Quant funds, quite popular abroad, are still few and far between in India.

There are only three funds (two of recent vintage) in the category with a rather small corpus as of October 2020 — Nippon India Quant (₹23 crore), DSP Quant (₹447 crore) and Tata Quant (₹92 crore). While the Nippon fund has been around since 2008, the DSP fund (set up in June 2019) and the Tata fund (January 2020) are of recent vintage.


Joining this small club is ICICI Prudential Quant, the new fund offer (NFO) of which closes on December 7.

It is an open-ended scheme, available for purchase at its net asset value (NAV) even after the NFO.

Similar to the other funds in the category, ICICI Prudential Quant seeks to invest based on a pre-defined, quantitative-based model. The idea behind quant funds is that by limiting human intervention and building portfolios based on set rules, behavioural biases in investing can be avoided.

These funds lie in a space between passively run exchange-traded funds (ETFs) and actively managed funds.

Different quant funds often follow different strategies; as such, their performance may not be comparable. The DSP fund, for instance, eliminates highly leveraged companies and chooses stocks based on high return on equity, and earnings growth consistency and potential. Tata Quant uses artificial intelligence and machine-learning to pick stocks, while Nippon India Quant follows a quality-cum-momentum strategy.

ICICI Prudential Quant Fund will filter stocks from the S&P BSE 200 index using a four-step approach — negative screening (eliminating stocks that don’t meet parameters), selecting (shortlisting companies based on macro, fundamental and technical factors), scoring (composite score for each company by giving equal weights to each parameter) and sizing (building portfolio of 30-60 stocks using the composite score and market-cap).

Model construct

The fund will be largely model-based with human intervention limited to eliminate stocks, based on corporate governance, at the first level.

To start with, the model will use four core fundamental parameters (factors) — price-to-earnings, dividend yield, return on equity, and analyst ratings — that will be given equal weights to arrive at the composite score for each stock.

Macro factors, other fundamental parameters and technical indicators such as RSI (relative strength index) and MACD (moving average convergence divergence) could also be used in the model, if need be.

There will be a cap of 10 per cent of portfolio at the stock level and 30 per cent at the sector level.

Based on the composite score and the market cap, weights will be assigned to the stocks to build a portfolio of 30-35 stocks; this could go up to 60 stocks if the corpus becomes large. The fund will have a large-cap tilt with the market-cap playing a part in the stock weightage. The scheme will have a non-tilt portfolio — neither pure value nor pure growth.

The fund will be fully invested in equity (minimum 95 per cent). Similar to other quant funds, it will be benchmarked to the BSE 200 TRI.

The portfolio will be rebalanced monthly to start with, and the parameters for stock selection will be reviewed on an annual basis.


ICICI Prudential Quant Fund’ model is said to have outperformed the benchmark in 10 out of 14 years till 2019, based on back-testing of data.

The performance of the existing three quant funds has been mixed. While the DSP and Nippon funds have done better than the benchmark over the past year, the Tata fund has lagged.

Also, over longer periods, Nippon has lagged the benchmark.

Quant funds are in an evolutionary phase in India — nascent but with scope for growth. This could be driven by, among other factors, the squeeze in alpha-generation (excess, risk-adjusted returns over the benchmark) especially in large-cap funds and the demand for more objective ways of stock selection.

That said, quant funds may not always work, especially in mid- and small-cap funds that offer good scope for alpha-generation. Also, the past is not always indicative of the future, and models, unless tweaked to reflect changing realities, may not work. Besides, swift, sharp market swings or major policy change impacts might be better-handled by quick human intervention than by quant-based models.