eSLR Adjustments and Stock Price Reactions of Eight Global Systemically Important Banks

Abstract

On Wednesday, 25 June 2025, the Federal Reserve voted to make changes to the Tier 1 Capital requirements for eight global systemically important banks. The changes include a cut to the enhanced supplementary leverage ratio (eSLR). The purpose of this study is to examine the immediate impact of this announcement on the stock prices of these eight systemically important banks. Using event study analysis and controlling for interest rate movements and general market conditions, we find that most of these banks generated superior returns during the event period. When compared to the KBW Index, however, results are mixed. Some banks do have superior returns even on pre-event day. The heterogeneous effects among banks emphasize that the benefits of capital regulation changes depend on a bank’s size, structure, and scope of operations.

1. Introduction

Banks traditionally earn low Return on Assets (ROA) (Federal Reserve Bank of St. Louis, n.d.-b) but significantly higher Return on Equity (ROE) (Federal Reserve Bank of St. Louis, n.d.-c). Comparing the two ratios over time on the St. Louis Fed’s website, one can see that bank equity relative to total assets is very low. Furthermore, if the equity requirement is reduced, ROE will become even higher than ROA. This means that any relaxation in the capital requirements will increase a bank’s ROE and will mean greater returns for the bank’s stockholders.

Bank failure usually means that the depositors’ money will have to be repaid by the FDIC. Therefore, banking is one industry where several regulatory agencies monitor capital requirements and asset quality. For this, justification is provided that the industry occupies the role of delegated monitor for a society’s savings, and hence banks have capital ratios, a scenario wherein banks’ minimum capital requirements are subject to law and regulation. This feature is unique in the corporate world. Regulators routinely check the capital adequacy of banks as part of the CAMELS ratios used to monitor these intermediaries.

On Wednesday, 25 June 2025, the Fed advanced a plan to ease bank leverage requirements. A Reuters report by Schroeder (2025) states:

“The Federal Reserve unveiled a proposal on Wednesday that would overhaul how much capital large global banks must hold against relatively low-risk assets, as part of a bid to boost participation in U.S. Treasury markets.

That plan, which the Fed voted to advance by a vote of 5-2, is the first step in what could be several deregulatory initiatives helmed by the Fed’s new top regulatory official, Vice Chair for Supervision Michelle Bowman.

The proposal would reform the so-called "enhanced supplementary leverage ratio" so that the amount of capital banks must set aside is directly tied to how large a role each firm plays in the global financial system.”

Additionally, it states:

“Depository institution subsidiaries at those banks would see capital requirements fall by an average of 27%, or $213 billion. Global bank holding companies would see a 1.4% capital reduction, or $13 billion.”

This is explained in another article on the website of BankingDive by Ennis (2025), who mentions, “Tier 1 capital requirements would drop by 27% for the bank subsidiaries of the nation’s eight global systemically important banks under a proposal the Federal Reserve Board issued Wednesday.

Fed governors voted 5-2 in favor of the proposal, which would cut the enhanced supplementary leverage ratio—a measure of how much capital banks must hold relative to their assets—to between 3.5% and 4.5% for bank holding companies and their depository institution subsidiaries. That’s down from 5% for the former and 6% for the latter.”

The list of Global Systemically Important banks (Financial Stability Board, 2024) includes eight banks from the United States. These are: JPMorgan Chase, Citigroup, Bank of America, Goldman Sachs, Bank of New York Mellon, Morgan Stanley, State Street, and Wells Fargo. The focus of this paper is to examine whether this change in eSLR resulted in a gain for the stockholders of these eight banks. The easing of any form of capital requirements increases the equity multiplier, thus providing higher ROE ratios from similar ROA ratios. If the market determines that these eight banks benefit from reduced eSLR, these eight stocks would have high abnormal returns during the announcement period.

The remainder of the paper is organized as follows: We briefly review the relevant literature in Section 2, including the motivation for the study. We present research methodology in Section 3 and empirical evidence in Section 4, followed by the conclusion and implications in Section 5.

