What factor investing actually is
Factor investing is the practice of selecting stocks based on measurable, persistent characteristics that academic research has shown drive long-term returns — instead of selecting by stories, news, or tips. You define a rule, rank every eligible stock by that rule on rebalance day, hold the top names, rebalance again on a schedule, and let the math compound.
The framework was formalised by Eugene Fama and Kenneth French in the early 1990s with their three-factor model (market, size, value), later extended to five factors (adding profitability and investment) in 2015. Real-world implementations now drive trillions of dollars of assets — AQR, Dimensional Fund Advisors, Robeco, Vanguard factor funds, and a growing number of smart-beta ETFs.
Six factors that the academic literature treats as well-established:
- Momentum — recent winners tend to keep outperforming over a 3 to 12 month horizon.
- Value — stocks cheap on price-to-book, price-to-earnings, or price-to-cash-flow tend to outperform expensive ones.
- Quality — companies with high return on equity, stable earnings, and low debt outperform low-quality firms.
- Low Volatility — surprisingly, low-volatility stocks deliver higher risk-adjusted returns than high-volatility ones, contradicting the simple CAPM prediction.
- Size — small-caps outperform large-caps over multi-decade windows, though with much higher drawdown variance.
- Profitability — high gross profitability and high operating margin firms beat low-profitability ones, especially when combined with quality.
Most modern quantitative strategies are combinations of these — Quality × Momentum, Low Volatility × Profitability, and so on. The reason combinations work better than individual factors is diversification: factors aren't perfectly correlated with each other, so blending them dampens drawdowns without sacrificing all the return.
Why factor research is harder than it looks (and most of the public Indian backtests are wrong)
Factor research is uniquely sensitive to backtest bias. The reason is that factor returns are typically a few hundred basis points per year above the index. Any backtest artefact that inflates returns by 2-3% per year fully consumes the entire factor premium — and makes a useless strategy look brilliant.
Two artefacts dominate:
- Lookahead bias on fundamental data. Indian listed companies file quarterly results with the exchange somewhere between 30 and 60 days after the period end. A naïve backtest stores the result indexed by the period-end date and lets the strategy use it on day 1 of the next quarter — when no investor in reality could have known it. We covered this in detail here. The inflation is typically 1.5 to 3.5% CAGR — enough to make a mediocre quality strategy look fantastic.
- Survivorship bias on the universe. If you backtest on "today's Nifty 500" instead of "the Nifty 500 as it existed on each rebalance date," your universe excludes every stock that fell out of the index in the meantime. Those exclusions are not random — they're the underperformers. More on this here. Typical inflation: 1 to 3% CAGR.
Add both biases and a 12% CAGR strategy can look like 18% — without the strategy doing anything different. This is why public Indian factor backtests must be read with skepticism. A "20% CAGR momentum strategy" claim that doesn't explicitly disclose how it controls for both biases is, more likely than not, wrong.
Every strategy below is run on an engine that anchors fundamental rows to their actual XBRL filing date and rebuilds index/sector membership at every rebalance date. The numbers reported are what the strategies would have earned with only the information available on each historical day.
The five factor families running live on ftInvstr
Across the 20 live strategies, we organise the work into five factor families. Each family targets a distinct return source. Within a family, multiple variants test different parameter choices — lookback length, weighting scheme, rebalance frequency — so the family's robustness can be evaluated rather than just one cherry-picked variant.
1. Pure Momentum — three variants
Buy what's been winning. Sell what hasn't. The most academically validated factor outside of value, and one of the few that has held up out-of-sample since its initial documentation in Jegadeesh & Titman (1993). The classical formulation is "12-1": rank by return from 12 months ago to 1 month ago, skip the most recent month (which exhibits short-term mean reversion), hold the top decile, rebalance monthly.
| Strategy | What it ranks by | CAGR | Sharpe | Max DD |
|---|---|---|---|---|
| 12–1 Momentum Factor (Weekly) | Return from 252 days ago to 20 days ago | 34.8% | 0.97 | −37.3% |
| Simple Price Momentum | Return over the last 20 trading days | 29.7% | 0.86 | −42.7% |
| Multi-Horizon Momentum Blend | Weighted blend of 60-day and longer-horizon momentum | 32.8% | 1.19 | −21.8% |
The Multi-Horizon Blend is the clear standout. It compounds at 32.8% with a Sharpe of 1.19 and a max drawdown of only −21.8% — substantially milder than either the 12-1 or the simple 20-day variant. The lesson is one of the cleanest factor-investing findings: blending lookback horizons improves risk-adjusted returns more than picking the optimal single horizon. A 252-day momentum signal misses fast regime changes; a 20-day signal whips around on noise. Together they're complementary.
