How Valuations Shape Returns and Risk in Select NSE Indices: A 6-Index, 6-Year Study of Return and Risk Across Valuation Regimes 21Apr2026
(This is my 505th blog since 2010. Over the years, I have covered global financial markets, with a focus on India, and continue to share insights to help readers understand complex topics in simple language.
The views expressed here are for information purposes only and should not be construed as a recommendation or investment advice. While the author is a CFA Charterholder with nearly 25 years of experience in financial markets, this content is intended to share general insights and does not constitute financial guidance.
Please consult your financial adviser before taking any investment decision. Safe to assume the author has a vested interest in stocks / investments discussed if any.)
PURPOSE OF THE STUDY
Objective:
To understand how valuation levels influence future returns and risk across selected Nifty indices.
Core question:
How do forward returns and volatility differ between low and high valuation regimes?
In the absence of downside risk (which would have a better metric for investors to understand risk intuitively) data, we used standard deviation (volatility) as a proxy for risk.
What is being tested?
Whether higher valuations are associated with lower future returns and higher risk, and the extent of this relationship.
Practical investing lens:
Investing decisions involve adjusting exposure, not binary choices. This includes increasing or reducing allocation, staggering investments, and being more cautious at high valuations and more aggressive at low valuations.
Behavioral question:
How should exposure change when valuations are high versus low?
Scope:
This is a historical, descriptive analysis of valuation, returns, and risk. It does not recommend investment actions.
Final framing:
How do return and risk outcomes vary across valuation regimes, and what does this imply for allocation behaviour?
DATA SET AND STRUCTURE
Source and indices:
Quarterly data from NSE India (NiftyIndices.com) for six select indices:
Nifty 50,
Nifty Midcap 150,
Nifty Smallcap 250,
Nifty 100 Low Volatility 30,
Nifty 200 Momentum 30, and
Nifty 200 Quality 30.
While momentum factor may be better captured using monthly data, this study uses quarterly data to reduce noise, ensure comparability across indices and align with longer-term valuation signals.
Index selection rationale:
The study covers six Nifty indices to represent both broad market segments and factor-based strategies. Nifty 50, Nifty Midcap 150 and Nifty Smallcap 250 capture the large-cap, mid-cap and small-cap segments of the Indian equity market.
In addition, three "smart beta" (factor investing) indices are included to reflect systematic investment styles beyond market capitalisation. Two of these are widely followed with significant assets tracking them, while the third is included due to its relatively higher return potential during the study period.
Period and constraints:
TRI-based (Total Returns Index) data is available from Dec 2019 to Mar 2026 (26 quarters).
This was further reduced to 21 quarters to maintain comparability, as NSE India changed the Nifty 50 PE calculation methodology in Mar 2021 from standalone earnings to consolidated earnings, creating a structural break in the data.
Total returns include price returns and dividends.
Final dataset:
21 quarters of consistent data for each Nifty index.
DATA VARIABLES
Returns:
1-year (absolute), 3-year CAGR, 5-year CAGR
Risk:
Standard deviation
Valuation:
PE, PB, Dividend Yield
For PE and PB, lower values generally indicate lower valuations and higher values indicate higher valuations; in contrast, dividend yield works inversely, where higher yields generally indicate lower valuations.
DATA FILTERING
Removed variables:
1-month and 3-month returns (too noisy)
Beta, correlation, R squared (not relevant to this study)
Final variables used:
Returns (1-year, 3-year, 5-year), standard deviation, PE, PB, dividend yield
DESCRIPTIVE FRAMEWORK
For each index, the following metrics are summarised:
PE, PB, dividend yield, standard deviation
Each is analysed using:
Minimum, 25th percentile, median, 75th percentile, maximum
INTERPRETATION APPROACH
Valuation regimes:
Low (bottom quartile), median, high (top quartile)
Purpose:
To assess how returns and risk shift across valuation bands.
