Corporate bankruptcy prediction using Deep learning techniques

Corporate Bankruptcy Prediction using Deep
Learning Techniques
Research in Computing Proposal
MSc Data Analytics
Shantanu Deshpande
x18125514
School of Computing
National College of Ireland
Supervisor: Anu Sahni

Corporate Bankruptcy Prediction using Deep
Learning Techniques
Shantanu Deshpande
x18125514
Abstract
Corporate bankruptcy is a major cause of concern for the economic stakeholders
including the investors, management, etc. The major challenge in predicting the
cause of a business failure is the wide number of factors responsible for a financial
crisis. Although concern topic has been of particular interest and widely explored
by the researchers, relying on a single predictive model is not sufficient due to
the vastness of underlying factors leading to bankruptcy. While the recent studies
have seen the use of machine learning techniques like SVM, Decision Trees and
Neural Networks for improved prediction accuracy, very few studies have explored
the discriminatory power of textual disclosures in conjunction with the financial
ratios (FRs). This research focuses on predicting corporate bankruptcy by making
use of not only the financial ratios of a firm but also integrating it with textual
disclosures from the MD&A section of 10-K annual filings of the firm. This study
intends to use the Long Short-Term Memory (LSTM) network, a type of artificial
Recurrent Neural Network (RNN) model that has never been used in the past for
corporate bankruptcy prediction. The models performance will then be evaluated
using different methods such as AUC-ROC, Accuracy Ratio and Cumulative decile-
ranking.
Keywords: Bankruptcy Prediction, Deep Learning, Recurrent Neural Net-
work, NLP
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Contents
1 Introduction 3
1.1 Research question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.1 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Plan of Paper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Literature Review 4
2.1 Prediction using Statistical Techniques and Ensemble Models . . . . . . . 5
2.2 Prediction using Semi-Supervised Models and Deep Learning Models . . 8
3 Methodology 15
3.1 Data Gathering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Data Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3 Data Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4 Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.5 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.5.1 Word Embedding Model . . . . . . . . . . . . . . . . . . . . . . . 17
3.5.2 Skip-Gram Negative Sampling Model . . . . . . . . . . . . . . . . 17
3.5.3 Long Short Term Memory Network (LSTM) Model . . . . . . . . 17
3.6 Evaluation Metrics for Measuring Model Performance . . . . . . . . . . . 18
3.6.1 Area Under the Receiver Operating Curve (AUROC) . . . . . . . 19
3.6.2 Accuracy Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.6.3 Cumulative decile-ranking . . . . . . . . . . . . . . . . . . . . . . 20
3.7 Project Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4 Summary 21
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1 Introduction
Bankruptcy is one of the major causes of concern for the creditors as they are the major
stakeholders in any organization in terms of the financial worth. Too many competitors
and the changes in the global economy resulting in unfair product prices and financial
crunch are few of the key drivers of corporate bankruptcy. The damage incurred through
these financial losses could not be overstated. Corporate bankruptcies create a negative
impact on the economy and thereby propagates recession Bernanke (1981). In terms of
economic decision making, prediction of bankruptcy is of great importance as any type of
business, whether large or small, concerns industry participants, investors, community of
local people and influences the global economy and policymakers. As economy is greatly
dependant on the corporate affairs, it is of foremost importance for a creditor to validate
number of parameters to predict the probability of bankruptcy like the number of existing
loans, regularity in the payments etc. These types of decisions play a crucial role in the
development of not only the company but also of the entire economy of a country.
While keeping in mind the global financial crisis faced in the recent years, the im-
portance of credible and timely bankruptcy forecasting is easily understood. There are
two different approaches that are been widely followed to predict whether there is any
possibility of a company going bankrupt. They are a) Structural approach where the
firm attributes and the interest rates are closely examined to determine the outcome of
the default probability and secondly the Statistical approach where the outcome of the
default probability is predicted through mining of the data. Various statistical methods
have been studied and implemented in Balcaen and Ooghe (2006) and in Kumar and Ravi
(2007) statistical as well as intelligent methods are explored to predict the bankruptcy
rate. Further there have been researches in this field based on different methodologies like
multidimensional analysis and generalized linear models. However, due to the increas-
ing volumes of data, the linear models such as logistic regression models are not able to
distinguish in an effective way the relationship within the econometric indicators. The be-
ginning of the 20th century has seen the use of artificial intelligence and machine learning
to determine the bankruptcy prediction. In a more recent work Alexandropoulos et al.
(2019) the author has used deep dense artificial neural network for bankruptcy prediction.
A basic and common element of all these models is the use of accounting-based and
market-based variables which are normally numeric data in a well-structured format.
Along with this, textual disclosures, which is a form of unstructured, qualitative data
equally plays an important role in the way information is propagated to the public. A
great proportion of the companys annual filing to the regulatory agencies are in the form
of textual disclosures. Also, the policy makers and market participants rely significantly
on the financial reports and news articles. Despite knowing the importance and effective-
ness of textual disclosures, there has not been much work done to integrate this type of
data due to the difficulty in obtaining as well as quantifying the textual data. Through
a recent study Campbell et al. (2014) Loughran and McDonald (2011) the author has
highlighted the fact that valuable information about credit risk is contained in the qual-
itative corporate filings. In order to leverage the potential of textual disclosures, there is
a need for effective algorithms for improving the prediction rate.
In this research, we will focus on a new deep learning method for bankruptcy forecast
by assessing the predictive power of textual disclosures. In deep learning approach, we
combine multiple layers of neural networks in order to learn the representations of data
along with multiple levels of abstraction. This approach has shown promising results in
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various areas like language processing, image recognition, machine translation etc. be-
cause of their ability to extract features from unstructured data such as text or image.
Therefore, the aim of this research is to use deep learning approach to forecast bank-
ruptcy using textual disclosures. The database for the purpose of this research is the
numerical variables generated from stock market and accounting data. Further, Mana-
gerial Discussion Analysis (MDA) section from the firms annual filing is extracted and
matched with our observations. Our study points to a fresh region that can be supported
by deep learning studies.
