With investors weighing the resilience of small caps against the valuation comfort offered by large caps, value investing within the Nifty 50 is gaining renewed attention. The index’s revised methodology seeks to identify relatively inexpensive companies among India’s largest and most liquid businesses, using earnings, sales, book value and dividend yield as key valuation signals.
In an interview, Chintan Haria, Principal – Investment Strategy at ICICI Prudential AMC, explains the investment case for the strategy, the risk of value traps, the impact of sector biases and the trade-offs involved in a valuation-led approach. He also discusses why the strategy may be better suited as a satellite allocation for investors with a five-year or longer horizon. Edited excerpts from a chat:
Investors appear divided between retaining conviction in small caps and seeking valuation comfort in large caps. What is the investment case for a value-oriented approach within the Nifty 50 at this stage?
The case rests on staying within a universe where the scale, liquidity and established business franchises are generally stronger than in the broader market, while still recognising that large-cap companies can have business and valuation risks. The Nifty 50 holds India's largest and most liquid companies, so a value screen applied here is not necessarily hunting for troubled businesses; it is choosing between established businesses on price and valuation. After a stretch in which index returns have been flat, the dispersion within the Nifty 50 has widened, some names are expensive and some are not. A value approach lets an investor tilt towards the cheaper part of that basket without stepping down the quality or liquidity ladder.
A market-cap-weighted index increases a company's weight as its valuation rises, but rising market capitalisation can also reflect stronger earnings and business quality. When does a valuation-aware strategy improve outcomes, and when might it prematurely reduce exposure to genuine compounders?
Valuation-aware weighting tends to help when leadership is narrow and a few stocks have run far ahead of their fundamentals, because it reduces the portfolio's exposure to stocks whose valuations have become stretched. It can also help during market rotations or the early stages of a recovery, when relatively inexpensive stocks can re-rate. However, that is not a reliable timing signal: cheap stocks can remain cheap, while expensive companies can continue to compound earnings and justify apparently high multiples.
Value style tends to be out of favour in trending, growth driven markets. The honest framing is that value weighting is a discipline against overpaying, not a forecast, and it will systematically underweight companies that appear expensive on valuation grounds. That creates a genuine opportunity cost. A valuation-aware index can lag a conventional market-cap-weighted index for extended periods if the market continues to reward high-quality compounders despite their premium valuations.
What shortcomings in the earlier methodology prompted the June revision? Was the change driven by historical performance, evolving market structure or a need for broader valuation signals? The NSE's June 2026 notification specifies the changes, but does not explicitly attribute them to particular performance or market-structure considerations. It is therefore better to describe the design changes rather than present a specific rationale as NSE's stated reason.
The first is standardised multi-factor scoring. Fragmented individual metrics have been replaced with an equal-weight composite score built on fundamental ratios, namely earnings, sales, book and dividend yields a change in the valuation signals and their weights. The earlier methodology used ROCE, P/E, P/B and dividend yield with weights of 40%, 30%, 20% and 10%, respectively. The revised methodology replaces these with four valuation measures; Earnings/Price, Sales/Price, Book Value/Price and Dividend Yield at 25% each.
The second is greater consistency with the valuation framework used in other NSE value indices, including Nifty200 Value 30, which uses E/P, B/P, S/P and dividend yield and a value-score-based tilt.
The third is the introduction of tilt weighting, moving away from rigid free-float weighting alone and blending market capitalisation with individual value scores, so the index captures the actual value premium more directly without introducing extreme concentration risk a tilt-weighting mechanism, under which free-float market capitalisation is multiplied by the stock's value score.
The revision also raises the number of compulsory inclusions based on value score from five to ten and changes the review/rebalancing cycle to semi-annual, in June and December. The revised methodology assigns equal 25% weights to Earnings/Price, Sales/Price, Book Value/Price and Dividend Yield. Why these four, and what is the rationale for weighting them equally?
