Sustainable Visual Analysis for Bank Non-Performing Loans and Government Debt Distress
Round 1
Reviewer 1 Report
In this paper, visualization of linear and functional principal component analysis (FPCA) and biclustering take place with NPLs and government debt distress for 25 EU and BRICS countries during the period of 2006 to 2017. This paper is significantly improved in comparison to the earlier version. This holds both in terms of expression and of economic soundness.
I believe that little space exists for further improvements. It is necessary that proofreading takes place by a professional with knowledge of economics. There are some parts, eg. in the Literature where the authors have just added according the remarks of reviewers, but more attention should be paid to the cohesion of the paper.
The economic underpinnings in the new version are not bad. This is not a paper that will make a great contribution to the relevant field, but it is of interest to relevant researchers.
Author Response
First of all, we’d like to express our gratitude to the referees for their careful and critical reading of our manuscript.
As described in a reply below to the reviewers, we've revised our manuscript following suggestions line by line.
Overall, our revised manuscript has been considerably improved due to the revision we've made according to the reviewers' suggestions.
We hope that our revised manuscript would be suitable for the publication in the Sustainability.
Point 1: In this paper, visualization of linear and functional principal component analysis (FPCA) and biclustering take place with NPLs and government debt distress for 25 EU and BRICS countries during the period of 2006 to 2017. This paper is significantly improved in comparison to the earlier version. This holds both in terms of expression and of economic soundness.
I believe that little space exists for further improvements. It is necessary that proofreading takes place by a professional with knowledge of economics. There are some parts, eg. in the Literature where the authors have just added according the remarks of reviewers, but more attention should be paid to the cohesion of the paper.
The economic underpinnings in the new version are not bad. This is not a paper that will make a great contribution to the relevant field, but it is of interest to relevant researchers.
Response 1: According to the referee’s valuable comment, the introduction is revised adding the paragraph below:
In recent years, several studies have examined the spillover effect of the European sovereign debt crisis on the finance system. Gertler and Kiyotaki (2012) study how fundamental risks and government credit policies pre-affected the vulnerability of the financial system and the effect of macro prudential policies aimed at offsetting risk-taking motives. Pagano and Sedunov (2016) find that the aggregate systemic risk exposure of financial institutions is positively related to sovereign debt yields in European countries in an episodic manner, and they find evidence of a simultaneous relation between systemic risk exposure and sovereign debt yields. Further studies find that the unconventional action of the Federal Reserve weakens the impact of the monetary policy of the Federal Reserve on the stock indexes of emerging countries (Papadamou et al., 2019a). Subsequently, Papadamou et al. (2019b) use meta-analysis techniques to capture the impact of unconventional monetary policies on key macroeconomic indicators. In a recent study, Morais et al. (2019) confirmed that a softening of foreign monetary policy has expanded the credit supply of foreign banks and produced a strong practical effect at the enterprise level. The results support the international risk-taking channels and spillover effects on emerging markets of core countries' monetary policy, whether in the softening or tightening part of monetary policy.
Another strand of the literature is focused on the relation between government debt defaults and financial crises. Government debt defaults spread to the financial system when banks hold large amounts of government debt in their portfolios (Reinhart and Rogoff, 2009, 2010). NPLs in the Greek banking system can be explained mainly by public debt, which relates rising sovereign debt to higher NPLs (Louzis et al., 2012). Furthermore, all financial systems in Europe have been affected by sovereign debt defaults, and the sovereign debt defaults have exacerbated the risks of the financial system (Reboredo et al., 2015b). Makri et al. (2014) reveal strong correlations between NPLs and public debt of the Eurozone’s banking systems for the period 2000-2008, and fiscal problems may raise bad loans in this region. Ghosh (2015) finds that liquidity risk, greater capitalization, greater cost inefficiency, poor credit quality and banking industry size significantly increase bank non-performing loans. Similarly, inflation, state unemployment rates, and US public debt significantly increase bank non-performing loans. Reboredo and Ugolini (2015a) find that, before the debt crisis, sovereign debt markets were all coupled, and systemic risk was similar for all countries. However, with the onset of the Greek crisis, debt markets decoupled, and the systemic risk of the countries in crisis (excepting Spain) for the European debt market as a whole decreased, whereas the systemic risk of non-crisis countries increased to a small degree. However, even though government debt and NPLs are important aspects of contagion, which can be quantified as the impact of extreme downward movement of one market on other markets, no study has visualized NPLs and government debt distress data integration and an outcome classification. With recent technological advances, visualization of categorical data by means of statistical methods has attracted considerable interest in recent years because the visualization capabilities of statistical software have increased during this time. This paper attempts to fill this gap, especially in three ways to contribute to the existing literature.
