Net interest margin (NIM) is a bank's interest income minus its interest expense, divided by its average interest-earning assets. It shows how much a bank earns on loans and securities after paying for deposits and other funding. This free six-sheet Excel workbook tracks six large US banks (JPM, BAC, C, WFC, GS, MS) around Q1 2026 earnings, pairing yield-curve scenarios (10-year vs 2-year) with MarketXLS formulas for price, P/E, EPS, revenue, ROE, debt to equity, beta, trend, and dividends. MarketXLS does not return NIM as a single formula, so you enter each bank's reported NIM from its earnings release next to the live data.
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The workbook was built for mid-April 2026, when Q1 bank earnings were starting and Treasury yields were elevated. Investors were watching loan growth, deposit costs, trading revenue, credit quality, and the shape of the yield curve at the same time, which is why the template puts rates, valuation, and profitability on one sheet.
Net interest margin tracker snapshot for Q1 2026
Before getting into workbook design, it helps to frame the market setup in plain terms.
| Bank | What the market is watching now | Why it matters in Q1 2026 | Useful MarketXLS angle |
|---|---|---|---|
| JPM | Consumer banking, investment banking rebound, deposit mix | A diversified read on the whole sector | =ReturnOnEquity("JPM") |
| BAC | Deposit sensitivity and net interest income trend | Often treated as a yield curve and consumer rate proxy | =PERatio("BAC") |
| C | Global consumer and institutional mix | Useful for comparing valuation against profitability | =EarningsPerShare("C") |
| WFC | Loan growth and efficiency discipline | A cleaner read on core U.S. banking trends | =QM_Last("WFC") |
| GS | Capital markets and deal activity | Less rate-sensitive than pure spread lenders | =Revenue("GS") |
| MS | Wealth and institutional securities trends | Mix of fee income and market activity | =DividendYield("MS") |
That table does not tell you what to buy or sell. It gives you a framework for why a net interest margin tracker is useful right now. The market is not only asking who beat earnings estimates. It is asking which business models are more sensitive to deposit repricing, which franchises are more diversified, which names still look inexpensive, and which price charts are confirming the story.
Why a bank tracker was useful in April 2026
Mid-April 2026 is one of those moments when macro and company-level analysis are colliding. A broad market rally can still coexist with a lot of uncertainty inside the financial sector. Treasury yields remain high enough that funding pressure and asset repricing still matter. Bank earnings are starting to turn that background into hard numbers. Investors want to know whether large banks can protect spreads, grow fee income, and keep credit quality stable in a market that still has geopolitical risk, sticky inflation, and shifting rate expectations in the background.
A net interest margin tracker in Excel helps for three reasons.
First, the yield curve matters again. Even a small change in the spread between short-term and long-term yields can affect how investors frame earnings quality for deposit-heavy institutions. A modestly steeper curve can help bank sentiment. A flatter setup can push the discussion back toward expense control and fee income.
Second, big bank earnings often reset expectations for the whole sector. When JPMorgan, Bank of America, Citi, and Wells Fargo report, they do more than move their own share prices. They change how the market interprets credit quality, consumer resilience, investment banking activity, and loan demand.
Third, many investors still want structure. The financial sector is easy to discuss in headlines and harder to compare in a consistent way. One bank can look cheap on P/E, another can look stronger on ROE, and another can carry a better income profile but weaker trend support. Without a spreadsheet, those trade-offs blur together.
The goal is not prediction. The goal is an educational, repeatable process for evaluating bank earnings season.
What a useful net interest margin tracker should include
A weak bank workbook is just a list of ticker symbols and prices. A useful one should combine several layers of analysis that reflect how financial stocks are actually evaluated during earnings season.
1. Price and trend context
You need to know whether a stock is holding trend support before you layer on earnings interpretation. If a bank is already trading above its 50-day moving average, the market may be confirming the story. If it is below that trend line, even a decent headline can be interpreted more cautiously.
In MarketXLS, a simple starting block looks like this:
=QM_Last("JPM")
=SimpleMovingAverage("JPM","50")
=FiftyTwoWeekHigh("JPM")
=FiftyTwoWeekLow("JPM")
Formula documentation: QM_Last, SimpleMovingAverage, FiftyTwoWeekHigh, FiftyTwoWeekLow
That gives you the current price, the medium-term trend reference, and the stock's annual range in one place.