1.1 Research question
Does bankruptcy prediction using financial ratios and textual disclosures as part of a Long
Short-Term Memory network model outperform the Convolutional Neural Network model
prediction that involves only the financial ratios?
1.1.1 Research Objective
The primary objective of this study will be to build such a predictive model that will be
robust and efficient in forecasting corporate bankruptcy based on not only the financial
indicators but also the textual disclosure.
• To find out if the addition of textual disclosures to financial ratios improves predic-
tion performance.
• To find out if deep learning technique like Recurrent Neural Network has better
prediction ability than Convolutional Neural Network.
1.2 Plan of Paper
The project proposal is categorized in five sections that would help in providing the ap-
proach this research will adopt for addressing the posed research question and objective.
Section 2 provides the information about the past studies related to this domain. This
section has three additional subsections. Section 3 precisely explains the proposed meth-
odology that will be followed for answering proposed research question. This section
further has been divided into six subsections that include the models that will be built
and also the evaluation methods for evaluating models performance. Lastly, section 4
summarizes the project proposal document and contains a brief of all the sections.
2 Literature Review
In this section, we will go through the past studies in this field and the various techniques
used to predict corporate bankruptcy and each study hereto tries to improve the predic-
tion rate and minimise the limitations of previous works. Numerous studies have made
use of statistical and intelligent techniques in the past to solve the bankruptcy prediction
problem. The techniques used in these studies varied based on the source of data, the
financial ratios used as explanatory variables and the size of the sample. According to
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Kumar and Ravi (2007) the health of a firm is highly dependent on the following factors-
1) at the time of inception how much financially solvent it is 2) the ability and efficiency
to create cash from ongoing operations 3) the level of access it has to the capital markets
4) incase of cash short-falls, the duration it can stay in power i.e. its financial capacity.
2.1 Prediction using Statistical Techniques and Ensemble Mod-
els
The initial studies primarily focused on using statistical techniques for solving the bank-
ruptcy prediction problem. The various methods that were explored included Linear dis-
criminate analysis (LDA), univariate analysis, multivariate discriminate analysis (MDA),
factor analysis (FA) and logistic regression (logit). Incase of a univariate bankruptcy
prediction model, estimation of a cut-off point is done for each ratio or measure and for
each measure the classification procedure is carried out separately. No prior statistical
knowledge is required thus making it one of the simplest technique. Also, it is assumed in
this method that there exists a linear relationship between the failure status and all the
related measures. Balcaen and Ooghe (2006) Multiple discriminant analysis is a combin-
ation of number of variables that works in the best way to distinguish between bankrupt
and non-bankrupt firms. Linear MDA is one of the most popular MDA methods. In this,
the firm characteristics are combined to form a single multivariate discriminant score.
The discriminant score determines the financial health of the company and a cut-off
point is identified to distinguish between failing and non-failing firms. If the discriminant
score is below the cut-off point the firm is classified as failing and non-failing if it is above
the cut-off point. Models based on logistic regression assume a logistic distribution of the
data required for training the model.
According to Balcaen and Ooghe (2006), the statistical bankruptcy prediction model
consists of a classification procedure that will classify the firms as failing or non-failing.
In this scenario there are two types of misclassification that can take place. Firstly, incase
a failing firm is classified as a non-failing firm then it is termed as Type1 error whereas
Type2 error occurs if a non-failing firm is wrongly classified as a failing firm.
As time progressed, studies were more based on intelligent techniques and the methods
used were different neural network architectures, probabilistic neural network, multi-layer
perception, decision trees, soft computing or hybrid intelligent systems etc. A thorough
review of previous studies till the year 2005 have been carried out by Kumar and Ravi
(2007) wherein a brief summary of all studies in the field of bankruptcy prediction is de-
rived and compared. As per the general observation, the intelligent technique like neural
network architectures viz. BPNN outperformed all the statistical techniques such as the
LDA, MDA, logistic regression and FA. The reason BPNN outperformed all the other
methods is that BPNN involved logistic activation function which can be perceived as
a collection of multiple logistic regressions that are fitted together in parallel. There-
fore, the non-linearity that is present in the data can be modelled better with the use
of BPNN method. As with the increased use of intelligent techniques and their out per-
formance over the previous methods, statistical techniques are no longer an opted choice
also because of their low accuracy.
An SVM based prediction model has been put forth by Ding et al. (2008) wherein the
author has used grid-search technique by making use of 10-fold cross validation in order
to find out the most favourable parameter value of the kernel function of support vector
machine. The dataset has been collected from A-share markets of two Chinese cities which
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contains financial ratios and includes 250 observations; 194 non-Special Treatment cases
and 56 ST cases. Different kernel functions like RBF, polynomial, linear and sigmoid has
been used to conduct experiments and the results showed that RBF SVM outperformed
the BPNN and MDA. The RBF SVM gave an accuracy of 95.2% on training data and
83.2% on test data whereas BPNN gave an accuracy of 77.6 % on training data and 76.8
% on test data.
Performance of BPNN model is tested and compared with a generalized regression
neural topology in Lahmiri and Bekiros (2019). The use of mimetic intelligent emulating
techniques is explained that involved ANN with qualitative information. The various
types of considered neural networks in this study are multi-layer back-propagation neural
network, radial basis functions, probabilistic neural network, and generalized regression
neural network. BPNN generally consists of three layers, first layer being the input
layer the second layer being hidden layer that captures non-linear relationship amidst
the variables and lastly the output layer that provides the predicted values. PNN is a
type of Bayesian classifier that merges non-parametric estimator and Bayesian decision
making strategy in order to obtain a probability density function. Generalized regression
neural network is a memory-based and parallel system that does the work of estimation
of the regression surface of continuous variable. The results showed that GRNN model
outperformed the other models and yielded accuracy of 99.96%.
