In a year marked by sharp volatility, stretched valuations, and cross-asset turbulence, protecting downside risk became as critical as generating returns.
In this edition of ETMarkets PMS Talk, Karan Aggarwal, Co-founder and CIO of Ametra PMS, explains how a tactical allocation strategy—with nearly 70% exposure to debt and gold—helped cushion portfolios during FY26.
He discusses the role of asset allocation and factor rotation in navigating uncertain markets, the importance of reducing timing risk in equities, and how a rule-based, multi-asset approach can deliver more consistent outcomes across market cycles. Edited Excerpts –
Q) Thanks for taking the time out. The strategy delivered around 10% return in the last 1 year (FY26). How do you interpret this performance in the context of a volatile market environment?
A) Last 12 months have been quite volatile across asset classes. While midcap and largecap remained time correction mode till Feb 2026, oil shock in March 2025 triggered broke patience of investors and triggered a 10% correction across benchmarks.
Deadly cocktail of faltering EPS growth (5%-10%) and high valuations (22-23x for largecaps and 32x for midcaps) ensured all attempts at breakouts above Sep 2024 failed amidst positive policy action such as repo rate cuts, GST moderation and US-India deal.
Things have been worse in smallcaps and microcap space with benchmarks spending most of year at drawdowns of 10%-20% against all-time time highs of Dec 2024.
Even in fixed-income space, yield rose sharply in last 9 months, leading to negative returns in long-term fixed-income securities. On the other hand, gold and silver delivered a one-dimensional one-in-a-generation rally with triple-digit returns in 2nd half of 2025.
If we look back at history of Indian financial markets, these kinds of trend-neutral periods marked by rich equity valuations, EPS stress, yield risk, violent moves in commodities and high geoeconomic risk has happened before as well as seen in 2011-2013 and 2017-2019.
Factor income has been tested for delivering double-digit returns in such trend-neutral market regime with tactical exposure to equities, debt and gold. In this context, 10% returns from strategy were on expected lines in times when most hybrid schemes are struggling to deliver low single-digit returns.
Q) Compared to the benchmark, the 1-year performance appears relatively resilient. What worked in FY26—asset allocation, factor rotation, or risk management?
A) Asset allocation and factor rotation decision for strategy are taken about a proprietary tactical model which provide a medium-to-long term leading indication about market volatility.
Based on tactical model, our view was cautious with nearly 70% allocation to debt and gold while 30% allocation was towards low-risk equities concentrated in largecap and midcap stock scoring high on low volatility, dividend and quality factors.
While for first 11 months, both asset allocation and factor rotation worked in our favor with gold rally and low-risk equity delivered outperformance over equity/hybrid funds, heavy debt allocation in cut down downside by nearly 50% during a period marked by oil shock in March 2026, accounting for 100% outperformance in the month.
Q) Would you classify FY26 as a year of defensive outperformance or missed upside, given the strategy’s diversified nature?
A) FY26 was a year marked by diversification-led defensive outperformance when benefits of asset diversification and negative correlation among gold, equity and debt protected the gains made in good months against market volatility during bad months.
As FY 2026 was marked by failed breakouts and eventual break down at end of year, defensive posturing helped in protecting returns in last 2 months of the year.
Having said that, our rule-based approach restricted our gold exposure at some pre-defined levels which restricted our upside to a significant extent. However, these are the opportunity costs that comes with risk management.
Q) Your strategy combines asset diversification and factor investing. What makes this combination more effective than traditional equity-heavy portfolios?
A) Factor investing technique revolves around identification of fundamental and technical attributes (referred as factors in technical parlance) which explain outperformance of winning stocks over broader market with value, dividend, low volatility, quality, Momentum, Alpha and Size are identified as common factors.
Interestingly, each factor comes with its own unique market cycle and risk-return trade-off. For example, factors such as low volatility, dividend and defensive quality maximize their outperformance during bearish phase and more suitable for low-risk investors while high-risk factors such as Momentum, Smallcap and Jenson’s Alpha maximize outperformance during bullish phase which create opportunity to generate ‘alternate beta’ which is missing in traditional equity products.
By tactically rotating into suitable factor in line with market conditions, investors can create all-season outperformance for 3-5 year holding period across market conditions.
For example, during 2008 crash, most equity-heavy portfolios delivered losses of 60%-80% while a concentration in low-risk equity factors reduce the losses by 50% to around 30%. Here, asset diversification actually sweetens the deal even more.
As debt, gold and equities have either negative or near-zero correlation, their additional to the mix can make ‘alternate beta’ even more attractive. For example, asset diversification towards gold and debt further cut the downside to mere 15% during 2008 crash.
Continuing our example, as markets turned the tide in 2009, not only weight of equities was increased to capitalize on bullish trend but equity slice risk was also increased with bias towards high-risk factors Momentum and smallcap.
This mix of Asset rotation and factor rotation deliver all-season alpha neutralizing timing risk associated with traditional equity strategies.
To further build on example, while most equity-heavy portfolios delivered negative to zero returns over 6-year period of Dec 2007-Dec 2013, strategy model delivered double-digit returns of 17.26% over the same period.
