Momentum Investing Beyond Stocks: How Momentum Signals Drive Asset Allocation Between Equity and Gold

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India's investing world has discovered momentum. The Nifty 200 Momentum 30 Index has delivered roughly 18.5% CAGR since 2005, trouncing the Nifty 50's ~13%. Momentum index funds have attracted thousands of crores. PMS strategies built on the factor are multiplying. And yet, almost every momentum strategy in India does the same thing: it uses momentum to pick stocks. This is like discovering electricity and using it only to power lightbulbs. Momentum's most powerful — and most under-exploited — application isn't in choosing which stocks to own. It's in deciding when to own stocks at all, and when to shift to gold.

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The Momentum Effect: A 212-Year Anomaly That Refuses to Die

Before we discuss what most of India's momentum strategies are missing, it's worth understanding why momentum works at all — and how robust the evidence actually is.

In 1993, professors Narasimhan Jegadeesh and Sheridan Titman published a landmark paper at UCLA demonstrating that stocks with strong recent returns continue to outperform, while recent losers continue to underperform. This "momentum effect" persisted across holding periods of 3 to 12 months.

What followed was one of the most extensively validated findings in all of financial economics.

Nobel laureate Eugene Fama — the father of the Efficient Market Hypothesis, who spent his career arguing that markets are rational — grudgingly called momentum "the premier anomaly." Coming from a man whose life's work was proving that anomalies don't exist, this is the academic equivalent of the Pope acknowledging that miracles are statistically significant.

The numbers are staggering:

Table 1: The Academic Evidence for Momentum — A Global Summary

Study	Dataset	Key Finding
Jegadeesh & Titman (1993)	US equities, 1965–1989	Buying past winners and selling past losers generates significant positive returns over 3–12 month horizons
Asness, Moskowitz & Pedersen (2013)	Stocks, bonds, currencies, commodities across 40 countries	Value and momentum generate abnormal returns across virtually every asset class and geography
Moskowitz, Ooi & Pedersen (2012)	58 futures/forward contracts, 1985–2009	All 58 instruments exhibited positive time-series momentum; 52 of 58 statistically significant
Hurst, Ooi & Pedersen (2017)	67 markets, 1880–2016	Positive returns in every decade for 137 years; performed well in 8 of 10 worst crisis periods
Rouwenhorst (1998)	12 European equity markets, 1978–1995	International momentum strategies profitable across countries, not explained by risk
Capitalmind India Research (2019)	NSE-listed equities, 15-year backtest	Even "naive" momentum outperformed Nifty after accounting for costs and taxes

This isn't a backtesting artefact. This isn't a statistical fluke. This is one of the most persistent, pervasive, and well-documented phenomena in the history of financial markets.

In India specifically, backtests by both NSE Indices and independent researchers confirm the effect:

Table 2: Momentum vs Broad Market Returns in India

Index / Strategy	CAGR (since Apr 2005)	5-Year Return (annualised)	Methodology
Nifty 200 Momentum 30	~18.5%	~29.7%	Top 30 stocks by volatility-adjusted 6m + 12m momentum from Nifty 200
Nifty 50	~13.0%	~15.8%	Top 50 by free-float market cap
Nifty 200	~13.5%	~17.2%	Top 200 by free-float market cap

Source: NSE Indices, data as of late 2024. Past performance is not indicative of future results.

So far, so good. Momentum works. Everyone agrees. The money is pouring in.

But here's the problem, when it didn't deliver since last 2 years.

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What India's Momentum Strategies Are Missing

Virtually every momentum strategy in India — whether index fund, PMS, or smallcase — applies momentum to one dimension: stock selection within equities. They use momentum signals to determine which stocks to own. The portfolio is always fully invested in stocks, always 100% equity, always long-only.

This means that when the broad market falls 40% — as it did in March 2020, when Nifty plunged from 12,100 to 7,600 — a momentum equity strategy falls with it. Perhaps it falls 30% instead of 40%. Perhaps 35%. But it falls, because it was designed only to pick the best horses in the race, not to decide whether it's a good day for horse-racing at all.

This is the limitation that nobody in India's momentum investing conversation is talking about.

Because the academic evidence for momentum doesn't stop at stocks. It doesn't stop at any single asset class. The most powerful application of momentum is across asset classes — using the same signal that works for stock selection to decide the allocation between fundamentally different assets: equities and gold, equities and bonds, risk-on and risk-off.

