EPS considers what would happen if dilutive securities were exercised.
Figure 3d
exhibits the dependent plot for diluted EPS (X25). It can be seen in Figure 3d that
there exists a negative relationship between X25 and SHAP values. When X25 increases,
SHAP values tend to decrease, reducing the probability of financial distress. However,
SHAP values have fluctuated when X25 is less than 0 and greater than 4000.
5. Conclusions
In this study, we employed machine learning models to predict financial distress in
listed companies in Vietnam from 2010 to 2021. The results showed that XGB and random
forest were two models with higher recall, F1 scores and AUC than other models. In
addition, we also used SHAP values to analyze the impacts of each feature on the forecast
results. Features such as long-term debts to equity (X4), account payable to equity (X10),
enterprise value to revenues (X22), and diluted EPS (X25) showed an significant impact on
forecast results and were generally in accordance with the knowledge from experts.
Based on this study, managers, policymakers, and credit rating agencies have equipped
tools to understand and interpret results from complex machine learning models. This
research has shed light on using XAI to make decisions in economics and finance.
The study also has some limitations, such as low sample size, especially low proportion
in financial distress companies. We hope the following studies can expand the sample size
by researching countries with similar characteristics. Moreover, the research sample can be
expanded to other fields, such as consumer lending or P2P lending.
In addition, the features used in this study are financial indicators, which are based
on the assumption that information about companies is reflected in the financial position.
However, this assumption is unrealistic in Vietnam, whose financial market is inefficient.
We hope that the following studies can add more behavioral features, such as ownership
structure, number of independent BOD members, industry, and diversity of business lines.
Author Contributions:
Conceived the idea, wrote the Introduction, D.T.N.; wrote literature review,
H.A.L.; wrote methodology, results and discussions, conclusion, K.L.T.; revised the manuscript,
T.H.N. All authors have read and agreed to the published version of the manuscript.
Funding:
The study was supported by The Youth Incubator for Science and Technology Program,
managed by the Youth Development Science and Technology Center—Ho Chi Minh Communist
Youth Union and Department of Science and Technology of Ho Chi Minh City, the contract number is
“14/2021/ HÐ-KHCNT-VU”.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data for this study can be found on our GitHub page: https://
github.com/anhle32/Explainable-Machine-Learning-.git (accessed on 12 October 2022).
Conflicts of Interest: The authors declare no conflict of interest.
Data 2022,7, 160 11 of 12
Appendix A
Table A1. Summary of Variables.
Symbol Input Features Category
X1 Cash Ratio Liquidity risk
X2 Quick Ratio Liquidity risk
X3 Current Ratio Liquidity risk
X4 Long term Debts to Equity Financial risk
X5 Long term Debts to Total Assets Financial risk
X6 Total Liabilities to Equity Financial risk
X7 Total Liabilities to Total Assets Financial risk
X8 Short term Debt to Equity Financial risk
X9 Short term Debt to Total Assets Financial risk
X10 Account Payable to Equity Business Risk
X11 Account Payable to Total Assets Business Risk
X12 Total Assets to Total Liabilities Business Risk
X13 EBITDA to Short term Debt and Interest Business Risk
X14 Price to Earning Market factor
X15 Diluted Price to Earning Market factor
X16 Price to Book Value Market factor
X17 Price to Sales Market factor
X18 Price to Tagible Book Value Market factor
X19 Market Capital Market factor
X20 Price to Cashflow Market factor
X21 Enterprise Value Valuation
X22 Enterprise Value to Revenues Valuation
X23 Enterprise Value to EBITDA Valuation
X24 Enterprise Value to EBIT Valuation
X25 Diluted EPS Valuation
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