2. Quality — four variants
Buy companies with growing earnings and clean balance sheets. The "quality" factor is most rigorously associated with Asness, Frazzini & Pedersen (2013) — they showed quality stocks earn excess returns the CAPM can't explain. The signal is robust globally and works particularly well during drawdown periods, when low-quality firms tend to get punished the most.
Our quality family ranks stocks on two stacked dimensions: EPS growth (positive change in Diluted EPS over the lookback) plus low leverage (low Borrowings / Total Assets ratio). Stocks that are growing profits without piling on debt rank top. The variants test 60-day vs 90-day lookback and equal-weight vs target-weight portfolio construction.
| Strategy | Lookback / Weighting | CAGR | Sharpe | Max DD |
|---|---|---|---|---|
| Quality Earnings, Low Debt (90d) | 90-day · target-weighted | 24.4% | 0.82 | −41.8% |
| Quality Earnings, Low Debt (60d) | 60-day · target-weighted | 24.2% | 0.83 | −40.5% |
| Quality Earnings, Low Debt (Score-Weighted) | 60-day · score-weighted | 21.8% | 0.71 | −44.0% |
| Quality Earnings, Low Debt (Equal Weight) | 60-day · equal-weighted | 21.7% | 0.71 | −43.9% |
The lookback choice barely moves the result (24.4% vs 24.2%). The weighting scheme matters more — target-weighted construction adds 2-3% CAGR over equal-weight and score-weighted. This is one of the underappreciated practical takeaways: in factor portfolios, portfolio construction is often more impactful than parameter tuning of the signal itself. The same signal, weighted differently, can vary by 250 bps annually.
3. Quality × Momentum — two variants
The textbook quant combination. AQR's Cliff Asness has written extensively about quality combined with momentum as one of the most robust pairs in factor investing: quality smooths the drawdowns that pure momentum suffers in trend reversals, and momentum captures price reactions to good fundamentals that pure quality alone misses.
Our Quality × Momentum strategies rank stocks on the sum of two components: price momentum (return from N days ago to today) plus low leverage (low Borrowings / Total Assets). N is the variable — we test 90 days and 120 days. Both run monthly rebalance, top 10 stocks from a 500-stock universe.
| Strategy | Lookback | CAGR | Sharpe | Max DD |
|---|---|---|---|---|
| Low Debt + Momentum (120d) | 120 trading days | 37.1% | 1.07 | −35.0% |
| Low Debt + Momentum (90d) | 90 trading days | 36.9% | 1.07 | −40.8% |
Both variants are top-of-portfolio for CAGR. Critically, neither blows up: max drawdowns of −35% and −40.8% are inside the typical Indian small/mid-cap envelope, not extreme tail outcomes. The 120-day lookback edges the 90-day on drawdown control (−35.0% vs −40.8%) while delivering essentially the same return, supporting the broader factor-research finding that longer momentum lookbacks tend to draw down less without giving up much performance.
4. Profitability & Growth Trends — five variants
This family is the closest cousin to Fama-French's profitability factor. Rather than ranking by static profitability (which favours mature mega-caps), we rank by trend in profitability: companies whose operating margins are expanding, whose earnings are growing, whose comprehensive income is rising. The economic story is straightforward: a company improving its profitability now is more likely to reprice upward than a company already optimised.
This family is heterogeneous — five distinct strategies test five flavours of the same idea:
| Strategy | Trend signal | CAGR | Sharpe | Max DD |
|---|---|---|---|---|
| Earnings & Margin Expansion | 120-day net profit growth + operating margin | 31.4% | 0.96 | −32.5% |
| Comprehensive Income Growers | 120-day change in comprehensive income | 24.2% | 0.77 | −42.0% |
| Operating Margin Expansion (90d) | 90-day pct change in OPM + sales | 23.0% | 0.68 | −44.0% |
| Operating Margin Expansion (60d) | 60-day pct change in OPM + sales | 20.0% | 0.59 | −44.5% |
| Steady Cash Compounders (60d/90d) | CFO-to-OP ratio + consistency | 17.9%–23.7% | 0.51–0.70 | −47% to −50% |
The standout — Earnings & Margin Expansion — combines a 120-day net profit trend with current operating margin. At 31.4% CAGR and 0.96 Sharpe with a relatively contained −32.5% drawdown, it's one of the most robust strategies in the entire library. The cash-flow-quality strategies (Steady Cash Compounders) perform worst on absolute return, but conceptually they're a defensive bet: cash flow stability matters most during corrections, and 2020-2026 was not a particularly correction-heavy period. We would expect them to relatively outperform during a true bear cycle that this backtest window doesn't include.
