Bank Earnings vs. Macro Policy: Model How a Credit-Card Rate Cap Would Reprice Bank Valuations

Hook — Why regulatory surprises that can erase a decade of earnings growth for consumer banks should be on every investor’s radar in 2026

Investors, analysts and risk teams are facing a familiar pain point: regulatory surprises that can erase a decade of earnings growth for consumer banks in a single policy change. Late 2025 and early 2026 brought renewed political momentum for an interest-rate cap on credit cards — a hypothetical that market participants now treat as a plausible regulatory shock rather than a thought experiment. This piece gives you a reproducible valuation model and scenario sensitivity analysis that quantifies how a cap on credit-card APRs would reprice major banks’ revenue, margins and price targets.

Executive summary — the short answer for portfolio managers

Our quant baseline and three regulatory scenarios (mild, moderate, severe) show:

• Moderate cap (18% APR with a 60% pass-through) produces a 4–8% decline in bank net income for typical large US banks; banks most concentrated in cards (Citigroup, BofA) see the largest EPS hit.
• Severe cap (15% APR with a 90% pass-through) can cut card pre-tax profits by ~60% and reduce bank-level net income by ~13–23%, producing double-digit downside to share prices before any multiple compression.
• Valuation impact is a function of: card exposure as a share of earnings, repricing speed, behavioral effects (balances, charge-offs, fees) and market re-rating risk. Diversified banks (JPMorgan, Goldman) are less sensitive; card-heavy franchises (Citi, some regional banks) are most exposed.

Why this matters now (2026 context)

The regulatory risk is not theoretical in 2026. Public statements in late 2025 and Q1 2026 signaled renewed appetite in Washington to limit consumer APRs. Simultaneously, banks’ January 2026 earnings reports revealed softness in consumer lines, growing sensitivity to fee compression, and higher-than-expected expenses. Against an elevated interest-rate backdrop and active political debate about consumer credit, even a low-probability cap event deserves portfolio-level stress testing.

Model structure — how to reproduce this analysis

The model is intentionally modular so you can swap inputs with current filings or your own projections. Key blocks:

1. Inputs: credit-card outstanding balances (B), baseline average APR (r0), funding cost allocated to cards (f), baseline fee income rate (fee%), baseline charge-off + provision rate (loss%), allocated card opex rate (opex%). Bank-level baseline pre-tax income and net income are used to scale to EPS and price. (Grab up-to-date figures from 10-Q / 10-K filings and use research tools or browser extensions to speed data collection.)
2. Card P&L (pre-reg change):

   Pre-tax card profit = B * [ (r0 - f) + fee% - loss% - opex% ]

3. Regulation shock: cap (rcap) and pass-through factor (p) determine the immediate reduction in gross APR on the affected portion of balances. Behavioral adjustments (Δbalances, Δloss%, Δfee%) are applied.
4. Bank-level mapping: change in card pre-tax profit flows to bank pre-tax and after-tax income. Translate to %EPS change and new implied price under constant multiple and under a re-rating shock.

Formulas — plug-and-play

Use these formulas in Excel or Python:

• Delta APR = (r0 - rcap) * p
• New card gross yield = r0 - Delta APR
• Change in NII = Delta APR * B
• Change in pre-tax card profit = -Change in NII - (Δfee% * B) + (Δloss% * B)
• Change in net income = Change in pre-tax * (1 - tax rate)
• Implied price change (constant multiple) = Change in net income / baseline net income

Base-case inputs and assumptions (you can replace these with your own)

To keep the model concrete we used representative public-filing-style inputs as of Q4 2025. Replace these with your own updated numbers from 10-Q / 10-K for precision.

• Baseline APR (r0) = 22%
• Funding cost allocated to cards (f) = 4%
• Fee income = 1.0% of balances
• Provision / loss = 3.5% of balances
• Allocated opex = 6.0% of balances
• Tax rate = 21% (effective)
• Representative card balances (B): JPMorgan $160bn, Bank of America $95bn, Citi $125bn, Wells Fargo $70bn
• Representative bank-level baseline metrics (rough, illustrative): JPM pre-tax 70bn / net 50bn / market cap $500bn; BofA pre-tax 45bn / net 33bn / market cap $260bn; Citi pre-tax 35bn / net 25bn / market cap $120bn; Wells pre-tax 30bn / net 22bn / market cap $140bn.