2. Literature Review and Motivation

Previous studies have examined the impact of capital requirement changes on banks’ stock returns. In a study on the stock market effects of the adoption of risk-based capital requirements, Cooper et al. (1991) found significant declines in equity returns for U.S., Canadian, and U.K. banks in response to news announcements, with U.S. bank returns showing the largest negative reaction. In another study, Pelster et al. (2018) analyzed the effect of bank capital on annual performance of global banks during the period 1999–2012. They found that higher Tier 1 capital decreases a bank’s stock performance over the entire sample period. Based on these studies, it is apparent that stricter capital requirements imply lower stock market returns for the banks.

In an earlier study, Van Roy (2008) examined the adjustments made by banks from six G10 countries to their capital levels and risk-weighted assets following the implementation of the 1988 Basel Accord, thereby contributing to the existing literature. The study reports that only U.S. banks with inadequate capitalization elevated their capital ratios more swiftly than their adequately capitalized peers, with no notable disparities in the adjustment of risk-weighted assets. Importantly, the results highlight the role of market discipline: weakly capitalized U.S. banks that did not face market pressure failed to increase their capital ratios at a faster rate, indicating that market forces were instrumental in driving the capital build-up of the early 1990s.

In another study, Barth and Miller (2018) emphasize that while Basel-style capital adequacy standards have evolved to become stricter, they have also grown excessively complex, especially in the U.S. context. They further show how the capital requirements vary for banks of different asset sizes. The authors show that multiple overlapping capital ratios often provide conflicting signals about a bank’s capitalization, suggesting that regulatory simplicity may be more effective for ensuring financial stability. In another study, Cohen (2013) finds that for a sample of 82 large global banks from advanced and emerging countries, banks that came out of the global crisis with higher capital ratios and stronger profitability were able to more aggressively expand lending. Capital requirements also impact bank lending and bank behavior.

A recent study by Fraisse et al. (2020) found that a one percentage point increase in capital requirements reduces lending by 2.3–4.5%. This implies that a reduction in capital requirements would likely increase lending and perhaps profits for a bank. This study, along with the studies of Cooper et al. (1991) and Pelster et al. (2018), serves as a primary motivation for our work. If increased regulatory capital reduces stock market returns, a reduction in required capital ratios would likely increase bank stock returns and positively impact bank lending and thereby bank profits. The potential reduction in capital requirements for the nation’s eight globally systemically important banks may impact their stock returns immediately following the Fed’s policy change.

Over the last thirty-five-plus years, the banking industry has undergone a metamorphosis in size and numbers in the U.S. As per data from FRED1, there were over 14,000 commercial banks in the country in 1984. As of the end of 2020, the number declined to 4375. Laws were enacted, such as the Interstate Banking and Branching Efficiency Act of 1994 (IBBEA) and the Financial Services Modernization Act of 1999 (FSMA), which encouraged banks to merge and consolidate in a de facto way. In an earlier study, Nippani and Green (2002) found that the performance of banks improved in the post-IBBEA period. When controlled for general economic conditions and interest rate movements, the impact of IBBEA on bank performance appears insignificant.

Chang et al. (2011) examined whether there is an optimal asset scale for interest spread to affect banks’ profits. They report evidence suggesting that there is an optimum size for banks. There is also evidence from the passage of the FSMA that is relevant to bigger banks. Akhigbe and Whyte (2001) found that bank gains at the passage of the act are positively related to the level of capitalization. Carow and Heron (2002) suggest that the largest returns of the act were realized by large investment banks and large insurance companies. Interestingly, they also report that the stock prices of banks of all sizes were unaffected by the legislation.

In another study, Hendershott et al. (2002) examined the market response to the FSMA and found that there was an insignificant response among commercial banks. In the U.S., the larger banks have profited more than their smaller counterparts over the last three decades. There is evidence from Nippani and Washer (2005) that smaller banks underperformed their larger counterparts following the implementation of IBBEA. In a more recent study, Nippani and Ling (2021) compared the post-financial crisis performance of US banks with their performance before the financial crisis of 2007–2009. For their analysis, they used bank size and financial ratios, including ROA and ROE. They conclude that small banks have a significant disadvantage in the industry in the post-financial-crisis era compared to both big banks and their own pre-financial crisis performance. This implies that larger banks have an even greater chance of higher profits should their capital requirements be lowered. This serves as the second motivation for the study.

