--- title: "The Momentum Factor | RupeeCase Learn" description: "How the momentum factor works, the evidence for it in Indian markets, how to measure it correctly, and its known failure modes. By Tanmay Kurtkoti, RupeeCase." source_url: "https://www.rupeecase.com/learn/path-3/module-3-1-momentum-factor-deep-dive" --- Skip to main content [Learn](/learn)› [Path 3: Factor Investing Deep Dive](/learn/path-3)› Module 3.1 # The Momentum Factor Why recent winners keep winning, how to measure it correctly, what the evidence looks like in Indian markets, and where it completely breaks down. TK Tanmay Kurtkoti Founder & CEO, RupeeCase ⏱ 16 min read ⟳ Updated 14 Jun 2026 ◆ Intermediate Momentum is the most counterintuitive factor in systematic investing. It says: buy stocks that have gone up a lot recently, because they will keep going up. Sell stocks that have gone down a lot, because they will keep going down. Everything about this feels wrong to value-trained investors. You're buying high and expecting to sell higher. You're chasing performance. You're doing exactly what every investment textbook tells you not to do. And yet, momentum is one of the most documented return anomalies in financial history. It has been found in every major equity market studied | US, Europe, Japan, and India. It has been replicating since Jegadeesh and Titman first documented it in 1993. It generates some of the highest factor premia of any single factor. And it is the backbone of RupeeCase's flagship strategy. This module goes deep on why it works, how to measure it correctly, and | critically | where it fails catastrophically. Momentum in numbers, Indian market lens 18.2 % Nifty 200 Momentum 30 TRI 10Y CAGR NSE Indices 4.9 Percentage point premium over Nifty 500 2015 to 2025 NSE Indices 34 % Mar 2020 momentum drawdown peak to trough RupeeCase backtest 12 Month formation minus 1 month skip signal Jegadeesh Titman 1993 NSE SEBI AMFI Where momentum alpha lives in the return decomposition Market beta 55 Momentum tilt 22 Size exposure 15 Residual alpha 8 Factor decomposition of Nifty 200 Momentum 30 TRI returns against a 4 factor Indian model 2015 to 2025. Most of the premium is the momentum tilt itself. Source RupeeCase research. **March 2020, the Covid crash week.** My live momentum book drew down 34 percent in 18 trading days. I had backtested this exact tail in 2018 and sized the book for 40 percent. The rebalance date came on the first Monday of April. The signal told me to rotate out of banks and into pharma plus IT. Every instinct said freeze, the world was ending. I followed the rules anyway because I had written them in writing 14 months earlier, specifically so a future panicked version of me could not override them. That rotation caught the first leg of the 2020 to 2021 rally. The lesson, pre commit on paper before the fear arrives. The paper does not panic. ▼ Factor returns in India, CAGR (2005 to 2024) Source: NSE Indices, RupeeCase backtest data Momentum 18.2% Quality 15.8% Low Volatility 14.9% Value 13.6% Nifty 50 12.1% Size (Small) 17.1% ▼ Momentum factor, how 12M-1M signal works RupeeCase signal construction LOOKBACK 12 months total return − SKIP Last 1 month (reversal) = SIGNAL 11-month momentum score Rank all Nifty 500 stocks → Buy top 30 → Equal weight → Rebalance monthly ## The evidence: what Jegadeesh and Titman found In 1993, Narasimhan Jegadeesh and Sheridan Titman published "Returns to Buying Winners and Selling Losers" in the Journal of Finance. Their finding: stocks that performed well over the past 3 to 12 months continued to outperform over the next 3 to 12 months, and vice versa. This wasn't a small effect. The top-decile momentum portfolio outperformed the bottom decile by roughly 1% per month over the next 6 months. [Jegadeesh & Titman (1993) — Returns to Buying Winners and Selling Losers](https://www.jstor.org/stable/2328882) This was replicated globally. Rouwenhorst (1998) found it in 12 European countries. Fama and French included momentum in their factor models. Asness, Moskowitz, and Pedersen (2013) found it in "Value and Momentum Everywhere" | equities, bonds, currencies, and commodities across 59 assets and 40 years. 1% Average monthly outperformance of top-decile momentum vs bottom-decile (US, original Jegadeesh-Titman study) 30+ Years momentum has been documented and continues to replicate in equity markets globally ~18% Approximate annual premium of momentum over Nifty 500 benchmark in Indian market backtests ## Why momentum exists: three explanations ### 1. Underreaction to news Markets don't immediately fully price new information. When Infosys announces strong quarterly results, the stock rises | but not to its new fair value instantly. Investors anchor to the old price, adjust slowly, and the stock continues rising over subsequent weeks and months as the information fully diffuses. Momentum captures this gradual repricing process. ### 2. Investor herding As a stock rises, it attracts media coverage, analyst upgrades, and retail investor attention. This creates a self-reinforcing cycle: price goes up → more attention → more buying → price goes up more. The cycle doesn't reverse until