Scenario definitions

• Mild: cap = 24% APR, p = 0.0 (negligible impact)
• Moderate: cap = 18% APR, p = 0.6; behavioral: balances +2%, loss% -10%, fee income -10%
• Severe: cap = 15% APR, p = 0.9; behavioral: balances +5%, loss% -20%, fee income -25%

Key intermediate calculation — baseline card economics

Under our assumptions, baseline pre-tax card profit margin = (r0 - f) + fee% - loss% - opex% = (22% - 4%) + 1.0% - 3.5% - 6.0% = 9.5% of balances. That yields baseline pre-tax card profits:

• JPM: $160bn * 9.5% = $15.2bn
• BofA: $95bn * 9.5% = $9.025bn
• Citi: $125bn * 9.5% = $11.875bn
• Wells: $70bn * 9.5% = $6.65bn

Results — moderate scenario (18% cap, 60% pass-through)

Delta APR = (22% - 18%) * 60% = 2.4% reduction in gross yield. Net NII declines by 2.4% * balances. After accounting for behavior (smaller provisions, lower fee income), pre-tax card profit reductions are:

• JPM: pre-tax card profit down $3.44bn (-> $11.76bn). Bank pre-tax falls ~4.9%; net income falls ~5.4%.
• BofA: pre-tax card profit down $2.04bn (-> $6.98bn). Bank net income falls ~4.9%.
• Citi: pre-tax card profit down $2.69bn (-> $9.19bn). Bank net income falls ~8.5% (larger sensitivity because of lower baseline earnings).
• Wells: pre-tax card profit down $1.51bn (-> $5.15bn). Bank net income falls ~5.4%.

Implied price moves if market keeps multiples constant: JPM ~-5.4%, BofA ~-4.9%, Citi ~-8.5%, Wells ~-5.4%. If you widen the analysis to include a 10% multiple compression (market re-rating due to regulatory uncertainty), add ≈10 percentage points to the downside.

Results — severe scenario (15% cap, 90% pass-through)

Delta APR = (22% - 15%) * 90% = 6.3% reduction. This is a large shock that destroys most card economics absent cost structure changes. Key outcomes:

• JPM: pre-tax card profit down $9.36bn (pre-tax card profit falls from $15.2bn to $5.84bn). Bank net income down ~14.8%; implied price decline ~15% before any multiple effects; with 10% multiple compression the downside approaches ~25%.
• BofA: pre-tax card profit down $5.56bn (-> $3.47bn). Bank net income down ~13.3%.
• Citi: pre-tax card profit down $7.31bn (-> $4.56bn). Bank net income down ~23.1% — Citi shows the largest proportional hit in our model.
• Wells: pre-tax card profit down $4.10bn (-> $2.56bn). Bank net income down ~14.7%.

Takeaway: severe caps materially impair bank profitability for card-dependent franchises and can trigger meaningful share-price declines even without credit stress.

What drives dispersion across banks?

• Card balance share of total earnings: Two banks with similar card losses will see different EPS impacts if one bank’s cards represent a larger share of overall earnings.
• Diversification: Banks with larger trading/wealth/IB revenue (Goldman, MS, some parts of JPM) have buffer capacity.
• Product mix: High-fee, co-branded and private-label portfolios generate more non-interest income (late fees, interchange) and are more exposed to fee compression. Watch interchange and fee-rule announcements closely.
• Repricing mechanics: The speed at which a cap applies to variable-rate balances matters. Credit cards often reprice quickly, so p (pass-through) can be high.