Finally, there is the motivation of the systemically important banks being too big to fail. Several studies have investigated the characteristics and implications of systemically important financial institutions. Castro and Ferrari (2014) focus on developing methods to identify and assess such institutions, while Bongini et al. (2015) highlight the market and regulatory implications of being designated as systemically important. Thomson (2009) further contributes by proposing a supervisory framework to mitigate risks posed by these entities.

Bigger banks can afford to take greater risks, and this will only be enhanced by Too Big To Fail (TBTF). An example of a study that examines TBTF banks is by Afonso et al. (2014), which shows the effect of potential government support on banks’ appetite for risk. Using balance-sheet data for 224 banks in 45 countries starting in March 2007, they found that higher levels of impaired loans after an increase in government support. Using support rating floors (SRFs)—a new measure introduced by Fitch Ratings that isolates potential sovereign support from other external support—the authors found that a one-notch increase in SRF raises the impaired loan ratio by approximately 0.2, which represents about an 8 percent increase for the average bank. They also show similar effects on net charge-offs for U.S. banks only. Given their size, interconnectedness, and potential to transmit systemic shocks, systemically important financial institutions play a critical role in financial stability, making them an essential focus for examining the effectiveness of regulatory capital standards and risk management practices. Since the banks in this study are TBTF and are globally systemic, a lowering of capital requirements would likely enable them to grow profitably.

Williams (2025) argues that the degree to which financial institutions form expectations of policy intervention despite their own risk appetites lies at the heart of macro-financial regulations such as the Dodd–Frank and Consumer Protection Acts. He argues that the effectiveness of these policies hinges on the assumption that large banks are the only banks that are TBTF. His results over the last two decades suggest that most TBTF banks have exhausted their economies of scale concurrently alongside the shrinking competitive landscape.

In an earlier study, Grammatikos and Papanikolaou (2018) compare TBTF with Too Small To Save (TSTS) banks. They mention that the shareholders and other uninsured creditors of a distressed bank are not bailed out if the bank is TSTS. In another work, Markoulis et al. (2022) ask if being considered a global systemically important bank (G-SIB) is a blessing or curse. While being considered G-SIB is perceived to be a good thing by the market, as it is accompanied by positive abnormal returns, it also becomes clear that this designation is accompanied by specific regulatory attachments, as there is a negative market reaction. Thosar and Schwandt (2019) show that it is premature to believe that TBTF has been solved, but macro-prudential regulation is much more effective after a financial crisis, and consequently, banks are on a considerably sounder footing since the depths of the crisis. This serves as the third motivation for the study.

The uniqueness of banks has been the subject of many studies. They are the only corporations whose capital requirements are monitored by regulators. The first of the measures commonly known as CAMELS ratios is capital adequacy. As Lopez (1999) mentions, CAMELS ratings are highly confidential. The tradeoffs of the release of information about banks are discussed by Leitner (2014). Hoe et al. (2017) used mathematical modeling to provide regulators with a practical method for determining optimally whether and when to disclose CAMELS ratings. As mentioned in the introduction, banks tend to have low ROA ratios and high ROE ratios. A quick perusal of the ROA chart provided for all US banks by the St. Louis Fed shows that the ROA seldom exceeds 1.4%. On the other hand, banks’ ROE is usually significantly higher, typically around 10% or more. Since ROA, when multiplied by the equity multiplier, yields ROE, it is quite possible that a reduction in Tier 1 capital requirements would further increase this ratio for the eight systemically important global banks. They would be able to generate higher ROEs due to lower capital requirements, despite no significant change in ROA.

In summary, for the eight systemically important banks, the Fed recently proposed a lower capital requirement, which could potentially lead to higher profits and stockholder returns. The literature provides empirical evidence that an increase in Tier 1 requirements leads to lower returns, suggesting that a decrease would result in higher stockholder returns. Additionally, the literature provides evidence that large TBTF banks have outperformed their smaller counterparts. Based on all this, we examine the following null hypothesis:

There is no immediate reaction to the Fed’s proposal to reduce the enhanced supplementary leverage ratio and other capital reduction measures on the stock returns of the eight global systemically important banks.