First, linear principal component analysis (PCA) is used to process the NPLs and the government debt, which is proposed by Jiang and Yan (2014) and generalized by Bakdi and Bensmail (2017). To capture specific features in the financial market, PCA is used to extract the low-dimensional and efficient feature information. The empirical results show that the training accuracy and efficiency have been improved (Yu et al., 2014). Although numerous successful applications have been reported, PCA performs poorly in dealing with nonlinear processes because it characterizes only the linear correlation among variables and does not explore the nonlinear relationships. Several nonlinear monitoring methods have been proposed to deal with the nonlinearity of a process (Mazumder et al., 2010; Candès et al., 2013). Florackis and Krisztián (2012) use nonlinear principal component analysis to study the relation between corporate governance and performance. This method enables the extraction of complex features from highly dimensional datasets. To enhance the overall efficiency of Romanian banks, Stoica et al. (2015) apply PCA to classify the banks into different operational strategies groups based on their relative efficiency scores. The results show that very few banks have utilized Internet banking services in their production process to increase their level of efficiency. The research of Jiang and Yan (2018) confirms that PCA is successful in stopping the increasing stochastic trend of NPLs and in bringing stability (stationarity) to the banking system.
Second, to obtain the clustering pattern of the time-course data in a given period of time, we employ functional principal component analysis (FPCA) to process the NPLs and the government debt because linear PCA only shows the clustering pattern of the whole data at a certain time (Kim and Jung, 2017). FPCA is a popular statistical analysis technology for financial data because it can capture the direction of variation and reduce the dimensions of data. Some studies demonstrate that FPCA can extract the collective characteristics of the financial system (Jaimungal et al., 2007; Densing, 2012; Guharay et al., 2016). Morseletto (2017) proposes a framework for the analysis of influential visualizations and defines criteria for studying their visual characteristics. The criteria are applied to two case studies, the “traffic light” and the “planetary boundaries” diagrams. Kim et al. (2017) investigate the technological evolution of Apple through high-dimensional visualization by functional data analysis. The results show that the company will be able to understand changes in consumer demand through clustering visualization figures. Pan et al. (2018) use FPCA to reduce the dimensionality of Intrinsic Mode Functions (IMFs) and to generate a set of information-rich features.
Last, several clustering techniques on time series datasets have been used to identify relevant groupings (Zhang et al., 2005), and a new biclustering algorithm is proposed to extract time series biclusters and apartment price data sets in metropolitan areas (Lee et al., 2010). Therefore, we use clustering techniques to find a group of countries that showed a homogeneous pattern of NPLs and government-to-GDP ratio in a certain period in this paper. Huang (2011) applies a biclustering algorithm to explore inconsecutive co-movement patterns of different foreign exchange rates across non-consecutive time periods. A detected bicluster demonstrates the co-moving behaviors of a subset of currencies in inconsecutive time periods, indicating that the currencies moved in different manners in some specific time periods. Xue et al. (2015) use a biclustering algorithm to find local patterns in the quantized historical data. A Biclustering-Based Intelligent System could find different patterns which contain a subset of technical indicators with different periodic parameters.
Finally, we really appreciate the referees’ critical but valuable comments which have been guidance in revising our manuscript. We believe that our manuscript has been much improved due to the revision based on the referees’ suggestions.
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Author Response File: 
Author Response.doc
Reviewer 2 Report
Dear Authors
The title of the article is too long – incomprehensible.
In article I do not see appropriate abstract with correct goal and hypothesis.
I saw goal in Introduction but it also should be in the abstract.
Poor interpretation of some results, especially those related to PCA and FPCA (tables 1,2,3,4)
The origin of sex lies in the creation of money – what does this sentence mean?
In conclusions - the added value of the research carried out in the article should be more emphasized
I do not see the correct discussion in the article.
Author Response
First of all, we’d like to express our gratitude to the referees for their careful and critical reading of our manuscript.
As described in a reply below to the reviewers, we've revised our manuscript following suggestions line by line.
Overall, our revised manuscript has been considerably improved due to the revision we've made according to the reviewers' suggestions.
