In 1992, Eugene Fama and Kenneth French published a paper that changed academic finance permanently. They showed that the market portfolio alone couldn’t explain stock returns, two other measurable characteristics did most of the work: company size and book-to-market ratio. The CAPM model that had dominated for decades was incomplete.
That paper opened a 30-year research programme into what are now called factors, measurable stock characteristics that predict returns systematically, across markets, across decades, and across countries. The most important ones have been replicated in Indian markets too.
A factor is a measurable characteristic of a stock that has been shown through extensive academic research to systematically predict returns above the market benchmark. Same signal, all stocks, applied consistently over many years.
Why factors beat stock picking: the Indian proof
30
Years of research
Fama French 1992 onwards
5
Major factors in India
Value, Momentum, Quality, Low Vol, Size
4.9
Pct long term premium
Nifty 200 Momentum vs Nifty 50
500
Stocks in universe
Nifty 500, RupeeCase default
NSE Indices
SEBI Smart Beta
AMFI Factor Funds
The Fama-French foundation
Fama & French (1992), Three-Factor Model
Expected Return = Market Risk + Size Premium + Value Premium
The original paper showing small-cap stocks and high book-to-market (cheap) stocks earned returns unexplained by market risk alone. The formal beginning of factor investing as an academic discipline.
Fama, E. & French, K. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance.
Fama & French (2015), Five-Factor Model
Expected Return = Market + Size + Value + Profitability + Investment
Two decades later: Profitability (companies that earn more consistently) and Investment (companies that invest conservatively) added as additional return predictors. Current academic standard.
Fama, E. & French, K. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics.
From market beta to 5 factors: the evolution of what matters
1964
CAPM
Market beta only
1992
3 factor
+ Size + Value
1993
Momentum
Jegadeesh + Titman
2015
5 factor
+ Profitability + Investment
2020s
Multi factor
Blended in Indian ETFs
The five major factors
Value
Cheap stocks outperform expensive ones over time
Signal: Low P/B · Low P/E · Low EV/EBITDA
Stocks trading at a low price relative to fundamentals, book value, earnings, or cash flow, have historically earned higher returns than expensive glamour stocks. Markets overprice hype and underprice boring, unloved businesses. Mean reversion does the rest.
In India: Strong value factor returns in Nifty 500, particularly post-corrections when cheap stocks are deeply discounted. PSU banks and commodity stocks periodically appear as deep value plays. NSE tracks this via the Nifty Value 20 Index.
Momentum
Recent winners keep winning over 3 to 12 months
Signal: 12M-1M trailing price return
Stocks that outperformed over the past 6 to 12 months (excluding the most recent month, which tends to reverse) continue to outperform over the next 3 to 12 months. Rationale: markets underreact to positive news, causing gradual price adjustment rather than immediate repricing.
In India: Momentum is among the strongest factors in NSE data. The Nifty Alpha 50 and Nifty Momentum 30 indices track this. RupeeCase’s strategies are built on cross-sectional momentum across Nifty 500, with strong statistical significance in NSE research papers.
Quality
Profitable, low-debt businesses outperform over cycles
Signal: High ROE · Low D/E · Stable earnings
Companies with high profitability, low financial leverage, and consistent earnings growth tend to compound wealth more reliably than low-quality businesses. The market only partially prices in the value of durable competitive advantages. Linked to Fama-French’s Profitability factor.
In India: Quality companies like BAJFINBajaj Finance, HDFCBKHDFC Bank, and ASIANPTAsian Paints have consistently rewarded long-term holders. NSE’s Nifty Quality 30 Index tracks this systematically. Powerful in India because corporate governance varies widely across Nifty 500.
Low Volatility
Less volatile stocks deliver better risk-adjusted returns
Signal: Low 1-year daily return standard deviation
The Low Volatility anomaly is counterintuitive: theory says higher risk = higher return. In practice, lower-volatility stocks have delivered comparable or higher returns with less risk. Rationale: investors overpay for “lottery ticket” volatile stocks and underpay for stable, boring ones.
In India: FMCG, pharma, and consumer staples like NESTLNestle India often rank high on Low Volatility. NSE tracks this via the Nifty Low Vol 50 Index. Works well as a complement to Momentum, combining them reduces drawdown significantly.
Size
Small-caps earn higher returns than large-caps over time
Signal: Low market capitalisation
Smaller companies have historically earned higher returns than larger ones, the original Fama-French finding. Rationale: small caps are less covered by analysts, more illiquid, and often mispriced. Investors demand a premium for the extra risk. The size premium is real but lumpy and can disappear for years.
In India: Significant premium in Nifty Midcap and Smallcap segments. But liquidity constraints matter, many small-cap stocks have insufficient daily volume to deploy meaningful capital without moving the price against you. RupeeCase’s Nifty 500 universe filters for minimum liquidity to address this.