Neural network based study for bankruptcy prediction has also been put forth in
Brédart (n.d.) that made use of only the major three financial ratios that are normally
easily available. The dataset consisted financial ratios of 3,728 Belgian firms out of which
1,854 had gone bankrupt. A high discriminating power is shown by liquidity, solvency
and profitability for bankruptcy detection. These ratios are further given as input to
neural network model for prediction and the results showed that on the training set the
model achieved accuracy of 83.6% and on the test set it scored 81.5% However, this
study is limited to only financial ratios and data from single-country hence has potential
to extend further for testing better prediction.
A study Alexandropoulos et al. (2019) has been conducted using the similar data as
used in Karlos et al. (2016) wherein the author has tested the bankruptcy prediction
rate using a deep dense neural network. 50 bankruptcies were considered and for each,
two healthy firms were taken which led to a sample size of 150 firms and 450 firm year
observations. Deep learning methodology has been implemented widely and successfully
in various difficult scenarios like speech and image recognition, image processing etc. In
this work, the bankruptcy prediction task is addressed using a Deep Dense Multilayer
Perceptron (DDMP) and the artificial neural network consists of two hidden layers. Care
should be taken while selecting the number of neurons as too many of it would lead to
over fitting which occurs when the training set does not contain enough information to
train all the neurons in the model. In the first layer, two thirds of the input attributes
were used along with activation function, ReLU and in the second hidden layer, one third
of the input attributes were used and overall 10% dropout technique has been used. The
DDMP approach using Keras library has been compared with other popular methods like
multilayer perceptron model, logistic regression, and Nave Bayes approach. On the basis
of chosen dataset, DDMP achieved the highest prediction accuracy of 73.2% for one year
prior prediction whereas MP, LR and NB achieved accuracy of 64.8%, 64.6% and 64.7%
respectively. The author has further suggested using additional quantitative attributes
as only financial ratios are considered in this study.
Another research Naidu and Govinda (2018) is based on similar grounds as in Geng
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et al. (2015) that shows the use of artificial neural networks and random forest in pre-
dicting the corporate bankruptcy. The dataset consisted 10503 financial statements of
Polish companies among which 10008 companies avoided bankruptcy and 495 represented
bankrupt companies thus showing a high extent of class imbalance. A sigmoid activa-
tion function is used as an activation function in the artificial neural network model and
bootstrap aggregating or bragging technique is used to train the model. Out of the 65
financial attributes, the three chosen ones based on previous literature and importance
are, Solvency, Ebit and Liquidity. The three attributes are found to be non-dependent
on each other and the Pearson correlation coefficient is maximum between Ebit and
solvency at 0.50. However, a higher degree of error i.e 5.1954 % was found in random
forest classification technique.
In a paper Geng et al. (2015) based on data mining techniques, the prediction has
been conducted using the process of knowledge discovery. Various classifiers were chosen
for this study, namely, Decision trees, Neural network and support vector machine. Com-
panies that were about to go bankrupt were denoted as ST (Special Treatment) firms.
The dataset utilized for building a model consisted of financial data that dated back 3-4
years prior to the company receiving an ST notice. For testing the models performance,
another control sample consisting of the financial data of companies that have not been
listed as ST has been chosen by the author. The dataset consisted of 31 financial indicat-
ors making use of which, 10 models were built on the basis of the statistical probabilistic
theory, specifically, NN, C5.0 DT, CR Tree, logit, bayes probability, discriminant ana-
lysis, and SVM. The models built on support vector machine, C5.0 decision trees, neural
networks outperformed all the remaining techniques and were thereby chosen for data
mining models. The models performance was measured in terms of the following factors:
the accuracy, determined by the ratio of correctly predicted records of both ST non-ST to
the total number of records. Secondly, recall i.e. the ratio of the accurately predicted ST
cases to the total count of actual ST cases and lastly, the precision that is determined by
the ratio of accurately predicted ST cases to total count of predicted ST records. Based
on the statistical results, the author highlighted that the prediction accuracy of neural
network was notably higher than that of support vector machine and decision trees. The
prediction accuracy of neural network in a 3-year window with train-test ratio of 60:40
was found to be 78.82%. Various train-test ratios were used and the results varied sig-
nificantly. This shows that the train-test ratio affects the prediction accuracy i.e. the
model that is created with high partition ratio is highly likely to overfit to the training
set and thus has a poor predictive performance. Thus, contrary to Ding et al. (2008),
in terms of performance, the neural network built on prune algorithm is observed to be
superior than that of the BPNN and SVM.
Although in the above works, it is important to note that only financial ratios are
used extensively; therefore in another research work Liang et al. (2016), the author has
explored the use of corporate governance indicators (CGIs) along with the financial ratios
to assess the prediction performance of corporate bankruptcy. CGIs are classified into five
broad categories: the board structure, ownership structure, cash flow rights, key person
retained and others. The author has used the method of stratified sampling in order to
collect similar number of bankrupt and non-bankrupt firms so as to address the problem
of class imbalance. The dataset contained seven FRs and five CGIs for 239 bankrupt
and 239 non-bankrupt cases. The financial ratios included profitability, cash flow ratios,
turnover ratios, capital structure ratios, growth, solvency whereas the CGIs included the
cash flow rights, ownership structure, retention of key personnel, board structure and
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others. Feature selection has been carried out to reduce irrelevant features by selecting
more representative features that have the more discriminative powers within the chosen
dataset. In this study, five techniques have been tried, tested and compared viz. k-
nearest neighbour, support vector machines, classification and regression trees, nave bayes
classifier and multilayer perceptron. The results showed that models build using CGIs
alone gave worse performance than models that were built using the FRs in terms of the
prediction accuracy and Type1/2 errors. Therefore, the comparison has been carried out
between FRs and FRs with CGIs and it showed that SVM model outperformed others
with prediction accuracy of 79.1% and 81.3% for FRs and FRs with CGIs respectively
followed by CART with prediction accuracy of 78.4% and 78.6% and KNN with 76.5%
and 74.5%.Although the research showed improved performance prediction upon the use
of CGIs, it also highlighted the fact that the usefulness of CGIs is dependant on market
condition.