We hope that our revised manuscript would be suitable for the publication in the Sustainability.
Point 1: The title of the article is too long - incomprehensible.
Response 1: According to the referee’s valuable comment, new title will be shorten as "Sustainable Visual Analysis for Bank Non-Performing Loans and government Debt Distress".
Point 2: In article I do not see appropriate abstract with correct goal and hypothesis. I saw goal in Introduction but it also should be in the abstract.
Response 2: According to the referee’s valuable comment, the abstract is revised by adding the paragraph below:
This article visualizes bank non-performing loans (NPLs) and government debt distress data integration and an outcome classification after the outbreak of European sovereign debt. Linear and functional principal component analysis (FPCA) and biclustering are used to show the clustering pattern of NPLs and government debt for 25 EU and BRICS countries during the period of 2006 to 2017 through high-dimensional visualizations. The results demonstrate that the government debt markets of EU countries experienced a similar trend in terms of NPLs, with a similar size of NPLs across debt markets. Through visualization, we find that the government debt and NPLs of EU and BRICS countries increased drastically after crisis, and crisis countries are contagious. However, the impact of the Greek debt crisis is lower for non-crisis countries, because the debt markets of these countries are decoupled from the Greek market. We also find that sovereign debtors in the EU countries have much closer fiscal linkages than BRICS countries. The level of crisis in the EU countries will be higher than that in the BRICS countries if crisis is driven by the common shocks of macroeconomic fundamentals.
Point 3: Poor interpretation of some results, especially those related to PCA and FPCA (tables 1,2,3,4)
Response 3: According to the referee’s valuable comment, the section 3 is revised by adding the paragraph below:
3.1 Linear PCA for Visualization of NPLs and Government Debt
As the first data analysis, linear PCA is used to process the NPLs ratio and the government-to-GDP ratio of 30 sample countries. In particular, the linear PCA variance proportion and cumulative variance proportion results of 30 countries' NPLs are shown in Table 1, corresponding to NPLs classification. Each principal component represents its percentage contribution to the whole density variation. The ranking of the principal components explains the density variation based on the corresponding contribution of each factor. It can be seen that the dimensions of NPLs characteristic data are dropped to three dimensions. In particular, the first dominant principal component (PC1) accounts for 53.07%, the second principal component (PC2) explains another 23.73% of variability and the third principal component (PC3) explains 12.64% of the whole variance proportion for FPCA. The variance contribution rate of the first three principal components together account for 89.43% of the whole variability (to take the cumulative proportion that is more than 85%). The results show that the first three principal components reflect 89.43% of the total information in the original index, and the data characteristics of the NPLs ratio can be well described by the first three principal components, which has a good extraction effect.
Similarly, the linear PCA variance proportion and cumulative variance proportion results of 30 sample countries in government-to-GDP ratio explained by the components are shown in Table 2. The first dominant principal component (PC1) accounts for 72.67%, the second principal component (PC2) explains 18.21% and the third principal component (PC3) explains 4.56% of the whole variance proportion for FPCA. The first three principal components account for 95.44% of the whole variability, and the cumulative variance contribution rate is above 95%. The results show that the first three principal components reflect 95.44% of the total information in the original index, and the data characteristics of the government-to-GDP ratio can be well described by the first three principal components.
3.2 Functional PCA for Visualization of NPLs and Government Debt
Table 3 shows the proportions of variance proportion and cumulative variance proportion results in NPLs ratio explained by the components. Similar to the PCA method, every principal component represents its percentage contribution to the overall density change. Specifically, the first dominant principal component (PC1) accounts for 53.1%, the second principal component (PC2) explains 46.84% and the third principal component (PC3) explains 0.03% of the whole variance proportion for FPCA. It is clear that the first three principal components account for 99.97% of the whole variability (to take the cumulative proportion that is more than 99%), which is close to 100%. The results demonstrate that the first three principal components reflect 99.97% of the total information in the original index, and data characteristics of the NPLs ratio can be well described by the first three principal components. In addition, comparing Table 1 and Table 3, it is found that two kinds of the variance contribution rate of the first three principal components obtained by FPCA and PCA differ in nearly 10% points, and the variance contribution rate of the first three principal components obtained by FPCA is significantly higher than PCA. From the above analysis, it can be found that the first three principal components obtained by FPCA have a good dimensionality reduction effect, and the first three principal components contain more data information.
