Are there conditions that can predict when an M&A works? The case of Italian listed banks

Arbolino, Roberta; Boffardi, Raffaele; Kounetas, Konstantinos; Marani, Ugo; Napolitano, Oreste Article Are there conditions that can predict when an M&A works? The case of Italian listed banks Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Arbolino, Roberta; Boffardi, Raffaele; Kounetas, Konstantinos; Marani, Ugo; Napolitano, Oreste (2023) : Are there conditions that can predict when an M&A works? The case of Italian listed banks, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 3, pp. 1-33, https://doi.org/10.3390/economies12030058 This Version is available at: https://hdl.handle.net/10419/328984 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Arbolino, Roberta, Raffaele Boffardi, Konstantinos Kounetas, Ugo Marani, and Oreste Napolitano. 2024. Are There Conditions That Can Predict When an M&A Works? The Case of Italian Listed Banks. Economies 12: 58. https://doi.org/ 10.3390/economies12030058 Academic Editor: Robert Czudaj Received: 27 October 2023 Revised: 11 February 2024 Accepted: 18 February 2024 Published: 26 February 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Are There Conditions That Can Predict When an M&A Works? The Case of Italian Listed Banks Roberta Arbolino 1,*, Raffaele Boffardi 1, Konstantinos Kounetas 2, Ugo Marani 1and Oreste Napolitano 3 1Department of Human and Social Science, University of Naples L’Orientale, 80134 Naples, Italy; [email protected] (R.B.); [email protected] (U.M.) 2Department of Economics, University of Patras, 26504 Patras, Greece; [email protected] 3Department of Business and Economic Studies, University of Naples Parthenope, 80132 Naples, Italy; [email protected] *Correspondence: [email protected] Abstract: This paper investigates the impact in the short/medium term of M&As made by 14 Italian banks quoted on the stock exchange for the period 1999–2016. After dividing the banks into two groups by size and degree of internationalisation, we sought to ascertain whether different initial conditions produce different final effects. Based on three assumptions, supported by three separate econometric approaches, our empirical analysis shows that the stronger banks increased their competitiveness while the weaker banks did not achieve the same results since they were motivated to grow “by desperation”. Keywords: M&A; bank lending; listed banks; bank shocks; Italy 1. Introduction Although the economic literature supports the thesis according to which mergers and acquisitions (both domestically and across borders) are a useful tool for achieving the consolidation of banks (or a reduction in the overall number of them), there is no agreement on the effects that such action entails (Zhang and Zhang 2015;Kandilov et al. 2017). The main objective of the consolidation process is to boost profits: this can be reached according to reductions in expenses, increasing market power and decreasing the volatility of revenue (Pathak 2016) or eliminating unnecessary managerial positions and closing overlapping branches of banks, which may reduce expenses (Rahman et al. 2018). Thus, recombining existing assets with the complementary ones of an acquired bank or successfully rearranging its assets to enter new markets appears to be a suitable strategic change in order to reinvigorate a bank’s assets (Yang et al. 2019). This change is mainly linked to a bank in search of quick growth (Kim et al. 2015). According to this notion, several research works show that acquisitions often fail to create value for shareholders (Friedman et al. 2016). The high premium required for acquisition implies that the acquiring bank must achieve a higher value to amortise the expenditure incurred (Haunschild 1994). High acquisition premiums are frequently cited as one of the leading causes of acquisition failure (Uhlenbruck et al. 2017). In the last twenty years, as in many other countries, Italian listed banks have experienced successive waves of M&As (Mastromatteo and Esposito 2016). Since the banks themselves are heterogeneous, M&As cannot be analysed as if both the main Italian groups and the smallest banks were part of the same strategic action. For instance, the rationale behind the creation of Intesa or the UniCredit group, which compete at the continental level, is different from the aggregation of medium-size banks. M&As have been designed by some stronger listed banks to cope with the limited banking concentration, which is lower in Italy than in most other European countries. By contrast, others, on a less firm financial footing, have mainly resorted to M&As to solve their “weak profitability”, thereby Economies 2024,12, 58. https://doi.org/10.3390/economies12030058 https://www.mdpi.com/journal/economies Economies 2024,12, 58 2 of 33 partly resolving the problem in the short run by their increasing market power and share value. In the latter case, the motivation moving the managers toward inappropriate and often risky actions is what is called “desperation” (Kim et al. 2011). This desperation, in turn, makes managers prone to high-risk strategies, being particularly motivated to seize growth opportunities. This risk can also mean paying a much higher price for a target bank than its market value. Starting from this de facto situation, the present research aims to test the behaviour of Italian banks during the successive waves of M&As in order to capture the rationale driving banks’ behaviour in the process. It pursues the goal of understanding whether there are conditions that can predict when an M&A has positive effects on banks and when it does not. To achieve this goal, we test three different research hypotheses, referring to different aspects related to the decision of performing mergers and acquisitions. The first one (Hypothesis 1—H1) studies the preconditions leading banks to the decisions to make M&As, i.e., the more negative/positive the initial condition of Bank j, the more likely it is that Bank jwill undertake M&A activities with Bank i. The second one (H2) studies the short-term effects of M&As on banks as follows: the effect of the M&A operations performed by Bank jincreases the market value of Bank j,i in the short term. Finally, H3 focuses on the structural impact of M&As on banks’ financial indicators in the long term: the effect of M&A operations undertaken by Bank jdecreases/increases the structural indicators of Bank i,j in the long term. Hence, the present work offers an overview of the phenomenon of mergers and bank acquisitions in Italy. We focus on strategies for researching the competitive advantage in external growth processes and on an analysis of the characteristics of M&A operations in recent years, also examining the implications as part of the delicate post-M&A phase. The M&A market has developed considerably in recent years at a global scale. Mergers and acquisitions, which traditionally were considered to be strictly extraordinary in character, have become a common phenomenon. Studying the Italian case is quite interesting because of the historical evolution of the Italian M&A market, which can be divided into two major periods: the first, before the euro, between 1988 and 1998, linked to the start of privatisation, and the second (1999–2016), marked by Italy’s entry into the euro and by globalisation processes and major global economic and financial crises. The ever-increasing European economic integration, the globalisation of markets and the development of information technology pushed lenders to research new strategies for achieving and maintaining a competitive advantage. Therefore, the Italian banking system underwent a profound transformation, which influenced the management, organisational and operational strategies of credit companies. This work tries to interpret M&A processes in selected Italian listed banks by studying their characteristics and how M&As have changed their structure and size. We will then analyse the problems that characterised the poor effective returns of M&As for 29 Italian listed banks, such as managers’ desperation to grow and overconfidence, highlighting how these operations are very complex and should be managed