Large US Banks Research Articles

Connectedness and systemic risk between FinTech and traditional financial stocks: Implications for portfolio diversification

The rapid growth in FinTech is attracting significant investor attention. There are concerns that FinTech stocks may be a source of systemic risk and this has implications for the stability of the financial sector. This study uses a TVP-VAR model to estimate the dynamic connectedness between global FinTech stocks and the stocks of large US banks, regional US banks, US insurance, and global private equity. The findings reveal that FinTech and insurance stocks are net receivers of shocks while large US banks are a major source of shocks. Thus, FinTech stocks are not a source of systemic risk but can be affected by financial contagion and systemic risk originating from large US banks. The highest connectedness was observed at the onset of the COVID-19 lockdowns illustrating the dramatic impact that a health pandemic can have on systemic risk. Systemic risk also increased during the 2023 US bank panic. Connectedness has implications for constructing investment portfolios. The equally weighted, risk parity, and risk parity connectedness portfolios have similar portfolio summary statistics, but lower risk adjusted returns than the maximum Sharpe ratio portfolio which better captures the low correlation between FinTech stocks and traditional financial stocks. These results are robust across various portfolio rebalancing periods (daily, weekly, monthly).

Model risk management in stress testing: The road up to here

This paper reviews the historical evolution of the quantitative models, model usage and model risk management (MRM) in large US banks since the 1980s. It comments on the most significant model-related events through this period. Some of these events were associated with contexts of great stress to the financial system. The paper identifies the main features of modelling in finance and economics that distinguish it from its application in the natural sciences. Then it presents the different types of models used in the large banks' stress testing programmes, the specific characteristics and risks of each type of model and the best practices for the implementation of sound model risk management, and it suggests a proper way to interpret the results of stress testing and capital assessment in the presence of model risks. The paper discusses the future evolution of MRM towards the implementation of a `fully fledged risk management framework` along the lines of other risk management disciplines, including risk identification, measurement, monitoring, reporting, limiting and capitalisation. It acknowledges the enhancements and opportunities offered by advances in the use of artificial intelligence and machine learning as well as increases in computational power. The paper concludes by noting that the model risk management framework that has been enforced by the Federal Reserve's stress testing and capital planning programmes has substantially strengthened the capital position, profitability and resilience of the large banks in the US.

The impact of digitalisation on the profitability of large US banks

The impact of digitalisation on the profitability of large US banks

Assessing the resilience of the US banking sector under a macroeconomic stress testing

PurposeThe increased capital requirements and the implementation of new liquidity standards under Basel III sparked various concerns among researchers, academics and other stakeholders. The question is whether Basel III regulation is ideal, that is, adequate to deal with a crisis, such as the 2007–2009 global financial crisis? The purpose of this paper is threefold: First, perform a stress testing exercise on the US banking sector, while examining liquidity and solvency risk indicators jointly under the Basel III regulatory framework. Second, allow the study to cover the post-crisis period, while referring to key Basel III regulatory requirements. And third, focus on the resilience of domestic systemically important banks (D-SIBs), which are supposed to support the US financial system in times of stress and therefore whose failure causes the entire financial system to fail.Design/methodology/approachThe authors used a sample of the 24 largest US banks observed over the period Q1-2015 to Q1-2021 and a scenario-based vector autoregressive conditional forecasting approach.FindingsThe authors found that the model successfully produces accurate forecasts and simulates the responses of the solvency and liquidity indicators to different real and historical macroeconomic shocks. The authors also found that the US banking sector is resilient and can withstand both historical and hypothetical macroeconomic shocks because of its compliance with the Basel III capital and liquidity regulations, which consist of encouraging banks to hold high-quality liquid assets and stable funding resources and to strengthen their capital, which absorbs the losses incurred in a crisis.Originality/valueThe authors developed a framework for testing the resilience of the US banking sector under macroeconomic shocks, while examining liquidity and solvency risk indicators jointly under Basel III regulatory framework, a point not yet well studied elsewhere, and most studies on this subject are based on precrisis data. The authors also focused on the resilience of D-SIBs, whose failure causes the failure of the entire financial system, which previous studies have failed to examine.