We examine the above hypothesis by testing whether the stock returns for these banks have higher returns during the two-day announcement window. The focus of this paper is on short-term stock performance for the eight U.S.-based global systemically important institutions. In the next section, we provide empirical evidence from testing this hypothesis.

3. Research Methodology

3.1. Data and Sample

This study investigates the short-term stock market reaction to the Federal Reserve’s 25 June 2025, proposal to lower the enhanced Supplementary Leverage Ratio (eSLR) requirements for large U.S. banks. The sample includes the eight U.S.–based Global Systemically Important Banks (G-SIBs) identified by the Financial Stability Board: JPMorgan Chase, Citigroup, Bank of America, Goldman Sachs, Bank of New York Mellon, Morgan Stanley, State Street, and Wells Fargo.

The data for the study were acquired from the Wharton Research Data Services (WRDS) dataset. We collected the daily closing values for each of the eight stocks of these companies, the S&P 500 index, the KBW Index, and the Fed Funds Rate for the period from 1 July 2024, to 27 June 2025. We then calculated the daily log returns for the eight stocks and the indices from the closing values. We had a total of 248 daily log returns for each of the stocks and the two indices. To calculate the daily log returns, we used the following formula:

R_it = ln(SI_t/SI_t−1)

(1)

where R_it represents the return of the stock/index i for day t, SI_t refers to the daily closing price of the stock/index i on day t, and SI_t−1 denotes the closing price of the stock/index on day t − 1. Using the above formula, we calculated the daily log returns for JPMorgan Chase (RETJPM), Citigroup (RETCITI), Bank of America (RETBAC), Goldman Sachs (RETGS), Bank of New York Mellon (RETNYM), Morgan Stanley (RETMS), State Street (RETSSC), and Wells Fargo (RETWFC), S&P 500 Index (RETS&P500), and the KBW Index (RETKBW). The summary statistics for the data are presented in

Table 1. This table reports summary statistics of daily log returns of eight banks (RETWFC, RETJPM, RETCITI, RETBAC, RETGS, RETMS, RETNYM, and RETSSC), S&P 500 daily log returns as represented by RETS&P500, RETKBW as the daily log return for KBW Index, and Fed Fund Rate.

Variables	Mean	Median	S.D.	Min	Max
RETWFC (%)	0.001	0.000	0.021	−0.096	0.123
RETJPM (%)	0.001	0.002	0.018	−0.084	0.109
RETCITI (%)	0.001	0.002	0.022	−0.129	0.088
RETBAC (%)	0.001	0.000	0.018	−0.117	0.081
RETGS (%)	0.002	0.002	0.022	−0.097	0.123
RETMS (%)	0.001	0.001	0.022	−0.100	0.110
RETNYM (%)	0.002	0.003	0.016	−0.086	0.077
RETSSC (%)	0.001	0.002	0.018	−0.083	0.089
RETS&P500 (%)	0.000	0.001	0.013	−0.062	0.091
RETKBW (%)	0.001	0.001	0.018	−0.104	0.102
Fed Fund Rate (%)	4.791	4.830	0.410	4.330	5.330

, which also includes the summary statistics of the Federal Funds rate.

3.2. Model Specification

To examine the study’s hypothesis, we employ event study-based regression analysis. We estimate two regression models for each bank, differing only in the market-control variable. The dependent variable in the regressions is the return of each of the eight banks. In Equation (2) below, the dependent variable BANKRET_j,t is the return of bank j for day t (RETWFC, RETJPM, RETCITI, RETBAC, RETGS, RETMS, RETNYM, RETSSC, respectively). The independent variables are RETS&P500, which controls for the general market movements. The Federal Funds rate is used as a control variable to control for the interest rate movements. We use PREEVENTDAY as the dummy variable that takes the value 1 for 24 June 2025 (the trading day prior to announcement), and 0 otherwise. It controls for any information leakage prior to the announcement date. While the news was not released until 25 June 2025, we wanted to check for the effects, if any, on the pre-event day. The main variable in the regression is EVENT, the dummy variable that takes a value of 1 for 25 June 2025 (the announcement day) and 26 June 2025 (the day immediately following the announcement), and 0 otherwise. We also use POSTEVENTDAY as a dummy variable that takes the value of 1 for 27 June 2025, and 0 otherwise.