with the maximum attention according to a well-defined strategic plan. Our research suggests that M&As undertaken by listed banks with different initial conditions have different final effects, which may be sequentially listed as follows: (i) several motivations encourage managers to implement the M&A process; (ii) this M&A process generates its effect (share market value) in the short run for the entire sample of listed banks, while (iii) in the medium/long run, the final effect is different because of the weaknesses/strengths of the banks’ financial structures. This study confirms the importance of the initial conditions in achieving the objectives (Hassan et al. 2018) and introduces an important new outlook to the economic literature: the role of bank-level “desperation” in the M&A process (Kim et al. 2011). Desperation occurs when there is a perception by managers that their banks are less profitable than others in an international context. Economies 2024,12, 58 3 of 33 The paper is structured as follows. Section 2explains the reasons to pursue M&As. Section 3describes the features of the Italian listed banks. Sections 4and 5show our hypotheses, data and the methodology used. The empirical findings are presented in Section 6, while conclusions are drawn in Section 7. 2. Literature Review on Mergers and Acquisitions Several explanations have been offered to explain why banks undertake M&As. The determinants of this process of the aggregation of the banking sector are manifold, and it is difficult to establish a specific classification (Badik 2007). In general, such operations respond to economic motivations concerning improvements in performance, growth and the creation of value for shareholders, increasing market power, economies of scale and synergy between the merged banks (Chu 2010). Asimakopoulos and Athanasoglou (2013) state that a willingness to increase in size, obtain value and enhance efficiency is the key to understanding banks’—especially small ones—decisions to undertake M&As. Zhang et al. (2018) claim that the value maximisation (VM) of the acquiring bank leads to improvements in its efficiency and profitability. Some of these topics belong to the neoclassical theory (Novickyt ˙ e and Pedroja 2015). Among these, merged institutions can increase their income according either to economies of scale or economies of scope (Dymski 2016). Economies of scale are achieved by decreasing the branch network and staff overhead and also by implementing information technology and risk management systems (Sharma 2013). Economies of scope can be obtained by increasing sales of services or placing emphasis on financial diversification, providing better services to consumers (Renaud 2016). Reducing operating costs, by merging branches and centralising back-office operations, is also included in VM (Kyriazopoulos and Drymbetas 2015). Moreover, M&As allow banks to (i) boost revenue—through network externalities and increased market power—(ii) reduce operation costs—saving costs related to marketing and distribution and human resource hiring—and (iii) create new growth opportunities— new markets and increased delivery channels (Fiordelisi 2009). Recently, the literature on banking sector businesses has shown that diversification is a central asset for increasing banks’ resilience to external factors (Ayadi et al. 2016;Michie and Oughton 2013;Ferri 2017). According to Badik (2007, p. 59), further external reasons are “globalization, deregulation, technological progress, introduction of Euro to name a few, that significantly affected the structure of the banking sector, creating pressures for change in the banking industry which might explain the recent pace of M&As activities”. With reference to the external factors leading to the development of M&As, the literature has also stressed the role of technological improvements, strengthened supervision of the banking system, increased integration and the globalisation of financial markets and—with reference to the EU context—the creation of a single market with a common currency (Asimakopoulos and Athanasoglou 2013). From a different perspective, it is often advocated for that diversified banking activities do not necessarily reduce the overall costs and risks associated with their activities (Goetz et al. 2016). Although banks emphasise several advantages of M&As (in terms of growth, the attainment of economies of scale and an increase in profitability), in practice, various operations may be referred to as motivated by non-value-maximisation (NVM) reasons. Behavioural theories classify the NVM motives into agency motives and hubris, which are, respectively, characterised by the rational or non-rational behaviour of managers. The main problem arises under the agency motives because managers do not represent shareholders’ interests and thus do not maximise profits for the shareholders. Dependency theory stresses the need for capital requirements to give stability to the banking system and manage the liquidity risk (Himalayan News Service 2015). Finally, agency theory supports market power synergy as a determinant of M&As, i.e., obtaining a stronger position in the market or better branding (Novickyt ˙ e and Pedroja 2015). Overall, agency theory seeks to explain the risk-taking behaviours of corporate strategic management and decision-makers (Hoskisson et al. 1993). This view has been Economies 2024,12, 58 4 of 33 applied to the finance sector, with the literature spending relevant efforts on developing models to explain risk-taking in the banking sector (Donnellan and Rutledge 2016;Palia and Porter 2007;Berger and Di Patti 2006). According to Jensen and Meckling (1976), agency problems can arise when the share of the bank owned by each shareholder is small and thus the incentive to monitor the behaviour of managers is missing. As regards hubris, a manager’s non-rational behaviour or overconfidence concerning the expected interplay resulting from M&As might carry to overpay the acquired bank. Thus, the buyers may achieve negative profits whereas the stockholders of the target bank might see value creation. The hubris hypothesis, proposed by Roll (1986), is based on the assumption that managers follow their personal benefits in term of power, wages and prestige, but to this end, they act against the owners’ interests. However, the priorities and interests of the managers often cause the acquisition to fail. In an agency-based theoretical framework, Milbourn et al. (1999) identifies two contrasting rationales driving managers to merge. First, managers decide to merge in order to increase their reputation or obtain higher compensation, at the cost of the shareholders. Secondly, uncertain future market opportunities and low levels of competition lead managers to expand their market power in order to create a competitive advantage, for the benefit of the shareholders. In conclusion, the literature has widely addressed the external factors and short- and long-term objectives leading banks to undertake M&A. However, as far as we know, a gap lies in the absence of studies aiming at the identification of the preconditions leading banks to the decision to merge with or acquire other banks. Moreover, another clear gap refers to literature studying the process of M&As happening in Italy. Among others, Focarelli et al. (2002) analysed the Italian banking system’s M&As between 1984 and 1996, finding that merging decisions were derived from a willingness to expand the customer base (i.e., achieve a larger market power), while acquisitions were mainly aimed at enhancing the value of the acquired bank. More recently, Coccorese and Ferri (2020) studied the wave of M&As undertaken by Italian mutual cooperative banks by focusing on their effectiveness in increasing the system efficiency. They found a relatively small increase in banks’ efficiency and conjectured that there were adverse effects on development and inequality. Indeed, it is timely to fill this gap, especially considering that, when compared with its main European competitors, the Italian banking system has several distinctive features due to its particular economic conditions and policies, which date back to the last century (Zedda 2016). By aiming to study the behaviour of Italian banks to understand the preconditions, effects and rationale driving banks’ behaviour in the M&A process, the present research tries to fill the gaps identified in the literature. 