Measuring responsible artificial intelligence (RAI) in banking: a valid and reliable instrument

AbstractWidespread use of artificial intelligence (AI) and machine learning (ML) in the US banking industry raises red flags with regulators and social groups due to potential risk of data-driven algorithmic bias in credit lending decisions. The absence of a valid and reliable measure of responsible AI (RAI) has stunted the growth of organizational research on RAI (i.e., the organizational balancing act to optimize efficiency and equity). To address this void, we develop a novel measurement instrument to assess RAI maturity in firms. A review of the nascent literature reveals that there is a wide distribution of RAI capabilities. The RAI instrument that we advance is based on the exhaustive review of this dispersed literature. Analyses of data from large US banks show strong evidence of validity and reliability of the RAI maturity instrument.

Born after the Volcker Rule: Regulatory change, managerial remuneration and hedge fund performance

AbstractSubstantial remunerative benefits accrue to managers of new hedge funds launched after the implementation of the Volcker Rule if their previous employer is a large US bank. After the rule, ex‐bankers' funds charge higher management fees and receive more flows as compared with other new hedge funds established during the same period. This phenomenon is related to changes in investor perception of the distribution of skills of new fund managers rather than to the actual differences in skills. Ex‐bankers' funds are indistinguishable from other funds in terms of performance, risk, and liquidation probability, both before and after the Volcker Rule.

Bayesian Artificial Neural Networks for frontier efficiency analysis

Artificial neural networks have offered their share of econometric insights, given their power to model complex relationships. One area where they have not been readily deployed is the estimation of frontiers. The literature on frontier estimation has seen its share of research comparing and contrasting data envelopment analysis (DEA) and stochastic frontier analysis (SFA), the two workhorse estimators. These studies rely on both Monte Carlo experiments and actual data sets to examine a range of performance issues which can be used to elucidate insights on the benefits or weaknesses of one method over the other. As can be imagined, neither method is universally better than the other. The present paper proposes an alternative approach that is quite flexible in terms of functional form and distributional assumptions and it amalgamates the benefits of both DEA and SFA. Specifically, we bridge these two popular approaches via Bayesian artificial neural networks while accounting for possible endogeneity of inputs. We examine the performance of this new machine learning approach using Monte Carlo experiments which is found to be very good, comparable to, or often better than, the current standards in the literature. To illustrate the new techniques, we provide an application of this approach to a data set of large US banks.

Impact of Leveraged Lending Guidance: evidence from nonbank participation in syndicated loans

PurposeThe purpose of this study is to examine the impact of supervisory Leveraged Lending Guidance (LLG) (2013–2014) on risk and structure of syndicated loans arranged by the largest US banks with participation of nonbank lenders.Design/methodology/approachThis study uses supervisory shared national credit loan-level data from 2010 to 2015 and DealScan loan origination data and use linear regressions with clustered standard errors.FindingsThis study finds that the impact of the LLG was mixed. Incidence and risk of leveraged lending declined following the Guidance, as reflected in lower nonbank syndicate participation. However, the covenant protections weakened and loan spreads at origination declined. This study also provides evidence that some risky lending originations shifted to nonbank entities outside of the banking regulatory environment.Originality/valueThis study contributes and expands literature on the impact of regulatory guidance on loan risk, terms and structure, focusing on nonbank participation in syndicated commercial loans.

The impact of bank opacity on bank risk‐taking behavior

AbstractWe examine the impact of opacity on bank stability using a large US bank sample from 2001 to 2021. We observe that increasing opacity erodes bank stability. Our results indicate that the spillover effects of opacity induce greater price synchronicity and consequently increase systematic risk. These stability‐destroying effects of bank opacity are transferred via reduced profitability, deteriorated asset quality, and increased earnings volatility. The negative effects of opacity on bank stability are more pronounced in financially stronger and larger banks. They are muted in dividend‐paying banks. This study supports the concerns of prior researchers over too‐big‐to‐fail institutions and should be of interest to policymakers and other bank stakeholders.

Did small or large US banks transmit more risk during the Subprime crisis?

This paper investigates bank size as a factor of risk transmission in the US banking sector during the Subprime crisis. Risk transmission is examined in two directions: from large to small banks and from small to large banks. To do this, we estimated a Spatial Autoregressive model using information on all US commercial banks from 2005 to 2010. Our results show that risk transmission from small to large banks appeared during early stages of the Subprime Crisis and transmission from large to small banks appeared later but was more long lasting.

