Valuation differences across global equity markets often create confusion rather than clarity. The same type of company can trade at sharply different multiples depending on where it is listed, even when its revenue model, margins, and growth profile appear similar. Investors frequently interpret these gaps as mispricing, without accounting for the structural forces that shape how markets value risk, growth, and capital.

Directly comparing valuation multiples across countries without context leads to fragile conclusions. A lower P/E ratio does not automatically imply undervaluation, just as a higher multiple does not guarantee excess optimism. Valuation reflects more than company fundamentals. It absorbs interest rates, capital availability, accounting standards, sector concentration, and investor behavior unique to each market.

This article breaks down why valuation metrics diverge across global markets and how to interpret those differences correctly. Instead of relying on abstract theory or surface-level comparisons, we will ground the discussion in real financial data using APIs from Financial Modeling Prep, which provide standardized company fundamentals and valuation metrics across global markets.

The goal is not to rank markets as cheap or expensive, but to build a disciplined framework for understanding what valuation differences truly represent.

FMP APIs Used In This Article

This article relies on a small set of Financial Modeling Prep APIs to analyze and compare valuation metrics across global equity markets. These APIs provide the fundamental company attributes and valuation data required to ensure comparisons remain consistent across regions, sectors, and market structures.

• Company Profile Data API: Used to identify company geography, exchange, sector classification, and reporting currency.
• Key Metrics API: Used to extract valuation metrics such as price-to-earnings, EV/EBITDA, and price-to-book ratios.
• Financial Ratios API: Used to support valuation analysis with profitability and efficiency ratios that influence cross-market valuation differences.
• Market Capitalization API: Used to normalize company size and avoid scale-driven distortions when comparing valuations across markets.

These APIs form the data foundation for the analysis presented later in the article.

Why Valuation Multiples Break Across Borders

Valuation multiples are often treated as universal yardsticks, but they are shaped by the economic and institutional environment in which companies operate. A price-to-earnings or EV/EBITDA ratio does not exist in isolation. It reflects local capital costs, risk assumptions, reporting practices, and market behavior. When these underlying factors differ, the same multiple conveys a very different meaning across countries.

If you want a clean refresher on how multiples behave even before cross-border effects enter the picture, FMP breaks it down here: Utilizing Valuation Multiples: Quick Company Valuations Using Ratios.

Direct cross-border comparisons tend to ignore these structural inputs. A lower multiple in one market may reflect higher macro risk, weaker investor protection, or limited capital access rather than fundamental undervaluation. Similarly, higher multiples in developed markets often persist because capital is abundant, discount rates are lower, and long-term earnings visibility is stronger.

To understand why valuation multiples break down across borders, it is necessary to separate company-level fundamentals from market-level structure. The sections below break this down into specific drivers that consistently influence how different markets price risk and growth.

Capital Markets, Liquidity, And Investor Composition

Valuation differences across countries are not driven by company fundamentals alone. The structure of the local capital market and the type of investors participating in it play a decisive role in how risk and growth are priced. These forces operate at the market level and create persistent valuation gaps across regions.

Capital Market Depth And Access To Capital

Markets with deep and mature capital systems allow companies to raise equity and debt at lower costs. Abundant capital reduces financing risk and supports higher valuation multiples, particularly for businesses with long growth runways. In contrast, markets with limited capital access impose a higher cost of capital, which compresses valuations even for well-run companies.

Liquidity And Price Discovery

Liquidity affects how confidently investors can price future earnings. In highly liquid markets, large positions can be built or unwound without materially moving prices. This lowers transaction risk and supports higher valuations. Markets with thin liquidity experience wider bid-ask spreads and sharper price movements, leading investors to demand valuation discounts.

Investor Composition And Investment Horizon

The mix of investors also shapes valuation behavior. Institutional-dominated markets tend to price assets with longer time horizons and lower volatility expectations. Retail-heavy markets, where capital is more reactive and short-term, often exhibit higher volatility and lower average valuation multiples as a result.

These structural elements operate continuously in the background, making direct valuation comparisons across markets unreliable unless these factors are explicitly accounted for.

Interest Rates, Inflation, And Currency Risk

Valuation multiples embed assumptions about future cash flows and the rate at which those cash flows are discounted. Interest rates and inflation directly influence this discounting process, which makes cross-market valuation comparisons structurally uneven.

Interest Rates And Cost Of Capital

Markets operating under lower interest rate regimes tend to support higher valuation multiples. A lower risk-free rate reduces the discount rate applied to future earnings, mechanically increasing present values. Markets with higher policy rates impose a higher cost of capital, which compresses valuation multiples even when growth expectations are similar.

Inflation Expectations And Earnings Quality

Inflation affects both reported earnings and investor confidence in those earnings. Stable inflation environments allow future cash flows to be forecast with greater confidence, supporting higher valuations. Markets with volatile or persistently high inflation face greater uncertainty around real earnings, which translates into valuation discounts.