2.2 Prediction using Semi-Supervised Models and Deep Learn-
ing Models
In a recent study Karlos et al. (2016), the use of semi-supervised schemes is discussed
to address the problem of bankruptcy prediction. The reason to proceed with semi-
supervised learning schemes is that for supervised schemes, the dataset requirement is
large and only then it can perform well and thus semi-supervised models provide efficient
solution by using limited data for achieving better prediction performance. The dataset
consisted of three years prior financial records of Greek firms before they declared bank-
ruptcy and for every bankrupt firm, two additional non-bankrupt firms were taken as
control samples that belonged to the same industry. The feature set consisted of 10 fin-
ancial attributes and the learning algorithms applied separately on both supervised and
on SSL schemes were K-nearest neighbour, C4.5 decisiomn trees, and Sequential Minimal
Optimization (SMO). The dataset has been partitioned based on 10-fold cross valida-
tion technique and feeded as input to SSL algorithms and their corresponding supervised
models and the results are then compared. The model based on Rel-RASCO scheme
achieved the best performance after combining it with C4.5 which is a popular decision
tree algorithm and it showed that models based on SSL performed better than supervised
models however the limitation of this study is that only the financial ratios are considered
whereas other quantitative variables like stock market data and qualitative information
like reputation, leadership, MDA could be considered to improve the prediction rate as
the literature in previous study Mayew et al. (2015) and Cecchini et al. (2010) informed
readers about the importance of these variables.
Another recent study Barboza et al. (2017) is based on the comparison of machine
learning models with that of statistical models and assess their prediction performance.
For the key concepts and definition of different statistical methods, we refer readers to
du Jardin (2016). The dataset included balanced set of solvent and insolvent firms and
predictor variables like profitability, liquidity, productivity, leverage, and asset turnover.
The various techniques that were applied included bagging, boosting, SVM with two ker-
nels (radial basis and linear), random forest, artificial neural network, logistic regression
and MDA. The models were implemented using R statistical software packages. The
packages used were random-Forest, mboost, e1071, ada, nnet, aod and MASS to imple-
ment random forest, boosting, SVM, bagging, ANN, logit and MDA respectively. The
results showed that RF and bagging techniques showed high accuracy in the training
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phase which was expected as both use decision trees that causes model overfitting. The
overall results signified that traditional models that were built using MDA, ANN, LR
had lower predictive accuracy (52% to 77%) whereas machine learning models performed
better (71% to 87%). However there are some drawbacks of machine-learning models.
SVM-Lin results show that there are more misclassifications because it is hard to address
non-separable datasets. Also this study lacked the use of feature selection that is found
to be a common approach in the recent studies.
As observed in previous study, one of the methods used for bankruptcy prediction
is the boosting technique. A similar study Takata et al. (2017) was conducted wherein
a boosting-based method is examined. This method choses a combination of financial
ratios and does the work of deriving a discrimination function. Time-series financial
data spread over two years is utilized as indicators. The dataset consisted of profitloss
statement, balance sheets and cash flow statements of 94 Japanese bankrupt companies
and 2,287 healthy companies. AdaBoost is used to extract the right combination of
ratios. AdaBoost is defined as an ensemble learning algorithm that aims to produce
a single classifier that has a high accuracy from multiple combination of low accuracy
classifiers. The performance of the proposed method is assessed through the process of
leave-one-out cross-validation. After thorough evaluation of the experiments, the study
observed that the prediction rate one year ahead of bankruptcy based on the proposed
AdaBoost model is 82.9%.
Further to the above works, an author du Jardin (2016) suggested designing various
profiles that mimic various situations that the firms may encounter and which may lead
to failure and to build that many models as the number of profiles rather than building a
single model or an ensemble based on algorithm on overall set of variables. The perform-
ance of this technique was studied by using several modelling methods on both single
models viz. Decision tree, logistic regression, neural networks; and ensembles of models
viz. bagging, boosting, random subspace. The results are compared with models that do
not take profiles into consideration. The data contained income statements and balance
sheets of French firms and the model has been designed with the training data from
given year N and testing data from given year N + 1. Bagging is a process that involves
extracting bootstrap samples from initial sample and further to this design of number of
models equal to bootstrap samples, using regression or classification technique. The fi-
nal prediction is observed once the predictions of individual models is combined through
majority voting scheme. Boosting involves sequential model creation in order to give
more weight to the cases that are wrongly classified. The outcome of this study is that
profile-based models provide better forecasts than traditional methods and performance
would further improve when combined with ensemble techniques. The gains achieved by
PBM over TM is 2.57% whereas the gain achieved by adding ensemble technique to PBM
model is 4.74%.
Several recent studies have investigated the use of ensemble based learning method
for classification, regression, one such technique is the Random Forest. A study Rustam
and Saragih (2018) on the prediction of bank failure made use of Random Forest as a
classifier method in order to predict the failure rate of banks in Turkey with the use of
bank financial statements containing 20 financial ratios. The advantage of proceeding
with Random Forest is that it does not require data preparation i.e. it is able to handle
categorical variables, numerical variables and binary variables without the requirement
of scaling and it is also possible to overcome missing values and noise. The bootstrap
method is used wherein a new training set is created by randomly drawing training set
9

with replacement several times. Random forest is a modification of bagging and is an
ensemble method that instead of one, uses multiple analytical models and it creates
forest from decision tree as an input classifier. The idea behind the use of random forest
technique is to reduce the amount of correlation among the trees without the increase in
the variance. The result showed that the testing set achieved accuracy of 94% with all
the ratios and 96% with 6 ratios in the prediction of bank failure rate.