Large US bank takeovers in 2008: performance and implications

PurposeBecause systemically important banks' takeovers in the US were expected to contain the 2008 global financial crisis (GFC) but were found to have imposed large cost on shareholders, this paper examines the effectiveness of these acquisitions during the GFC and investigates what went wrong with the market for corporate control of large banks.Design/methodology/approachThis paper presents a model of the disciplinary takeover based on the efficient market hypothesis which provides appropriate measures for it to examine the financial performance of acquiring banks after takeover.FindingsThe results indicate that the takeover market for large banks was ineffective in two aspects: the market did not distinguish strong banks from weak banks before the crisis and acquirers performed worse after takeover. Such ineffectiveness reflects the fundamental deficiencies of large bank takeovers arising from some key distinguishing characteristics of large banks.Research limitations/implicationsThe sample size of systemically important banks' takeovers is small so large-sample standard statistical inferences cannot be used.Practical implicationsThe deficiencies of large bank takeovers need to be rectified in order to aid in resolving future crises.Originality/valueThis paper provides rare and detailed insight based on case studies of large US bank takeovers during the GFC.

Measuring the systemic importance of large US banks

<ns2:p>The failure of large and connected financial institutions often leads to system-wide financial crises and economic downturns (Labonte 2015). Even absent outright failure and bankruptcy, perceived weakness of a large and connected financial firm can result in decrease valuation of other firms – due to perceived linkages – and overall decrease in market liquidity.</ns2:p>

Organizational Guilt Management: A Paradox Perspective

Alleged organizational wrongdoings are often characterized by high levels of uncertainty about what happened, which can take years to be established judicially. In this study, we examine organizations’ efforts to manage their stakeholders’ impressions of their possible guilt in this period of uncertainty. The study examines the discursive guilt-management strategies organizations employ in such situations to embrace the paradoxical tensions that emerge between their routine, positive self-presentations as responsible organizations and their communication about their possible guilt. Taking departure in impression management and a paradox perspective, we conceptualize guilt management as a discursive practice enacted in times of uncertainty. Specifically, we conduct a microlevel discourse analysis of corporate social responsibility (CSR) reports published by large US banks after the financial crisis and analyze how these banks managed impressions of their possible guilt, before they eventually agreed to legal settlements. We identify amending, bracketing, shifting locus of control, implicating, as well as reattributing and extending moral agency as central guilt-management strategies that embrace the paradoxical tensions between the banks’ positive self-presentations and their communication about their possible guilt. We conclude with a discussion of theoretical and methodological contributions to organization studies.

A test of the inherent predictiveness of the RU, a new metric to express all forms of operational risk in banks

In 2016 Allan D. Grody and Peter J. Hughes proposed a method and system termed ‘Risk Accounting’, an integrated financial and risk accounting framework. Risk Accounting incorporates a novel operational risk exposure quantification technique based on the Risk Unit (RU), a new common additive metric designed to express all forms of operational risk in banks. In this paper, we report on initial tests of the inherent predictiveness of the RU. The test focused on the period leading up to the global financial crisis of 2007-8 and involved the restatement into RUs of publicly available accounting data in the United States relative to a subset of large US banks. We contend that the RU’s inherent predictiveness could be concluded if it is demonstrated that an accelerated increase in trended operational risk RUs and subsequent material unexpected losses are positively correlated. We further describe how a monetary value can be stochastically derived and assigned to the RU over time. The inclusion of valued RUs in accounting systems will potentially enable the systematic adjustment of financial performance and condition relative to accepted nonfinancial risks to complement the accounting treatment already applied to financial (credit and market) risks. The resulting harmonisation of the accounting treatment applied to both financial and nonfinancial risks based on stochastic modelling will enable risk-adjusted economic profit to be adopted as the primary business performance metric and economic capital as the primary method of determining both operating and regulatory capital requirements. The real-time or near-real-time production of portfolio views of operational risk exposures based on the RU adds analytical rigour to their management and causes risk mitigation to become both a risk reduction and a profit optimisation initiative. The more effective management, oversight and governance of exposures to operational risks is the anticipated outcome.

The Influence of Liquidity Information on Liquidity Holdings in the Banking System

I study how liquidity information influences banks' liquidity holdings using the disclosure of liquidity coverage ratio (LCR) mandated for a group of large US banks. While the disclosure rule aims to increase liquidity in the banking system, non-disclosing banks responded by reducing liquidity holdings. Using bank network relationships to measure how much a bank learns from LCR disclosures, I find that banks learning more cut their liquidity significantly more. In the aggregate, liquidity in the banking system declined and systemic risk increased after the disclosure rule adoption. My findings highlight an important, and potentially unanticipated, effect of liquidity disclosure.

