The initial public offering (IPO) of Data Patterns India was subscribed 3.3 times on December 14, the first day of bidding. The issue received bids for 2.34 crore equity shares against the IPO size of 70.97 lakh equity shares. Retail investors submitted bids 5.89 times of the portion reserved for them.
Non-institutional investors placed bids 1.46 times the allotted quota. Qualified institutional buyers applied for 1,650 shares against the reserved portion of 19.58 lakh shares. The initial public offer comprises a fresh issue component of up to Rs 240 crore and an offer for sale up to 59,52,550 equity shares.
The share sale has a price band of Rs 555-585 per share. The company collected Rs 176 crore from anchor investors on December 13, a day ahead of the IPO.
The company intends to utilise the net proceeds from the fresh issue for repayment of the debt, funding its working capital and up-gradation and expansion of its existing facilities besides general corporate purposes.
The issue through which the firm plans to collect Rs 588.2 crore will close on December 16.
Shares of the IPO will be allotted to eligible investors on December 21 and the stock is likely to be listed on December 24, 2021. Investors can bid for a minimum of 25 equity shares or one lot by spending Rs 14,625. A retail individual investor can apply for up to 13 lots or 325 shares by spending Rs 1,90,125.
IIFL Securities Ltd and JM Financial Consultants are the book running lead managers to the issue.
Data Patterns is backed by former Blackstone head Matthew Cyriac via Florintree Capital Partners LLP, which holds a 12.8 per cent stake in the company.
Founded by Srinivasagopalan Rangarajan and Rekha Murthy Rangarajan, Data Patterns is a vertically integrated defence and aerospace electronics solutions provider that caters to requirements across space, air, land and the sea.
Its core competencies include design and development across electronic hardware, software, firmware, mechanical, product prototype besides its testing, validation and verification.