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Summary: Axis MF's fund manager explains why data-led strategies demand discipline, how their models adapt to shifting market drivers and why five years is the right lens for factor funds.
Quant and momentum investing may sound like buzzwords, but for Karthik Kumar, they boil down to discipline and data. The fund manager at Axis Mutual Fund steers the Axis Quant and Axis Momentum funds, along with several passive schemes, overseeing assets worth Rs 17,700 crore. Kumar argues that quant strategies move in cycles, delivering strongly in some phases while lagging in others. What matters, he says, are the “underlying drivers.”
In this conversation, Kumar explains how Axis’s quant model balances quality, growth and valuations, why momentum strategies can deliver outsized gains but also sharp drawdowns and why factor funds need at least a five-year horizon to prove their worth.
How does your quant framework respond to various market environments?
It’s not so much about whether markets are sideways or volatile; what matters are the underlying drivers. Those drivers determine whether a quant process works or not. And just to add a caveat, like every investment approach, quant models also go through cycles. Just as a fundamental manager has good and bad periods, quant strategies too won’t win all the time.
The underlying drivers play a key role. Let me give you an example. Last month was challenging for growth strategies. On August 15, the Prime Minister announced GST restructuring. The biggest beneficiaries were consumer-oriented sectors—consumer services, discretionary, etc. But prior to that, the earnings prospects of these companies were lukewarm. Guidance for the next few quarters was muted, and hence, growth expectations were low.
Then, after the Prime Minister’s announcement, consumer stocks rallied sharply in anticipation, despite no earnings upgrades. Now, a quant strategy is data-driven; it relies on reported growth metrics. So, in such an event-driven rally, the model can lag because the data hasn’t yet reflected the change. How it plays out later depends on what happens next. If consumer spending truly picks up and analysts revise earnings estimates upward, the quant model will capture those stocks when it rebalances. But if growth doesn’t materialise and earnings estimates stay flat or decline, the rally will reverse, and the model will benefit by avoiding these stocks. This is just an example of how a particular style can temporarily struggle in response to sudden market shifts.
Do you believe factor-based strategies like quant or momentum are more vulnerable in sideways or volatile phases, especially in sentiment-driven markets like India?
If markets are being driven by event-based factors, then it’s challenging for quant models. For example, since the US President took office, he has made a lot of policy changes, which have been difficult to predict. In such cases, factors and quant strategies may respond with a lag, as it takes time for data to reflect the new realities of the market.
But let’s contrast that with Covid. It was a volatile period, yet quant strategies performed very well as policy responses evolved over time, giving the market and the models enough data to adjust and adapt. That allowed factor-based approaches to reposition effectively.
So again, it’s not about whether markets are sideways or volatile. What matters are the underlying drivers. If those drivers are systematic and unfold over time, quant strategies adapt. If they’re purely event-driven and unpredictable, that’s when factors can face a tougher environment.
Can you explain in simple terms what the Axis Quant Fund model is and how it works? What are the main inputs you look at, and how do stocks eventually flow into the portfolio?
Put simply, the Axis Quant Fund follows the philosophy of growth at reasonable prices. Our benchmark is the BSE 200, and we work with that universe of stocks. Each company in this universe is ranked on three dimensions: quality, growth and valuations.
With quality, we study the company’s financial history—the balance sheet, income statement and cash flow—to see how the business has evolved over time. This helps us understand how management treats the business and its stakeholders.
Growth, on the other hand, is a forward-looking concept. We analyse earnings estimates for the next two to three years and also look at how those estimates are evolving. Macro factors also feed into this naturally.
Valuations act as a balancing factor. The goal is not to build a portfolio of only the cheapest stocks, but to make sure we are not overpaying at a portfolio level.
Each company then receives a combined score across these three pillars. The higher the score, the greater the weight in the portfolio. We also run the portfolio through a risk and liquidity framework to ensure it remains diversified, with no single stock or sector dominating. The portfolio is rebalanced at least once a month so that it continuously adapts to new data. That, in essence, is how the Axis Quant Fund works: a disciplined, data-driven approach that seeks growth at reasonable prices while keeping risk under control.
Quant Fund shows high exposure to large caps with limited allocation to mid and small caps. Is this a natural outcome of the liquidity and risk filters in the model, or is it a conscious design to avoid volatility?
The current allocation of around 67–68 per cent to large caps and the rest in mid and small caps (as of September 2025) is purely a bottom-up outcome. Of course, liquidity and risk filters play a role, but the larger driver is that the large-cap names we hold also look attractive at this point. It’s important to remember that our benchmark is the BSE 200. Compared to this benchmark, the fund actually has a higher allocation to mid caps.
How does your Momentum Fund work?
The Axis Momentum Fund is structured a bit differently. Most passive indices, like the Nifty Momentum indices, only look at six- and 12-month momentum. Our model is proprietary and considers momentum across multiple time periods, which adds more depth to the signal. The investment universe is closer to the Nifty 500, and each stock is scored on its momentum across these time frames. Higher scores imply higher expected returns, while lower scores suggest weaker prospects.
We also apply the same risk management framework as in the Axis Quant Fund because momentum is inherently a riskier strategy. While we cannot eliminate this risk, we attempt to mitigate it through constraints on stock and sector weights. Another key difference is rebalancing. Passive momentum portfolios are typically rebalanced every six months, but given that momentum is a fast-moving signal, we rebalance our portfolio at least once a month, which allows us to capture shifts more effectively.
The Axis Quant Fund has underperformed the benchmark over the past year. Does this reflect a weakness in the factors driving the model, or is it simply part of a natural down-phase of the quant cycle?
Both are linked. If the underlying factors have not worked, it becomes part of the quant cycle. This year has indeed been challenging because growth has struggled, momentum hasn’t worked, and most styles have barely kept up with the market. The only factors that have worked are value and, more importantly, low volatility. Low-volatility stocks, especially consumer names, have done particularly well. But when performance is so concentrated in just one or two styles, a diversified quant process tends to lag.
What about momentum specifically? Many of the schemes launched in 2021–23 are in the negative. Why has momentum struggled?
The last eight to nine months have been challenging for momentum. But let’s look at it in context. In 2023, both the Nifty 200 Momentum 30 Index and the Nifty 500 Momentum 50 Index outperformed their respective benchmarks by nearly 15 per cent. In 2024, they beat the benchmarks by high single digits. So, momentum strategies had two very strong years. After such a strong performance, it is natural for the style to take a breather, and that is exactly what we have seen in 2025.
Momentum, by design, tends to deliver outsized returns during its good phases but can also have sharper drawdowns when the cycle turns. This is why we’ve always emphasised that momentum is a higher-risk strategy, and allocations should be made accordingly. The recent underperformance is painful, but it is consistent with the natural cycle of momentum.
For retail investors who are seeing the recent underperformance, how should they think about factor funds? What is the realistic time horizon before one should judge them fairly?
Factor funds can be a useful diversification tool alongside traditional actively managed funds. The stock-selection process is entirely different, resulting in low overlap, which helps reduce concentration risk in a portfolio. That said, investors should view quant or factor funds through the same lens as they would any fundamental fund. These strategies are not designed for short-term performance. A realistic horizon would be around five years. Over such a period, the cycles of different factors play out, and investors get a fair picture of the fund’s ability to deliver.
Also read: 'Premiums in mid and small caps don't sustain; be cautious'
Disclaimer: This content is for information only and should not be considered investment advice or a recommendation.
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