Over the last decade, Indian markets have quietly undergone a structural shift. Trading screens may still be full of tickers and charts, but behind them, a growing share of activity is now driven by code. Algorithmic and quantitative strategies already account for roughly half of total trading volumes in India, powered by faster systems, cheaper data and better regulation.
“In 2025, this ‘quant revolution’ has moved decisively from prop desks and foreign institutions into the portfolios of Indian high-net-worth individuals (HNIs). Nearly half of all new PMS launches this year are quant or factor-based, signalling that data-driven strategies are no longer niche - they are becoming the default way sophisticated investors try to beat benchmarks,” says Rohit Jha, Co-founder & CTO of ArthAlpha, a leading quantitative investment firm.
However, for HNIs, the real question is not “Is it high-tech?” It is: Does it deliver consistent, explainable outcomes without nasty surprises?
Traditional discretionary investing for HNIs often revolved around a star fund manager’s ‘feel’ for markets. When it worked, returns could be spectacular. When it didn’t, drawdowns were painful and hard to explain.
Data-driven investing flips this logic. Instead of a few big, heroic calls, it is built around rules and probabilities:
Define what has historically worked (for example, quality + reasonable valuations + earnings momentum).
Express that view as a set of rules or factors.
Apply those rules consistently across a broad universe of stocks.
Manage risk systematically at the stock, sector and portfolio level.
“The result is not a promise of outsized returns every year, but a higher probability of reasonable returns across market cycles. For HNIs juggling businesses, family responsibilities and global exposure, this focus on consistency matters more than chasing the next multibagger,” says Jha.
In the Indian context, ‘algorithmic’ or ‘quant’ can mean many things - from simple screeners to high-frequency trading. For HNIs investing through Portfolio Management Services (PMS), alternative investment funds (AIFs) or specialised funds, it typically means:
Factor-based equity portfolios – Portfolios tilted towards characteristics such as quality, value, momentum, low volatility or size, based on decades of academic and market research.
Rules-based risk management – Position sizing, stop-losses, hedging and diversification governed by pre-defined rules rather than emotion.
Evidence-led portfolio changes – Rebalancing driven by fresh data (earnings, price moves, fundamentals), not headlines or hunches.
“It does not mean black-box gambling in options or intraday F&O punting. In fact, SEBI’s recent push to regulate algo strategies and create specialised vehicles for higher-risk strategies is precisely to separate serious, regulated quant products from unregulated ‘tips-as-algos’,” informs Jha.
For an HNI, the safest rule of thumb is simple: if a strategy cannot be explained in plain language and backed by long-term data, it doesn’t qualify as genuinely data-driven.
One criticism of algorithms is that they feel opaque. HNIs are understandably wary of handing over crores to a ‘black box’. The good news is that serious quant investing is becoming more - not less -explainable.
A well-designed data-driven strategy should be able to answer, in simple terms:
1. What are the signals?
For example: “We prefer companies with high and stable return on equity, moderate leverage, improving earnings and reasonable valuations.”
2. Why do they work?
“Historically, portfolios built on these characteristics have outperformed the market over long periods because they capture investor under-reaction to improving fundamentals.”
3. When will it struggle?
“In sharp momentum-driven rallies led by low-quality or speculative stocks, the strategy may lag, but tends to catch up as fundamentals reassert themselves.”
4. How is risk controlled?
“We cap exposure to single stocks and sectors, keep cash or hedges in extreme conditions, and have drawdown rules that trigger de-risking.”
Explainability does not mean predicting every month’s return. It means you and your manager share a clear mental model of how the strategy behaves across different regimes, and you see that behaviour reflected in your portfolio.
The regulatory push in India is steadily nudging sophisticated investors towards structured, rule-based products. For instance, SEBI’s specialised investment fund framework for long-short and other advanced strategies with a minimum Rs 10 lakh ticket size.
For HNIs, the message is clear:
Avoid excitement masquerading as sophistication.
Prefer boringly consistent, rules-based approaches over adrenaline-fuelled bets.
Judge strategies on process quality and risk control, not on a single year’s headline return.
“Data-driven investing is not about replacing humans; it is about augmenting them,” says Jha.
According to him, algorithms are excellent at:
Processing large amounts of market and fundamental data,
Enforcing discipline, and
Reacting to predefined triggers without emotion.
Human investors and CIOs are essential for:
Setting the investment philosophy,
Choosing which risks to take (and which to avoid),
Interpreting regime changes (policy shifts, liquidity shocks, geopolitical events), and
Communicating with clients.
“The most robust HNI solutions in 2025 are not ‘man versus machine’ but ‘man with machine’ - clear human accountability sitting on top of a data-driven engine,” observes Jha.
In that sense, 2025 may well mark a turning point: the year Indian HNIs stop asking, “What’s the hottest idea right now?” and start asking, “What’s the most consistent, explainable process I can back for the next decade?”
That, more than any buzzword, is the real mantra of data-driven investing.
