WHAT WE UNDERSTAND BY FINANCIAL STABILITY: TEXT ANALYSIS WITH NETWORK APPROACH

Claudiu Albulescu

Politehnica University of Timisoara, Romania claudiual@yahoo.com

Kristijan Breznik

International School for Social and Business Studies, Slovenia kristijan.breznik@mfdps.si

Valerij Dermol

International School for Social and Business Studies, Slovenia valerij.dermol@mfdps.si

Abstract: During the last decade the financial stability became one of the key concepts in the macroeconomic and financial literature and represented one of the main concerns of monetary authorities. However, neither the financial stability definition, nor its assessment, is commonly agreed between academics and policy makers. In this context our purpose is to identify the key elements underlining the financial stability concept, using both a clustering and a networking approach. More precisely we conduct an investigation over a sample of 145 papers including in their title the concept “financial stability”, starting from financial stability related keywords advanced in the literature. We establish a network and performed a hierarchical and k-means clustering analysis which helps us to identify the clusters of keywords related to the financial stability concept. In addition, we apply a Pathfinder algorithm which helps us to identify the most important links and nodes from the network. Our results show that the interest for the “financial stability” increased exponentially after the recent financial crisis. Further, the keywords related to financial stability are grouped in four clusters which might be associated with systemic risk, banks, monetary policy and economic context. However, the clustering analysis does not lead to clear results even if the hierarchical and k-means clustering present similar findings. Finally, our network analysis identifies the banks on the one hand, and the central banks and their monetary policies on the other hand, as the most important elements for the financial stability. Keywords: financial stability, clustering, networking, Pathfinder algorithm.

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1. INTRODUCTION The financial stability issues are not new, but they became one of the main concerns for the authorities, in particular after the successive financial crises in Asia and Latin America in the 1990s. The recent global crisis increased the interest of both academics and policy makers for understanding and ensuring the financial stability. The financial stability generally refers to the stability of the financial system as a whole, and not to the financial strength of an individual institution, or to the stability of an asset. However, the stability of financial institutions and markets represents a prerequisite for a stable financial system, which is rather a shock absorber. Consequently, in order to assess the financial stability, the relations and networks among financial institutions is equally important. The deregulation and the liberalization processes also have to be addressed in defining the financial stability. Even if the financial stability definition can appear as a simple exercise at a first glance, there is no consensus among scholars and policy makers in terms of how this phenomenon can be defined. An ample study related to the methods for defining financial stability was performed by Schinasi (2004) who established five key principles which have to be considered when defining the financial stability: (i) financial stability represents a large concept, including different aspects of the financial system as the infrastructure, institutions and markets; (ii) financial stability does not involve only the efficient allocation of resources, the risk management, the mobilization of savings and the facilitation of welfare accumulation, but also the adequate operation of the payments system; (iii) financial stability is connected not only with the absence of financial crises, but also with the system’s capacity to limit the imbalances and the contagion phenomenon; (iv) financial stability has to be approached depending on the potential consequences on the real economy; (v) financial stability can be analyzed when it stands for a continuous phenomenon. Nevertheless, after establishing some basic principles for defining financial stability, scholars did not find a consensus regarding its definition. A first set of definitions does not directly approach the phenomenon, but its features. For instance, Foot (2003) associates certain elements of the macroeconomic stability with the financial stability. The author asserts that the financial stability exists when there is monetary stability, an unemployment rate close to the natural one, a confidence in respect of markets and key financial institutions and when there is no movement of the real or financial assets prices which could lead to an increase of the inflation rate or of the unemployment level. A second set of definitions assigned to the financial stability directly refers to the concept of stability (Jacobson et al., 2001; Schinasi, 2005). A third set of definitions states that financial stability is opposed to the instability situation, and then define its opposite (Mishkin, 1997; Crockett, 2000; Allen and Wood, 2006). Another category of papers define financial stability from the perspective of a level indicator, and focus thus on financial stability assessment (Goodhart, 2006; Woodford, 2012). The subsequent works are mostly related to the empirical investigation of financial stability determinants and implications (Blot et al., 2015). None of these papers address the factors characterizing the financial stability and their interaction in a clustering and network perspective. Indeed, Oosterloo et al. (2007) make a literature assessment of financial stability reviews published by central banks in order to see which information related to financial stability is disclosed to the public. However, the authors do not address the journal papers or the working papers series published by international financial institutions, to assess the financial stability concept. Against this background our paper makes to major contributions to the literature. First, in our review paper we assess 145 articles and working papers which contain in their title the term “financial stability”. Based on their abstract and a list of keywords used in these works, we make an ample analysis of the financial stability concept and we identify the key elements related to the financial stability definition. Second, this is the first paper which uses a cluster and a network analysis to examine which are the groups of keywords which characterize the financial stability, and which are the most important links and nodes from the network. For this purpose we use both a hierarchical and a k-means clustering approach, while the network analysis is performed based on the Pathfinder algorithm. We finally make a refinement of the network analysis using a Boolean matrix development from the original network adjacency matrix, as the keyword “central bank” includes the keyword “bank” and a separation seems necessary.