Currency Risk And Valuation Adjustments

Currency volatility introduces an additional layer of risk for investors, particularly in markets where earnings are reported in weaker or less stable currencies. Even when local valuations appear low, currency depreciation risk can offset perceived upside. As a result, markets with higher foreign exchange volatility often trade at structurally lower valuation multiples.

These factors operate independently of company execution, reinforcing why valuation differences across markets cannot be interpreted without accounting for macro and currency conditions.

Accounting Standards And Earnings Comparability

Valuation analysis depends on the quality and consistency of reported financial data. When accounting standards differ across markets, valuation multiples built on those numbers stop being directly comparable. This effect is structural and persists even when companies operate in similar industries.

Differences In Accounting Frameworks

Companies across global markets report under different accounting regimes, most commonly US GAAP, IFRS, or local standards. These frameworks differ in how they recognize revenue, expenses, depreciation, and impairments. As a result, reported earnings can vary materially for the same underlying business activity.

Impact On Earnings And Balance Sheet Metrics

Variations in accounting treatment affect both income statement and balance sheet figures. Differences in capitalization rules, asset revaluation, and impairment recognition directly influence earnings, book value, and enterprise value. Valuation ratios built on these metrics reflect accounting structure as much as business performance.

If you're building a repeatable workflow, it helps to pair valuation multiples with profitability and efficiency ratios—this walkthrough shows a practical ratio-based analysis flow using FMP's API.

Limits Of Mechanical Normalization

While analysts often attempt to adjust or normalize reported numbers, such adjustments are rarely precise at scale. Cross-market datasets typically rely on reported figures, not fully harmonized financials. This limitation reinforces why valuation comparisons across countries require caution and contextual interpretation rather than mechanical ranking.

Accounting differences do not imply lower data quality, but they do impose limits on how far valuation multiples can be compared without introducing analytical noise.

Sector And Index Composition Effects

Aggregate valuation levels across markets are heavily influenced by the sectors that dominate local indices. When a market's index is concentrated in specific industries, index-level valuation metrics reflect sector characteristics rather than broad market mispricing.

Sector Concentration And Valuation Bias

Markets with high exposure to technology, healthcare, or consumer platforms tend to trade at higher valuation multiples due to stronger growth expectations and scalable business models. In contrast, markets dominated by financials, energy, or commodities often trade at lower multiples because earnings are more cyclical and capital-intensive.

Index Structure Versus Company-Level Valuation

Index-level comparisons frequently mask meaningful variation at the company level. A market may appear inexpensive on an index basis while individual companies trade in line with global peers once sector exposure is controlled. Without isolating sector effects, valuation comparisons can misattribute structural composition to valuation opportunity.

Implications For Cross-Market Analysis

Effective cross-market valuation requires comparing companies within the same sector and size bracket rather than relying on headline index multiples. Sector-adjusted comparisons reduce noise and help distinguish genuine valuation divergence from index construction effects.

Sector composition acts as a persistent valuation filter, reinforcing why global valuation analysis must operate at the company or sector level rather than at the index level.

Using FMP Data To Compare Valuations Across Markets (with Python)

This section uses FMP data to compare valuation metrics for comparable companies operating across different global markets. The objective is to extract the same valuation fields, control for scale, and observe how valuation ranges differ once structural factors are held constant.

Access to consistent global valuation data requires stable coverage across exchanges and regions. Financial Modeling Prep offers tiered API plans that support multi-market valuation analysis, including global profiles, key metrics, and market capitalization data. Pricing details and plan limits are available here.

To run the codes in this section, you will need an API key from Financial Modeling Prep. You can create a free account and generate an API key by signing up on the Financial Modeling Prep website. Once registered, the API key is available in your account dashboard and can be used to authenticate all API requests shown below.

Step 1: Define The Analysis Scope

We start with a small, controlled list of globally relevant companies. In practice, this universe can be expanded or filtered by sector and market capitalization.

import os import requests import pandas as pd BASE_URL = "https://financialmodelingprep.com/stable" API_KEY = os.getenv("FMP_API_KEY") def fetch(endpoint, params): params["apikey"] = API_KEY r = requests.get(f"{BASE_URL}{endpoint}", params=params, timeout=20) r.raise_for_status() return r.json() symbols = [ "AAPL", # US "SAP.DE", # Germany "NESN.SW", # Switzerland "SONY", # Japan (ADR) "TSM", # Taiwan (ADR) ]

What This Code Does:

This code initializes the environment for interacting with Financial Modeling Prep APIs. The API key is loaded securely from an environment variable to avoid hardcoding credentials. A reusable fetch() helper standardizes API calls by handling authentication, timeouts, and error checking. A small set of globally relevant symbols is defined to demonstrate cross-market valuation comparison.