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Time-Series Momentum: The Bigger Discovery

In 2012, Moskowitz, Ooi, and Pedersen at AQR published a paper titled "Time Series Momentum" in the Journal of Financial Economics that should have changed how every asset manager in the world thinks about portfolio construction. For many, it did. For India's PMS industry, the memo seems to have been lost in transit.

What they found: across 58 liquid futures and forward contracts spanning equity indices, currencies, commodities, and government bonds, over 25 years of data — every single instrument exhibited positive time-series momentum. Not most. Not many. Every one.

Time-series momentum is different from the cross-sectional momentum most Indian investors are familiar with:

Table 3: Two Types of Momentum — And Why Both Matter

	Cross-Sectional Momentum	Time-Series Momentum
Core question	"Which stocks are outperforming other stocks?"	"Is this asset trending up or down versus its own history?"
Application	Stock selection within equities	Asset allocation across equity, gold, bonds, commodities
What it buys	Relative winners (top decile stocks)	Any asset with positive recent returns
What it avoids	Relative losers (bottom decile stocks)	Any asset with negative recent returns
Can it exit equities entirely?	❌ No — always 100% in stocks	✅ Yes — can move fully to gold or cash
Drawdown protection	Moderate (better stock selection)	Strong (exits declining asset classes)
Used by	Momentum index funds, equity PMS	Managed futures, tactical asset allocation, multi-asset quant
India adoption	Growing rapidly	Almost non-existent

This distinction is the crux of this entire article.

The key finding from the AQR research: a diversified portfolio of time-series momentum strategies across all asset classes delivered substantial returns with little exposure to standard asset pricing factors. More importantly, the strategy performed best during extreme markets — precisely when investors needed protection most. During October, November, and December 2008, at the height of the Global Financial Crisis, time-series momentum delivered large positive returns while equity markets were collapsing.

AQR's subsequent "Century of Evidence on Trend-Following Investing" (Hurst, Ooi, and Pedersen, 2017) extended the dataset back to 1880 and found that in each decade — through two World Wars, the Great Depression, stagflation, the dot-com crash, and the 2008 crisis — time-series momentum delivered positive average returns with low correlations to traditional assets. Every decade. For 137 years. Across 67 markets. With an average Sharpe ratio of approximately 0.4 per market.

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The Gold-Equity Relationship: India's Most Under-Exploited Diversifier

Now let's bring this back to India and to a specific asset pair: equities and gold.

Gold occupies a unique position in Indian portfolios — culturally revered but strategically underutilised. Most Indian investors either over-own gold (physical jewellery with no investment rationale) or under-own it (dismissing it as an unproductive asset). What almost nobody does is use gold systematically as a portfolio counterweight to equities, allocated dynamically based on momentum signals.

The data on gold's behaviour during Indian equity crises is striking:

Table 4: Gold vs Nifty During Indian Market Crises

Crisis Period	Nifty 50 Return	Gold (INR) Return	Gold Outperformance
2008 GFC (Jan 2008 – Mar 2009)	−60% (21,000 → 8,200)	+40% (₹10,800 → ₹15,500 per 10g)	+100 percentage points
COVID Crash (Feb – Sep 2020)	−37% then recovery	+50% (₹34,500 → ₹51,600 per 10g)	+87 percentage points
2022 Correction (Jan – Mar 2022)	−12% (18,300 → 16,200)	+5% (₹50,100 → ₹52,700 per 10g)	+17 percentage points
2015–16 Crash (Jun 2015 – Feb 2016)	−23% (Sensex fell 1,624 pts in a single day)	+8% (modest rise amid global turmoil)	+31 percentage points
Demonetisation (Nov 2016)	−6% (Nifty fell 540 pts)	+3% (flight to safety)	+9 percentage points

Sources: NSE, MCX, historical gold rate data. Approximate figures for illustrative purposes. Past performance is not indicative of future results.

Academic research confirms this pattern is structural, not coincidental. A study from the London School of Economics on the Indian market found that during all three waves of COVID-19, heavy selling volumes in Nifty were matched by heavy buying volumes in gold. The World Gold Council's research, using data from 1973 to 2024, found that gold has a correlation of just 0.01 with equities — essentially zero — making it statistically independent from stock market movements.

