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SBI Mutual Fund, the largest fund house of the country, will launch one of its kind active Quant fund based on four factors including momentum, value, quality and growth.
The new fund to be launched on Wednesday will use a rule-based approach for portfolio construction, guided by an in-house quant model.
The portfolio will consist of 30-35 stocks from top-200 stocks filtered by quant model developed by the fund house. The scheme will also invest in units of mutual fund schemes (including domestic and overseas ETFs) up to 20 per cent of the net assets of the scheme.
It may also seek investment opportunities in foreign securities including ADR/GDR/Foreign Equity, Overseas ETFs and Debt Securities subject to regulations. Such investment may not exceed 35 per cent of the net assets of the scheme and will be in line with the maximum limits available, it said.
India growth story
DP Singh, Deputy MD and Joint CEO, SBI Funds Management, said the SBI Quant fund is for those investors who believe in the India growth story and want to invest in equity with the benefit of periodic reviews through a rule-based investing framework.
By integrating established equity factors, each with distinct risk/return profiles, the fund aims to deliver optimal risk-adjusted returns and minimise behavioural biases, he said.
The multi-factor model incorporates factors such as momentum, value, quality and growth to optimise performance across various market cycles, he added.
The in-house algorithm will adjust the weightage of four factors based on their performance and will be readjusted to equal weight of 25 per cent each during risk-on times when weightage of the outperforming factor reaches the upper band 35 per cent. Similarly, rebalancing can also be triggered by the worst-performing factor if it reaches the lower band minus 8 per cent).
Nand Kishore, MD & CEO, SBI Funds Management, said multi-factor investing combines various factors rather than focusing on a single one, helping to smooth out the cyclicality of returns and reduce behavioural biases in factor selection.
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