Another similar bankruptcy prediction study Joshi et al. (2018) has been carried
out using the financial ratios as attributes with the use of Random Forest. The ratios
that influence the most for predicting the bankruptcy rate are chosen on the basis of
Genetic Algorithm that does the work of filtering the most important ones from different
bankruptcy models. The process includes analysis of the non-linear relationship between
the financial ratios of various bankruptcy prediction models and further classifying these
set of ratios into influential and non-influential ratios. For the identification of the most
influencing ratios, five different bankruptcy models are taken into consideration. Decision
trees generally face with the problem of high variance hence to address this problem
Bootstrap Aggregation can be used that reduces the variance for those the algorithms
having high amount of variance. Limited dataset of only 14 companies is chosen for this
study and the data is slit in 80-20 ratio for training and testing purpose respectively.
The model is successful in predicting the bankruptcy in certain cases however the limited
nature of dataset, it does not guarantee a strong prediction rate.
The role of textual disclosures is further explored in Mayew et al. (2015) to predict the
firms ability to continue without going bankrupt. The dataset consists of 211 firms that
have filed for bankruptcy, the exact date of bankruptcy obtained from 8-k filings by the
bankrupt firms. Equal number of control observations are taken for the empirical analysis
along with the data of market capitalization and the stock returns. Conditional logistic
regression models are used as sample consists of matched pairs and because the dependent
variable is dichotomous. A dictionary-based technique Loughran and McDonald (2011)
is used to capture the overall linguistic tone of the complete MDA. Assessment of textual
disclosures so as to predict bankruptcy is done via referring to the statistical significance
of the interest coefficients. The extent of accuracy in predicting the bankruptcy with the
help of textual disclosures is assessed by measuring the predictive accuracy through the
ROC curve i.e. via the area under the receiver operating curve or the AUC, and also
from the goodness of fit. The coefficients on all the three variables, namely, GC MGMT,
POSMDA, NEGMDA have the predicted sign and are also statistically significant having
an AUC of 85% and 32% of Pseudo R2. An excellent discriminatory ability is indicated
if the AUC lies between 80-90%. Thus, based on the current variables selected and the
linguistic tone of the MDA disclosure, it can be said that the model possesses an ex-
ceptional bankruptcy prediction accuracy. The results showed that the predictive ability
of the MDA disclosures is remarkable as visible from the 85% being Area under Curve
(AUC). The author also drew attention to an interesting observation that MDA disclos-
ures have the incremental ability and predictive power to predict bankruptcy as much
as three years in advance to the bankruptcy. Also a negative aspect observed in this
study is that if only the management opinion is considered while keeping other factors
in isolation, over 60% of the firms did not have any going concern outlook even though
they filed for bankruptcy the following year.
There is one other source of corporate governance indicators, the Management Dis-
cussion Analysis sections (MDA) of 10-K filings which the researchers have recently
started to look at in the financial statements in order to better understand the dynamics.
10

In a study Cecchini et al. (2010) the researcher creates a unique methodology for use
by finance researchers by defining a dictionary of key terms from a financial statement.
The technique used is Vector Space Model (VSM) wherein after the preprocessing, the
document is further converted to form a vector of key word counts. A methodology is
developed for automating the creation of ontology which includes preprocessing, obtain-
ing the frequency of key terms, further to this the use of WordNet to convert the term
counts to concept counts, and then the development of domain relevant ontology that will
be based on the concepts with the largest discriminating power, multi-word phrases are
then scored and then finally these top-scored multi-word phrases are then converted to
vector of values. The testing methodology involved aggregation of the token counts into
one vector per company, then running vectors through support vector machine model
to find the weights on the individual tokens and testing using leave-one-out analysis for
determining the out of sample ability of prediction of the ontology. The result obtained
showed that the model was able to discriminate between bankrupt firms from the non-
bankrupt firms with an accuracy of 80% further when the text data was combined with
quantitative data, the prediction accuracy improved to 83.87% thus highlighting the fact
that the text from MDA and quantitative financial information complements the overall
performance.
A rather different and unique approach to predict the corporate bankruptcy can be
seen in Antunes et al. (2017). In this a stochastic and Bayesian approach called as Gaus-
sian process is applied and compared with SVM and LR. GP provides a framework that
is highly flexible and it models the complex non-linear relationships between bankruptcy
risk and accounting ratios with improved potential. A probability distribution over a
large set of possible functions is termed as a Gaussian process wherein one can use Bayes
rule for updating the distribution of functions through the observation of training data.
Therefore the main difference between various discriminative classification technique and
the GP is that every prediction is in the form of a probability. Data containing the fin-
ancial ratios of French firms is considered in this study. The GP model was implemented
along with the Laplace approximation method and the Logistic likelihood function and
the results showed that the SVM was outperformed by the GP model based on the chosen
kernel parameters. The author hinted at exploring various other kernels to foresee the
prediction accuracy.
Another similar study conducted using the Gaussian process model in a Bayesian
framework Seidu (2015) for the classification problem. The effectiveness of the expect-
ation propagation approximation and the Laplace approximation is investigated using
several kernels. Large dataset of 2000 corporate firms and their financial ratios is con-
sidered for this study. As mentioned in Antunes et al. (2017) GP is a continuous stochastic
process that can be perceived as probability distributions over functions due to the fact
that the covariance and mean are functions of the inputs. The results showed that mul-
tivariate Gaussian process classifier along with squared exponential kernel provides the
ability to improve bankruptcy prediction with an accuracy of 90.19% in comparison to
the linear logistic model that shows accuracy of 83.25%. Another study Hosaka (2019)
based on convolutional neural network has been put forth recently. The author has used
financial attributes as images and given this as input to the convolutional neural net-
work model to predict the corporate bankruptcy. CNN models are not widely explored
on this topic since it is suitable for application to images and somewhat less suitable
for numerical data. A total of 153 Japanese firms that went bankrupt along with 2450
continuing firms are considered for this study. Each financial ratio is assigned a specific
11

pixel position i.e. x,y coordinates and according to the basis of value of the financial
ratio the brightness of the pixel is set. The generated images are further feeded to the
CNN that is based on GoogLeNet. A higher identification performance of 92% could be
noted for continuing enterprises in the proposed method as compared to other methods
like LDA, CART, AdaBoost, SVM and MLP. SVM showed 82% whereas AdaBoost had
84%. In the prediction performance for bankrupt enterprises, the LDA method performed
better in some regions than our proposed model since the identification through LDA is
largely biased towards the bankruptcy cases. Proposed method showed 88% prediction
performance whereas LDA had a prediction performance of 86% when 10 financial ratios
are considered.