5. Flow & Conviction Signals — five variants
This family departs from textbook factors. Instead of fundamental or price-derived signals, it ranks on who is buying: FII flows, promoter holdings, volume-backed price action, and stocks breaking out of recent price ranges with profit confirmation. The economic intuition: when sophisticated holders increase ownership while price confirms, returns tend to follow.
| Strategy | Conviction signal | CAGR | Sharpe | Max DD |
|---|---|---|---|---|
| Profitable Breakouts (Weekly) | Price near 20-day high + positive net profit | 33.4% | 1.02 | −38.8% |
| Lean Inventory Operators | Low inventory-to-sales + profit growth | 28.8% | 0.90 | −46.2% |
| FII Buying Meets Price Momentum | 20-day change in FII holdings + 20-day momentum | 28.7% | 0.85 | −44.1% |
| Volume-Backed Momentum (120d) | Long-horizon momentum + relative volume | 25.5% | 0.71 | −37.6% |
| High Promoter Stake + Profit Growth | Promoter holding + 20-day net profit delta | 24.5% | 0.74 | −43.2% |
Profitable Breakouts (Weekly) is the standout — combining a technical signal (price near 20-day high) with a fundamental filter (positive net profit). The combination is what makes it work: pure breakout strategies are notorious for whipsaws on low-quality names. Filtering for profitability filters out the speculative penny stocks that account for most breakout false signals.
What the cross-family data actually shows about factor investing in India
Aggregating across all 20 strategies and the 2020-2026 window:
- Every strategy beats Nifty. Nifty 50 compounded at roughly 11-12% over this window. The lowest-CAGR strategy in the library returned 17.9% — still 6 percentage points above the index. The median strategy returned 26%. The top quartile returned more than 32%.
- Quality × Momentum is the dominant pair. The two Low Debt + Momentum variants produced the highest CAGRs in the library (36.9% and 37.1%) while keeping max drawdowns in line with the broader Indian small/mid-cap universe. This matches what AQR, Robeco, and the broader academic literature have found globally.
- Portfolio construction beats parameter tuning. Within the Quality family, the target-weighted variants outperformed equal-weight and score-weighted by 2-3% CAGR — a wider spread than the gap between 60-day and 90-day lookback choices. How you weight matters more than the exact horizon.
- Longer lookbacks draw down less. The 120-day momentum variants consistently held up better through corrections than 60-day variants. This is the well-documented "slow momentum" effect — fast momentum signals are noisier and whip harder during regime changes.
- Maximum drawdowns are real. Even the best Sharpe strategy in the library hit −21.8%. The median strategy hit roughly −42%. Anyone running concentrated factor portfolios on Indian small/mid-caps must be emotionally prepared for routine 30-50% drawdowns. Drawdown is a feature, not a bug — read this for context.
Why a single backtest CAGR is not the same as the result you'll see
Every CAGR above is a single realisation — the strategy compounding through one specific historical sequence of daily returns. Real money managers know that the path matters: a strategy with the same final CAGR can deliver radically different investor experiences depending on when the drawdowns happen.
Every active strategy on ftInvstr is run through a stationary block-bootstrap Monte Carlo nightly. The procedure constructs 1,000 alternate-history paths by resampling the strategy's own daily returns in short consecutive blocks (preserving autocorrelation), then walking each path to track the running maximum drawdown — not just the final return.
Across the library:
- 12 of 20 strategies fall in the "volatile path" category. Their 5th-percentile max drawdown exceeds −55% or their probability of drawing down 50% along the path exceeds 15%.
- 8 of 20 are "moderate." Drawdown distributions are wide but not extreme.
- Zero strategies are "smooth" by our threshold (median path drawdown shallower than −20%). The honest takeaway: concentrated 10-stock factor portfolios on Indian small/mid-caps are inherently path-volatile. This isn't a bug. It's the geometry of the asset class.
- Multi-Horizon Momentum Blend is the lowest-risk path — median worst dip of only −24.5%, less than 1% probability of touching −50% along the path. It's not the highest-CAGR strategy in the library, but it has the cleanest risk profile.
