I've been researching what is driving bank stock valuations. It has been a subject that has lived rent free in my head for years. What I would like to drive valuation: long-term EPS growth, financial performance and financial condition. There is correlating factors to these drivers. But I also can't ignore how much tangible book value per share (TBVPS) growth is also a key driver.
You can see this play out with banks that have poor earnings. They trade at relatively high P/EPS multiples versus very profitable banks. It is common to see a very profitable bank trade at less than 10x P/ EPS while a terrible earner trading at 15x-20x. The poor earner might be trading at book, while the good earner at 1.25x book. Telling you all that you need to know about the peculiarities of bank stock valuation.
To do a deeper dive, I ran a lot of data. I downloaded the following data points for years 2021 - 2026Q1:
TBVPS Growth, EPS Growth, Leverage Ratio, NPAs/Assets, ROAA, ROAE, Net Interest Margin, Cost of Funds, Yield on Earning Assets, Efficiency Ratio, P/EPS, P/TBVPS
I asked Copilot to find correlations between the financial performance and financial condition factors and compare it to investment banks and consulting firms' observations on bank stock valuations. Here are the most salient findings with accompanying tables and charts.
Interesting that Copilot made no correlation to Cost of Funds, even though those publicly traded bank with the lowest cost of funds traded at the highest P/TBVPS, and second highest P/EPS (see table below). Low COF banks trading at higher multiples than higher Yield on Earning Assets banks has been consistent over the 20 years I have made the comparison.
Here is what Copilot had to say after consuming all of the data and referencing industry experts.
✅ A. ROE / ROTCE ↔ P/TBV (strongest positive correlation)
Conclusion:
👉 This is the #1 driver of bank valuation multiples
- Banks with higher ROE consistently show higher TBV growth and would command higher P/TBV
Supporting evidence:
- Price-to-book rises with expected ROE and capital strength [bis.org]
- Bank analysts explicitly link P/TBV to ROE vs cost of equity [breakingin...street.com]
The dataset implication:
- ROAE + EPS growth columns will show highest explanatory power for valuation dispersion
✅ B. ROA ↔ Multiples (very strong, often underappreciated)
Conclusion:
👉 ROA drives quality of earnings, and therefore valuation durability
- Clean ROA → high NIM, low NPAs, strong efficiency
- High ROA banks don’t rely on leverage → more sustainable
Observed pattern in your data:
- Banks with:
- ROAA > ~1.2%
- Low NPAs
- Efficiency <55%
→ also show:
- Strong EPS growth
- Strong TBVPS compounding
Industry confirmation:
- ROA reflects true operating performance and asset quality [linkedin.com]
- Markets reward “clean ROE built on ROA,” not leverage-driven returns [linkedin.com]
✅ C. EPS Growth ↔ P/E (very strong)
Conclusion:
👉 Growth is the primary driver of P/E dispersion
From your dataset:
- EPS growth volatility is very high across banks
- High-growth names (especially >15–20%) stand out sharply
Relationship:
- Higher forward EPS growth → higher justified P/E
Analyst view:
- Bank valuation embeds expected earnings growth + cost of equity [breakingin...street.com]
✅ D. TBVPS Growth ↔ P/TBV (structural correlation)
Conclusion: 👉 TBV growth is the long-term anchor of returns
In the dataset:
- TBVPS growth aligns tightly with:
- ROE
- retained earnings
- efficiency
Banks with steady TBV compounding:
- Show stable ROE
- Have better efficiency ratios
✅ E. Asset quality (NPAs) ↔ Multiples (negative correlation)
Conclusion: 👉 One of the strongest negative drivers
From the data:
- NPAs vary dramatically
- Higher NPAs → lower ROA → lower ROE → compressed multiples
Industry evidence:
- Credit quality directly affects risk and valuation via expected losses [intrinsic-...vestor.com]
✅ F. Efficiency ratio ↔ Valuation (negative correlation)
Conclusion: 👉 Efficiency drives operating leverage and ROA
From the data:
- Banks with efficiency <50–55% consistently show:
- higher ROA
- better EPS growth
Interpretation:
- Markets reward cost discipline + operating leverage
✅ G. NIM ↔ ROA ↔ Multiples (moderate–strong chain)
Conclusion: 👉 NIM is an indirect but important driver
From your dataset:
- Higher NIM → higher ROA → higher ROE → higher valuation
Industry context:
- NIM compression in 2025 is already affecting profitability expectations [mckinsey.com]
✅ H. Leverage ↔ Multiples (non-linear / weak-to-negative)
- High leverage can boost ROE
- But:
- increases risk
- lowers valuation quality
The dataset:
- Shows mixed relationship
- Supports known reality: markets discount “leveraged ROE”
3) Synthesis: What actually explains bank multiples (ranked)
Tier 1 (dominant drivers)
- ROE / ROTCE
- EPS growth
- TBVPS growth
Tier 2 (core operating drivers)
- ROA (asset-level profitability)
- Efficiency ratio
- NIM
Tier 3 (risk modifiers)
- Credit quality (NPAs)
- Capital / leverage
4) How this aligns with recent investment-bank / industry commentary
🔹 1. Profitability (ROE) is still the anchor
- Valuation gaps across banks persist largely due to ROE dispersion
- Investors reward sustainable ROE above cost of equity [bis.org]
🔹 2. Growth skepticism is suppressing multiples
- Despite strong 2024–2025 earnings:
- bank P/B and P/E remain structurally low
- Investors are questioning long-term growth durability [mckinsey.com]
🔹 3. Markets increasingly distinguish “quality of earnings”
- Banks are not valued just on ROE:
- but how ROE is generated
- Clean ROA + low credit cost outperform leverage-driven models [linkedin.com]
🔹 4. Strategy + credibility now matter more
- Bain: valuation dispersion driven by:
- execution consistency
- growth narrative
- capital allocation credibility [bain.com]
🔹 5. Structural valuation framework
Investment banks (FIG coverage) consistently anchor:
- P/TBV ↔ ROE / ROTCE
- P/E ↔ EPS growth + cyclicality
- Adjusted for:
- credit risk
- capital strength [acetheround.com]
5) Bottom line (what the dataset is telling us)
🔴 The single most important takeaway:
👉 ROE (and ROTCE) is the strongest direct driver of bank trading multiples
🔵 But the true causal chain in your data is:
NIM + Efficiency + Credit quality → ROA → ROE → TBV growth → Multiples
🟢 And the market overlay is:
- Growth (EPS, TBV) determines P/E vs P/TBV premium
- Risk (NPAs, leverage) determines discount vs premium
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