India’s mutual fund industry has 55 fund houses managing over 1,900 open-ended schemes, including more than 500 actively managed equity funds.
To differentiate themselves, a group of new fund houses is adopting a data-led approach rather than traditional active fund management. By allowing data and fixed rules to guide decisions, this system-driven strategy aims to eliminate fund-manager bias.
The approaches range from using factors based on back-tested market data to using a combination of factors and other data points.
Dynamic factors
Capitalmind Mutual Fund scores its investable universe on five factors—momentum, low risk, quality, profitability and value—but the weight each factor carries shifts with what it calls market-regime classification, said Anoop Vijaykumar, head of equity, Capitalmind Mutual Fund.
This regime interpretation is also entirely data-driven, evaluating metrics such as market breadth, the performance of an equal-weighted index relative to a market cap-weighted index, and how wide factor valuation spreads are compared to historical trends. These dynamic factor weights yield a composite score for every stock, which the model then uses to generate a portfolio.
A market regime refers simply to the current market phase—whether it is rising, falling, or moving sideways. Under this approach, individual factors target distinct stock characteristics: momentum favors equities in an uptrend; low risk selects stocks that are steadier than the broader market; quality targets companies with strong balance sheets; profitability focuses on operational efficiency; and value identifies stocks trading at reasonable valuations relative to their peers.
Some discretion is used if there are any major changes in corporate governance disclosures that are yet to show up in data, or if there is heavy concentration to a single business group.
Samco Mutual Fund runs a rules-based, model-driven strategy with momentum as its core factor. "The model tweaks the momentum strategy depending on the market regime. We've also built fundamental checks into the system, so it's momentum first, with a quality overlay," said Umeshkumar Mehta, chief investment officer, Samco Mutual Fund.
"Those checks screen for price-to-earnings multiples, leverage (debt) and free-cash-flow conversion — quality filters that sit inside the model, not outside it," he added.
"Momentum does best in strong bull markets. In sideways, bearish or mean-reverting phases, the model itself trims momentum exposure and rotates into quality or value names. The regime call is systematic, not discretionary," Mehta said.
A mean-reverting phase occurs when price trends reverse—as recent top performers lose steam and lagging stocks recover—a shift that can cause the momentum factor to underperform.
In the case of NJ Mutual Fund, the primary factor is quality. “We start with the quality filter, where we look at ratios such as return on equity, return on capital employed (the company’s internal capital-efficiency ratios), debt-to-equity ratio, dividend payout ratio, operating cash flow, etc,” said Nirmay Choksi, executive director, research, NJ Mutual Fund.
Operating cash flow acts as a crucial reality check, confirming whether a company's reported profits reflect actual cash generated by the business. On-paper profits that lack matching cash flows can often signal potential accounting red flags.
“Once we have a set of quality-filtered stocks, we follow a multi-factor adaptive approach. So, depending on the market environment, our model would suggest whether we need aggressive or defensive allocation. For example, in a risk-on environment, the model may suggest a more momentum-oriented portfolio within our quality basket, while in a risk-off environment, it might switch to a low volatility-oriented portfolio,” said Choksi.
A risk-on environment is when the market sentiment is strong and investors are willing to chase returns; risk-off is the opposite: nervier markets in which investors retreat to safer, steadier names.
Alternative data
Jio BlackRock Mutual Fund describes its investment strategy as "systematic active". Under this framework, the fund house evaluates about 400 data signals per Indian stock, actively deploying 30 to 50 metrics per fund. These signals are categorized into distinct factor buckets—such as valuation, quality, sentiment, and fundamentals momentum. "The sentiment bucket... is more about analyst sentiment," said Rishi Kohli, chief investment officer at Jio BlackRock Mutual Fund.
It also taps alternative data such as job postings. "If job postings at a company or in a sector are rising or falling, that can provide information about how the company or sector is doing," Kohli said, adding that news and online-sales feeds are also used.
AlphaGrep Mutual Fund combines a multi-factor, adaptive model with other data sources. “We have incorporated all major factors or styles (value, quality, momentum, etc) into our model and then adapt allocations as different factors come in and out of favour,” said Bhautik Ambani, chief executive officer of AlphaGrep Mutual Fund.
He added that while the portfolio diversifies across factors, it can be relatively overweight or underweight to certain factors depending on market conditions, which are analyzed by its model.
As for data, the model draws on analyst estimates and earnings revisions, news and social sentiment, order-book and flow & stock ownership data, and analyst call transcripts. "The idea is to capture both long-term drivers and short-term signals that move prices," Ambani said.
Uncharted waters
Many of these strategies are developed and back-tested using historical market data. While these fund houses aim to deliver more resilient, tech-driven models by drawing on broader datasets, their real-world performance across full Indian market cycles remains to be proven.
“Several of these rule-based strategies have been widely tested globally and have demonstrated performance. So there is a case for having some portfolio allocation to data-based strategies. But at the same time, these strategies have been backtested on historical data. So we need to see how these strategies do over longer periods in Indian markets,” said Vishal Dhawan, founder of Plan Ahead Wealth Advisors.