Similarly, the proportions of variance proportion and cumulative variance proportion results of total variation in the government-to-GDP ratio explained by the components are shown in Table 4. Specifically, the first dominant principal component (PC1 or Harmonic Ⅰ) accounts for 61.75%, the second principal component (PC2 or Harmonic Ⅱ) explains 38.2% and the third principal component (PC3 or Harmonic Ⅲ) explains 0.02% of the whole variance proportion for FPCA. From Table 4, it can be found that the effect of using FPCA for data dimensionality reduction is obvious; the first three principal components account for 99.99% of the whole variability (to take the cumulative proportion that is more than 99%), which is close to 100%. The results demonstrate that the first three principal components reflect 99.99% of the total information in the original index, and data characteristics of the government-to-GDP ratio can be well described by the first three principal components. In addition, a comparison of Table 2 and Table 4 shows that the effect of the first three principal components obtained by FPCA is significantly better than the results obtained by PCA.
Point 4: The origin of sex lies in the creation of money - what does this sentence mean?
Response 4: This is a mistake in writing, we delete this sentence to avoid misunderstanding.
Point 5: In conclusions-the added value of the research carried out in the article should be more emphasized
Response 5: According to the referee’s valuable comment, the conclusions is revised by adding the paragraph below:
In this study, we examined the visualization of NPLs and government-to-GDP ratio integration and an outcome classification in Eurozone and BRICS countries after the global financial crisis of 2008 and the subsequent European debt crisis of 2009. In particular, our sample countries contain developed countries and developing countries, and they also include countries with debt crises and no debt crises over the same time period. We first provide evidence, by using PCA and FPCA, that the variance contribution rate of the first three principal components obtained by FPCA is significantly higher than PCA. It can be found that the first three principal components obtained by FPCA have a good dimensionality reduction effect, and the obtained first three principal components contain more data information. We also found that the government debt and NPLs of EU and BRICS countries increased drastically after a crisis, and crisis countries are contagious. However, the impact of the Greek debt crisis was lower for non-crisis countries, because the debt markets of these countries are decoupled from the Greek market. Furthermore, evidence also confirms that sovereign debtors in the EU countries have much closer fiscal linkages than BRICS countries. The level of crisis in the EU countries will be higher than that in the BRICS countries if crisis is driven by the common shocks of macroeconomic fundamentals.
Our findings in this article confirm that countries with high government debt have experienced a significant increase in their contribution to systemic risk since 2008, especially for EU countries. The results may have some meaningful implications for policymakers because unsustainable government debt can lead to payment defaults, which will impose more problems on the stability of the region. Furthermore, the findings in this paper can also help to establish a better monitoring mechanism and ultimately impose penalties on countries that violate regulations.
Point 6: I do not see the correct discussion in the article.
Response 6: According to the referee’s valuable comment, the manuscript is revised by adding the section 3.4 below:
3.4 Discussion
This article visualizes bank non-performing loans (NPLs) and government debt distress data integration and an outcome classification after the outbreak of European sovereign debt. To extract the main feature of the sample data, dimensionality reduction was done by linear PCA. The results obtained from linear PCA suggest that the variance contribution rate of the first three principal components accounts for more than 89% of the cumulative proportion. Moreover, we have shown that some countries are clustered together by using 2-D and 3-D visualizations.
We employed FPCA to extract more complex clustering features. The results indicate that the first three principal components obtained from FPCA explain a higher percent of the cumulative variance contribution rate as compared to the linear principal components. It is shown that FPCA explains more of the total data variance than linear PCA, the dimension reduction effect of FPCA is good and extracted principal components contain more information. This finding is in line with the existing results of Jiang and Yan (2018). In addition, the figures of 2-D and 3-D visualizations have shown the clustering pattern of NPLs and government debt data.
A biclustering method was used to discover biclusters in the sample data. Our experimental results show that the pattern of biclusters represents significant meaning. It can be seen that columns of bicluster A and bicluster B denote different groups of NPLs (or government debt) that different countries have in common.
The implication of these results is that the government debt and NPLs of EU and BRICS countries increased drastically after the global financial crisis and the European debt crisis. Specifically, most Southern and Western European countries are under the greatest pressure whether on NPLs or government debt, followed by Eastern European, Central European and Northern European countries. It should be noted that there is a sharp increase in NPLs and government debt for BRICS countries after a crisis, which means that those crises spread to emerging countries. Our findings provide support to several recent studies that government debt incentives to default increase if a government cannot pay its debts, and their effects on the economy would be amplified through the impact on banks’ balance sheets (see Gennaioli et al., 2014).