Practical investment actions and risk-management steps

Use these concrete steps to apply the model to your portfolio:

1. Obtain up-to-date inputs: download card balances, APR yields, card net interest income, and fee/provision splits from each bank’s latest 10-Q / 10-K or investor deck. Replace the model’s illustrative numbers with those values.
2. Run scenario sweeps: vary rcap across 24%, 20%, 18%, 15% and p across 30–90% to build a sensitivity surface that maps cap severity to EPS shock. If you need a formal playbook for scenario runs, adapt the logic from standard incident playbooks (see incident response workflows for structuring runbooks).
3. Model behavioral offsets: include potential balance growth, lower delinquencies and fee erosion. These partially offset interest loss but rarely offset it fully unless structural changes to card economics occur.
4. Translate to valuation: compute implied price targets under constant multiple and under multiple compression scenarios (-5%, -10%, -20%). Investors can use these to size positions and set stop-loss or hedge triggers.
5. Hedging ideas: buy protective puts on the most exposed tickers or short bank-specific AT1 perpetuals/credit instruments for directional exposure to regulatory risk. For a lower-cost hedge, consider buying downside protection on ETF baskets (e.g., XLF) but be mindful of basis risk.

Case study: JPMorgan vs. Citi — same headline, different sensitivity

Our moderate and severe runs show a clear divergence: JPMorgan absorbs shocks better because card profits are a smaller share of total earnings while Citi, with a larger consumer-card footprint relative to earnings, sees a substantially higher EPS decline. That means investors should not treat all large banks as identical exposures to card-cap risk — quantify card exposure per dollar of net income before taking a view.

Model limitations and caveats — what the model doesn’t capture

• Macroeconomic feedback loops: a cap could change consumer spending patterns, employment outcomes and macrocredit conditions that feed back into charge-offs differently than our linear behavioral assumptions.
• Strategic bank responses: pricing changes across product sets, cost cuts, re-underwriting, shifts to secured products, or lobbying/structural legal moves can materially alter outcomes over 12–24 months.
• Market recognition: our multiple compression assumption is a stylized scenario. Actual market re-rating will depend on policy detail, judicial challenges and whether central banks or fiscal policy offset impacts.

Signals to monitor (early-warning system)

• Congressional bills and White House statements about APR ceilings or co-branded-card rules
• CFPB guidance or state AG actions targeting late fees / interest practices
• Bank-level disclosures: management commentary on card attrition, originations, promotional programs, and litigation reserves
• Interchange and fee-rule announcements from Visa / Mastercard that could amplify fee revenue shifts

How to operationalize this model in your workflow

Excel implementation steps (5 minutes if you have the inputs):

1. Sheet 1: input balances and baseline rates per bank.
2. Sheet 2: compute baseline card P&L per bank using the formula for pre-tax card profit = B * margin.
3. Sheet 3: create scenario table for rcap and p; compute Delta APR, ΔNII, Δfees, Δprovisions and new card P&L.
4. Sheet 4: map card Δpre-tax to bank pre-tax, then to net income and implied price under different multiple assumptions.
5. Sheet 5: create tornado charts and sensitivity matrices for easy visualization and share with PMs/ risk committees.

Final interpretation — what investors should do right now

In 2026 the regulatory channel is a valid and material risk for bank valuations. Use the model to:

• Re-weight portfolios toward banks with diversified revenue and lower card exposure if you are risk-averse.
• Maintain explicit scenario-based position sizing: allocate capital assuming a severe cap scenario (15% APR) as a stress-case.
• Consider hedges that protect against regulatory-driven multiple compression, not only EPS declines.

“Regulatory shocks are valuation shocks — quantify them before they arrive.”

Actionable takeaways — checklist for investors and analysts

• Run this model with each bank’s current card balances and yields — don’t assume the 2025 numbers remain static.
• Focus on the pass-through rate (p) and the bank’s disclosure about repricing speed — this parameter drives most of the outcome variance.
• Stress-test for multiple compression as a separate risk dimension — regulatory events often trigger sentiment-driven re-ratings.
• Use hedges where the upside to being right (avoiding a large drawdown) exceeds the cost of protection.

Call to action

Want the spreadsheet used for this analysis and an interactive bank screener that auto-populates card balances from filings? Visit our Quant Resources hub at stock-market.live (Screeners & Quant Resources) to download the model template, or contact our research desk for a customized run for your portfolio. Run the scenarios now — regulatory risk is priced only after it becomes probable, and by then it may be too late to hedge efficiently.