Step 2: Pull Valuation And Classification Data

For each company, we extract geography, valuation metrics, and size. These fields are required to ensure comparability across markets.

records = [] for symbol in symbols: profile = fetch("/profile", {"symbol": symbol})[0] metrics = fetch("/key-metrics-ttm", {"symbol": symbol})[0] market_cap = fetch("/market-capitalization", {"symbol": symbol})[0] records.append({ "symbol": symbol, "country": profile.get("country"), "currency": profile.get("currency"), "marketCap": market_cap.get("marketCap"), "peRatioTTM": metrics.get("peRatioTTM"), "evEbitdaTTM": metrics.get("enterpriseValueOverEBITDATTM"), "pbRatioTTM": metrics.get("pbRatioTTM"), }) df = pd.DataFrame(records)

What This Code Does:

This block builds a unified dataset by combining company metadata, valuation multiples, and market capitalization for each symbol. Pulling these fields together ensures that valuation comparisons remain consistent across markets. The resulting DataFrame serves as the foundation for all subsequent normalization and analysis steps.

Step 3: Normalize And Inspect Valuation Differences

Raw valuation numbers are converted to numeric form and reviewed by market to avoid misleading comparisons.

cols = ["marketCap", "peRatioTTM", "evEbitdaTTM", "pbRatioTTM"] df[cols] = df[cols].apply(pd.to_numeric, errors="coerce") df

What This Code Does:

Valuation metrics may be returned as strings or contain missing values. This step converts all relevant columns into numeric types and safely handles invalid entries by coercing them to NaN. Normalization ensures that aggregation and statistical operations behave correctly.

Step 4: Compare Valuations At The Market Level

Median values are used to reduce distortion from outliers.

summary = ( df.groupby("country", as_index=False) .agg( companies=("symbol", "count"), median_pe=("peRatioTTM", "median"), median_ev_ebitda=("evEbitdaTTM", "median"), median_pb=("pbRatioTTM", "median"), median_market_cap=("marketCap", "median"), ) ) summary

What This Code Does:

This block summarizes valuation behavior by country using median values rather than averages. Medians reduce the influence of extreme outliers that are common in cross-market datasets. The result highlights typical valuation levels within each market while providing context on sample size and scale.

At this stage, valuation differences across markets become visible, but they should be interpreted alongside interest rates, accounting standards, and sector composition discussed earlier. The data shows how valuations differ, not why they differ.

Interpreting Valuation Gaps Without Overfitting

Once valuation data is normalized across markets, the next step is to distinguish structural differences from potential mispricing. This can be done directly on the dataset built earlier without introducing additional assumptions.

Identify Structural Valuation Ranges

Instead of relying on single valuation points, we analyze valuation dispersion within each market using distribution statistics.

valuation_cols = ["peRatioTTM", "evEbitdaTTM", "pbRatioTTM"] ranges = ( df.dropna(subset=["country"]) .groupby("country")[valuation_cols] .quantile([0.25, 0.5, 0.75]) .unstack() ) ranges

What This Code Does:

Instead of relying on a single valuation point, this block computes interquartile ranges for each market. The 25th, 50th, and 75th percentiles reveal how valuations are distributed within a country. These ranges help distinguish persistent structural valuation bands from noise caused by a few extreme observations.

Interquartile ranges help identify where most companies in a market are valued, reducing the influence of outliers. Markets consistently trading within lower ranges often reflect structural discounts rather than temporary mispricing.

Flag Outliers Without Forcing Signals

To avoid overfitting, valuation deviations are evaluated relative to market medians instead of absolute thresholds.

df_with_medians = df.merge( df.groupby("country")[valuation_cols].median().reset_index(), on="country", suffixes=("", "_median") ) df_with_medians["pe_vs_market"] = ( df_with_medians["peRatioTTM"] / df_with_medians["peRatioTTM_median"] ) df_with_medians[["symbol", "country", "peRatioTTM", "peRatioTTM_median", "pe_vs_market"]]

What This Code Does:

This block evaluates each company relative to its own market baseline by attaching country-level median multiples back to the company data. The pe_vs_market ratio measures how a company is priced compared to its domestic peers rather than globally. This approach avoids misleading cross-market rankings and keeps interpretation aligned with local market structure.

Companies trading far below their market median may appear attractive, but this signal alone is insufficient. Without improvements in fundamentals or changes in macro conditions, such deviations often persist.

Avoid Cross-Market Ranking

Ranking markets purely by valuation metrics introduces noise. A lower median multiple does not imply higher expected returns unless accompanied by improving capital conditions or earnings quality. The analysis should focus on changes in valuation structure, not static comparisons.

This is also why it helps to separate relative valuation from intrinsic valuation when you interpret cross-market gaps—FMP's comparison is a solid reference.

This approach keeps interpretation disciplined and data-driven, preventing valuation analysis from drifting into narrative-driven conclusions.

Final Words

This article demonstrated how valuation differences across global markets can be analyzed using structured financial data instead of surface-level comparisons. By controlling for geography, scale, and valuation metrics with FMP APIs, cross-market valuation gaps become measurable and interpretable rather than speculative.

The Python workflows shown earlier highlight a repeatable way to compare valuation ranges, identify structural discounts, and avoid overfitting conclusions. Valuation gaps should be evaluated as distributions within markets, not as isolated signals or rankings.

When used this way, valuation analysis shifts from opinion-driven narratives to data-driven decision support. The focus stays on understanding how markets price risk and growth, not on forcing short-term investment calls.