Indian factor premium: excess return over Nifty 500 TRI, 2015 to 2025
Annualised excess return vs Nifty 500 TRI, 10 years to 31 Mar 2025. Source: NSE Indices. No factor wins every year. Combining Momentum + Low Vol historically beats every single factor on Sharpe.
Why the 5 factors win: the 3 drivers of persistence
Behavioural (investor bias)45%
Structural (benchmarking, mandates)35%
Risk compensation (real risk)20%
Approximate split of academic explanations. The behavioural plus structural share is what keeps the premium alive in India. Indian mutual fund managers cannot hold a 30 pct Value sleeve for 3 underperformance years without losing AUM.
Why factors persist
If factors generate excess returns, why doesn’t everyone exploit them until they disappear? Three complementary answers:
1
Risk-based explanations
Some factors may represent compensation for real economic risk. Value stocks are often companies in financial stress, they’re cheap because they’re risky. You earn higher returns because you bear real risk of permanent capital loss.
2
Behavioural explanations
Some factors persist because of systematic human bias. Momentum exists partly because investors underreact to news, prices adjust slowly. Low Volatility persists because investors overpay for exciting, volatile stocks and underpay for boring stable ones. As long as these biases exist, the premia should persist.
3
Structural/institutional explanations
Fund managers benchmark against large-cap indices, they’re evaluated quarterly and can’t hold Value positions that underperform for 2 to 3 years even when the thesis is correct. These structural constraints create persistent pricing inefficiencies that systematic investors exploit.
The honest caveat: Factor premia are real but not smooth. Value underperformed for a decade before the 2022 rotation. Momentum can crash 30 to 40% in sharp reversals. Size premia disappear in large-cap bull markets. Factors require patience measured in years, not months, which is why combining multiple factors smooths the ride.
~18%
Approximate Momentum factor annual premium above Nifty 500 (10Y NSE data, directional estimate)
~12%
Approximate Quality factor annual premium above Nifty 500 (10Y NSE data, directional estimate)
~8%
Approximate Value factor annual premium above Nifty 500 (10Y NSE data, directional estimate)
◆ How RupeeCase uses factors
Every strategy on RupeeCase is built on one or more of these five factors. The NSE Momentum strategy uses cross-sectional momentum across Nifty 500. The factor screener lets you rank all 500 stocks on any combination of signals. This is the engine under the hood.
Factors live in the terminal
Rank all 500 Nifty stocks by Value, Momentum, Quality and more
Factor scores updated nightly. Real NSE data. No spreadsheets.
TK | The day factors clicked for me
For my first 4 years as a trader I was a pure discretionary stock picker. Terminal in one hand, broker chat in the other, convinced the next Bajaj Finance was one earnings call away. I outperformed Nifty 50 in some years, underperformed in others, mostly noise. In 2018 I sat down with a proper multi factor backtest on Nifty 500, Value + Momentum + Quality, monthly rebalance. The equity curve was not sexy. It did not catch every multibagger. What it did was beat the index on a Sharpe basis in 8 out of 10 calendar years without me doing anything discretionary. That was the moment I stopped arguing with charts and started arguing with signals. Factors are not magic. They are what remains when you take out the ego. Every strategy on RupeeCase is a bet on that same lesson.
RC
Put learning into practice. Every concept in Path 2 maps directly to a tool in the RupeeCase terminal.
Glossary, Module 2.2
Factor
A measurable stock characteristic that systematically predicts returns above the market benchmark across time and markets.
Value factor
Stocks cheap relative to fundamentals (low P/B, P/E, EV/EBITDA) tend to outperform expensive stocks over time.
Momentum factor
Stocks that outperformed over the past 12 months (minus last 1 month) tend to continue outperforming over the next 3 to 12 months.
Quality factor
High profitability (ROE), low leverage, and stable earnings predict outperformance over full market cycles.
Low Volatility factor
Stocks with lower price volatility deliver better risk-adjusted returns than high-volatility stocks, opposite of what standard theory predicts.
Size factor
Smaller-cap stocks earn higher returns than large-caps historically, compensating for illiquidity and lower analyst coverage.
Factor premium
Excess return earned by a factor strategy above the market benchmark. E.g. if Nifty returned 14% and your momentum strategy returned 22%, the premium was approximately 8%.
Sources & further reading
- → Fama-French Data Library, Factor returns, research, methodology
- → NSE Research, Factor Returns in Indian Equity Markets (Working Paper)
- → NSE Indices, Strategy & Factor Index Family
- → Fama, E. & French, K. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance.
- → Fama, E. & French, K. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics.
- → Jegadeesh, N. & Titman, S. (1993). Returns to Buying Winners and Selling Losers. Journal of Finance.
All content on this page is original work by Tanmay Kurtkoti and QC Alpha Technologies Pvt Ltd. Protected under Indian copyright law. tanmay@rupeecase.com
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Factor Score Z-Normaliser
Multi-factor portfolios need normalised scores so no single factor dominates. The z-score puts every factor on the same scale.
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