An oversight framework to keep senior executives in control

Given the current regulatory focus on senior management responsibility and the resultant requirement for high-quality oversight of processes and controls, this paper sets out an approach to instantiating such an oversight process in the 1st line of defence. As most financial firms are subject to huge change, the paper then goes on to show how such an oversight process might dovetail with a regulatory change process. In order to more clearly explain the approach, the paper then describes a short case study from a large US bank who successfully ran such a project in 2019. Finally, the paper will set out the benefits to be accrued from taking this path to high-quality oversight.

Analyzing Impact of a Crisis on Bank Financial Ratios

In this study we provide a practical framework and methodology for analyzing the effects of banking shocks (economic or financial in nature) on bank fundamentals, that avoids the use of complicated econometrics methods. For this, we focus our attention to the effects of the 2007-2008 global financial crisis on the four largest US banks and examine the variation of trends in the select financial ratios for those institutions using quarterly regulatory data running from 2002-Q4 to 2020-Q2. We start by plotting time series charts of those financial ratios for each bank and compare the before-crisis, transition and after-crisis periods. For this, we simply fit trend lines with three parameters of shift, slope, and volatility to the banking data. The shift parameter describes the level change of the variable when before- and after-crisis periods are compared. The slope parameter pronounces the difference in steepness of the trend lines, while the volatility parameter is associated with all three periods and describe the variation in the data during each period. Our results indicate that capital ratios, an important regulatory financial ratio, are higher across the board in the after-crisis period compared to before-crisis period, suggesting a positive shift. We don’t see significant changes in slope parameter for the capital ratio series leading us to suggest the use of dummy variable regression model where slope is treated as a fixed constant. We further show that pre-crisis and transition periods are characterized by higher volatilities that ultimately subside in the after-crisis period. Lastly, we conclude by suggesting that financial practitioners use the shift, slope and volatility parameters in understanding trends in financial time series data since it is easy to implement and interpret the results compared to more sophisticated econometric models.

Analyzing Impact of a Crisis on Bank Financial Ratios

In this study we provide a practical framework and methodology for analyzing the effects of banking shocks (economic or financial in nature) on bank fundamentals, that avoids the use of complicated econometrics methods. For this, we focus our attention to the effects of the 2007-2008 global financial crisis on the four largest US banks and examine the variation of trends in the select financial ratios for those institutions using quarterly regulatory data running from 2002-Q4 to 2020-Q2. We start by plotting time series charts of those financial ratios for each bank and compare the before-crisis, transition and after-crisis periods. For this, we simply fit trend lines with three parameters of shift, slope, and volatility to the banking data. The shift parameter describes the level change of the variable when before- and after-crisis periods are compared. The slope parameter pronounces the difference in steepness of the trend lines, while the volatility parameter is associated with all three periods and describe the variation in the data during each period. Our results indicate that capital ratios, an important regulatory financial ratio, are higher across the board in the after-crisis period compared to before-crisis period, suggesting a positive shift. We don’t see significant changes in slope parameter for the capital ratio series leading us to suggest the use of dummy variable regression model where slope is treated as a fixed constant. We further show that pre-crisis and transition periods are characterized by higher volatilities that ultimately subside in the after-crisis period. Lastly, we conclude by suggesting that financial practitioners use the shift, slope and volatility parameters in understanding trends in financial time series data since it is easy to implement and interpret the results compared to more sophisticated econometric models.

Policy uncertainty and bank stress testing

The accuracy of dynamic stress-test capital models remains undocumented. Three methodologies: a CLASS-style approach, Bayesian model averaging, and a Lasso specification are used to forecast the performance of 14 large US banks during the financial crisis. Individual bank models are calibrated using bank historical data while regulatory models are calibrated using representative bank data. Representative bank model forecasts differ dramatically from the forecasts from bank-specific models and from actual outcomes. The Lasso methodology is most accurate, but its superiority may be sample-specific and is only apparent ex post. The results highlight the policy uncertainty inherent in regulatory stress tests.

The impact of digitalisation on the profitability of large US banks

The impact of digitalisation on the profitability of large US banks