As stated in previous studies Cecchini et al. (2010) and Mayew et al. (2015) about
the importance of textual disclosures in predicting the corporate bankruptcy, a recent
study Ahmadi et al. (2018) analysed the potentiality of deep sentiment mining in textual
disclosures of the management reports with an objective to identify signals of financial
distress. The various process involved collection of large number of business reports
that are analyzed qualitatively, based on Altman Z-score defining a non-trivial target
variable, then based on the class correlation pattern mining identifying and filtering the
sentences to reduce complexity of long texts and finally applying Dependency Sensitive
Convolutional Neural Network to build a prediction model. Although, financial distress
prediction is possible based on the complex texts, achieving high prediction performance
is not that easy task. The dataset for this study has been collected in the form of
business reports which include basically three things: the annual balances that portrays
relationship between the assets and the liabilities, the organizations profit/loss statement,
and the management letters that signifies the current situation of the company. Sentence
filtering process is carried out to reduce the long complex text into short meaningful
sentences that contains most information. Then it is feeded to DSCNN model which
consists of a convolutional layer on top of two LSTM networks. Each sentence is processed
separately in the first layer of LSTM so as to capture dependency within the sentences.
The second layer is present between convolutional layer and first layer to encode the
dependencies among the sentences. The results showed that CNN with static setting and
LSTM performed worse than the SVM whereas SVM performed worse than DSCNN. The
architecture of DSCNN and CNN differed in the utilization of LSTM layers for perception
of text information hence it is the reason for improved kappa and accuracy. However,
this study does not consider the quantitative attributes and only predicts the bankruptcy
using text-based data.
Further to the above study, the use of textual disclosures along with financial ratios
has been explored in Mai et al. (2019) where the prediction power is assessed by combin-
ing both these parameters. The techniques used are Word embedding and convolutional
neural network. The dataset has been formed by merging data from three sources; ac-
counting data, equity trading data and MDA textual disclosures from 10-K filings. A
time-varying panel dataset constructed for the explanatory variables with 36 numerical
predictor variables. The primary step involved the use of Natural language processing
(NLP) to convert textual data into numerical units as the textual databases are larger
in size as compared to numerical databases and thus information extraction plays key
role in the modelling process. For model implementation, Keras 2.0 with TensorFlow
backend has been used and a deep learning system with a feed forward model that maps
inputs to a binary output. Backpropagation algorithm is used to train the model. Eras-
ure method has been used to find out which words are important form the MDA section.
12

This method involves erasing of the individual words from input data and parallelly as-
sessing the performance of model as it degenerates. If the change in AUC is significant,
then the word is treated as important. The results showed that the models built on deep
learning framework outperformed the logistic regression and random forest with an AUC
value of 0.856 whereas that of logistic regression was 0.753 thus highlighting the fact that
deep learning models are able to capture relevant features from textual disclosures and
it complement sthe numerical data.
On the basis of the above literature works, it is well inferable that a wide number
of techniques have been used by researchers to solve the bankruptcy prediction problem
and also few recent studies like have seen the use of machine learning and deep learning
models to improve prediction accuracy however the vastness of the factors leading to
bankruptcy further deteriorates the models performance. Therefore in this study, we will
go beyond most studies by supplementing textual disclosures that are rarely used in the
past with the financial ratios and also use recent deep learning models that are suitable
for this study.
Following section elucidates the proposed approach for carrying out the research and
obtaining answer to proposed research question.
Study Data Model Results
Ravi Kumar
(2006)
Financial Ratios
Statistical &
Intelligent techniques
Intelligent techniques
like BPNN perform
better
Yongsheng Ding
(2008)
Financial Ratios
Support Vector
Machine with
radial basis function
SVM outperformed
BPNN and MDA
with accuracy of 83.2
% on test data
Salim Lahmiri
(2019)
Financial Ratios
Generalized regression
neural network
GRNN outperformed
BPNN, RBF, PNN with
accuracy 99.96%
Bredart (2014)
3 financial ratios
used
Artificial neural
network
Prediction accuracy of
83.6% on training and
81.5% on test data
Ruibin Geng
(2014)
31 financial
indicators
NN, C5.0 DT, CART,
logit, bayes, DA, SVM
NN built on prune
algorithm showed accuracy
78.82% and outperformed
BPNN & SVM
P Jardin
(2015)
Income statements/
Balance sheets
Ensemble models (bagging,
boosting, random subspace)
Gains of ensemble
technique to PBM is 4.74%
13

Yuta
Takata (2017)
P&L, balance
sheet & cash
flow statements
Boosting
Prediction accuracy of
AdaBoost model found
to be 82.9%
Deron Liang
(2016)
Financial ratios +
CGIs like cash flow
rights, ownership
structure etc
KNN, SVM, CART,
Nave Bayes,
multilayer perceptron
SVM outperformed CART
& KNN with accuracy of
81.3% whereas latter showed
78.6% and 74.5% respectively
Mark Cecchini
(2010)
MD&A text disclosures
+ financial ratios
Vector Space Model
Initially found accuracy to be 80%.