FPI Research (December 2025) found that the optimal allocation to gold in a diversified portfolio was 18% over their study period, based on maximising risk-adjusted returns. Gold's contribution wasn't just diversification in the polite, theoretical sense. It was active performance during the exact moments when equity portfolios were under maximum stress.

There is a nuance worth noting, and intellectual honesty demands it: gold's safe-haven behaviour isn't perfectly consistent. Research from Hood and Malik (2013), Akhtaruzzaman et al. (2020), and Cheema et al. (2020) found that during the very peak of the Lehman collapse and the initial weeks of the COVID crash, gold actually exhibited positive correlation with equities — falling alongside stocks before recovering. Gold's hedging benefit requires not just extreme but somewhat prolonged conditions of market stress, as Burdekin and Weidenmier noted.

This is precisely why you need a systematic signal to manage the allocation — not a permanent 60/40 split, not a "buy gold when scared" approach, but a quantitative framework that reads momentum across both asset classes and shifts accordingly.

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From Stock Selection to Asset Allocation: Where Momentum Gets Truly Powerful

Here's where the threads converge.

If momentum works for selecting which stocks to own (it does — 212 years of evidence), and if momentum works across asset classes including gold and equities (it does — 58 instruments, 137 years of evidence from AQR), and if gold moves inversely to Indian equities during market stress (it does — 2008, 2020, 2022 all confirm), then the logical question is:

Why isn't anyone in India using momentum to systematically shift allocation between equity and gold?

Mebane Faber of Cambria Investments demonstrated this concept elegantly in his widely cited paper "A Quantitative Approach to Tactical Asset Allocation" (2007, updated 2013). His system was breathtakingly simple: buy an asset class when its monthly price is above its 10-month simple moving average (the monthly equivalent of the 200-day SMA). Sell and move to cash when it drops below. Applied to a diversified portfolio of asset classes including equities, bonds, commodities, and real estate over more than a century of data, this approach achieved equity-like returns with bond-like volatility and drawdowns.

When applied specifically to the equity-gold pair, the logic works like this:

Table 5: The Momentum Signal Matrix — Equity × Gold

Equity Momentum	Gold Momentum	Portfolio Action	Rationale
✅ Positive	❌ Negative	Heavy equity allocation	Bull market regime; equities trending, gold fading
❌ Negative	✅ Positive	Heavy gold allocation	Risk-off regime; equities declining, gold rallying as safe haven
✅ Positive	✅ Positive	Split allocation (relative strength)	Both assets trending up; allocate based on which signal is stronger
❌ Negative	❌ Negative	Cash / reduced exposure	No clear trend; capital preservation mode

This is a simplified illustration of the principle. Actual implementation involves multiple lookback periods, volatility scaling, and systematic rebalancing rules.

The insight is deceptively powerful: you don't need to predict what the market will do. You need to observe what it is already doing and respond. Momentum is not forecasting. It is pattern recognition. It exploits the empirically documented tendency of trends to persist — driven by behavioural biases (anchoring, herding, under-reaction to information), institutional frictions (fund mandates, rebalancing constraints), and macroeconomic feedback loops (strong markets boosting consumption, boosting earnings, boosting markets further).

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Why Cross-Sectional Momentum Alone Isn't Enough

Let me be specific about what a pure equity momentum strategy cannot do, even a great one.

The Nifty 200 Momentum 30 Index, for all its outperformance over long horizons, experienced a drawdown of approximately 40% during the March 2020 COVID crash — similar to the broad market. Why? Because every stock in its portfolio was an Indian equity. When the market-wide selling hit, momentum leaders fell just as hard as everything else.

This is not a flaw of the momentum factor. It is a structural limitation of applying momentum within a single asset class. No matter how brilliantly you select stocks, you cannot diversify away the systemic risk of being 100% equity. The only way to manage that risk is to step outside equities entirely — into an asset class with a fundamentally different return driver.

Table 6: Pure Equity Momentum vs Cross-Asset Momentum — A Structural Comparison

Characteristic	Pure Equity Momentum	Cross-Asset Momentum (Equity + Gold)
Asset classes	Equities only	Equities + Gold (dynamic)
Bull market returns	High (concentrated in best stocks)	Slightly lower (some gold dampens upside)
Bear market drawdown	Full equity drawdown (30–50%+)	Structurally lower (10–20% typical)
Max drawdown (2008-type)	−50% to −60%	−15% to −25% (estimated)
Can exit equities entirely?	No	Yes
Recovery time after crash	2–4 years	Months
Behavioural advantage	Moderate	High — investors less likely to panic sell
Sharpe ratio (long-term)	Good (~0.6–0.8)	Better (~0.8–1.0+)
Available in India	Yes — many options	Rare

Estimated figures based on historical simulations and academic research. Actual results will vary. Not a guarantee of future performance.