2. Valuation
Bank stocks are often discussed in relative valuation terms. A lower P/E can look attractive, but only if profitability and balance sheet quality support it. Comparing P/E across major banks gives you an immediate first pass.
=PERatio("BAC")
=PERatio("C")
=PERatio("WFC")
Formula documentation: PERatio
This is especially useful in Q1 2026 because the market is deciding whether earnings growth deserves a better multiple or whether the sector should still trade at a discount to broader equities.
3. Earnings and revenue support
Price and P/E are not enough. You also want to know whether trailing earnings per share and revenue scale support the market narrative.
=EarningsPerShare("JPM")
=Revenue("GS")
=EarningsPerShare("MS")
Formula documentation: EarningsPerShare, Revenue
For large banks, this can help separate franchises that are relying more on net interest income from those with bigger contributions from trading, advisory, wealth management, or other fee streams.
4. Profitability and balance sheet quality
For educational screening, return on equity and debt to equity are useful shorthand measures. They do not replace a full banking model, but they help compare business quality quickly.
=ReturnOnEquity("JPM")
=ReturnOnEquity("WFC")
=TotalDebtToEquity("C")
Formula documentation: ReturnOnEquity, TotalDebtToEquity
ROE gives you a broad profitability lens. Debt to equity can highlight differences in leverage profile. Neither should be read in isolation, but together they help organize the comparison.
5. Income profile
Some investors looking at bank stocks care about income and capital return as much as price appreciation. Dividend yield and dividend per share belong in the same workbook because they change how a position fits into a broader portfolio.
=DividendYield("BAC")
=DividendPerShare("JPM")
=DividendYield("MS")
Formula documentation: DividendYield, DividendPerShare
That can be especially useful for advisors who want to balance bank exposure with portfolio income goals.
The MarketXLS formulas used in this workbook
The workbook relies on these MarketXLS functions:
=QM_Last("TICKER")for current snapshot price=PERatio("TICKER")for trailing price-to-earnings ratio=EarningsPerShare("TICKER")for trailing EPS=Revenue("TICKER")for revenue context=ReturnOnEquity("TICKER")for profitability comparison=TotalDebtToEquity("TICKER")for leverage comparison=Beta("TICKER")for market sensitivity=SimpleMovingAverage("TICKER","50")for trend context=FiftyTwoWeekHigh("TICKER")and=FiftyTwoWeekLow("TICKER")for annual range analysis=DividendYield("TICKER")for trailing dividend yield=DividendPerShare("TICKER")for annual dividend dollars per share=Sector("TICKER")and=Industry("TICKER")for classification
That formula set is enough to build a reusable bank earnings dashboard. Net interest margin itself is entered from each bank's earnings release.
What is inside the Net Interest Margin Tracker Excel workbook
The downloadable package includes two files built for different jobs.
Download the templates:
- - Pre-filled with current sample values and visible MarketXLS formula references
- - Live-updating formulas with no static market data in the core data fields
Both files use the same six-sheet structure.
1. How To Use
This opening sheet explains what each tab does, links to MarketXLS and the book a demo page, and explains how the bank watchlist is intended to be used. In the sample version, it also includes a visible Data as of date so users know the values are a snapshot.
2. Main Dashboard
This is the center of the workbook. It tracks JPM, BAC, C, WFC, GS, and MS across sector, industry, last price, P/E, EPS, revenue, ROE, debt to equity, beta, 50-day SMA, 52-week high, 52-week low, and dividend yield. It also includes a transparent score so users can quickly sort the watchlist.
That score is intentionally simple. It rewards a mix of stronger profitability, lower valuation, trend support, and income. It is not a recommendation engine. It is a triage tool that helps you decide where to spend more time.
3. Scenario Analysis
NIM discussions only make sense if they are paired with a rates framework. This sheet lets the user compare a bull steepener, base case, bear flattener, and rate shock setup. The purpose is educational. It helps you think about what changes in the 10-year, 2-year, and the spread between them might mean for the way investors read bank earnings.
4. Strategy Actions
This section is deliberately framed as an educational workflow. It gives users a place to record trend checks, income checks, valuation checks, and follow-up observations after earnings. That is useful for advisors and active investors who want a repeatable review process.