After combining text data accuracy
improved to 83.87%
William Mayew
(2015)
MD&A text disclosures
+ financial ratios
Conditional logisic
regression model
Predicitive ability of the model
with MD&A disclosure found
out to be 85%
Stamatis
Karlos (2016)
10 financial indicators
Semi-supervised
models on KNN,
C4.5 DT, SMO
Semi supervised models performed
better than supervised models and
C4.5DT achieved best performance
with Rel-RASCO scheme
Flavio Barboza
(2017)
Financial indicators
of balanced set
of bankrupt/non-
bankrupt firms
Bagging, boosting,
SVM, RF, ANN,
LR and MDA
RF and Bagging techniques showed high
accuracy between 71% to 87% than
traditional methods
Stamatios
Aggelos (2019)
Financial ratios
of 150 firms
Deep Dense Neural
Network
DD Multilayer perceptron achieved
highest accuracy of 73.2% followed
by MP, LR and NB with 64.8%, 64.6%
and 64.7% respectively
F Pereira
(2017)
Financial indicators
of French firms
Gaussian Process
with Laplace
approximation method
GP model outperformed the SVM
model based on chosen kernels
MN Seidu
(2015)
Financial ratios of
2000 firms
Gaussian Process
GP classifier predicted with accuracy
of 90.19% whereas linear logistic model
showed accuracy of 83.25%
Zuherman
Rustam (2018)
20 financial ratios
used
Ensemble model,
Random Forest
Accuracy of 94% achieved with all
ratios and 96% with 6 ratios
Shreya Joshi
(2018)
Financial ratios
chosen based on
Genetic Algorithm
Random Forest
Random Forest based model
outperformed the other four
chosen models
Zahra Ahmadi
(2018)
Textual disclosures
Convolutional Neural
Network
DSCNN outperformed SVM whereas
SVM outperformed CNN in terms
of prediction accuracy
Feng Mai
(2018)
MD&A text
disclosures + 36
financial ratios
Network
CNN model achieved AUC of 0.856
whereas logistic regression
achieved 0.753
Pranav Naidu
(2018)
Financial statements
of 10503 companies
Artificial neural
network,
Random Forest
Random Forest based model achieved
bankruptcy prediction accuracy
of 94.81%
T Hosaka
(2018)
Image based financial
indicators
Network
Proposed method showed prediction
accuracy of 88% whereas LDA showed
86% for 10 financial ratios
14

3 Methodology
The common process followed in all the above mentioned studies is Knowledge Discovery
in Database (KDD) Fayyad et al. (1996) discovered the process of KDD wherein several
scenarios are explored and the advantage of following KDD approach is specified for im-
proved efficiency and one such explored area is fraud detection. Here the author has cited
an example of a financial company whose activities had a malicious money laundering
intent. These frauds or money laundering activities can be a cause for bankruptcy, so
this study will also be following the similar approach. Figure 1 portrays the flow of the
KDD process and in the subsequent section, we will be discussing each step of the KDD
process in brief:
Process.PNG Process.PNG
Figure 1: Knowledge Discovery in Databases
3.1 Data Gathering
As discussed in section 2, in this research we will be considering the financial ratios
(includes accounting data and equity trading data) and textual disclosures data from the
10-K annual filings for extraction of information pertaining to the cause of bankruptcy
of an organization. The data will be collected from three sources: Compustat for the
accounting data, Center for Research in Security Prices (CRSP) for the equity trading
data and Securities Exchange Commission (SEC) for textual disclosure data from 10- K
filings. It will include yearly data of publicly traded organizations from 1994 to 2014.
The primary sample of the financial ratios includes more than 10,000 firms with
close to 95,000 to 100,000 firm-year observations. And for the numerical predictors, 36
predictor variables have been identified based on above literature works and by con-
sidering only those values that are responsible for companys profitability, liquidity and
liability status. Few of the columns are: Current Assets/Current Liabilities, Accounts
Payable/Sales, Cash/Total Assets, Earnings before Interest and Tax/Total Asset etc.
The innovative and less explored approach of this research is the consideration of textual
data source Form 10-K for forecasting the financial distress of an organization. From the
10-K we will be focussing on the Management Discussion and Analysis (MDA) section.
This section contains an explanation about the companys operations in such a way for an
average investor to easily understand. As we need a section-based text data and need to
15

merge it with the accounting and equity data, all this process would require a lot of pre-
processing so as to be used for further address our research question and the objectives
of the study.
3.2 Data Cleaning
The datasets explained in the above section are to be extracted from three different
sources and lot of pre-processing would be required before merging them as one of the
sources contains textual disclosure data. The accounting data would most likely contain
missing values and outliers that need to be taken care of. Also, we need to extract MDA
section from the 10-K form and perform text pre-processing. To address these tasks, we
will be using R Studio. Following are a few steps we will do on our extracted data-
• Addressing the outliers and missing values
Based on the works of Yadav and Roychoudhury (2018), we will be making use
of an R package known as MissForest for the imputation process. This package is
suitable for our study because the literature states that if MissForest is used on data
with records around 100,000, the imputation time decreases. Outliers if identified,
would be removed from the dataset.
• Text Preprocessing
Text-based data being natural language, we need to use Natural Language Pro-
cessing (NLP) to transform textual data to numerical units. Firstly, the raw data
from MDA section is converted to clean plain text form in these steps- tokenizing
each section into individual words by making use of NLTK (Natural Language Tool
Kit) NLP with Python (2009), each word is then lemmatized and returned to basic
form. Ex. Sold and selling becomes sell and low-frequency words will be removed,
and only most frequent words will be included. Next step in the process would be
to use feature extraction to convert the textual data into numerical values.
3.3 Data Exploration
Transformation of the data as per the requirement of the study is one of the most im-
portant aspect of the entire process. For building our prediction model, we would need
a binary response variable i.e. bankruptcy indicator to predict the bankruptcy of a firm.
Further to the extraction of data sources and pre-processing of the text data, the dataset
will be linked based on the firm-year and each firm-year will be a separate observation.
3.4 Data Mining
In order to build a model for prediction, we will need to split the data into training data
and test data. In this study we will use the k-fold cross validation split method. In a
general cross validation scheme, the train and test sets will cross-over in sequential rounds
in such a way that each data value is tested for prediction 1
. The process repeats itself
until all the unique groups are used as test sets 2
. Therefore, the model will be trained
1
https://towardsdatascience.com/cross-validation-in-machine-learning-72924a69872f
2
https://blog.contactsunny.com/data-science/different-types-of-validations-in-machine-learning-
cross-validation
16

and built on training dataset with 70%-80% of data and will perform predictions on test
data and thus will be evaluated on the test data.
