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Among the various ways of picking stocks, one is to screen them using quantitative criteria. If you do not have the time or skill to do so, there are mutual fund schemes to help you, technically called ‘quant funds’ or quantitative funds. SBI Mutual Fund (MF) and Motilal Oswal MF are expected to unveil their offerings soon. Eight funds in this category currently have assets under management worth Rs 5,471.6 crore.
Data-driven approach
Quant funds use a rule-based approach for portfolio construction based on quantitative models. The model selects stocks from a predefined universe without any human intervention. “Quant investing is primarily data-driven investing. Historical market data is interpreted to make investment decisions. Unlike other funds, they do not focus on fundamental stock-specific research, nor analyse company financials to create a portfolio,” says Sukanya Ghosh, quant analyst, SBI Mutual Fund. The eight quant schemes in the market show a wide range of returns, highlighting the variance in their quantitative models.
Many variables employed
A quantitative model typically considers multiple variables, including quantifiable factors like quality, growth, momentum, valuation, and macro factors such as investor sentiment, volatility, and money flow. The portfolio is rebalanced at regular intervals. “Quant funds gather extensive historical and real-time data, including market prices, financial statements, economic indicators, and even non-traditional data sources like social media sentiment,” says Pratik Oswal, head of passive funds, Motilal Oswal Asset Management.
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No fund manager risk
Quant funds eliminate the risk of fund manager decisions going wrong. “They are free of emotions and human biases. Systematic investment brings discipline and prevents emotional decisions,” says Utpal Sarma, head of business analytics, Tata Asset Management.
Less fleet-footed
These data-driven funds may struggle to quickly adapt to a rapidly changing environment. “Quant funds are mandated to churn or trade only at specific times. Hence, they find it difficult to react quickly to sudden market changes,” says Sarma.
“The reliance on historical data can limit their adaptability to new market conditions. Also, ‘overfitting’ is a common risk where models perform well on historical data but poorly in live markets, leading to suboptimal returns,” says Oswal. A quant-based approach may find it difficult to spot turnaround stories ahead of the market. This approach may also not work with smaller-sized companies with inadequate disclosures. Their models, if not tweaked periodically, could become irrelevant.
For seasoned investors
Quant funds are ideal for sophisticated investors. “They are suitable for investors who understand financial markets well,” says Oswal. Ghosh says these funds are ideal for investors keen to diversify their investments to rule-based strategies along with existing investments in active funds. Investors who choose quant funds should be prepared for volatility and drawdowns.
“Quant funds are for investors with higher risk tolerance. The potential for high returns comes with the possibility of significant losses,” says Oswal. Investors building a satellite portfolio may allocate up to 10 per cent of their equity portfolio to these schemes through systematic investment plans. “Allocation should depend on the investor’s risk profile. A higher allocation is recommended for those investing for the medium to long term,” says Sarma.
New or first-time investors can skip them. “The complexity of quant fund models can make it difficult for some investors to understand how their money is managed, leading to transparency-related concerns,” says Oswal.