The methodology, briefly
For completeness — the engineering that makes the numbers above credible:
- Point-in-time XBRL fundamentals. Every quarterly result row in the data store carries the date it was actually filed with the exchange. The backtest engine only allows strategies to read a row on or after its filing date. More detail here.
- Universe reconstruction at each rebalance. Index and sector membership is computed fresh on each rebalance date from the period's actual constituent list — not from today's snapshot. Stocks that were dropped from the index in subsequent years still participate in the backtest until the date they were actually dropped. More detail here.
- Trade execution simulated at next-day open. Orders generated on close-of-day are filled at the following morning's open — not at the close they were generated from. This prevents the "see-the-close, trade-at-the-close" artefact.
- Cash drag modelled. Cash sitting in the portfolio between rebalances doesn't earn return. The current cash position is tracked daily; idle cash dilutes equity-driven returns the same way it would in real money.
- Trade charges applied. STT, brokerage, exchange transaction charge, stamp duty, SEBI fee, and GST on charges are computed per trade. The CAGRs reported are net of these costs.
- Risk-free rate is the live 10-year G-Sec yield. Sharpe, Sortino, and Information Ratio use the current G-Sec yield fetched from market data each evening, rather than a hardcoded assumption like 6% or 0%. The fetched rate is applied uniformly across the historical computation.
A practical reading guide — how to use this
If you're new to factor investing, three places to start:
- Browse a single factor family. Pick Momentum or Quality, look at all three or four variants in that family, and notice how parameter choices affect the result. The point is to see the shape of a factor's behaviour, not to pick a winner.
- Compare two families. Run a side-by-side comparison of, say, the best Quality strategy against the best Quality × Momentum strategy. See where their equity curves diverge — typically Quality × Momentum outperforms during trending markets, Quality alone outperforms during corrections.
- Read the Monte Carlo card on each strategy detail page. The "Resilience" badge on each dashboard card is a one-glance summary of path risk. The full Monte Carlo card below Yearly Stats shows the underlying numbers — median path drawdown, 5th percentile worst path, probability of touching −50%.
If you're already familiar with factor investing and just want the punchline: Multi-Horizon Momentum Blend (Sharpe 1.19, drawdown −21.8%, median path dip −24.5%) is the cleanest single-strategy in the library — and Low Debt + Momentum (120d) is the highest absolute CAGR strategy with a still-defensible risk profile (drawdown −35.0%, Sharpe 1.07).
What this is not
This is research output, not investment advice. Every claim is grounded in historical data and a specific methodology. Historical performance does not guarantee future returns — particularly for factor strategies, which are known to go through multi-year periods of underperformance even when the long-horizon edge holds. The 2020-2026 backtest window does not include a true bear cycle on Indian equities; results during a bear cycle could differ materially.
ftInvstr is a research and backtesting platform. It is not SEBI-registered as an investment adviser or research analyst. The strategies above are educational research outputs designed to illustrate factor-investing methodology — not signals to act on. Anyone considering deploying real money against a factor strategy should consult a SEBI-registered investment adviser, understand the tax implications (LTCG, STCG, STT, dividend taxation), and stress-test the strategy on their own personal risk tolerance through a full bear-market cycle.
Where the field goes from here
The frontier in Indian factor investing today is, in our view, three places:
- Sector-neutral factor construction. The factor strategies above don't sector-neutralise. A momentum signal in a banking-led bull market will accidentally become a banking-heavy bet. Sector-neutral construction (ranking within each sector, then aggregating) gives a purer factor exposure.
- Multi-factor blending. Single-factor strategies are easier to reason about, but multi-factor blends typically have higher Sharpe ratios. The next wave of Indian smart-beta products will likely converge on multi-factor portfolios with explicit risk budgeting per factor.
Factor investing isn't magic. It's a disciplined application of measurable signals to a wide enough universe, with bias-controlled methodology, run long enough that the math compounds. The 20 strategies above are a working tour of what that discipline looks like on Indian equities. The framework is open — you can fork any of them in Strategy Lab, change the lookback or the universe or the weighting, and run your own variant.
Disclaimer. ftInvstr is a research and backtesting platform. We are not SEBI-registered investment advisers or research analysts. All performance figures cited are hypothetical backtest results from a point-in-time, survivorship-controlled simulation engine, net of estimated transaction costs and taxes. Historical performance is not indicative of future results. Nothing on this page is a recommendation to buy or sell any security or to follow any strategy. Consult a SEBI-registered investment adviser before deploying real capital.