BANKRET_j,t = β₀ + β₁*RETS&P500_t + β₂*FEDFUNDS_t + β₃*PREEVENTDAY +
β₄*EVENT + β₅*POSTEVENTDAY + ε_j,t

(2)

In Equation (3), we employ KBW Bank Index, which more specifically captures sector-wide banking performance, instead of S&P500. We regress each of the bank returns with RETKBW in place of RETS&P500 as the control variable. RETKBW is the daily log return for the KBW Index. The equation used for the regression is given below.

BANKRET_j,t = β₀ + β₁*RETKBW_t + β₂*FEDFUNDS_t + β₃*PREEVENTDAY +
β₄*EVENT + β₅*POSTEVENTDAY + ε_j,t

(3)

To ensure the validity of our regression estimates, we test for potential econometric issues. Autocorrelation is examined using the Durbin–Watson test, and heteroskedasticity is assessed through both the Breusch–Pagan and White tests. The results indicate no evidence of autocorrelation; however, heteroskedasticity is detected. Accordingly, we employ heteroskedasticity-robust standard errors to obtain consistent and reliable inference.

4. Empirical Evidence

This section presents the empirical results using event-study regressions modelled in Equations (2) and (3).

Table 2. Impact of eSLR on Globally Systemically Important Banks’ returns using S&P500 Index as control.

Variables	RETWFC	RETJPM	RETCITI	RETBAC	RETGS	RETMS	RETBNYM	RETSSC
EVENT	0.05 ***	0.01 ***	0.01 ***	0.00 ***	0.01 ***	0.01 **	−0.01 ***	0.01
	(5.98)	(6.38)	(4.22)	(3.00)	(7.58)	(2.24)	(−4.16)	(1.63)
PREEVENTDAY	0.03 ***	−0.00	0.01 ***	−0.00 **	0.01 ***	−0.00	0.01 ***	0.00 ***
	(2.97)	(−1.03)	(3.61)	(−2.20)	(4.54)	(−0.90)	(5.45)	(3.95)
POSTEVENTDAY	0.05 ***	−0.01 ***	−0.01 ***	−0.01 ***	−0.00 **	−0.00	0.01 ***	0.00
	(6.00)	(−10.31)	(−5.76)	(−9.63)	(−2.33)	(−0.62)	(6.85)	(0.29)
RETS&P500	0.51	0.99 ***	1.30 ***	1.01 ***	1.37 ***	1.36 ***	0.90 ***	1.02 ***
	(0.79)	(10.79)	(9.80)	(7.33)	(15.01)	(14.84)	(12.33)	(14.13)
FEDFUNDS	−0.25 ***	−0.00	−0.00	−0.00	−0.00	0.00	0.00	0.00
	(−21.31)	(−0.41)	(−0.79)	(−0.28)	(−0.28)	(0.32)	(0.88)	(0.66)
Constant (C)	5.39 ***	0.00	0.01	0.00	0.00	−0.00	−0.01	−0.01
	(94.96)	(0.51)	(0.84)	(0.30)	(0.38)	(−0.24)	(−0.72)	(−0.57)
Observations	248	248	248	248	248	248	248	248
Adj. R2	0.63	0.49	0.56	0.47	0.64	0.62	0.53	0.53

Note: This table presents regression results for Equation (2). t-statistics are given in parentheses. **, and *** represent significance at the 0.05, and 0.01 levels, respectively.

reports results using the S&P 500 Index as a proxy for overall market performance, while

Table 3. Impact of eSLR on Globally Systemically Important Banks’ return using KBW Index as control.