Finally, we really appreciate the referees’ critical but valuable comments which have been guidance in revising our manuscript. We believe that our manuscript has been much improved due to the revision based on the referees’ suggestions.
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Author Response File: Author Response.doc
Round 2
Reviewer 2 Report
Dear Authors
I my opinion your article was improved and could be publish.
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
[1] Several symbols are missing from the submitted version. Make sure you submit a pdf and the pdf is free from such errors. For example lines 178-194 are virtually unreadable.
[2] don’t you have to use some prior information of \Phi?
[3] Equation 7 or 8 is finally used? I don’t see how bi-clustering is developed based on this (ANOVA) decomposition.
[4] I don’t see why figures are useful here. You might as well plot debt versus NPLs or another variable. Moreover PCs are unique series. How can you plot PC1 versus PC2 for different countries? Similarly I am not sure what Fig 3 represents.
[5] From page 12 onwards all equations are misaligned.
[6] Without rotations I don’t think PC plots such as those appearing after p. 12 are of any use.
Reviewer 2 Report
Dear Author (s)
In article I do not see appropriate abstract and introduction with correct aim, hypothesis
especially please improve most figures/charts - they are very unreadable
the results should indicate more recommendations,
the conclusions should clearly indicate what results from this study - the results should be described in more detail -specially with add value in this article
Reviewer 3 Report
This study employs visualization of linear and functional principal component analysis (FPCA) and biclustering with NPLs 17 and government debt distress using Eurostat and IMF data for 25 EU and BRICS countries during 18 the period of 2006 to 2017. The authors find that find that government debt markets decoupled with the breakout of the debt crisis, and crisis countries are contagious.
There are some minor typos, such as in line 53, should be: ‘‘confirm that’’.
The literature review part is not bad, as a number of relevant studies related to the methodology adopted are laid out. There are also included a number of studies of economic significance focusing on non-performing loans. Further bibliography related to non-performing loans or macroeconomic conditions during crises, would be:
Gertler, M., Kiyotaki, N., & Queralto, A. (2012). Financial crises, bank risk exposure and government financial policy. Journal of Monetary Economics, 59, S17-S34.
Ivashina, V., & Scharfstein, D. (2010). Bank lending during the financial crisis of 2008. Journal of Financial economics, 97(3), 319-338.
Morais, B., Peydró, J. L., Roldán‐Peña, J., & Ruiz‐Ortega, C. (2019). The International Bank Lending Channel of Monetary Policy Rates and QE: Credit Supply, Reach‐for‐Yield, and Real Effects. The Journal of Finance, 74(1), 55-90.
Papadamou, S., Kyriazis, N. A., & Tzeremes, P. G. (2019). Spillover Effects of US QE and QE Tapering on African and Middle Eastern Stock Indices. Journal of Risk and Financial Management, 12(2), 57.
Papadamou, S., Kyriazis, Ν. A., & Tzeremes, P. G. (2019). Unconventional monetary policy effects on output and inflation: A meta-analysis. International Review of Financial Analysis, 61, 295-305.
Papadamou, S., Spyromitros, E., & Kyriazis, N. A. (2018). Quantitative easing effects on commercial bank liability and government yields in UK: A threshold cointegration approach. International Economics and Economic Policy, 15(2), 353-371.
Description of the methodology is good. There has also been an effort to provide the economic implications of the results in this study. Nevertheless, the authors should improve economic analysis. They should provide an economic reasoning why there are alterations between countries included in categories of Table 5 in relation to countries included in categories of Table 6. Therefore, how has the crisis affected GDP differences among countries?
Moreover, in line 415 it is written that crisis has spread from Europe to Russia and South Africa. Better elaboration is needed on the connection between Europe and South Africa. Why should the crisis spread to South Africa? What is the economic base for this phenomenon?
The study would benefit a lot from a discussion of the channel linking NPLs with the government-to-GDP ratio. Some of the papers I have listed above would be very useful for this economic analysis. This way, the paper will render more articulate in an economic sense, and will not look just as an application of methodologies.
Overall, I believe that this paper should be accepted after major revisions connected to the part of economic implications.