The four cover different parts of a company's financial profile: earnings, revenue, balance sheet and cash returned to shareholders. Each is imperfect on its own. Earnings can be distorted by one-off items, book value means little for some asset-light firms, sales ignore profitability, and dividend yield can be high simply because a stock has fallen. Using all four together provides a broader valuation assessment than relying on one ratio alone.
Equal weighting reflects the fact that there is no robust basis for claiming one valuation signal is reliably better than another. It is important, however, to present this as a design choice rather than as proof that all four signals are equally predictive across every sector or market cycle. With ROCE no longer central to selection, is there a risk the index favours statistically cheap companies with weak fundamentals? How does the methodology protect against value traps?
The revised index is fundamentally a value index, not a value-plus-quality index. Its starting universe is the Nifty 50, which provides scale and liquidity filter, but it does not eliminate value traps. A large, established company can still suffer from deteriorating earnings, poor capital allocation or a structural change in its industry.
The revised methodology therefore does not provide a direct quality safeguard comparable to a ROCE or profitability screen. Instead, it diversifies the value assessment across E/P, S/P, B/P and dividend yield, uses the Nifty 50 as the eligible universe, and applies a value-score-based weighting rather than simply equal-weighting the cheapest stocks. Investors should therefore view the strategy as a pure valuation tilt, with the trade-off that some exposure to fundamentally challenged but statistically cheap companies is possible.
How does the value-score multiplied by free-float-market-cap weighting work in practice? To what extent does valuation influence stock weights compared with size?
Each stock's weight is based on its free-float market capitalisation multiplied by its value score, so size and relative cheapness both matter. The value score is derived from four valuation measures—earnings-to-price, sales-to-price, book-value-to-price and dividend yield. Each measure is standardised relative to the Nifty 50, and the four scores are combined to arrive at the stock's overall value score.
A company that looks cheaper than the index average will generally have a value score above one, which lifts its weight, while a relatively expensive company will tend to have a score below one, pulling its weight down. In practice, size remains an important driver. A very large company will carry a significant weight even at a modest value score, but value score can materially tilt the final weight. The 15% single-stock cap, applied semi-annually, then limits how far any one name can dominate.
Valuation metrics have different relevance across sectors, such as book value for financials versus sales for asset-light businesses. How does the index ensure comparability and avoid sector bias? Because every ratio is standardised using value scores across the eligible universe, the four measures can be combined on a common statistical scale. This improves comparability across metrics, but it does not make the underlying economics of those metrics equally relevant across sectors.
What it does not do is neutralise sectors. The scoring is done across the whole Nifty 50, not within sectors, so there is no explicit sector cap or sector-relative scoring specified in the revision. That means a structurally cheap sector, financials being one example because of the relevance of book value in analysing financial companies, can become overweight. Using four parameters rather than one softens this, but investors should expect some sector tilt.
Could periodic rebalancing lead to higher turnover, transaction costs or repeatedly selling strong performers? How should investors evaluate these implementation risks?
Some turnover is inherent, since the strategy rebalances towards stocks whose relative value characteristics have become more attractive and away from those whose value characteristics have deteriorated. The revision actually increases the frequency of reconstitution from annual to semi-annual, in June and December, which can raise the potential for churn relative to the old design.
However, more frequent rebalancing does not necessarily mean realised turnover will be higher in every period; that will depend on how much constituent rankings and value scores change between reviews. Should investors view this as a replacement for a conventional large-cap allocation or as a complementary, satellite exposure? What horizon and risk profile is appropriate?
It is better understood as a complement than a replacement. With 20 stocks drawn from the Nifty 50 and a valuation tilt, it is more concentrated than the parent index and is likely to deviate from it from time to time. It may be considered as a satellite holding alongside a broad large-cap core rather than the core itself. It suits investors who understand how a value tilt may perform in different market cycles. A horizon of five years or longer is more appropriate for evaluating the strategy, because factor cycles can persist and value can underperform for extended periods.