5. Portfolio Allocation
This sheet converts target weights into dollar allocations, approximate shares, expected dividend income, and ROE context. It is helpful when the question changes from "which bank looks interesting" to "how would this fit into a real portfolio under risk limits?"
6. Correlation Comparison
This sheet compares bank pairs on price gaps, ROE gaps, yield gaps, and valuation gaps. It is not a statistical correlation engine. It is a comparison layer that helps users judge overlap, concentration, and relative attractiveness inside a bank basket.
Every sheet includes a MarketXLS Functions Used section so users can see the exact formulas behind the live template and adapt the workbook later.
Why the yield curve belongs in a bank earnings workbook
The yield curve belongs in a bank workbook because it drives the spread banks earn.
Net interest margin is influenced by the spread between what banks earn on assets and what they pay on liabilities. Real life is more complicated than that sentence, but it is still a useful framing tool. If the 10-year yield rises relative to the 2-year yield, the market may interpret that as better support for lending spreads and earnings quality, particularly for more traditional lenders. If the curve flattens, investors may care more about fee income, expense control, and trading activity.
That is why the workbook includes yellow input cells for:
- portfolio size
- max position weight
- 10-year yield
- 2-year yield
- curve spread
- preferred watchlist count
Those inputs flow into the scenario sheet so the workbook stays dynamic instead of static.
A bank earnings workflow should not pretend that one macro regime fits all names equally. JPMorgan and Goldman Sachs are not the same business. Bank of America and Morgan Stanley are not the same business. Some names are more directly read through a spread lens. Others are more diversified and less dependent on pure net interest income. The yield curve tab helps keep that distinction visible.
How to read each bank inside this template
The point of this section is not to issue a recommendation. It is to explain why each ticker deserves a slightly different lens.
JPMorgan
JPMorgan is often treated as the sector benchmark because it combines consumer banking, commercial banking, capital markets, and broad operating scale. In a tracker like this, JPM can act as the reference line. If the sector narrative is strong, JPM often helps confirm it.
Useful formulas include:
=QM_Last("JPM")
=PERatio("JPM")
=ReturnOnEquity("JPM")
=DividendPerShare("JPM")
Formula documentation: QM_Last, PERatio, ReturnOnEquity, DividendPerShare
Bank of America
Bank of America is frequently discussed as a yield-sensitive name because of its deposit base and rate exposure. That makes it a good candidate for the scenario sheet. If you are testing how the market might react to a modest steepening or flattening in the curve, BAC is often one of the clearest examples.
Citigroup
Citigroup is useful because it often trades at a valuation discount relative to peers. That creates a natural question for the dashboard: is the discount justified, or is it an area where profitability and execution could drive re-rating if earnings improve? A spreadsheet helps because you can compare the discount directly against ROE, EPS, and trend support.
Wells Fargo
Wells Fargo is a strong read on core U.S. banking conditions. Loan growth, consumer activity, and cost discipline matter here. In the workbook, WFC is useful for comparing traditional bank earnings quality against more diversified capital markets names.
Goldman Sachs
Goldman Sachs is less of a pure NIM proxy and more of a capital markets and advisory read. That difference matters. If a user wants to compare banks purely through a yield-curve lens, GS can look like an outlier. That is actually helpful because it reminds you not to overgeneralize the sector.
Morgan Stanley
Morgan Stanley adds wealth management and institutional securities balance to the comparison set. In the template, its dividend yield, valuation, and profitability can be compared against both deposit-heavy banks and capital markets peers.
How the scoring model helps without becoming a black box
Many workbooks become less useful when they pile up dozens of metrics without any way to prioritize them. This template uses a simple score that combines a few transparent checks:
- whether ROE clears a baseline threshold
- whether P/E is below a rough comparison threshold
- whether price is above the 50-day SMA
- whether dividend yield clears a minimum income threshold
- whether leverage looks elevated relative to the group
This does not make the model predictive. It makes it usable.
That distinction matters. Advisors and self-directed investors often need triage more than certainty. The score highlights where further analysis may be warranted. It is a time-saving device, not a substitute for judgment.
How to use the sample file versus the live template
The static sample file is designed as a learning tool. It includes pre-filled values, formula references, a visible data date, and MarketXLS branding throughout. That means a user can open it, understand the structure immediately, and see exactly which MarketXLS functions power the live version.
