Combining Factors

In the previous five modules, you studied each factor individually. Now comes the insight that makes all of it actionable: factors are most powerful when combined.

No single factor works every year. Momentum crashes in sharp reversals. Value underperforms for years at a time. Quality lags in bull markets. Low Volatility underperforms when risk appetite is high. But these failure modes don't all happen at the same time | and that's the key. When factors are negatively or lowly correlated, combining them produces a portfolio that's more stable, with lower drawdowns, than any individual factor alone.

This isn't financial magic. It's the mathematics of diversification applied to factor returns rather than individual securities.

Why factor correlation is the key

Consider two investments, each earning 15% annually but with different behaviour:

• Investment A does well in years when B does poorly, and vice versa (negative correlation).
• Investment A and B combined 50/50 earn roughly the same 15% CAGR | but with dramatically lower volatility and drawdowns than either alone.

This is the core principle of factor combination. The returns are additive; the risks are less than additive if the factors are not perfectly correlated. The lower the correlation, the more diversification benefit you get.

Factor correlation matrix

Here is an approximate correlation matrix for the five major factors in Indian equity markets (annual returns, Nifty 500 universe, illustrative based on academic literature and Indian market research):

The key observations: Momentum and Value are negatively correlated (−0.35) | a powerful pairing. Quality and Low Volatility are positively correlated (+0.32) | they tend to move together, so combining them adds less diversification. Momentum and Low Volatility are negatively correlated (−0.28) | excellent for smoothing the momentum crash problem.

The two approaches to combining factors

Approach 1: Composite Score (bottom-up)

Calculate a single composite score for each stock by combining multiple factor signals. For example: Composite = 0.4 × Momentum Score + 0.3 × Quality Score + 0.3 × Value Score. Select the top N stocks by composite score.

• Advantage: Finds stocks that rank well on multiple factors simultaneously | strong confirmation signal
• Disadvantage: If one factor dominates (very high momentum stock with poor quality), a stock could rank high overall but is essentially a single-factor bet

Approach 2: Portfolio Blending (top-down)

Build separate factor portfolios and allocate capital between them. E.g., 50% in a momentum portfolio + 50% in a quality portfolio, rebalanced annually.

• Advantage: Clean factor exposure, easier to understand and monitor. Drawdowns from one factor are explicitly offset by the other.
• Disadvantage: Higher turnover and costs. May hold some stocks in both portfolios with offsetting positions.

Proven factor combinations in Indian markets

What happens to costs in multi-factor strategies

Multi-factor strategies typically have higher turnover than single-factor strategies | because more signals can trigger rebalancing. If momentum says "buy Stock A" but quality says "sell it," the portfolio is in constant tension. This creates more trades, more costs, and more tax events.

The cost-management discipline for multi-factor strategies:

• Use the composite score approach | fewer stocks enter and exit per rebalance compared to blending multiple separate portfolios
• Set rebalancing thresholds | only trade a position if its rank has changed significantly (e.g., a stock drops from top-30 to top-35 doesn't need to be sold)
• Favour quarterly rebalancing for slow-moving factors (value, quality) even if momentum rebalances monthly

Indian factor indices, what NSE actually publishes

Every theoretical factor combination has a live counterpart on NSE Indices. The methodologies are public, the constituents update on a fixed schedule, and the historical data goes back a decade or more. Six worth knowing.

Nifty Alpha 50. Pure momentum. Picks the 50 stocks with the highest 6-month and 1-year alpha against Nifty 50 from the broader Nifty 500 universe. Quarterly rebalance. Highest CAGR among the factor indices over 10 years, highest volatility, deepest drawdowns. The textbook momentum factor in Indian form.

Nifty 200 Quality 30. Pure quality. Top 30 names from Nifty 200 ranked on ROE, debt-to-equity stability and EPS growth stability. Semi-annual rebalance. Lower CAGR than Alpha 50 but materially shallower drawdowns. The defensive factor.

Nifty Low Volatility 30. Pure low-vol. The 30 lowest-volatility stocks in Nifty 100, weighted inversely to their volatility. Quarterly rebalance. Best risk-adjusted return among single-factor indices on a multi-decade comparison. Underperforms in narrow bull rallies, holds up in stress.

Nifty Alpha Low-Volatility 30. A two-factor blend. Alpha plus low-vol, equal weight on score, on the Nifty 100 universe. Captures momentum upside while restraining drawdowns. The composite-score approach in published form. Used as a benchmark for hedge-fund-style equity products in India.

Nifty Alpha Quality Low-Volatility 30. Three-factor composite. Alpha, quality, low-vol equally weighted. Quarterly rebalance, Nifty 100 universe. Smoother than any single factor, with the diversification benefit of three uncorrelated signals. The closest published index to a true multi-factor strategy.

Nifty100 Quality 30. Quality on the Nifty 100. Narrower universe than the 200 Quality 30, slightly different constituent mix, similar return character.

The lesson from comparing these indices over 10 years is consistent. Multi-factor composites do not always beat the best single factor in any given year, but they avoid the worst single-factor years, which is the actual point. Compounded over a decade, the smoother ride wins because investors stay invested.

The drawdown rotation no one talks about

Most investor literature stops at "factor diversification reduces drawdown". The harder lesson is that factor leadership rotates inside the drawdown itself. In a sharp drawdown, the order in which factors crack matters.

Momentum cracks first. The names that led on the way up tend to be the most overextended on the way down, so a pure-momentum book often hits its trough before the broader market. Quality holds longer because high-ROE balance sheets weather macro shock better. Low-vol holds longest because the constituents are by definition the least cyclical. Value tends to lead the recovery because deep-discount names benefit first from any sentiment turn.

For a multi-factor strategy, the practical implication is that the rebalance immediately AFTER a sharp drawdown is the highest-information moment of the year. Momentum scores will look terrible on names that just sold off; value scores will look strong on the same names. A thoughtful composite weighting captures the rotation. A naive equal-weight composite captures it less efficiently. This is one of the few legitimate cases where factor weightings can be adjusted with the regime, as long as the rule is set in advance and not in the moment.

Glossary