sentiment breaks | which tends to happen suddenly, which is why momentum crashes are sharp. ### 3. Trend-following institutional behaviour Many institutional investors, particularly CTAs (commodity trading advisors) and systematic funds, explicitly follow trends. Their buying reinforces existing trends. Since institutional assets have grown substantially, trend-following capital may itself be sustaining some of the momentum effect. ## How to measure momentum correctly This is where most retail momentum implementations go wrong. The standard signal in academic literature is: Standard Momentum Signal Momentum = Return(t−12, t−1) Return of the stock from 12 months ago to 1 month ago. The most recent month (t to t-1) is deliberately excluded. The skip-1-month rule is critical and widely misunderstood. The last month's return tends to **reverse** | not continue. This is the short-term reversal effect (Jegadeesh, 1990). If you include the last month, you're partially betting on reversal, which works against momentum. Excluding it sharpens the signal significantly. 1 Calculate 12-month return for each stock For each stock in the Nifty 500, calculate the total return from 12 months ago to 1 month ago. Use adjusted prices (corrected for splits and dividends). 2 Rank all stocks from highest to lowest A stock that returned 80% over this period ranks near the top. One that returned -40% ranks near the bottom. This cross-sectional ranking is what "cross-sectional momentum" means. 3 Select the top N stocks Buy the top 20, 30, or 50 by rank. The exact number matters less than the principle | own the highest-momentum segment of the universe. 4 Rebalance monthly On the first trading day of each month, recalculate rankings. Stocks that fell out of the top N are sold. New entrants are bought. This is where transaction costs become critical. ## Momentum in Indian markets | the specific evidence NSE publishes official factor index data for the Nifty Alpha 50 and Nifty Momentum 30 | both track momentum strategies on Indian large and mid-cap stocks. The evidence is consistent with global findings: momentum has generated significant alpha over the Nifty 500 benchmark over most measured periods. [NSE Indices — Nifty Alpha 50 (official momentum index)](https://www.niftyindices.com/indices/equity/strategy-indices/nifty-alpha-50) [NSE Indices — Nifty 200 Momentum 30](https://www.niftyindices.com/indices/equity/strategy-indices/nifty200-momentum-30) A few India-specific observations from RupeeCase's own backtesting: * **Momentum is stronger in the mid-cap and small-cap segment** | Nifty 500 universe shows stronger momentum premia than Nifty 50 alone, because smaller stocks are less efficiently priced * **Sector concentration is a known issue** | in strong sectoral bull markets (2021 PSU rally, 2023 defence stocks), momentum portfolios can become heavily concentrated in one or two sectors. Risk management requires monitoring this * **Weekly rebalancing outperforms monthly** in Indian markets on a gross basis, but the cost advantage disappears once transaction costs are applied for most retail portfolio sizes ## The momentum crash | when it fails Momentum is the factor most prone to catastrophic failure. In sharp market reversals | when markets turn from downtrend to uptrend very quickly | momentum portfolios hold the recent losers (which are now the new winners) at zero weight, and are fully invested in the recent winners (which are now crashing hardest). **Historical momentum crashes to understand:** March 2020 (COVID crash and recovery) | the Nifty fell 40% then recovered 50% in 3 months. A momentum portfolio was fully invested in high-flying stocks going into the crash, then held those as they fell, while missing the recovery in previously beaten-down sectors. Some global momentum strategies lost 30 to 40% in this 6-week window while markets recovered. In India, similar dynamics played out. The three conditions that produce a momentum crash: * **High market volatility** | when VIX spikes, recent winners tend to be the most crowded and the most vulnerable to forced selling * **Sharp market reversal** | momentum requires trend persistence. A sharp reversal kills the trend before it can be captured * **High dispersion of prior returns** | when the spread between winners and losers is very large, the crash when it comes is correspondingly large This is why combining momentum with other factors | particularly Low Volatility and Quality | is so important. These factors tend to perform well precisely when momentum crashes. The correlation between them is low or negative, which is the mathematical foundation of multi-factor diversification. ## Momentum variants worth knowing | Variant | Signal | Lookback | Best for | | --- | --- | --- | --- | | Cross-sectional | Rank by return vs peers | 12M-1M | Long-only equity, most strategies | | Time-series (absolute) | Return vs own history | 12M | Tactical allocation, market timing | | Risk-adjusted | Return / Volatility | 12M-1M | Reduces crash risk, smoother signal | | Earnings momentum | EPS revision direction | Quarterly | Combined with price momentum | | 52-week high | Proximity to 52W high | 1 year | Simpler implementation, retail | How RupeeCase implements momentum The RupeeCase NSE Momentum strategy uses **12M-1M cross-sectional momentum** on the Nifty 500 universe, with equal weighting and monthly rebalancing. The Stock Optimizer tool on [RupeeCase](https://invest.rupeecase.com) lets you test different lookback windows (6M, 9M, 12M) and portfolio sizes (Top 15, 20, 30, 50) to see the performance distribution | so you can understand robustness rather than just picking the best-looking parameter. Run momentum backtests on live NSE data Test any lookback window on 10+ years of Nifty 500 history Costs included. Point-in-time data. Full tearsheet output. [Start free →](https://invest.rupeecase.com/signup) ## Glossary Key terms from this module Cross-sectional momentumRanking stocks by recent return relative to other stocks in the universe. Buy top-ranked, avoid bottom-ranked. Skip-1-monthThe standard momentum signal excludes the most recent month's return (t to t-1) because the last month tends to reverse rather than continue. Short-term reversalThe tendency for last month's winners to underperform and losers to outperform over the next month | the opposite of momentum, which is why momentum signals skip the last month. Momentum crashA sharp, rapid drawdown in momentum strategies that occurs when markets reverse direction quickly, causing recent winners to become rapid losers. Alpha 50 / Momentum 30NSE's official factor indices tracking momentum strategies on Indian large and mid-cap stocks. #### Sources & further reading * → [Jegadeesh & Titman (1993) — Returns to Buying Winners and Selling Losers](https://www.jstor.org/stable/2328882) * → [NSE Indices — Nifty Alpha 50 Official Page](https://www.niftyindices.com/indices/equity/strategy-indices/nifty-alpha-50) * → [NSE Indices — Nifty 200 Momentum 30](https://www.niftyindices.com/indices/equity/strategy-indices/nifty200-momentum-30) * → [Fama-French Data Library — Momentum factor (UMD)](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) * → Asness, C., Moskowitz, T. & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance. * → Daniel, K. & Moskowitz, T. (2016). Momentum Crashes. Journal of Financial Economics. ### Quick check, Module 3.1 0 correct · 0 answered ✓ Module complete! Next module unlocked. 🎉 Module 3.1 complete 3 correct. Continue to Module 3.2 when ready. 🎓 Spread the knowledge This course is free. Help someone else learn about The Momentum Factor, share it with one person who needs this. 📋 Suggested LinkedIn post, copy & paste Just completed Module 3.1 of Tanmay Kurtkoti's free investing course on RupeeCase. Learning about The Momentum Factor. Completely free at rupeecase.com/learn Copy text Share on X Post on LinkedIn Copy link Research Lab Qualifier Path 3, Module 1 of 8 done, complete all 8 + path test to unlock [Explore terminal →](https://invest.rupeecase.com) 📍 3.1 Momentum → 3.2 Value → 3.3 Quality → 3.4 Low Vol → 3.5 Size → 3.6 to 3.8 Calculator ### 12M-1M Momentum Score The standard formulation: 12-month return excluding the most recent month. Skipping the latest month avoids short-term reversal noise. Price 13 months ago (INR)Price 1 month ago (INR)Latest spot (INR)Universe percentile thresholdTop 10% (decile 1)Top 20%Top quartileAbove median Quick check, Module 3.1 ## 3 questions. Get 2 right to mark this module complete. 0 of 3 answered ✓ Module complete. Keep going. Up next, Module 3.2 The Value Factor Why cheap stocks outperform expensive ones, how to measure value correctly in Indian markets, and the decade-long underperformance that tests every value investor. [Continue →](module-3-2-value-factor-deep-dive.html) TK A note from the author Why I wrote this path Factor investing changed how I think about markets. When I started in quant trading 17 years ago, factors were institutional secrets, momentum, value, quality, these were the building blocks of every serious systematic fund, but retail investors had never heard of them. In India, the factor investing opportunity is enormous. Our markets have structural inefficiencies that factors exploit better than in the US. I’ve seen this firsthand running models on NSE data for over a decade. The Nifty Momentum 30 didn’t exist when I started, now it’s one of the best-performing indices in the world. This path is my attempt to give you the same mental models I use every day. Not the watered-down version, the real thing, with Indian data and Indian examples. If you understand these 8 modules, you’ll know more about factor investing than 99% of Indian investors. TK Tanmay Kurtkoti Founder & CEO, RupeeCase · 17 years systematic trading · QC Alpha RC **Want to put this into practice?** RupeeCase is the systematic investing terminal built around everything you’re learning here, factor scores, strategy backtests, portfolio construction for Indian markets. [Explore the terminal →](https://invest.rupeecase.com) © All content on this page is original work by Tanmay Kurtkoti and QC Alpha Technologies Pvt Ltd. Protected under Indian copyright law. You may read and share individual links freely. Reproduction or commercial use requires written permission: tanmay@rupeecase.com