3. M&A Italian Listed Banks versus M&A European Listed Banks The Italian banking system presents strong differentiation points in comparison with other ones, which are mainly derived from its peculiar economic conditions and policies characterising the end of the 1990s (Zedda 2016). The troubled harmonisation process regulating both the banking sector and market integration led Italian banks to experience a delay in the consolidation process. This delay was mainly due to both the policy of the supervisory authorities, a low degree of competition and the presence of inefficient banks (Pannetta 2017). In fact, until the 1990s, the Italian banking system was still largely dominated by government-owned entities, while it managed to open up competition, becoming dynamic and efficient, in more recent times (Hagendorff et al. 2007). Italian institutions (banks) differ considerably, which is why the average data may mask the persistence of critical situations, and this is one of the most critical issues in the Italian banking sector (Bank of Italy 2019b). Indeed, according to the institutional classification of the Bank of Italy (2019a), the Italian banking system is highly heterogeneous: it comprises listed banks, cooperative banks (banche popolari), small cooperative (mutual) banks and subsidiaries of foreign banks. There emerges a puzzling framework in which the Economies 2024,12, 58 5 of 33 concentration level of Italian banks is lower than in other European countries; the number of non-performing loans (NPLs) is large, and profitability is weak, linked to poor asset quality (Weber 2017). In the last thirty years, in order to improve its competitiveness on European and international markets, fundamental changes have been made to organise the banking system more efficiently. Among such changes, stronger banks have started a privatisation process. There are currently 29 listed banks on the stock exchange out of a total of 493 (enrolled in the Register of Italian banks), in turn clustered into 53 banking groups (Bank of Italy 2019b). In order to solve the low level of concentration and overcapacity, some of the listed banks have launched a growth and aggregation process by implementing M&As (Baglioni et al. 2018) 1 . Due to the lack of available data, we considered 14 banks listed before 2010 that undertook the M&A process from 2010 to 2016. In order to understand the M&A process undertaken by the 14 Italian listed banks, which own about 97% of the total assets of all the listed banks, an analysis of the main operations was implemented. Due to the heterogeneous nature of the banks in question, differing in their international presence and strategic objectives, it does not make sense to lump them together as if they were part of some common trend (Esposito 2014). According to the Bank of Italy’s classification (2017), we clustered the listed banks into two groups according to their size (assets below/above €30 million) and degree of international openness (<4 foreign bank branches versus >4 foreign bank branches). The idea is that when Italian banks are solid, they go abroad (Paladino 2007;Esposito 2014). The details of this classification are reported in Appendix A(Table A1). Table 1shows, for each group, the M&As undertaken by the main listed banks in Italy from 2011 to 20162. Table 1. M&As undertaken by Italian listed banks (group 1 and group 2); 2011–2016. Group 1 2011 2012 2013 2014 2015 2016 1 BPER Banca SpA (BPER) 0 0 0 1 1 0 2 Banca Mediolanum SpA (Medionalum) 0 0 0 0 0 1 3 Credito Emiliano SpA (CREDEM) 1 1 0 0 0 0 4 Banca Piccolo Credito Valtellinese (CREVAL) 0 1 1 1 1 0 5 Banca Carige SpA (Carige) 1 0 0 0 0 1 6 Banco di Desio e della Brianza (DESIO) 0 1 1 0 0 0 7 Banca Generali SpA (Generbanca) (BG) 0 0 1 0 0 0 Group 2 2011 2012 2013 2014 2015 2016 1 Banca Monte dei Paschi di Siena SpA (MPS) 0 0 0 1 0 0 2Banca Popolare di Sondrio, Societa Cooperativa per Azioni (popso) 100000 3 Banco Popolare di Milano (BPM) 1 0 0 1 1 0 4 Intesa Sanpaolo (Intesa) 0 1 1 1 1 1 5 Mediobanca SpA (Mediobanca) 0 0 1 0 0 1 6 UniCredit SpA (UniCredit) 1 1 1 0 0 0 7 Unione di Banche Italiane SCpA (UBI) 1 1 1 0 0 0 In order to verify the results obtained by the banks subsequent to the M&As, the main banking indicators were analysed. According to the classification by KPMG (2017), we chose five main classes of indicators describing the various aspects characterising each bank from 2011 to 2016 (Tables 2and 3). Each class of indicators shows the following features: • Liquidity: A bank’s ability to quickly convert assets into cash. (Federal Reserve 2014; Chen et al. 2018); • Performance: A bank’s ability to provide its services to consumers and businesses while generating sustainable profitability (Anbar and Alper 2011); • Profitability: A bank’s ability to generate revenue that can cover costs, thus being profitable. This result is crucial for both the ongoing activity of the bank and its investors to obtain fair returns. Moreover, this index is carefully observed by the Economies 2024,12, 58 6 of 33 supervisory authorities, as it ensures more resilient solvency ratios, particularly in the context of a riskier entrepreneurial environment (Abdul 2017;Athanasoglou et al. 2008); • Quality: This set of indicators analyses the quality of the customer portfolio based on the quality of non-performing loans (Chiorazzo et al. 2008); • Structural/Capital ratio: This indicates the level of capitalisation of the banks and their ability to cope with lean periods using their own resources. Capital takes on the role of a financial cushion to tackle unexpected losses. (Posner 2015). A full description of the variables is set out in Appendix A(Table A2). To gauge the growth of the Italian listed banks, by using the Bureau van Dijk Orbis dataset, we calculated the trends in the main indexes of not only the banks in the sample, clustered into groups 1 and 2, but also compared all the listed banks in the EU operating during the study period. Our analysis is twofold: the first part compares the averages of the indexes achieved by each group with those attained by the 96 listed banks in the 27 EU countries which undertook M&As; the second part duplicates the analysis by referring to the 52 listed banks belonging to the top five countries in the Euro Area (France, Germany, the Netherlands, the UK and Spain). The results obtained by each bank are reported in Appendix A(Tables A3–A17). A comparison with the averages of both European countries and the top five is useful to understand the nature of the specific fragility of the Italian listed banks. Overall, the results show that both groups achieve below-average results, both compared to the whole European area and the top five countries. However, significant evidence can be highlighted in the magnitude of the data: group 1 shows lower values than group 2. These findings underline that group 1 is less sound compared with the stronger banks. In general, it may be observed that the weakness of the Italian system was aggravated by the long recessional phase during the years 2008–2013, which made the banks even more fragile (Engler and Klein 2017;Farinha et al. 2019). Nevertheless, the impact of the crisis was amplified by elements of deep-rooted structural vulnerability (Borio and Gambacorta 2017). This applies to the main indexes belonging to the two groups, from which a strong discrepancy originates vis àvis the European average: liquidity, profitability and performance indexes (Mastromatteo and Esposito 2016). An important consideration must be made in terms of the quality indexes that are mainly explained by the presence of NPLs. The findings within group 2, being above average (except for Mediobanca and Banca Popolare del Sondrio), highlight the presence of a large quantity of insolvent loans that generate disruption in the system; in contrast, group 1 shows lower values of NPLs than those found Europe-wide. Indeed, in the years following the