The live template is the real operating file. It keeps the market data cells formula-driven so the workbook can continue working after the blog post date passes. The user can swap tickers, refresh formulas, change weights, and adapt the watchlist without rebuilding the workbook.
The sample file lets you understand the workbook before installing anything. The live file needs MarketXLS installed to refresh.
Example formula block for one dashboard row
If you wanted to build a single bank row from scratch in Excel, this is the kind of block you would use:
=Sector("JPM")
=Industry("JPM")
=QM_Last("JPM")
=PERatio("JPM")
=EarningsPerShare("JPM")
=Revenue("JPM")
=ReturnOnEquity("JPM")
=TotalDebtToEquity("JPM")
=Beta("JPM")
=SimpleMovingAverage("JPM","50")
=FiftyTwoWeekHigh("JPM")
=FiftyTwoWeekLow("JPM")
=DividendYield("JPM")
=DividendPerShare("JPM")
Formula documentation: Sector, Industry, QM_Last, PERatio, EarningsPerShare, Revenue, ReturnOnEquity, TotalDebtToEquity, Beta, SimpleMovingAverage, FiftyTwoWeekHigh, FiftyTwoWeekLow, DividendYield, DividendPerShare
With that block in place, you can answer several questions quickly:
- Is the stock above or below medium-term trend support?
- Does the valuation look demanding or reasonable versus peers?
- Is profitability strong enough to justify the multiple?
- Does the dividend profile change the way it fits a portfolio?
- Is the name near the top of its yearly range or still recovering?
That is the real advantage of a structured spreadsheet. It compresses decision support into a format that is easy to review, update, and share internally.
Internal resources that fit this workflow
If this topic is relevant to your work, a few MarketXLS resources pair naturally with it:
- MarketXLS homepage for overall platform context
- Book a demo if you want a guided walkthrough
- Pricing for plan details
- Bank Earnings Tracker Excel: Q1 2026 for a broader earnings tracker angle
- Regional Bank Earnings Tracker Excel Yield Curve Watchlist for a smaller-bank comparison workflow
- Understanding Yield Curve for a more basic rates refresher
FAQ
What is net interest margin in a simple Excel workflow?
Net interest margin = (interest income - interest expense) / average interest-earning assets. A bank that earns 5.0% on its assets and pays 2.0% to fund them has a NIM of roughly 3.0%. In this Excel workflow, you enter each bank's reported NIM from its earnings release and compare it with price, P/E, ROE, revenue, and yield curve assumptions pulled by MarketXLS.
Why does the yield curve matter for bank stocks?
The yield curve can influence how investors think about lending spreads, deposit costs, and earnings quality. A steeper curve can be seen as supportive for some banks, while a flatter curve can push attention toward fee income and cost discipline.
What is the difference between the sample workbook and the live template?
The sample workbook is pre-filled and designed for learning. It includes visible formula references and a data date. The live template uses MarketXLS formulas for the core market data fields so users can refresh and customize the watchlist over time.
Which MarketXLS formulas are most useful for a bank earnings tracker?
The core set in this workbook includes QM_Last, PERatio, EarningsPerShare, Revenue, ReturnOnEquity, TotalDebtToEquity, Beta, SimpleMovingAverage, FiftyTwoWeekHigh, FiftyTwoWeekLow, DividendYield, DividendPerShare, Sector, and Industry.
Does this workbook give investment advice?
No. The workbook is built for educational analysis, comparison, and research workflow. It is designed to help users organize current market information, not to tell anyone what to buy or sell.
Can I swap in regional banks or ETFs instead of the default list?
Yes. The template is meant to be flexible. Users can replace the default tickers with regional banks, financial ETFs, or a custom watchlist as long as the formulas are supported by MarketXLS.
The bottom line
A net interest margin tracker in Excel puts rates, funding pressure, profitability, and valuation for large banks on one page during earnings season. A spreadsheet will not remove uncertainty, but it can make uncertainty easier to analyze.
That is what this workbook is for. It gives you a six-sheet process for comparing large U.S. banks, testing yield-curve scenarios, logging post-earnings observations, sizing positions, and keeping formula-driven analysis in one place.
If you want to build more structured financial workflows in Excel, visit MarketXLS. If you want help adapting a bank earnings workbook, sector dashboard, or advisor workflow to your own process, you can also book a demo.