Variables	RETWFC	RETJPM	RETCITI	RETBAC	RETGS	RETMS	RETBNYM	RETSSC
EVENT	0.05 ***	0.00	0.01 **	−0.00 ***	0.00 ***	0.00	−0.01 ***	0.00
	(5.27)	(1.55)	(2.04)	(−3.08)	(2.60)	(0.24)	(−4.68)	(0.39)
PREEVENTDAY	0.03 ***	−0.00	0.01 ***	−0.00 ***	0.01 ***	0.00	0.01 ***	0.01 ***
	(3.45)	(−1.18)	(8.21)	(−4.17)	(10.39)	(0.85)	(6.60)	(5.13)
POSTEVENTDAY	0.05 ***	−0.01 ***	−0.00 ***	−0.01 ***	0.00 ***	0.01 ***	0.01 ***	0.00 ***
	(6.59)	(−10.10)	(−3.27)	(−11.81)	(3.91)	(6.44)	(11.01)	(5.97)
RETKBW	0.48	0.88 ***	1.09 ***	0.92 ***	1.10 ***	1.08 ***	0.65 ***	0.83 ***
	(1.41)	(18.79)	(20.50)	(18.96)	(27.96)	(30.72)	(8.70)	(12.04)
FEDFUNDS	−0.25 ***	−0.00	−0.00 *	−0.00	−0.00	−0.00	0.00	0.00
	(−21.46)	(−1.06)	(−1.71)	(−1.02)	(−0.95)	(−0.04)	(0.66)	(0.51)
Constant (C)	5.39 ***	0.01	0.01 *	0.01	0.01	0.00	−0.00	−0.00
	(95.47)	(1.12)	(1.74)	(0.97)	(1.01)	(0.06)	(−0.53)	(−0.44)
Observations	248	248	248	248	248	248	248	248
Adj. R2	0.64	0.81	0.82	0.84	0.87	0.84	0.59	0.75

Note: This table presents regression results for Equation (3). t-statistics are given in parentheses. *, **, and *** represent significance at the 0.1, 0.05, and 0.01 levels, respectively.

substitutes it with KBW Bank Index to capture banking-sector–specific movements.

The regression estimates in Table 2 employ heteroskedasticity-robust standard errors. The EVENT dummy, which captures the returns during two-day event window (25–26 June 2025) of the Federal Reserve’s announcement to lower the enhanced Supplementary Leverage Ratio (eSLR), is positive and significant for six out of the eight U.S. G-SIBs. This finding indicates that investors regarded the proposal positively, expecting enhanced shareholder returns resulting from a reduction in capital requirements. These results are consistent with the findings of Fraisse et al. (2020), which indicate that stricter capital regulations limit bank lending and profitability, suggesting that a relaxation of these regulations would yield a contrary, advantageous outcome.

However, Bank of New York Mellon (RETBNYM) shows a negative coefficient during the event window and the coefficient for State Street Corp. (RETSSC) is insignificant. One plausible explanation may lie in its smaller relative size2 and unique business model as a custodian bank, which limits the extent to which it benefits from reduced leverage requirements. Prior literature supports the notion that smaller banks or less diversified institutions tend to underperform larger peers during deregulatory or expansionary episodes (Nippani & Washer, 2005; Nippani & Ling, 2021). This asymmetric reaction is also consistent with the view of Barth and Miller (2018) that complex U.S. capital regulations impose uneven effects across banks depending on asset composition, and with Thomson (2009) and Castro and Ferrari (2014), who highlight that systemic importance influences how regulatory changes are priced by the market.

It can be seen from the results presented in Table 2 that the market returns, as represented by RETS&P500, have a positive and significant coefficient at 0.01 level in most regressions. It appears that general market movements significantly influence the returns of these eight stocks. The FEDFUNDS rate that controls for interest rate movements is not significant in most of the regressions. For the PREEVENTDAY dummy variable, there are positive and significant coefficients for five regressions, while one company Bank of America has a negative and significant coefficient, and the remaining two are insignificant. These mixed results point to some leakage of information in the market. However, the information was limited to the meeting but not the outcome. The POSTEVENTDAY coefficients are mostly weak or negative, indicating that the positive stock reaction was short-lived and that markets efficiently incorporated the news immediately following the announcement3. Overall, it appears that the relaxation of eSLR requirements for these eight banks was a wealth-creating event for the stockholders of six of the eight companies after controlling for general market movements.

