1,721,008 research outputs found
Examining the Relationship Between the Takeover Premium and the Environmental, Social and Governance Performances of a Target Firm.
No full textIn a context characterized by an increasing number of mergers and acquisitions (M&A) deals, it is important to analyze the impact of sustainability performance on deal outcomes. Actually, the evidence
in the literature sometimes reaches quite different results. Thus, the study aims to investigate the possible existence of a relationship between the acquisition premium paid in an M&A transaction and a
target firm’s environmental, social and governance (ESG) performance, as well as to understand which ESG component has the strongest influence on the takeover premium. 111 and 102 M&A transactions closed, respectively, in 2021 and 2022, involving listed target companies active in both the European and North American markets, were analyzed through a connection analysis and a regression approach. The results obtained, in line with those of others in the literature, show that the acquisition premium is
not linked in any way to ESG performances. These findings shed light on the actual importance of ESG factors in the valuation of the takeover premium
Measuring Systemic Risk: A Review of the Main Approaches.
No full textScholars, Regulatory and Supervisory Authorities have always been engaged in the search for efficient approaches to measuring systemic risk. Such procedures are extremely useful, first and foremost, in understanding and managing the stability and resilience of a financial-economic system as a whole, in forecasting possible crisis situations, and in implementing effective macro-prudential policies in response to the turbulence that can be generated by systemic risk in the financial system. Actually, over time, different approaches to measuring systemic risk have been defined. Undoubtedly, these methods are difficult to compare and often result in assessment parameters that are difficult to cointegrate. This chapter describes and analyses the main approaches for measuring systemic risk currently used in the literature. In more detail, it analyses the Probability Distribution Measures, the Network Analysis Measures, the Illiquidity Measures, the Contingent Claims and Default Measures and, last but not least, the Macro-economic Measures
Lending business models and FinTechs efficiency.
Full text linkThe aim of the study is to analyse which managerial issues can be considered the main efficiency drivers for all Italian FinTechs engaged in lending. We measure their efficiency in the period 2020–2022 via Stochastic Data Envelopment Analysis. The main determinants seem to be ROA and cost-to-income ratio; this means that the ability to control both the business risk level and costs is crucial for FinTechs’ managers and other players interested in M&A deals in this industry. The results are useful for FinTechs, other financial players, regulators and supervisors in defining homogeneous rules in the lending sector
Does Efficiency Matter in M&A of FinTech Firms?
No full textThe increasing presence of FinTech firms is reshaping the structure, behaviour and business models of the different players operating within the financial sector. The aim of this study is to understand whether there is any relationship between the achievement of specific efficiency levels by FinTech firms and their greater/minor involvement in M&A transactions. Since such a relationship could be in the opposite direction, the topic is important for a wide range of financial stakeholders, from individual investors to key market players and regulators. We measured efficiency via two different Stochastic Data Envelopment Analysis (SDEA) models. The results,
obtained starting from a hand-collected data set made up of Italian FinTechs operating in the period 2021–2023, show that M&A transactions essentially involved firms characterised by both
quite high or quite low levels of efficiency
High-tech investment activity and efficiency. Is there a stable relationship?
No full textOur study examines the high-tech investment activity of the banking system from 2009 to 2020. To gain understanding of the effect of the increasing interest of banks in high-tech investments, this research provides evidence of the relationship between the level of efficiency achieved by Euro Area banking groups and their high-tech investment aptitude. This paper is purely exploratory given that, to the best of our knowledge, it is one of the first to address this specific research question. We analyze in detail the association between bank efficiency (measured using a stochastic frontier approach) and different high-tech investment indicators, as well as the direction of this connection to provide an explanation for the relationship. We find a stable and overall significant relationship between banks’ efficiency and their high-tech investment aptitude. Moreover, we find that only medium efficiency banking groups that adopt a more diversified investment acquisition strategy have a positive relationship with high-tech investment aptitude; otherwise, the relationship between bank efficiency and high-tech investment indicators seems to be negative. Although banking groups are clearly fostering their high-tech investments to appear more innovative and to keep up with fintech and techfin, their high-tech investment strategies are implemented in the context of great competition in the technology sector that is populated by firms whose value is very difficult to evaluate, which leads to the possibility that some acquisitions are achieved at prices higher than the real fair value. We conjecture those new entrants to the banking market (e.g. fintech and techfin companies) that are more specialized in the production of patents and high-tech instruments can benefit from lower production costs compared with incumbent financial firms. For this reason, we suggest further research to investigate the differences between investments in high-tech internal divisions or companies and acquisitions of high-tech companies. Open banking regulation aimed to increase competition in the banking sector to reduce customers’ costs. However, our paper reveals that to compete with new entrants (fintech and techfin), banking groups are investing in resources that, on average, have not yet generated a positive effect on efficiency. Regulators should be aware of this acceleration in the enhancement of high technology either because it can reduce the stability of banking groups or because an increase of intangibles assets (such as patents) could also be motivated by an interest rather than by an earnings management strategy
An alternative proposal based on organizational effectiveness and efficiency's ratios for forecasting the financial status of a firm
No full textAssessing the insolvency risk is certainly a central issue for economic and financial analysis and of prime importance to financial intermediaries. Despite that, no agreement yet exists. Institutional factors specific to each country, as well as a large variety of other causes which can lead to the failure of a firm, obstruct the way to a general theory. It is instead necessary to deal with this issue, not only because it is central to credit management by banking operators, but also for its overall impact on the economy. This paper analyzes how to forecast the financial status (non-defaulting/defaulting) of a firm. To this aim, alternative procedures were tested on the same data set. Specifically, after analyzing the adequacy of Altman’s Z-score model, (i) it was attempted to solve its well-known limit due to the consideration of the same number of non-defaulting and defaulting firms in the group, (ii) explicative variables related to a firm’s risk of bankruptcy were selected, and finally (iii) an alternative approach based on panel data was used to divide firms in non-defaulting/defaulting sub-groups. In this way, a considerable reduction of errors in the prediction of a firm’s financial status was progressively obtained
Lending activity efficiency. A comparison between fintech firms and the banking sector
Full text linkThe FinTech phenomenon is undoubtedly increasingly changing the morphology of the global financial system, as well as the existing competitive levers in particular sectors, including lending. The aim of this study is to offer a comparative analysis of the level of efficiency exhibited by FinTech firms operating in this sector with that of banks, which have traditionally carried out this activity. We measure efficiency levels by implementing the Stochastic Data Envelopment Analysis (SDEA). The study, referred to 2021, analyses a data set composed of all the Italian FinTech firms engaged in the lending business and all the Italian banks. We find higher efficiency levels for banks compared to FinTech firms. The results are certainly interesting both at corporate level and for regulatory purposes
Too Useful to Fail. Il ruolo delle banche cooperative come mitigatrici del rischio sistemico
No full textAnalysing banks’ performance during the recent breakdowns. Which were its main drivers?
No full textWe observe the main e±ciency drivers of European Banking Groups after the burst of the
Global Financial Crisis. This analysis is a live issue within the studies in the field of intermediation. The observed period (2010–2021) is emblematic of the complexity of the financial market in the last two decades. The efficiency levels derive from a stochastic frontier approach; a
k-means cluster analysis distinguishes the units into three homogeneous groups, so that the main determinants of the higher level of e±ciency can be identified. They are linked to a particular business model, specific managerial choices, costs rationalization and liquidity optimization
- …