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The rest of the paper is as follows. Section 2 presents the data and some general statistics about our sample. Section 3 describes the methodology, while Section 4 shows the results. The last section concludes. 2. DATA AND GENERAL STATISTICS Our investigation is based on an EBSCO database search of the “financial stability” term. We have obtained 160 journal papers (published by 69 journals) and official working papers (published by the International Monetary Fund – IMF, the Bank for International Settlements – BIS, the National Bureau of Economic Research – NBER, the European Central Bank – ECB) which contain in their title the term “financial stability”. We have further identified a list of 31 keywords used in these papers for performing the clustering and network analysis. The investigation is made using the abstract of these papers. We have renounced to papers that do not have an abstract and the final sample contains 145 papers. Papers which were published first in working papers series and afterwards in journals are only considered once, as journal papers. Figure 1 presents the structure of our sample, where the journals or working papers series having published more than 5 papers on financial stability are considered as individual sources (all other journals or working papers series are include in the “others” category). Figure 1: Structure of the papers’ sample containing in their title the “financial stability” term

We notice that there are basically three journals (Journal of Financial Stability, Journal of Banking & Finance, and Journal of International Money and Finance) and three working papers series (BIS, IMF, NBER) which have published more than 40% of papers which contain in their title the “financial stability” term. Figure 2 shows that the first paper on financial stability was published in 1987 (Friedman, 1987). Since then and until 2004, just one or two papers including “financial stability” term in their title were published each year, which shows a limited interest for the concept. In 2006 the number of papers increased considerably but starting with 2007 (the financial crisis out-burst) and until 2010 (the year generally considered as the end of the crisis), the number of publications decreased. This is not surprising because during the crisis period most of researchers investigate the “financial instability” issues. However, in our sample we have not addressed this alternative and opposite concept for assessing the solidity and the performance of financial systems. Starting with 2011 the number of published works increased exponentially, with a pick in 2014 (over 30 publications). In 2015 a downturn was recorded, showing that in normal times people lose their interest for assessing financial stability issues. Figure 2: The years of appearance of “financial stability” papers

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Table 1 presents the list of keywords advanced in the “financial stability” papers, keywords which are related to the financial stability concept and represents the ingredients of our analysis. These keywords are representative given their frequency appearance. Therefore, we have associated to them different synonyms (keywords advanced in the analyzed papers, which have a smaller frequency appearance compared to the representative keyword. For example, we have associated to the keyword “monetary policy” other keywords as: “monetary policy transmission mechanisms”, “monetary economy”, “inflation target”, “preemptive tightening”, “monetary regimes”, “unconventional monetary policy”, “monetary transmission”. This approach affects the precision and the details level of our study, but allows for a simple and clear interpretation of the results. Table 1: Financial stability related keywords

asset prices contagion financial instability

interest rate rule monetary stability systemic risk

bank credit risk financial literacy lender of last resort

regulation

business cycles crisis financial sector liquidity shocks

capital economic policies financial stability index

market structure sovereign debt

central bank exchange rate general equilibrium

moral hazard stress-tests

corporate financial integration

hedging monetary policy spreads

Figure 3 shows the frequency appearance of these keywords, where the “bank” keyword leads with more than 250 appearances, followed by the “monetary policy” keyword with more than 50 appearances. Figure 3: Keywords frequency appearance

3. METHODOLOGY

3.1. Network of keywords

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A network is usually defined with the set of actors (presented as nodes) and the set of relations (presented as links) between them (Wasserman & Faust, 1994). In this study, we naturally determined keywords as actors, however the relation between keywords in defined as the appearance of them in the same article. In other words, two keywords are related (or linked on a graph) if they appear together in the same article. Moreover, links in the network of keywords can be weighted with the number of times two keywords appear together in the same article. In this way we obtained weighted and undirected network of keywords in articles considering “financial stability”. To visualize a network many standard procedures can be used. In this article, PathFinder algorithm (Schvaneveldt, 1990) was applied. It is known as very efficient tool for removing less important nodes from the network. Actually, as a result of applying the PathFinder algorithm it remains only the skeleton of the network. However, the algorithm preserves the connectivity of the network.