For Indian investors, gold is the most natural and accessible counterweight to equities. It is liquid (gold ETFs trade on NSE and BSE with tight spreads). It is tax-efficient relative to alternatives. It has deep cultural familiarity. And most importantly, it has the empirical track record of providing crisis-period returns that equity — no matter how cleverly selected — simply cannot.

A cross-asset momentum strategy that includes gold doesn't just outperform during crashes. It changes the investor's experience of volatility. When your portfolio drops 15% instead of 40%, you are far less likely to panic sell at the bottom. You are far more likely to stay invested through the recovery. And staying invested — as any honest advisor will tell you — is the single largest determinant of long-term wealth creation.

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The Compounding Maths: Why Avoiding Deep Drawdowns Matters More Than You Think

There is a simple arithmetic truth that most investors underappreciate:

Table 7: The Recovery Asymmetry — Why Drawdowns Destroy Wealth

Portfolio Loss	Gain Required to Recover	Time to Recover (at 15% annual return)
−10%	+11%	~9 months
−15%	+18%	~1.2 years
−20%	+25%	~1.7 years
−30%	+43%	~2.7 years
−40%	+67%	~3.8 years
−50%	+100%	~5.0 years
−60%	+150%	~6.7 years

The relationship is non-linear: each additional 10% of loss requires disproportionately more gain to recover. This is why drawdown management isn't just "nice to have" — it's the single biggest driver of long-term compounding.

Consider two portfolios over a hypothetical 10-year period that includes two significant market corrections:

Portfolio A (Pure Equity Momentum): Captures strong bull market returns — say 25% in up years. But experiences full drawdowns during crises: −40% in year 3, −30% in year 8.

Portfolio B (Cross-Asset Momentum, Equity + Gold): Captures slightly less in bull markets — say 20% in up years (because some allocation is in gold during equity rallies). But the drawdowns are structurally smaller: −15% in year 3, −10% in year 8.

Over 10 years, Portfolio B almost certainly ends with a higher terminal value. Not because it had higher returns in any single year, but because it avoided the deep holes that require disproportionate recoveries. This is the mathematical foundation of why risk-adjusted metrics like Sortino ratio and downside capture matter more than raw CAGR.

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What India Can Learn From Global Trend-Following

The managed futures industry — firms that apply systematic trend-following across asset classes — manages over $350 billion globally. These strategies delivered some of their best years precisely when traditional portfolios suffered most: 2008 (during the financial crisis), 2022 (during the inflation/rate shock), and multiple other periods of market stress.

AQR's research found that time-series momentum performed positively in 8 of the 10 largest crisis periods for a 60/40 portfolio over the past century. The strategy tends to be long equities during bull markets (because equities have positive momentum) and short or flat equities during bear markets (because momentum turns negative) — naturally shifting to assets like gold, bonds, or cash that are trending positively during stress.

In India, this approach has barely penetrated the investment landscape. There are hundreds of PMS strategies to choose from — the vast majority are either discretionary equity, or at best, systematic equity. The concept of applying quantitative momentum signals to asset allocation between equity and gold is essentially absent from the Indian PMS and AIF universe.

This is an opportunity, not a criticism. The Indian market's structural characteristics — high information asymmetry, strong retail participation driving behavioural patterns, gold's deep cultural significance — make it arguably more suited to cross-asset momentum strategies than Western markets.

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Frequently Asked Questions

What is cross-asset momentum investing? Cross-asset momentum applies the same momentum principle used in stock selection — buying recent winners and avoiding recent losers — across different asset classes like equities and gold. Instead of just picking which stocks to own, it determines how much to allocate to equities versus gold based on which asset class is showing stronger positive trends. This approach has been validated across 212 years of data, in 40+ countries, and more than a dozen asset classes.

