The approach can bring greater consistency to portfolio construction, but it also has limitations.
Since many quantitative models are based on historical market data, their performance can vary when market conditions change.
How quantitative investing works
Quantitative strategies use predefined rules to select stocks or construct portfolios. Depending on the strategy, models can consider factors such as value, quality, momentum and volatility.
For example, a value-based strategy may identify stocks that appear inexpensive on certain financial measures, while a momentum strategy may select stocks based on their recent price trends.
According to Rishabh Nahar, Partner and Fund Manager at Qode Advisors, a Mumbai-based, SEBI-registered quantitative wealth management and Portfolio Management Service (PMS) firm, quantitative investing shifts portfolio construction towards a more systematic, data-driven process. By following predefined rules, such strategies can reduce the role of emotions such as fear during market declines or the tendency to chase strong market rallies.
Quantitative investing was earlier more commonly associated with institutional and high-net-worth investors, partly because of the investment requirements and structure of alternative investment funds. The availability of quant-based mutual funds, Specialised Investment Funds (SIFs) and PMS strategies has expanded access to a wider set of investors, Nahar said.
Quant strategies are becoming more visible
The number of investment products using quantitative or factor-based approaches has also increased.
Bharath Rathore, Executive Director at Anand Rathi Wealth, an Indian non-bank wealth solutions firm, said passive quant-based, quant-themed and factor-based strategies have increased from around 50 in 2024 to more than 120 in 2026. He said assets under management in these strategies have risen from around ₹21,000 crore to ₹66,000 crore over the same period.
However, the growing number of products does not mean all quant strategies work in the same way. Different strategies can produce very different outcomes depending on the factor used and the prevailing market environment.
Historical data has its limitations
One of the key limitations of quantitative investing is its dependence on historical data.
A model identifies patterns from past market behaviour and uses predefined rules to make investment decisions. However, those patterns may not necessarily continue in the future.
Rathore noted that factors such as Value, Momentum and Quality can underperform for extended periods. He also pointed out that many quantitative strategies in India have relatively short track records, making it difficult to assess their performance across several market cycles.
Recent performance also demonstrates this variation.
Rathore said many quant strategies performed well in calendar year 2023, but did not perform as strongly in 2024 and 2025, when the broader market and diversified investment categories performed better.
This means investors cannot assume that a particular factor will outperform consistently across market cycles.
Volatility can test quantitative models
Sharp market movements can pose another challenge for quantitative strategies.
Models may struggle to respond to events that have little historical precedent, such as sudden policy changes or other unexpected shocks. A model may also take time to distinguish between a temporary market movement and a lasting change in market conditions.
Liquidity can become another concern during periods of market stress. If trading volumes fall sharply, exiting positions can become more difficult and may affect the price at which securities are sold.
At the same time, Nahar said quantitative approaches can be used to conduct scenario analysis and stress tests. These exercises can help fund managers assess how portfolios could behave under different market conditions and establish risk-management rules in advance.
Rebalancing can affect performance
The frequency at which a quantitative strategy changes its portfolio can also be important.
Rathore noted that many factor-based funds and ETFs track indices that are rebalanced quarterly or every six months. If a stock’s characteristics change between two rebalancing dates, the index-based strategy may not be able to respond immediately.
This is different from an actively managed fund, where a fund manager can generally change the portfolio based on their assessment of changing company or market conditions.
Quant investing does not necessarily replace fundamental investing
The rise of quantitative strategies does not mean traditional fundamental investing is becoming irrelevant.
Fundamental investing involves analysing factors such as a company’s financial performance, business prospects, valuation and management, with investment decisions based on the fund manager’s research and conviction.
Rathore said quantitative investing is unlikely to replace this approach. Nahar, meanwhile, sees quantitative and fundamental investing as approaches that can complement each other.
Human involvement also remains important in quantitative investing. Fund managers and investment teams design the models, select the factors, test strategies, monitor their performance and make changes when necessary.
What investors should understand
For investors considering a quantitative or factor-based strategy, the label alone does not provide enough information. The underlying methodology matters.
Investors should understand:
- which factors the strategy uses;
- how the stocks are selected and weighted;
- how frequently the portfolio is rebalanced;
- how the strategy has performed across different market conditions; and
- what risks arise if the underlying factor remains out of favour.
Quantitative investing can provide a systematic approach to portfolio construction, but it does not remove investment risk. Its effectiveness can vary across market cycles, and historical performance does not guarantee that the same factors will continue to work in the future.