The KBW NASDAQ bank index is described as “The KBW Bank Index is designed to track the performance of the leading banks and thrifts that are publicly traded in the U.S. The Index includes 24 banking stocks representing the large U.S. national money centers, regional banks and thrift institutions (NASDAQ, n.d.-a).” This index is specifically chosen because the twenty-four banks (NASDAQ, n.d.-b) included in the index includes the eight banks under study. This regression would measure if each particular bank has superior returns as compared with the leading 24 banks included in the index on the event days. Similar to Equation (2), we use EVENT, PREEVENTDAY, POSTEVENTDAY, and FEDFUNDS as independent and control.

A perusal of Table 3 shows that, consistent with the results from Table 2, the index variable in this index RETKBW has positive and significant (at 0.01 level) coefficients in all regressions except for RETWFC, where the coefficient is positive but not significant. This outcome is expected, as the KBW Index captures sector-specific dynamics and partially internalizes the market’s aggregate reaction to the Federal Reserve’s policy proposal. Consequently, after accounting for industry-wide performance, the incremental abnormal returns for individual banks appear smaller. Also consistent with the previous regression FEDFUNDS is not significant for most of the regressions. For the PREEVENTDAY, five of the banks accrued positive and significant returns at 1%, while one RETBAC has a negative return. The four banks with positive returns are RETWFC, RETCITI, RETGS, RETBNYM, and RETSSC. For the event period of two days, three banks, RETWFC, RETCITI, and RETGS have positive and significant coefficients. However, two banks, RETBAC and RETBNYM have negative and significant coefficients at 0.01 level. The EVENT coefficient shows mixed results, suggesting that the observed market response largely reflects an industry-level effect rather than idiosyncratic firm-level reactions. This pattern reinforces the argument of Barth and Miller (2018) that capital rule changes often produce heterogeneous but correlated impacts across large banks, given their exposure to similar regulatory and funding environments.

As in the earlier model, BNYM continues to show a negative event response, likely driven by its lower asset base relative to peers. The POSTEVENTDAY coefficient shows that the market reacted differently to the eight G-SIBs after the event on 27 June 2025, when the KBW Bank Index was taken into account. RETWFC, RETGS, RETMS, RETBNYM, and RETSSC all have positive and statistically significant coefficients. This means that investors are still adjusting positively or have delayed their adjustments as they think about the long-term effects of the Fed’s proposal to lower the enhanced Supplementary Leverage Ratio (eSLR). On the other hand, RETJPM, RETCITI, and RETBAC have small but significant negative coefficients. This is probably because investors are taking profits or correcting after the big gains on the day of the event. This pattern corresponds with the post-announcement drift and short-lived reversals frequently noted in event studies (MacKinlay, 1997). The statistical significance of post-event effects, even after controlling for industry-wide movements through the KBW Index, demonstrates the robustness of the findings and validates the selection of 27 June 2025, as the post-event day to observe delayed price adjustments and market normalization behavior. The implications of these results are explained in the next section.

5. Conclusions and Implications

Earlier empirical studies have indicated that bank stockholders get negative wealth effects when capital requirements are tightened. The purpose of the study is to investigate whether the Federal Reserve’s lowering the eSLR for Tier 1 Capital requirements for eight global systemically important banks has been a wealth-creating event for the stockholders of these banks. Using an event-study methodology with both broad-market (S&P 500) and sector-specific (KBW Bank Index) benchmarks, we find that the proposal elicited a positive and statistically significant reaction for most G-SIBs, implying that investors perceived the reduction in capital requirements as beneficial for shareholder value. The results support earlier findings by Cooper et al. (1991) and Pelster et al. (2018), who document that tighter capital regulations reduce equity returns, while relaxation measures tend to boost them through higher expected profitability and leverage-based gains in ROE.