3.2. Clustering One of the most important methods in network analysis is clustering of network units (actors of the network, i.e. nodes) into groups called clusters. There are many different clustering techniques, hierarchical clustering and K-means clustering that are used in this paper are only two of them. The combination of this two techniques has been met many times. Hierarchical clustering and its dendrogram is firstly used to determine the number of potential clusters. Later clusters are defined and/or confirmed using the K-means clustering algorithm. Hierarchical clustering is based on calculating dissimilarities between the units. In the first step, all units in the network are clusters of its own. From now on, hierarchical algorithm looks for a pair of units (actors in the network) that are the most similar (have the lowest dissimilarity). This two units are then joined and form a new cluster. Choosing dissimilarities between new born cluster and old clusters we can distinguish several methods of hierarchical clustering (Doreian, Batagelj and Ferligoj, 2005). We choose the most common Ward method. With K-means clustering we need to provide the number of clusters in advance. The algorithm later search for the best positions of all centroids in each cluster. Algorithm of K-means clustering stopped when centroids no longer move. 4. RESULTS

4.1. Network analysis The network analysis allows us to analysis in a different way the interdependencies existent between the retained keywords and the most representative keywords called nodes. Figure 6 presents a first series of results. Figure 6: Network of keywords after applying the PathFinder algorithm

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We observe the central role played by banks for the financial system stability. At the same time we notice the important role of monetary policy and regulation activities. We also notice some peripheral networks which can be easily explained by the economic theory, as the link between systemic risk – contagion – stress tests, or the link between liquidity – credit risk – corporations. However, the “central bank” keyword is placed in relation with the economic context and not in relation with the monetary policy and regulation – two of its activity. This inconsistency appears because the “central bank” keyword contains the “bank” keyword which makes the delimitation between them necessary. Therefore, to overcome this drawback, we use a Boolean matrix development from the original network adjacency matrix which provides attractive results (Figure 7). On the one hand we see the central role of banks in defining the financial stability. The Pathfinder algorithm also allows us to see that the bank-related keywords of high importance are “regulation”, “liquidity”, “capital”, “systemic risk”, “crisis” and the “financial stability index” – a measurement of financial stability which usually considers the banks solidity. On the other hand we notice the importance of central banks and their monetary policies. This time the “central bank” keyword is closely related to other keywords representing central banks activities, as well as keywords highlighting the general economic context and economic policies. We conclude that the separation between “bank” and “central bank” keywords considerably improve our findings and lead to the simplification of results interpretation. Figure 7: Network of keywords from Boolean transformation matrix

4.2. Clustering analysis Clustering units in a network offers supplementary information for our research. Figure 4 presents the hierarchical clustering and reveals the fact that the “financial stability” related keywords can be grouped in four clusters. Figure 4: Hierarchical clustering

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The first cluster is related to systemic risk and crisis issues and contains related keywords as “systemic risk”, “asset prices”, “crisis”, “liquidity”, “spreads” and “hedging”. All these elements show that the excessive liquidity and bubbles in asset prices create an environment favorable for the crisis appearance and thus, a threat for the financial stability. Unfortunately this cluster does not include the “stress tests” and the “financial stability indicator” – two measures of the systemic risk. The second cluster address the monetary policy issues, including keywords as “central bank”, “financial sector”, “financial instability”, “capital”, “monetary policy”, “regulation” and “shocks”. The results show thus that the central banks and their policies play a decisive role in ensuring the financial stability. However, other keywords reflecting central banks activities as the “lender of last resort” and “interest rule” do not appear in the second cluster as expected. The third and the forth clusters might be representative for banks on the one hand, and for the economic context on the other hand. However, these clusters include a mixture of keywords from the two categories, which makes hard their interpretation. Therefore, in the second step of the clustering analysis we use a normalized k-means cluster (with four clusters also), for comparison and robustness purpose (Figure 5). Figure 5: K-means clustering

We notice that a first cluster on the left side of Figure 5 include monetary policy issues, similar to the hierarchical clustering. The second cluster on the left side is similar to the first cluster of the

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hierarchical clustering addressing the systemic risk. However, similar to Figure 4, the K-means clustering analysis leads to mix results for the clusters 3 and 4, where both banking and economic context issues are found. All in all, we show that the central banks activities, the role of banks in the financial system, the systemic risk and crisis issues, as well as the general economic context represent key elements for defining and assessing the financial stability. 5. CONCLUSIONS The “financial stability” concept was intensively debated during last years. However, there is no consensus about defining and measuring financial stability. Against this background, our purpose was to provide additional insights about the elements characterizing the financial stability, and about their importance. Making thus a review analysis of 145 papers which contain in their title the “financial stability” concept, we use a clustering approach and a network analysis to see how the related elements can be grouped and how they are interlinked. Our findings shows that the representative 31 keywords related to the financial stability can be grouped in four clusters which address the systemic risk, monetary policy, banks and economic context issues. However, if the first two clusters are relatively well structured, the last two clusters presented a mixture of keywords associated with banks and general economic issues. These results are nearly the same under a hierarchical and a k-means clustering analysis. However, the network analysis shows the central role of banks on the one hand, and of the central banks and their monetary policies on the other hand, in defining and assessing the financial stability. The programs for data editing and producing networks were written in R (R Development Core Team, 2016). Likewise, program R was used for statistical analysis. For analysis of networks we used the Pajek program (Batagelj & Mrvar, 1996-2016). ACKNOWLEDGEMENTS Claudiu Albulescu acknowledges the fact that this work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-II-RU-TE-2014-4-1760. REFERENCE LIST

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