CONSISTENCY CHECKS
Expected patterns:
Momentum index to show highest volatility
Low volatility index to show lowest volatility
Quality index to trade at higher valuations
Smallcap highest volatility, midcap next, Nifty 50 lowest
DATA SOURCES
Nifty Indices - NSE Index factsheet
NSE Index Dashboard (Nifty Indices) - PDF for Mar2025
END NOTE
The dataset enables comparison of return and risk across valuation regimes. The next step is to map valuation levels to forward outcomes and test whether higher valuations consistently lead to lower returns and higher risk.
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Now, let us dive into the actual study.
(Please note as this is a data-driven article, it will some time to complete the blog, so please bear with me till then).
1 INTRODUCTION
Future returns are shaped by the expectations embedded in valuations at the start of the investment period, and the path markets take thereafter also matters (path dependence).
This study examines how starting valuation levels have historically aligned with subsequent returns and risk across select Nifty indices.
By comparing outcomes across valuation regimes, it seeks to understand how both starting points and market paths influence realised outcomes.
2 Nifty 50 Index Analysis
Valuation profile:
Nifty 50 valuations are generally stable around a central range. The PE ratio mostly stays between 21.4 and 23.2, showing a fairly consistent valuation band over time.
There is one notable spike up to 33.2, in Mar2021, during the post-COVID period. This is largely due to temporarily weak earnings after the severe lockdown shock, combined with markets pricing in a strong recovery ahead.
As a result, this peak reflects a distorted earnings base rather than sustained overvaluation.
The PB ratio also remains stable, mostly between 3.5 and 4.2, indicating steady valuation behaviour from a book value perspective.
Dividend yield moves inversely, ranging from 0.96 per cent to 1.44 per cent, with higher yields typically seen when valuations are lower.
Risk profile:
Short-term risk varies meaningfully over time. The one-year standard deviation ranges from 9.8 per cent in calmer phases to 22.5 per cent in stressed markets, with a typical level around 13.6 per cent.
This shows that risk is not constant and tends to cluster in certain market environments rather than remaining evenly distributed over time.
Return profile:
One-year returns show very high dispersion depending on entry point. They range from -4.0 per cent in weak periods to 72.5 per cent in strong rallies, with a typical outcome around 16.1 per cent.
Three-year returns are more stable, and five-year returns are even more compressed. This shows that longer holding periods reduce the impact of entry timing and smooth out short-term volatility.
Key takeaway:
Nifty 50 demonstrates strong path dependence in short-term outcomes, where entry timing significantly affects returns. Over longer horizons, returns stabilise and dispersion reduces.
Valuations tend to move in cycles rather than trends, and apparent extremes such as the March 2021 PE spike are largely driven by temporary earnings distortions rather than structural changes in valuation levels.
Sidenote: While this study focuses on equity valuations, future returns are also influenced by the prevailing cost of capital, typically reflected in the 10-year sovereign bond yield (10-year G-Sec bond yield in India). Changes in bond yields affect discount rates and, in turn, shape how equity valuations are interpreted across different time periods.
Chart 1 showing summary data for Nifty 50 index with valuation ratios, risk and returns >
Nifty 50 Valuation Entry Point Analysis:
The entry point analysis shows how investment outcomes change depending on when you enter the market and the valuation level at that time.
It compares short, medium, and long-term realised returns from the same starting point to highlight the role of holding period.
Overall, it helps understand how valuations and market cycles together influence return and risk over time.
How to read the above entry point analysis table:
Each row shows a specific point in time when an investor could have entered the Nifty 50, along with the valuation level at that time and the market conditions.
The “market phase” and valuation metrics (PE, PB, dividend yield) describe how expensive or cheap the market looked at the point of entry.
The return columns show what actually happened after investing at that point—first over 1 year, then over 3 years and finally over 5 years—so you can see how outcomes change depending on how long the investment is held.
In simple terms, the table connects “what the market looked like when you entered” with “what you actually got over time.”
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(the article is not yet completed, it will take some time to finish this, please bear with me till then)
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References:
Tweet 03Jun2021 - Nifty 50 PE calculation method change wef 31Mar2021
Tweet 01May2024 - Don't compare Nifty PE ratios on or after 31Mar2021 with those in prior periods
Tweet 07Jul2024 - NSE press release on change in Nifty 50 PE calculation method
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