Bandhan Mutual Fund files draft document with Sebi for quant fund

Bandhan Mutual Fund has filed a draft document with Sebi for a quant fund. Bandhan Quant Fund will be an open-ended equity scheme investing based on an artificial intelligence (‘AI’) / machine learning (‘ML’) i.e. an adaptive and evolving quantitative model theme.

The investment objective of the scheme will be to generate medium to long term capital appreciation by investing predominantly in equity and equity related instruments selected based on an AI / ML i.e. an adaptive and evolving quantitative model.

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The scheme will be benchmarked against BSE 200 TRI. This thematic fund will be managed by Nemish Sheth (equity investments), Brijesh Shah (debt investments), Ritika Behera and Gaurav Satra (overseas investments).

An exit load of 0.50% of the applicable NAV will be applied, if redeemed/switched out on or within 30 days from the date of allotment. The exit load will be nil, if redeemed/switched out after 30 days from date of allotment.

The minimum application amount for lumpsum investment will be Rs 1,000 and in multiples of Re 1 thereafter. For SIP, the minimum amount will be Rs 100 and in multiples of Re 1 thereafter with minimum six installments. The minimum amount for STP will be Rs 500 and any amount thereafter.

Bandhan Quant Fund will allocate 80-100% in equity and equity related instruments selected based on quantitative model theme, 0-20% in equities and equity related overseas securities, 0-20% in debt securities and money market instruments (including Government securities, Securitised debt), and 0-10% in units issued by REITs & InvITs.

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Bandhan Quant Fund will be an open-ended thematic scheme that has a data-driven investment approach by integrating diversified quantitative factors. The fund house aims to construct a well-balanced portfolio of predominantly large and mid cap companies that maximizes return while managing risk effectively.

The portfolio captures investment styles using conventional (such as Earning Quality, Value and Momentum) and alternative factor premiums (Analyst Revision, Analyst Estimates and Ownership data) combining AI / ML techniques with Fund Manager expertise.

The scheme will be suitable for investors who are seeking to create wealth over a long term and want investment predominantly in equity and equity related instruments based on an AI / ML i.e. an adaptive and evolving quantitative model.