crisis, the trend in NPLs (Appendix A(Tables A11 and A12)) was due to the length and ineffectiveness of the procedures used to recover guarantees. Furthermore, up to 2015, the fiscal regulations discouraged banks from making suitable changes or writing off deteriorated credit (Jassaud and Kang 2015). The indicators describing the liquidity of the banks show persistently inefficient values when compared to the international context (with the exception of Mediolanum in group 1). However, an improvement in the liquidity index occurred in 2012 when compared with EU_27 and in 2014 when compared with the top five countries (cfr. Appendix A(Table A4)). This highlights the role played by the economic crisis in the performance of the banking system, during which convergence to a lower similar value occurred (Zedda 2016). Economies 2024,12, 58 7 of 33 Table 2. Group 1—comparison between Italian banks and European top 5 banks. Average Europe 27 Average Europe 5 = 100 Index Class Indicator BPER Mediolanum CREDEM CREVAL Carige DESIO BG BPER Mediolanum CREDEM CREVAL Carige DESIO BG Liquidity Liquid assets/deposits and short-term funding ratio 22.8 115.9 28.9 18.5 28.2 23.5 31.4 18.9 94.5 24.0 15.4 23.7 20.6 26.3 Liquid assets/ Total deposits and loans ratio 20.1 103.0 25.1 17.3 21.6 21.9 37.0 16.7 77.0 20.0 14.3 17.6 17.7 30.5 Performance ROAA 0.6 469.8 111.2 −255.7 −180.5 336.8 312.9 8.0 607.3 139.5 −315.3 −137.6 548.8 802.0 ROAE 68.3 386.7 101.3 171.6 −204.3 374.0 524.1 30.3 225.4 63.5 −4.4 −264.6 223.1 320.8 RoRWA 7.0 468.2 76.8 −64.0 −148.2 379.1 487.9 15.5 374.5 67.2 −37.8 −105.5 294.2 400.2 Profitability Operating profit/average equity 36.3 282.7 110.7 −58.6 −327.8 461.5 387.2 37.8 229.9 93.9 −62.8 −167.9 396.5 317.4 Operating profit/total deposit 60.9 284.9 124.6 −0.6 −113.8 159.3 347.2 12.6 262.8 89.7 −33.6 −192.5 137.8 297.2 Profit before tax/total deposit 50.7 248.1 133.8 −74.5 −296.4 150.3 324.1 11.3 366.2 164.3 −216.0 −400.1 190.9 415.1 Quality Impaired/NPL/equity 316.2 28.5 100.2 319.3 359.2 277.1 24.5 418.4 22.2 124.1 467.1 557.9 315.2 24.3 Impaired/NPL 131.8 1.4 16.5 54.8 72.8 10.3 0.7 90.6 1.0 11.3 38.0 50.6 7.3 0.4 Structural Equity/total assets 76.5 46.8 59.0 67.0 60.4 56.0 66.6 70.7 44.1 54.7 62.2 56.7 49.8 60.2 Net profit/(loss) for the year from discontinued operations −2.1 0.0 2.2 1.4 19.5 0.0 0.4 −0.6 0.0 0.7 0.1 7.5 0.0 0.1 Capital Ratio Tier 1 ratio 61.1 94.3 65.4 59.9 52.5 80.9 79.3 72.3 110.2 78.0 70.7 61.3 97.5 95.6 Equity/net loans 35.7 71.6 32.4 32.0 32.2 50.1 99.0 24.4 49.1 22.2 21.9 22.0 34.4 67.8 Total capital ratio 59.9 81.3 63.1 61.0 53.2 65.3 70.6 60.0 80.6 63.4 60.7 52.3 65.1 70.3 Source: Bureau van Dijk Orbis. Economies 2024,12, 58 8 of 33 Table 3. Group 2—comparison between average value of each Italian listed bank and EU_27 bank. Average Europe 27 Average Europe 5 = 100 Index Class Indicator UniCredit Intesa BPM MPS UBI Mediobanca Popso UniCredit Intesa BPM MPS UBI Mediobanca Popso Liquidity Liquid assets/deposits and short-term funding ratio 68.9 90.4 7.5 50.8 28.0 79.2 35.9 52.9 58.2 5.2 39.0 20.0 39.1 37.2 Liquid assets/ total deposits and loans ratio 52.9 58.2 5.2 39.0 20.0 39.1 37.2 41.7 46.1 3.7 31.7 16.5 29.7 29.9 Performance ROAA −15.1 56.5 −10.5 −411.3 −18.7 73.0 54.5 −29.0 25.2 −10.2 −556.4 −64.7 68.8 66.3 ROAE −200.6 −233.8 16.0 1382.6 −122.7 −27.7 −49.9 −92.8 −61.7 13.4 −424.5 −73.8 28.7 50.0 RoRWA −61.6 −2.7 −16.7 −139.3 37.9 23.6 −53.3 −30.1 23.7 −16.2 −183.2 −6.1 19.9 30.5 Profitability Operating profit/average equity −93.8 −6.6 −22.9 −520.4 −9.0 39.9 76.0 −54.2 22.5 −31.1 −393.1 −22.7 30.5 62.8 Operating profit/total deposit −10.0 62.9 10.0 −163.7 4.5 52.6 52.6 −27.5 59.7 3.7 −174.2 15.6 67.3 35.7 Profit before tax/total deposit −127.6 −170.0 10.7 −396.3 −117.1 21.2 50.5 −70.5 45.0 3.9 −456.1 4.1 92.3 51.4 Quality Impaired/NPL/equity 255.9 193.4 136.5 789.3 192.0 23.5 186.1 1042.6 743.8 141.0 500.1 156.5 19.5 39.1 Impaired/NPL 1042.6 743.8 141.0 500.1 156.5 19.5 39.1 710.6 509.9 103.1 345.4 107.3 13.6 27.2 Structural Equity/total assets 57.9 67.7 16.9 36.1 77.2 76.3 61.6 229.2 515.8 0.2 0.7 0.1 0.0 0.0 Net profit/(loss) for the year from discontinued operations 229.2 515.8 0.2 0.7 0.1 0.0 0.0 −2.1 46.1 −0.2 −0.2 0.0 0.0 0.0 Capital Ratio Tier 1 ratio 61.6 76.9 26.2 59.6 68.2 58.6 54.3 75.3 92.9 27.0 73.6 82.4 67.6 64.9 Equity/net loans 34.3 42.6 10.9 19.2 37.1 54.8 29.6 23.4 29.1 7.6 13.1 25.4 37.9 20.3 Total capital ratio 64.0 74.7 20.3 67.1 74.1 59.1 55.1 65.0 74.5 16.0 68.4 75.7 58.7 54.8 Source: Bureau van Dijk Orbis. Economies 2024,12, 58 15 of 33 Economies 2024, 12, x FOR PEER REVIEW 15 of 35 banks, namely Generali and Mediolanum, exhibited for very early M&As the persistent effect of the coefficients. This long-run impact of the M&As is particularly evident for the first two M&As made by Banca Generali. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 COEF_INTERCLOSE2001 COEF_INTERCLOSE2003 COEF_INTERCLOSE2013 COEF_INTERCLOSE2014 COEF_INTERCLOSE2017 Interacrion coefficients - BPER 0.0 0.4 0.8 1.2 1.6 2.0 2.4 IV I II III IV III III IV I 2016 2017 2018 COEF_INTERCLOSE2015 COEF_INTERCLOSE2017 Interacrion coefficients - Mediolanum 0.0 0.4 0.8 1.2 1.6 2.0 10 11 12 13 14 15 16 17 18 COEF_INTERCLOSE2009 COEF_INTERCLOSE2011 Interacrion coefficients - CREDEM -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 IV I II III IV III III IV III III IV III III IV III III IV III III IV I 2012 2013 2014 2015 2016 2017 COEF_INTERCLOSE2011A COEF_INTERCLOSE2011B COEF_INTERCLOSE2012 COEF_INTERCLOSE2013 COEF_INTERCLOSE2014 COEF_INTERCLOSE2016 COEF_INTERCLOSE2018 Interacrion coefficients - Credito V altellinese -0.4 0.0 0.4 0.8 1.2 1.6 2.0 2.4 09 10 11 12 13 14 15 16 17 18 COEF_INTERCLOSE2009 COEF_INTERCLOSE2010 COEF_INTERCLOSE2014 COEF_INTERCLOSE2015 COEF_INTERCLOSE2016 Interacri on coefficients - Carige 0.0 0.4 0.8 1.2 1.6 2.0 III IV III III IV III III IV III III IV III III IV III III IV III III IV I 2012 2013 2014 2015 2016 2017 COEF_INTERCLOSE2011 COEF_INTERCLOSE2014 Interacrion coefficients - Desio -0.4 0.0 0.4 0.8 1.2 1.6 2.0 07 08 09 10 11 12 13 14 15 16 17 18 COEF_INTERCLOSE2007 COEF_INTERCLOSE2008 COEF_INTERCLOSE2012 Interacrion coefficients - Banca Generali Figure 1. Behaviour of the interaction coefficients with rolling regressions for group 1. 0.0 0.4 0.8 1.2 1.6 2.0 II III IV III III IV III III IV III III IV IV I 2013 2014 2015 2016 2018 COEF_INTERCLOSE2013A COEF_INTERCLOSE2013B Int e r a cri o n c o e f f ici e nts - M o nt e d e i P a schi di Si e n a -0.8 -0.4 0.0 0.4 0.8 1.2 1.6 2.0 II III IV III III IV III III IV III III IV III III IV III III IV 2011 2012 2013 2014 2015 2016 COE F_INTE RC LOS E 2011A COE F_INTE RC LOS E 2011B COE F_INTE RC LOS E 2011C COE F_INTE RC LOS E 2012 COE F_INTE RC LOS E 2014 COE F_INTE RC LOS E 2015 COE F_INTE RC LOS E 2016 Int e r a cri o n c o e f f ici e nts - B a nc a P o p o l a r e di S o ndri o 0.0 0.4 0.8 1.2 1.6 2.0 III III IV III III IV III III IV III III IV III III IV III III IV III III IV I 2011 2012 2013 2014 2015 2016 2017 COEF_INTERCLOSE2010 COEF_INTERCLOSE2013 COEF_INTERCLOSE2014 Int e r a cri o n c o e f f ici e nts - B a nc a P o p o l a r e di Mil a n o -2 -1 0 1 2 3 4 5 6 08 10 12 14 16 1 8 COEF_INTERCLOSE2008 COEF_INTERCLOSE2009 COEF_INTERCLOSE2011 COE F_IN TE R CLOS E 2012A COEF_INTERCLOSE2012B COE F_INTE RC LOS E 2013A COEF_INTERCLOSE2013B COE F_INTE RC LOS E 2013C COEF_INTERCLOSE2014A COE F_INTE RC LOS E 2014B COEF_INTERCLOSE2015A COE F_INTE RC LOS E 2015B COEF_INTERCLOSE2015C COE F_INTE RC LOS E 2016A COEF_INTERCLOSE2016B COE F_INTE RC LOS E 2016C Int e r a cti o n c o e f f ici e nts - B a nc a Int e s a -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 IIIIII IV III III IV III III IV III III IV III III IV I 2013 2014 2015 2016 2017 COEF_INTERCLOSE2012 COEF_INTERCLOSE2015 COEF_INTERCLOSE2017 Interacrion coeffi cients - Mediobanca -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 06 07 08 09 10 11 12 13 14 15 16 17 COEF_INTERCLOSE2006 COEF_INTERCLOSE2007 COEF_INTERCLOSE2008 COEF_INTERCLOSE2010 COEF_INTERCLOSE2011 COEF_INTERCLOSE2012 COEF_INTERCLOSE2013 COEF_INTERCLOSE2016 COEF_INTERCLOSE2017 COEF_INTERCLOSE2018 Interaction coefficients - Unicredit -0.4 0.0 0.4 0.8 1.2 1.6 2.0 III III IV