However, the reactions are not uniform across banks. The Bank of New York Mellon (BNYM) exhibited a negative and significant market reaction, likely due to its smaller relative asset size and distinct custodial business model that relies less on balance sheet expansion and more on fee-based income. This contrasts with large universal banks such as JPMorgan Chase and Bank of America, which are better positioned to exploit the higher equity multipliers resulting from relaxed capital constraints. Consistent with Nippani and Ling (2021) and Barth and Miller (2018), these heterogeneous effects emphasize that the benefits of capital regulation changes depend on a bank’s size, structure, and scope of operations.

When controlling for the KBW Bank Index, which captures sector-wide movements, the results indicate that much of the observed market reaction was driven by an industry-level effect rather than purely firm-specific responses. From Table 3, we can decipher that while some of these banks did well when controlled for the major bank KBW Index, it is not as good as them being compared to the general market. The implication for the difference in findings is potentially the composition of the KBW Index itself. It includes these eight banks out of the 25 components. That means their positive direction of the market is somewhat reflected in the index, thereby impacting that coefficient. A bigger implication is that other big banks in the index also potentially found their stock values increased. That could be because industry deregulations generally happen in patterns in the banking industry. Historically, regulation increased (from the 1930s to the 1980s) and then decreased (from the 1990s to 2007) in phases. The Glass–Steagall Act of 1933 was followed by the Bank Holding Company Act of 1956. Both were increasingly regulation-oriented. As compared with that, the Riegle–Neal IBBEA of 1994 and the Financial Services Modernization Act of 1999 were more deregulatory in nature. The implication from the reduction in eSLR could mean more deregulation is to follow. This implies that other banks in the KBW Index may have higher returns during the two-day event, as the market anticipates further deregulation to follow, and the reduction in eSLR is merely the first step. It could also mean that other banks, which are components of this index, are very close to achieving a similar status to these eight banks because they might be entering the TBTF territory. Thus, when it comes to bank indices, the results are mixed. That is because other banks could have had positive returns as well, thus impacting this more smaller and industry-oriented index.

There are several implications of this study, both for academics and portfolio managers. First, relative to the overall stock market, this was a wealth-creating event for seven of the eight banks. For academics, this is the first study to examine the implications of lowering capital requirements on banks’ stockholder wealth. All earlier studies have examined the implications of increasing capital requirements. These eight large banks have other advantages: they are large, hence considered TBTF, and can take more risks because of this. As such, lowering their equity requirements likely significantly increases their future ROE. As larger banks have been found to outperform their smaller counterparts in the post-financial crisis period, this implies that lowering capital requirements can only make these eight banks’ performance even significantly better as compared with other large banks. The implications for portfolio managers are that these banks could potentially have higher ROE as compared with the general industry because of this advantage given to them. Just as increasing restrictions reduce bank profits, lowering them potentially increases them. The significant event-day reaction and rapid post-event normalization suggest that investors closely monitor regulatory developments as signals of the future policy environment. This aligns with the view of Van Roy (2008) that market discipline can complement regulation by incentivizing banks to maintain prudent capital levels. The mixed post-event coefficients imply that while markets welcome deregulation, they also reassess risks in the following days—indicating an awareness of potential volatility stemming from looser oversight.

This study is subject to certain limitations. In particular, it focuses exclusively on the short-term market reaction to the Federal Reserve’s announcement, without examining its potential long-term implications. Future research could extend this analysis by investigating the enduring effects of such regulatory changes on bank performance, risk-taking behavior, and broader financial stability. Future research could benefit from using intraday or high-frequency data to more precisely capture immediate market reactions and to further investigate the possibility of information leakage or anticipatory trading prior to regulatory announcements, which could not be explored in this study due to data limitations. This study focuses exclusively on the eight global systemically important banks (G-SIBs) that were directly affected by the Federal Reserve’s eSLR ajustments. As the policy change did not apply to smaller institutions, they are not included in the current analysis. Future research could extend the analysis to include smaller banks in order to examine potential spillover effects of regulatory announcements, even when such institutions are not the direct targets of the policy. This could help assess whether market expectations or systemic linkages influence the broader banking sector’s response to regulation.