How is this different from a balanced fund or multi-asset mutual fund? Most multi-asset mutual funds in India operate with static or regulatory-mandated allocation bands — for example, minimum 10% in each asset class. A momentum-driven multi-asset strategy has no such constraint. Allocation can range from 0% to 100% in either equities or gold, driven entirely by quantitative signals. When equity momentum is strong, the strategy can be fully invested in stocks. When signals flip, it can shift entirely to gold. This flexibility is the core advantage. To understand how different approaches to PMS and AIF compare for sophisticated investors, the choice of vehicle matters alongside the strategy itself.

Does this mean market timing? Not in the way most people understand "market timing." Traditional market timing involves predicting future market movements based on macro forecasts, valuations, or gut feeling. Cross-asset momentum involves observing what has already happened — measuring the recent trend — and responding systematically. The AQR researchers described it well: the strategy isn't predicting which direction markets will move; it's observing which direction they are already moving and positioning accordingly. The distinction matters because predictions fail. Observations, systematically acted upon, have worked for over a century.

Why gold specifically? Why not bonds or cash? Gold has a near-zero correlation (0.01) with Indian equities, compared to higher correlations for bonds. More importantly, gold exhibits an asymmetric relationship — it actively rallies during periods of equity distress, not merely holding value. During the 2008 crash, gold in INR rose over 40% while Nifty fell 60%. During COVID, gold surged nearly 50% while Nifty fell 37%. Bonds in India, while useful, don't exhibit the same magnitude of crisis-period outperformance. Gold's dual role — inflation hedge and crisis-period performer — makes it the most effective counterweight to Indian equities in a momentum framework. Also bond market is not liquid.

What are the risks of a cross-asset momentum strategy? The primary risk is whipsaw — periods where the market oscillates above and below trend levels, generating frequent buy-sell signals that erode returns through transaction costs and taxes. This is most common in choppy, range-bound markets without clear trends. Additionally, momentum strategies can underperform during sharp V-shaped reversals, where the market crashes and recovers so quickly that the signal doesn't adapt in time. No strategy is immune to all market conditions. Understanding these limitations is part of evaluating any systematic strategy properly.

What is the minimum investment to access a multi-asset quant PMS? SEBI regulations mandate a minimum investment of ₹50 lakhs for all Portfolio Management Services in India, including multi-asset strategies. Grey Sky Capital's Smart Core PMS is SEBI-registered (INP000009694) and applies systematic momentum signals to dynamically allocate between Indian equities and gold. To understand how PMS fee structures affect net returns across different strategies, modelling the actual rupee impact over your investment horizon is essential.

How can I compare the performance of different multi-asset strategies? SEBI requires all registered portfolio managers to report performance data to the Association of Portfolio Managers in India (APMI). You can verify any strategy's returns on the APMI performance dashboard. For detailed risk-adjusted comparison across hundreds of PMS strategies — including Sharpe ratios, drawdowns, and rolling returns — our PMS Monitor tracks data across 300+ strategies.

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The Bigger Picture

India is at the beginning of its quant investing journey. The first wave — systematic stock selection using momentum — is already here and growing. The second wave — applying momentum signals to asset allocation itself — is where the real structural advantage lies, and it has barely begun.

The academic evidence is overwhelming. Across 212 years, 40 countries, 58 instruments, and multiple research teams at the world's leading universities and quantitative investment firms, the conclusion is consistent: momentum works across asset classes, it works for tactical allocation, and it works best precisely when traditional portfolios are under maximum stress.

Indian investors don't need to choose between equity momentum and gold. They need a framework that allocates between them — systematically, quantitatively, and without emotion. The same framework that has delivered consistent returns for global trend-following firms for decades, adapted for the specific characteristics of the Indian equity and gold markets.

The evidence suggests this isn't a speculative idea. It's an empirically validated approach that India's investment industry has simply not yet adopted at scale. The firms and investors who recognise this gap early will be positioned to capture the benefits as the Indian quant landscape matures.

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Disclaimer: This article is for informational and educational purposes only. It does not constitute investment advice, a recommendation, or an offer to buy or sell any securities. Past performance of any strategy, including quantitative strategies, is not indicative of future results. All investments carry risk, including the risk of loss of principal. Grey Sky Capital Private Limited is a SEBI-registered Portfolio Manager (Registration No. INP000009694). Investors are advised to consult their financial advisor before making investment decisions. The academic research cited herein is referenced for educational purposes; the studies' findings may not be replicated in future market conditions.

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