III III IV III III IV III III IV III III IV III III IV I 2012 2013 2014 2015 2016 2017 C OE F_11IN TE RC LOS E 11 COEF_12AINTERCLOSE12A C OE F_12IN TE RC LOS E 12 C OE F_13IN TE RC LOS E 13 C OE F_16IN TE RC LOS E 16 COEF_17AINTERCLOSE17A COEF_17BINTERCLOSE17B C OE F_17IN TE RC LOS E 17 Interaction coefficients - UBI Figure 2. Behaviour of the interaction coefficients with rolling regressions for group 2. In line with the results obtained for group 1, the behaviour of the share prices of the Italian banks in group 2 shows a similar path: a short-run process for almost all the banks. However, UBI and Monte dei Paschi with their first M&As show persistent effects, particularly for UBI. A consideration that concerns all groups is that the impact that the M&As had on the share prices of the banks was always short-term. More detailed analysis shows that the groups with a high performance experienced a shorter effect than groups with a low performance. Since it is beyond the scope of this study to identify the determinants of the Figure 2. Behaviour of the interaction coefficients with rolling regressions for group 2. A consideration that concerns all groups is that the impact that the M&As had on the share prices of the banks was always short-term. More detailed analysis shows that the groups with a high performance experienced a shorter effect than groups with a low performance. Since it is beyond the scope of this study to identify the determinants of the share price of the banks, the rolling analysis only allowed us to isolate and quantify the impact of the M&As on the value of the Italian banks. From this point of view, we can therefore hypothesise that the impact of the M&As that presented a low performance may have been amplified solely by the additional difficulties that these banks had to face, while, for the other two groups, such difficulties may have been neutralised, in these cases, by other confidence factors. These findings confirm H2. H3. The effect of the M&A operations performed by Bank j decreases the structural indicators of Banki,j in the long term. To determine the relationship in the long run between the M&A and the vector of the explanatory variables, we tested two different cases: OLS-FE and FE-DK. As the first step, we implemented the standard Hausman test (see the results in Table 3). The null hypothesis of the test is rejected. The bank-specific effects are correlated with the regressors. Since the random effects estimator is inconsistent, the appropriate model is a fixed-effects model, and, consequently, it is applied to test H3. With the fixed-effects model, we assessed the impact of the main financial statement/management indicators on the bank’s assets, as well as the M&A activity repeated by the banks. As a preliminary diagnostic, test for the model assumptions must be implemented. The three most essential assumptions of the fixed-effects estimator are no serial correlation, no contemporaneous correlation and homoscedasticity. Testing for the latter is performed using the modified Wald test for the null hypothesis of homoscedasticity, while Wooldridge’s serial correlation test is used for serial correlation. Testing for the absence of the contemporaneous correlation assumption, Pesaran’s CD test is performed. However, since the model has cross-sectional dependence, we cannot use the standard first-generation tests to check for a panel unit root because it could increase the probability of the existence of a spurious unit root. Thus, to overcome this problem, Pesaran proposed the CIPS test for the unit root test in the presence of heterogeneous cross-sectional dependence. Economies 2024,12, 58 16 of 33 Together, with the uniform results obtained from the first-generation unit root tests, in Table 5, the results of the CIPS test, with the selected average lag length of 1, show that our prior suspicion regarding the order of integration of the series in question still holds if we also account for cross-sectional dependencies. Finally, since the panel models could suffer from endogeneity problems, as the Tier 1 ratio could affect the merger decision, we then test for endogeneity. However, due to the small sample, we cannot apply the Wu–Hausman test. In fact, this test is only as good as the instruments used and is only valid asymptotically. This may be a problem in small samples, and so it should generally be used only with sample sizes well above 100. Therefore, we move to the two-step test. First, we regress the suspected endogenous variable (merger) using the instrument(s). We save the residuals as RES, and after, we include this residual as an extra term in the original model. In this new estimation, if we test whether the coefficient of RES is equal to zero (using a t-test). If it is, we can conclude that merger and error term are indeed correlated, that is, there is endogeneity in the model. Our results show that the t-stat of the RES coefficient is − 0.37; therefore, it is not statistically significantly different from zero, so we conclude that there is no endogeneity bias of the merger in the model. Table 5. Probit model. Group 1 Group 2 merger tier1_ratio −0.1072 * 2.02 ×10−8** (0.0647) (7.64 ×10−9) liquidass_Dep_Bor 0.0197 −0.0419 (0.0383) (0.0335) oper_profit_avg_equity −7.3794 46.209 ** (7.1516) (22.508) impair_npl_equ −0.0650 −0.0835 (0.2271) (0.2262) roae −0.07937 * −0.2432 (0.04418) (0.24001) _cons −5.0586 ** −5.3382 ** (2.0180) (2.1117) N42 42 pseudo R20.154 0.160 Robust standard errors in parentheses. ** p< 0.05; * p< 0.1. Table 6presents the outcomes of the panel estimations of the two groups. The first and most important result is that the impact of the M&As in the medium–long run is positive and significant only for group 2 when Panel OLS b is applied. The effect of the M&A operations performed by Bank j increases the structural indicators of Bank i,j in the long term for group 2, while for group 1, the sign of the coefficient is negative and significant, implying (when Panel OSL b is applied) the relevant negative role of M&As for the weaker listed banks. These findings confirm that M&As in Italy have played an ambiguous role. The structural indicator of all listed banks that merge in the short/long term shows that the main banking indexes (with the exception of the profitability index of group 1) contribute positively to the level of capitalisation of the banks and their ability to cope with stressful periods using their own resources. Economies 2024,12, 58 17 of 33 Table 6. Rolling regression coefficients. Group 1 α1Std. Error t-Statistic Obs 1 BPER Banca SpA (BPER) 0.996381 *** 0.000913 1090.845 7351 2 Banca Mediolanum SpA (Medionalum) 0.990958 *** 0.001742 568.8754 5409 3 Credito Emiliano SpA (CREDEM) 0.987000 *** 0.003474 284.1477 2346 4 Banca Piccolo Credito Valtellinese (CREVAL) 0.970691 *** 0.001068 933.6389 7895 5 Banca Carige SpA (Carige) 0.998903 *** 0.000804 1242.504 5862 6 Banco di Desio e della Brianza (DESIO) 0.995733 *** 0.001167 853.4398 5708 7 Banca Generali SpA (Generbanca) (BG) 0.00732 *** 0.001792 4.087185 2868 Group 2 α1Std. Error t-Statistic Obs 1 Banca Monte dei Paschi di Siena SpA (MPS) 0 0 0 2 Banca Popolare di Sondrio Societa Cooperativa per Azioni (popso) 0.998439 *** 0.000997 1001.880 4702 3 Banco Popolare di Milano (BPM) 0.006593 *** 0.038765 0.170079 5013 4 Intesa Sanpaolo (Intesa) 0.995146 *** 0.000981 1014.899 8379 5 Mediobanca SpA (Mediobanca) 0.992721 *** 0.001235 803.9053 8376 6 UniCredit SpA (UniCredit) 0.997471 *** 0.000772 1291.868 8374 7 Unione di Banche Italiane SCpA (UBI) 0.998719 *** 0.009735 102.5855 1561 *** p< 0.01. These findings confirm the claim of H3 that the effect of the M&A operations undertaken by Bank i,j decreases or increases the structural indicators in the long term on the basis of its initial conditions, following the logic of “consolidation” or “desperation to grow”. Instrumental Variable Approach In Table 5, the use of the covariates describing the index “tier1_ratio” is likely to produce endogeneity problems, which mainly arise from reverse causality and omitted variables since the index selected can include factors that have been omitted from the regression (Efendic et al. 2011). Furthermore, the