Q) You highlight “timing risk” in equities. How does your model specifically mitigate this risk across market cycles?
A) Our tactical model provides us with a medium-to-long term market volatility signal which triggered asset rotation and factor rotation for the strategy. For example, if volatility signal predict spike in volatility, allocation to debt and gold is increased while introducing heavy bias towards low-risk factors in equity slice.
On the other hand, if model indicate a volatility moderation in future, equity allocation is raised with bias towards high-risk factors.
Strategy plays on both end of spectrum by reducing the risk in bad times and increasing the risk in good times, ensuring outperformance across market conditions and neutralizing the timing risk.
Q) The portfolio allocates across equities, debt, commodities, and international exposure. How do you decide the optimal mix at different points in the cycle?
A) Traditionally, cross-asset correlations between debt, gold, equities and internation equities is negative or less than 0.40 – means assets rarely move together.
Going by the track record of last 20 years, equities as long-term investment has been a winner with nearly 12% annualized returns over long-term holding period with even worst 10-year holding period has delivered around inflation beating returns around 7%.
However, these inflation-beating returns comes with substantial risk of short-term drawdowns. Over the last 20 years, there have 3 instances where Nifty 500 went down by more than 30% from all-time highs. In these cases, investors starting their journey at peak have to wait for many years to see gains on their portfolio.
Strategy is heavily biased towards equity in bullish phase with 60%80% allocation to domestic and international equities most of the time.
However, based on tactical model signal, strategy increase debt and gold allocation to 70% during the bad times or period expected to deliver underwhelming equity returns, protecting against the losses and take the ‘timing risk’ out of equation.
Q) Your model uses tactical signals and factor rotation. Can you explain how these signals are generated and how frequently they lead to portfolio changes?
A) Strategy used a proprietary rule-based tactical model to generate signals which trigger asset rotation and factor rotation. Model used multiple market and economic parameters such as US VIX, India VIX, Nifty 50 line Regression premium, GoI bond yields, Nifty 50 P/E ratio, gold prices, Nifty broad benchmark levels to generate an output which provide insight about market direction in medium-to-long-term.
Though, signals are generated on daily basis but portfolio changes are triggered on an average in 6-18 months. For example, model has been tested over last 20 years and model has generated change only 15 times.
Q) The strategy shows relatively moderate volatility and controlled drawdowns. What are the key levers you use to manage downside risk?
A) Strategy has multiple protection shields in form of low-cross-asset correction among asset classes, asset rotation and factor rotation, which work on sync to keep with risk at 50% of traditional equity products while delivering almost similar or sometimes, even better returns.
Though multi-asset offerings come with lower risk on account of low corrections, strategy improves on return/risk trade-off by moving to risk extremes – high risk in good times (high equity allocation and high equity beta) and low risk in bad times (low equity exposure and low equity beta) – through tactical asset rotation.
These levers allow strategy to reduce drawdowns to 10%-15% when Nifty 50 was down by 40%-70% during 2008 and 2020 bear markets.
Q) The strategy has delivered over 20% CAGR since inception (back-tested). How should investors interpret these numbers given the role of back-tested data?
A) Strategy follows a rule-based approach during back testing around asset class exposure, tactical shifts and security selection mechanism with rules been tested across 20 years covering 3 bear markets, multiple bull markets, interest rate cycles, inflation cycles and geopolitical events.
In a country like India, 20 years provide a reliable dataset on capability of these rules in delivering outperformance across market conditions.
These same rules have been casted into rules and leverage in portfolio construction and management with the perception that strategy would continue to deliver similar performance over in 10-15 years as market cycles tend to repeat themselves over time.
Ideally, investors are advised to look at numbers to evaluation efficiency of model in its tactical calls around asset rotation and factor rotation.
Having said that, there is always a risk that some economic megatrend might not be covered in the model, but that risk comes with every investment vehicle as ‘past performance is not an indicator of future returns.
Q) The strategy aims to deliver regular income with inflation-beating compounding. How do you balance income generation with long-term capital growth?
A) Strategy is designed to deliver 18%-22% CAGR for 10-year holding period without participation in downside risk during bear market with investor having an option to withdraw 1% of principle in form of income every month – translating to 12% annual income for investors.
As most products delivering >12% returns come with substantial downside risk, income withdrawals are often unsustainable if you are starting at peak of market as corpus is burned out.
However, capability of strategy is delivering similar returns while restricting downside allow it to service income needs of investors without ‘timing risk’.
For example, even an investor investing at peak of 2007 would have continued to get regular income and ending with CAGR of 18% after 10 years.
Q) You follow a rules-based approach for security selection, weighting, and rebalancing. How much human discretion is involved versus model-driven decisions?
A) 90%-100% of portfolio construction including asset allocation, security selection, weighing and rebalancing are driven by rule-based tactical and factor models as back testing around these rules form the core of the strategy.
At times, around 5%-10% of funds are diverted in line with manager’s discretion on some high-conviction trades.
(Disclaimer: Recommendations, suggestions, views, and opinions given by experts are their own. These do not represent the views of the Economic Times)
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