After thorough review of the above literature, we found out that machine learning
models that are built on neural network methodologies are most likely suitable for answer-
ing our research question and objective. Based on literature review, we can also note that
statistical techniques are no longer recommended technique for prediction whereas vari-
ous machine learning models have outperformed in several studies. Particularly, neural
network-based models have been used in recent studies. Hence, in this study we will be
using different models based on different neural network methods and concepts and see
which model is able to answer the question posed in our research study. Thus, following
are the models that will be trained and evaluated on the testing data in order to forecast
the corporate bankruptcy.
3.5 Models
3.5.1 Word Embedding Model
As our data consists of large textual information which need to be understood, analyzed
and further linked with the numerical predictors for classifying the output variable, hence
classification models would be useful for our study. Word embedding model can be used
so as to better understand the context of a particular word. Word embeddings are
the vector representations of an individual word and the technique for learning word
embeddings from shallow neural network is termed as Word2Vec Mikolov et al. (2006).
The objective of this model is to identify words that have similar context and to ensure
they occupy close spatial positions. In our case, words like growth and profitability have
similar context and they should have a greater share of dependence. Mathematically,
between these vectors, the cosine should be close to 1.
3.5.2 Skip-Gram Negative Sampling Model
In order to identify words with similar context, two different models are suggested- Com-
mon bag of words and skip-gram model. We will adopt the latter one as it is suitable
for our dataset wherein the architecture is such that the target word is at the input
layer whereas the output layer contains the context words Rong (2014). As mentioned in
Guthrie et al. (2006), negative sampling helps us in dealing with the difficulty of having
numerous output vectors that require continuous updation after every iteration by only
updating a sample of them. From our textual data, we need to have our output word in
our sample, along with this we will have few more words in the sample that will be treated
as negative samples hence it is termed as negative sampling. Based on log probability,
we will identify the word embeddings that are required for training the model 3
.
3.5.3 Long Short Term Memory Network (LSTM) Model
In this research, we will adopt the LSTM approach which has not been applied before
in the past studies for bankruptcy prediction hence is a novel technique for our study.
LSTM network is based on the Recurrent neural architecture unlike feedforward neural
networks, feedback connections are present in a LSTM network. They have the capability
to process complete sequences of data and suitable for making predictions, classifying
3
https://www.researchgate.net/figure/The-architecture-of-Skip-gram-model-20f ig1322905432
17

Figure 2: Skip Gram Model
based on the time-series data Hochreiter and Schmidhuber (1997) which can thus be
useful for answering our research question. The architecture of an LSTM network consists
of- 1) a cell, that keeps track of the dependency among elements that are in the input
sequence.2) input gate, controls the ﬂow of new values in the cell. 3) forget gate, controls
the duration to which the value remains in cell and lastly the output gate, to determine
the degree to which cell value is made use of for computing the output activation4
.
Figure 3: Long Short Term Memory Network Architecture
3.6 Evaluation Metrics for Measuring Model Performance
Error generation is a part of any standard machine learning process which is also obvious
as the model is trained to predict based on certain algorithms which may not work
similarly on diﬀerent types of datasets. These errors are termed as Bias. In this research,
we will deploy some techniques to evaluate our models performance and estimate the
models accuracy-
4
https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-
lstm/
18

3.6.1 Area Under the Receiver Operating Curve (AUROC)
For measuring the performance of our classification model at various thresholds settings,
we use the AUC-ROC curve. While the AUC denotes the degree of separability, the ROC
is an probability curve. From an AUC-ROC curve, we can determine the extent to which
the model can distinguish between classes. Higher the AUC, the better the model turns
out in distinguishing between classes.
The ROC curve is plotted by having the True Positive Rate on y-axis and False
Positive Rate on x-axis.
Figure 4: ROC Curve
TPR(TruePositiveRate)/Recall/Sensitivity =
TruePositive
(TruePositive + FalseNegative)
Specificity =
TrueNegative
(TrueNegative + FalsePositive)
FPR(FalsePositiveRate) = 1 − Specificity
3.6.2 Accuracy Ratio
The ability to distinguish between bankrupt and non-bankrupt is termed as the Discrim-
inatory Power and the summary of the quantitative measure of the Discriminatory Power
is termed as the Accuracy ratio. It is the ratio of area above the power curve and under
the power curve. The closer it is to 1, the better the discriminating power of the model
in terms of classification 5
.
AR =
Ar
Ap
where Ar is the area of actual model and Ap is the area of perfect model
5
https://www.openriskmanual.org/wiki/AccuracyRatio
19

3.6.3 Cumulative decile-ranking
The process of evaluation through cumulative decile-ranking method is such that the
predicted probabilities of the companys are ranked into deciles with companies having
high default risk placed in the top decile and low default risk companies placed in bottom
decile. The interpretation from the deciles is such that a greater percentage in high
bankruptcy probability decile implicates better classiﬁcation power.
The overall methodology that need to be followed sequentially for achieving desired
results is mentioned in below process ﬂow diagram6
:
Figure 5: Proposed Process Flow Diagram
3.7 Project Plan
The below Gantt chart shows the time-line of execution of tasks that will be adhered to
in the next semester:
Figure 6: Gantt Chart
6
https://www.draw.io/
20

4 Summary
Based on the review of several studies in the domain of corporate bankruptcy, it can
be noted that however large scale the firm is, it can still come down to a situation of
bankruptcy and this can adversely affect a wide segment of people including investors,
employees, management etc. By thorough study of financial indicators that determines a
firms growth or failure, a research question and a research objective has been framed with
a proposal draft that includes all the methodologies and techniques that will be deployed
for addressing our research objective. Along with the draft, a systematic plan detailing
the timeline of execution of the required steps is mentioned that shall be followed to
achieve the desired results.
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