data referring to the structural indicators, especially those used for the construction of the ROAE, could be affected by measurement errors. Indeed, being based on the average shareholders’ outstanding equity, such data could clearly have a direct effect on the dependent variable (Pinotti 2015). In order to test the robustness of our results and obtain results that were unaffected by endogeneity, we had to use alternative econometric methods. We adopted an instrumental variable (IV) strategy where the indexes assessing the lagged independent variables are used as instruments. Indeed, the lagged IV method is considered acceptable and helpful for mitigating endogeneity, since they derive from consistent estimates that are less biased than OLS ones (Wang and Bellemare 2019). This approach has been exploited by the literature applying the IV approach to addressing endogeneity (e.g., Keong et al. 2003;Canale et al. 2018;Bonasia et al. 2022). The instruments should affect the dependent variables only indirectly, namely through their correlation with the variables identified as endogenous. In this framework, lagged independent variables might be correlated with the current value of the instruments—but not with the outcome. Indeed, the selected instrumental variables show a low correlation with the independent variables and a stronger correlation with the instrumented variables. IV estimation proceeded as follows. We pooled our panel and estimated an IV fixed-effects model with heteroscedasticityrobust and panel-corrected standard errors. The choice of pooling the data is justified by the fact that we mainly exploit cross-section variations among the Italian banks, and the pooled approach can also control for additional reverse causality. Application of the fixed-effects IV approach produces more consistent results in the presence of heteroscedasticity and cross-sectional dependence (Baltagi et al. 2016). The regression was estimated once by using the dependent variable “Tier 1 ratio”; a second regression was estimated for the robustness check by using a different dependent variable, called equity/net loans. Economies 2024,12, 58 18 of 33 Table 7shows the panel IV results for the group 1 and group 2 specifications that also include a different dependent variable (Equity/net loans). The first important result we can draw from both groups of regressions is that our analysis holds even once the endogeneity problem is accounted for. The validity of the instruments is set by the value of the Wald statistic. The correct interpretation for the Wald test is a test of the specified null hypothesis, namely that all coefficients are zero. Moreover, the value of the coefficient of the endogenous variable lies within the confidence region obtained after applying the conditional likelihood ratio test statistics (Moreira 2009), supporting the robustness of the results to weak instrument issues. In each specification, the null hypothesis of Sargan– Hansen’s J statistic that the instruments are valid is not rejected. Table 7. Results from the IV model. tier1_ratio (Group 1) Equ_netloans (Group 1) tier1_ratio (Group 2) Equ_netloans (Gropp 2) Merger −1.73094 * (0.98707) −1.425071 (1.35475) 1.70926 ** (0.85347) 0.420315 * (0.230402) Roae −0.354115 2.81872 ** 7.28038 *** 5.4082 ** (0.49284) (1.21779) (1.58717) (2.41192) Liquidass_Dep_Bor 0.13297 *** (0.049761) 0.04796 ** (0.022295) −0.07212 (0.126673) 0.256082 * (0.136152) Impaired_npl 3.36 ×10−7 (2.74 ×10−7) 3.49 ×10−7*** (5.59 ×10−8) 6.98 ×10−8*** (2.36 ×10−8) 3.74 ×10−8** (1.79 ×10−8) Oper_prof_avg 0.14014 * 0.136591 0.738308 0.231248 * (0.08072) (0.19947) (2.19074) (0.227972) _cons 7.31211 *** 4.81130 *** 9.25188 *** 6.97251 * (1.56646) (0.70399) (1.62849) (3.72589) N35 35 35 35 pseudo R20.30 0.41 0.37 0.64 Wald|χ2(5) 14.97 23.22 38.74 12.37 P-Val 0.0105 0.0003 0.0000 0.0300 Robust standard errors in parentheses. *** p< 0.01; ** p< 0.05; * p< 0.1. Instruments: lagged independent variables. It is worth emphasising that the estimation results have a rather high explanatory power considering the reported values of the pseudo R2. Of the two models, the first (group 2) has more explanatory power. Almost all the coefficients of the explanatory variables are significant and have the correct relationships. A comparison of the results obtained in Table 5regarding the role of the M&As in the structural indexes is substantially confirmed according to the IV analysis on the relationships of the coefficients but with different magnitudes. These results support the goodness of our approach. To sum up, from the panel fixed-effects model and panel IV method estimations, the effect of the M&A operations undertaken by the two groups of listed Italian banks in the last 15 years has increased their structural indicators in the long term. However, the M&As for group 1 have had a negative effect on the tier1_ratio. That is, for group 1, the M&As follow the logic of “desperation to grow”. Conversely, the estimates for group 2 (Panel and IV) confirm the intuition that, for group 2, the M&As have instead followed the logic of “consolidation”. A remarkable extension of the present work would be a more complete investigation into this practice, looking for the determinants of M&As for single banks. However, this would require a more meticulous dataset containing the specific characteristics of the individual banks. 7. Discussion and Conclusions The profitability gap of these Italian listed banks reflects several characteristics, such as the macroeconomic context, the banks’ business model and their policies (Albertazzi et al. 2016). The macroeconomic context in Italy, as in other countries, has been affected by Economies 2024,12, 58 19 of 33 a period of recession due to both the financial crisis and sovereign debt crisis, which has worsened the quality of bank credit, creating a huge number of NPLs (around 21 percent of GDP) (Weber 2017) and a corresponding fall in bank profits. The policies, interacting within a competitive system, followed the trend imposed by the European Banking Authority (EBA) toward both a more consolidated banking system and strict constraints concerning capital requirements. These directives arising from Basel 3 imply that banks’ profitability is bound to become even more an important component of financial. Finally, the focus of the bank business model in Italy is more on lending to households and firms compared to other EU countries (Weber 2017). Moreover, Italian banks show a significant degree of heterogeneity. Although the Italian authorities have passed a number of reforms both to transform banks’ governance structures and boost banks’ competitiveness, the results of the main banking indicators show lower values than the EU average. Indeed, some of them still have a certain degree of competitiveness on domestic and international markets compared with others that could be considered weaker concerning their assets and level of internationalisation. This paper highlighted the peculiarity of the Italian case, which needs to be considered when assessing the effects of the M&A process. We analysed the process of the mergers and acquisitions of the Italian listed banks, finding a conflicting situation in which banks with different initial conditions were driven by different motives with respect to those proposed in the conventional economic literature. Due to the fact that different initial structural conditions might have different impacts on the structural indexes, this study used a miscellaneous approach to ascertain whether the Italian M&A process has experienced different impacts depending on timing. Using several econometric models, our empirical analysis showed that the process of bank acquisitions in Italy is derived paradoxically from a situation of strength vs. weakness, with weak buying banks that struggle to become stronger. The initial condition of the strength of a bank leads to the strengthening of the bank itself; yet, starting from a weaker condition, its structural position become even more impaired. Specifically, even if, in the short term, the impact of the implemented policies shows the same results for both groups of banks, the initial differences influence the final effect when observed in the long term, highlighting the very limited and short-lasting effects for the weaker banks. Thus, the banks belonging to group 2, the stronger ones, present a behaviour consistent with theoretical and empirical analysis of M&A processes, showing that the process of banking concentration has lasting effects since the management of the merging banks is able to trigger real effects, i.e., the improvement of the structural indicators (Badik 2007). In contrast, the consolidation and concentration processes of the weaker Italian listed banks were partial, and their strengthening seems desirable to overcome the problems of efficiency and profitability, as emerges from the comparison with the European listed banks. Our empirical analysis for group 1 appears to support the existence of causal links according to which (i) the propensity to undertake mergers is positively correlated with the weakness of the starting conditions; (ii) equity capital gain is a short-term phenomenon; (iii) the subsequent profitability conditions do not improve; rather, they tend to worsen. Indeed, from the panel fixed-effects model and panel IV method estimations, the impact of the M&A operations undertaken by seven weaker Italian banks lowered their structural indicators in the long run. Frequently, a quick way a bank can capitalise on growth opportunities is through an acquisition by expanding into new geographic markets (Ullah et al. 2015). Consequently, the acquisition activity of banks that are in search of quick development can be stimulated by low economic growth. Even though acquisitions can be considered an effective business policy because they are considered a growth vehicle, they are essentially risky because they are related to significant uncertainty and potential financial loss (Ravenscraft and Scherer 1989;Kravet et al. 2018). Our research confirms the hypotheses of prior studies indicating that acquisitions often fail to create value for shareholders in the long Economies 2024,12, 58 20 of 33 term (Friedman et al. 2016). Finally, some of the M&As in Italy did not work in the way the standard literature has suggested. These conflicting results show an overall condition of the structural vulnerability of the Italian listed banks, which appear even weaker in comparison with the universe of the European banking system, highlighting the importance of clustering banks into homogeneous groups. Despite presenting useful results, the research shows some limitations which might pave the way for future research avenues. First, our estimation approach allows us to capture the effects of the M&As on both the short-term share value and long-term structural indicators of the banks, but it does not fully consider other determinants of these features. The second limitation concerns the time span analysed. Indeed, the recent trends characterising the Italian banking industry (such as the Cooperative Bank reform of 2015) cannot be not fully captured by our analysis. In addition, recent international crises (such as the COVID-19 shock or the Russo-Ukrainian war, with the sanctions imposed on Russian banks) have affected Italian banks’ operations. Subsequently, expanding the time coverage of the data might provide more insightful results. Third, the sample of banks analysed does not include Italian cooperative banks, which have undergone a strong reform process. Considering these banks in the analysis might provide evidence on a peculiar typology of banks that plays a non-negligible role in the Italian credit market. Finally, as far as data are available, expanding this analysis to other EU banks might provide an interesting comparative assessment of Italian banks’ performance. Author Contributions: Conceptualisation, R.A., U.M. and O.N.; data curation, R.A., R.B., K.K. and O.N.; formal analysis, R.A., K.K. and O.N.; methodology, R.A. and O.N.; supervision, R.A. and O.N.; validation, K.K.; visualisation, R.B. and K.K.; writing—original draft, R.A., R.B. and O.N.; writing—review and editing, R.A., R.B. and O.N. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: Data will be made available on request due to restrictions. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Table A1. Classification of Italian listed banks by assets, year 2017. Bank Name City Country Code Total Assetsm USD Country Rank World Rank 1. UniCredit SpA MILAN IT 1,003,562 1 30 2. Intesa Sanpaolo TURIN IT 955,675 2 36 3. Banco BPM SpA MILAN IT 193,335 3 142 4. Banca Monte dei Paschi di Siena SIENA IT 166,888 5 154 5. Unione di Banche Italiane SpA BERGAMO IT 152,762 6 167 6. BPER Banca SpA MODENA IT 85,557 8 278 7. Mediobanca SpA (Mediobanca) MILAN IT 84,288 9 285 8 Banca Mediolanum SpA BASIGLIO IT 51,890 13 426 9. Banca Popolare di Sondrio SONDRIO IT 49,920 14 448 10. Credito Emiliano SpA (CREDEM) REGGIO-EMILIA IT 49,872 15 449 11. Banca Piccolo Credito Valtellinese SONDRIO IT 29,931 20 673 12. Banca Carige SpA GENOVA IT 29,886 21 675 13 Banca di Desio e della Brianza DESIO IT 16,785 31 1036 14 Banca Generali SpA (Generbanca) TRIESTE IT 10,783 43 1363 A Total assets of 14 listed banks 2,956,461 B Total assets of all listed banks 3,040,606 C A/B 0.97 Economies 2024,12, 58 21 of 33 Table A2. Description of the variables. Macroarea Description N. Indicator Label Liquidity The extent to which banks have liquidity on hand and are funded by relatively stable and predictable (mainly retail) deposits, rather than by potentially more volatile wholesale debt funding 1Liquid assets/total deposit and borrowing liquidass_Dep_Bor 2Liquid assets/deposits and short-term funding liquidass_Dep_stfunding Performance The bank’s ability to provide its services to consumers and businesses 3 Return on average assets (ROAA) Roaa 4 Return on average equity (ROAE) Roae 5Return on risk-weighted assets (RORWA)—operating profit/RWA Rorwa Profitability The bank’s ability to generate revenue that can cover costs, thus being profitable 6 Operating profit/average equity oper_profit_avg_equity 7 Operating profit/total deposit operpro_tdep 8 Profit before tax/total deposit prof_bef_tax_totdep Quality Analyses the quality of the customers’ portfolio based on the quality of non-performing loans present 9 Impaired/non-performing loans/equity impair_npl_equ 10 Impaired/non-performing loans impaired_npl Structural/capital ratio Indicates the level of capitalisation of the banks and their ability to cope with stressful periods using their own resources 11 Equity/total assets equity_totassets 12 Net profit/(loss) for the year from discontinued operations Netprofit_disc 13 Total capital ratio tot_capital_ratio 14 Tier 1 ratio Tier_1 15 Equity/net loans equ_netloans Table A3. Liquidity indicators. Liquid Assets/Deposits and Short-Term Funding Ratio N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA 28.5 29.7 28.2 25.7 25.1 23.5 2. Intesa Sanpaolo 38.2 37.6 35.0 32.0 33.4 33.5 3. Banco BPM SpA 9.9 9.3 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA 13.5 20.9 15.8 17.6 19.9 28.8 5. UBI Banca SCpA 6.5 7.0 7.6 11.5 15.0 14.4 6. BPER Banca SpA 4.7 5.2 7.6 7.6 10.6 14.9 7. Mediobanca SpA 32.9 39.4 42.2 40.3 34.7 n.a. 8 Banca Mediolanum SpA 97.8 77.5 73.6 8.0 11.2 21.3 9. Banca Popolare di Sondrio SCpA 12.0 12.7 13.0 15.4 13.2 15.9 10. CREDEM SpA 14.4 11.9 9.9 9.3 10.0 11.2 11. Banca Piccolo Credito Valtellinese 5.1 4.8 5.4 6.0 9.5 10.2 12. Banca Carige SpA 13.1 8.4 5.7 8.2 12.2 14.7 13. Banca di Desio e della Brianza SpA 25.1 1.6 3.6 3.8 7.1 8.6 14. Banca Generali SpA 12.7 8.9 7.4 9.1 16.0 15.1 Average 21.6 18.9 18.7 15.0 16.8 17.7 Source: Bureau van Dijk Orbis. Table A4. Liquidity indicators. Liquid Assets/Total Deposits/Loans Ratio N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA 21.1 21.5 19.2 17.7 16.2 14.9 2. Intesa Sanpaolo 24.4 22.5 19.7 18.1 17.9 17.8 3. Banco BPM SpA 7.4 6.4 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA 10.7 14.3 11.0 11.5 12.1 16.9 5. UBI Banca SCpA 4.5 4.5 4.6 6.9 8.8 8.0 6. BPER Banca SpA 4.0 4.1 5.8 5.8 7.6 9.9 7. Mediobanca SpA 16.7 20.4 19.4 17.9 14.3 n.a. 8 Banca Mediolanum SpA 77.5 66.0 66.3 7.8 10.8 20.2 9. Banca Popolare di Sondrio SCpA 10.8 11.4 11.6 13.8 11.9 14.2 10. CREDEM SpA 11.2 9.2 7.6 7.3 7.8 8.4 11. Banca Piccolo Credito Valtellinese 4.4 3.9 4.2 4.8 7.3 7.5 12. Banca Carige SpA 9.7 5.9 3.8 5.5 7.9 8.9 13. Banca di Desio e della Brianza SpA 19.7 1.6 3.6 3.8 7.1 8.6 14. Banca Generali SpA 12.7 8.9 7.4 9.1 16.0 15.1 Average 16.2 13.9 13.7 10.0 11.2 12.5 Source: Bureau van Dijk Orbis. Economies 2024,12, 58 22 of 33 Table A5. Performance indicators. Return on Average Assets (ROAA) N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA −1.3 0.2 0.3 −1.5 0.1 −1.0 2. Intesa Sanpaolo 0.5 0.4 0.2 −0.7 0.3 −1.3 3. Banco BPM SpA −1.0 0.4 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA −2.0 0.2 −2.9 −0.7 −1.4 −2.0 5. UBI Banca SCpA −0.7 0.1 −0.6 0.2 0.1 −1.4 6. BPER Banca SpA 0.0 0.4 0.1 0.0 −0.1 0.4 7. Mediobanca SpA 0.9 0.8 0.6 −0.2 0.1 n.a. 8 Banca Mediolanum SpA 0.9 1.0 1.0 1.8 2.0 0.8 9. Banca Popolare di Sondrio SCpA 0.3 0.4 0.4 0.2 0.1 0.3 10. CREDEM SpA 0.3 0.5 0.5 0.4 0.4 0.3 11. Banca Piccolo Credito Valtellinese −1.3 0.4 −1.2 0.1 −1.1 0.2 12. Banca Carige SpA −1.1 −0.3 −1.4 −3.9 −0.1 0.4 13. Banca di Desio e della Brianza SpA 8.9 2.1 1.0 0.9 1.3 0.7 14. Banca Generali SpA 2.2 3.3 2.5 2.1 2.2 1.7 Average 0.5 0.7 0.1 −0.1 0.3 −0.1 Source: Bureau van Dijk Orbis. Table A6. Performance indicators. Return on Average Equity (ROAE) N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA −23.4 3.9 4.6 −23.3 2.0 −16.1 2. Intesa Sanpaolo 6.5 5.8 2.9 −9.6 3.4 −17.0 3. Banco BPM SpA −13.0 5.3 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA −40.2 5.1 −90.4 −22.8 −36.6 −42.7 5. UBI Banca SCpA −8.6 1.4 −6.5 2.5 0.9 −18.9 6. BPER Banca SpA 0.3 3.9 0.6 0.3 −0.7 5.1 7. Mediobanca SpA 6.8 7.1 6.2 −2.7 1.2 n.a. 8 Banca Mediolanum SpA 18.7 22.6 21.4 29.8 39.0 22.5 9. Banca Popolare di Sondrio SCpA 3.8 5.4 5.6 3.1 2.1 4.1 10. CREDEM SpA 5.3 6.8 6.7 5.6 6.7 5.9 11. Banca Piccolo Credito Valtellinese −16.7 5.8 −16.4 0.7 −15.4 3.1 12. Banca Carige SpA −12.8 −4.8 −31.6 −66.8 −1.9 6.6 13. Banca di Desio e della Brianza SpA 77.4 32.0 23.4 24.6 30.9 13.5 14. Banca Generali SpA 24.3 34.7 32.0 33.8 40.5 29.5 Average 2.8 9.9 −1.9 −1.9 5.5 −0.4 Source: Bureau van Dijk Orbis. Table A7. Performance indicators. Return on Risk-Weighted Assets (RoRWA)—Operating Profit/RWA N. Bank Name 2011 2012 2013 2014 2015 2016 1. UniCredit SpA −3.0 0.4 0.7 −1.8 −0.1 0.2 2. Intesa Sanpaolo 1.0 1.3 1.0 −0.9 1.0 0.3 3. Banco BPM SpA −3.1 0.6 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA −5.1 0.2 −9.7 −2.5 −2.3 −0.5 5. UBI Banca SCpA −2.0 0.4 0.2 0.3 0.2 −0.3 6. BPER Banca SpA 0.1 0.5 0.2 0.2 −0.1 1.1 7. Mediobanca SpA 0.9 0.9 0.4 0.3 0.3 n.a. 8 Banca Mediolanum SpA 5.4 7.1 6.2 10.2 8.8 3.2 9. Banca Popolare di Sondrio SCpA 0.5 0.8 0.8 0.5 0.4 0.6 10. CREDEM SpA 1.5 1.8 1.5 1.3 1.0 1.2 11. Banca Piccolo Credito Valtellinese −2.5 −1.1 −1.9 0.2 −0.7 0.6 12. Banca Carige SpA −2.5 −1.0 −2.8 −4.7 −1.4 1.2 13. Banca di Desio e della Brianza SpA 10.4 7.5 5.2 5.9 5.6 2.2 14. Banca Generali SpA 7.4 8.9 7.9 9.2 8.0 4.8 Average 1.1 2.3 0.7 1.4 1.6 1.2 Source: Bureau van Dijk Orbis. Economies 2024,12, 58 23 of 33 Table A8. Profitability indicators. Operating Profit/Average Equity N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA −24.0 3.1 5.2 −12.9 −0.8 2.0 2. Intesa Sanpaolo 5.6 8.0 5.7 −5.2 6.3 1.7 3. Banco BPM SpA −18.2 3.8 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA −41.5 2.2 −123.5 −33.8 −24.6 −4.7 5. UBI Banca SCpA −12.4 2.3 1.3 1.7 1.1 −2.8 6. BPER Banca SpA 0.6 3.8 1.2 1.7 −0.5 10.9 7. Mediobanca SpA 5.2 6.3 3.4 2.6 2.8 n.a. 8 Banca Mediolanum SpA 20.0 27.8 29.0 43.0 54.0 25.7 9. Banca Popolare di Sondrio SCpA 4.4 7.2 8.9 6.3 5.3 7.6 10. CREDEM SpA 8.3 9.7 10.9 10.2 8.9 12.6 11. Banca Piccolo Credito Valtellinese −18.5 −7.9 −16.4 1.3 −6.7 5.6 12. Banca Carige SpA −18.0 −9.6 −32.8 −38.4 −10.6 9.6 13. Banca di Desio e della Brianza SpA 81.1 48.6 35.4 41.6 48.6 21.3 14. Banca Generali SpA 28.8 40.6 40.2 45.1 51.7 33.9 Average 2.7 11.2 −0.4 4.9 10.4 10.3 Source: Bureau van Dijk Orbis. Table A9. Profitability indicators. Operating Profit/Total Deposits N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA 0.0 0.0 0.0 0.0 0.0 0.0 2. Intesa Sanpaolo 0.0 0.0 0.0 0.0 0.0 0.0 3. Banco BPM SpA 0.0 0.0 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA 0.0 0.0 −0.1 0.0 0.0 0.0 5. UBI Banca SCpA 0.0 0.0 0.0 0.0 0.0 0.0 6. BPER Banca SpA 0.0 0.0 0.0 0.0 0.0 0.0 7. Mediobanca SpA 0.0 0.0 0.0 0.0 0.0 n.a. 8 Banca Mediolanum SpA 0.0 0.0 0.0 0.0 0.0 0.0 9. Banca Popolare di Sondrio SCpA 0.0 0.0 0.0 0.0 0.0 0.0 10. CREDEM SpA 0.0 0.0 0.0 0.0 0.0 0.0 11. Banca Piccolo Credito Valtellinese 0.0 0.0 0.0 0.0 0.0 0.0 12. Banca Carige SpA 0.0 0.0 0.0 −0.1 0.0 0.0 13. Banca di Desio e della Brianza SpA 0.1 0.0 0.0 0.0 0.0 0.0 14. Banca Generali SpA 0.0 0.1 0.1 0.1 0.0 0.0 Average 0.0 0.0 0.0 0.0 0.0 0.0 Source: Bureau van Dijk Orbis. Table A10. Profitability indicators. Operating Profit/Total Deposits N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA 0.0 0.0 0.0 0.0 0.0 0.0 2. Intesa Sanpaolo 0.0 0.0 0.0 0.0 0.0 0.0 3. Banco BPM SpA 0.0 0.0 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA 0.0 0.0 −0.1 0.0 0.0 0.0 5. UBI Banca SCpA 0.0 0.0 0.0 0.0 0.0 0.0 6. BPER Banca SpA 0.0 0.0 0.0 0.0 0.0 0.0 7. Mediobanca SpA 0.0 0.0 0.0 0.0 0.0 n.a. 8 Banca Mediolanum SpA 0.0 0.0 0.0 0.0 0.0 0.0 9. Banca Popolare di Sondrio SCpA 0.0 0.0 0.0 0.0 0.0 0.0 10. CREDEM SpA 0.0 0.0 0.0 0.0 0.0 0.0 11. Banca Piccolo Credito Valtellinese 0.0 0.0 0.0 0.0 0.0 0.0 12. Banca Carige SpA 0.0 0.0 0.0 −0.1 0.0 0.0 13. Banca di Desio e della Brianza SpA 0.1 0.0 0.0 0.0 0.0 0.0 14. Banca Generali SpA 0.0 0.1 0.1 0.1 0.0 0.0 Average 0.0 0.0 0.0 0.0 0.0 0.0 Source: Bureau van Dijk Orbis. Economies 2024,12, 58 24 of 33 Table A11. Quality indicators. Impaired/Non-Performing Loans/Equity N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA 1.8 1.5 1.5 1.5 1.1 1.1 2. Intesa Sanpaolo 1.2 1.3 1.3 1.2 0.9 0.8 3. Banco BPM SpA 3.4 2.0 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA 6.9 4.6 7.1 5.0 3.9 1.8 5. UBI Banca SCpA 1.4 1.3 1.1 1.0 0.9 0.8 6. BPER Banca SpA 2.0 2.0 1.8 2.0 1.5 1.3 7. Mediobanca SpA 0.2 0.2 0.2 0.2 0.1 n.a. 8 Banca Mediolanum SpA 0.1 0.1 0.1 0.1 0.1 0.3 9. Banca Popolare di Sondrio SCpA 1.5 1.4 1.3 1.2 0.7 0.5 10. CREDEM SpA 0.6 0.6 0.5 0.5 0.5 0.5 11. Banca Piccolo Credito Valtellinese 2.9 2.4 2.1 1.7 1.3 1.0 12. Banca Carige SpA 3.4 2.6 3.4 3.1 0.7 0.7 13. Banca di Desio e della Brianza SpA 1.4 1.2 1.0 1.1 1.5 1.7 14. Banca Generali SpA 0.1 0.1 0.1 0.1 0.1 0.2 Average 1.9 1.5 1.7 1.4 1.0 0.9 Table A12. Quality indicators. Impaired/Non-Performing Loans N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA 75,483,530 80,005,187 77412983 74,310,248 69,602,096 62,011,648 2. Intesa Sanpaolo 57,853,000 62,142,000 58,559,000 52,619,000 42,851,000 36,452,000 3. Banco BPM SpA 25,888,394 26,429,293 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA 44,672,678 44,027,989 41,327,529 31,003,497 24,966,976 20,237,777 5. UBI Banca SCpA 12,407,687 13,196,123 11,641,365 10,967,663 9,584,547 7,514,979 6. BPER Banca SpA 11,015,891 11,110,712 10,064,663 9,393,477 7,314,886 5,919,843 7. Mediobanca SpA 1,998,478 1,930,737 1,927,976 1,133,655 909,043 n,a, 8 Banca Mediolanum SpA 112,837 107,114 87,210 61,983 55,370 170,222 9. Banca Popolare di Sondrio SCpA 4,087,552 3,768,117 3,105,902 2,485,175 1,435,197 1,009,034 10. CREDEM SpA 1,360,080 1,360,631 1,233,072 1,149,257 961,320 826,946 11. Banca Piccolo Credito Valtellinese 5,171,495 5,274,281 4,207,025 3,280,051 2,502,824 2,020,046 12. Banca Carige SpA 7,212,565 6,545,468 6,134,241 5,071,102 2,711,748 2,115,125 13. Banca di Desio e della Brianza SpA 1,761,638 667,992 430,480 435,427 467,885 330,005 14. Banca Generali SpA 41,859 43,486 54,271 51,293 39,155 49,844 Average 17,790,549 18,329,224 16,629,671 14,766,294 12,569,388 11,554,789 Table A13. Structural/capital ratio indicators. Solvency: Equity/Total Assets N. Bank 2011 2012 2013 2014 2015 2016 1. UniCredit SpA 5.0 6.2 6.3 5.9 7.2 5.9 2. Intesa Sanpaolo 6.8 7.2 7.0 7.2 7.5 7.5 3. Banco BPM SpA 7.1 7.7 n.a. n.a. n.a. n.a. 4. Banca Monte dei Paschi di Siena SpA 4.2 5.7 3.2 3.1 3.0 4.6 5. UBI Banca SCpA 8.1 9.0 8.5 9.0 8.0 7.6 6. BPER Banca SpA 8.6 9.2 9.1 7.6 7.7 7.7 7. 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