Equity benchmarks of Bombay Stock Exchange and National Stock Exchange continued to touch new highs on strong buying in the…
Bombay Stock Exchange (PHOTO: AFP)
Equity benchmarks of Bombay Stock Exchange and National Stock Exchange continued to touch new highs on strong buying in the run up to the Budget 2018-19 scheduled for 1 February.
Bulls were further charged up on account of firm domestic and external cues. Highlight of the day was Nifty Bank surpassing 26,000 milestone to all time high of 26,091.80 (+34275) points, mainly on strong buying in private lenders HDFC Group and ICICI Bank. Market analysts see clear signs of economic recovery and revival of industrial sector after several quarters of virtual stagnation. Overall earnings data so far have been on expected lines.
Advertisement
The Sensex in early morning deals surged to 34,963.69 (+371.30) points and Nifty peaked 10,782.65 (+101.40) points before slowing down a bit in the afternoon trade. These benchmarks closed the day at 34,843.51 (+251.12) points and 10,741.55 (+60.30) points respectively, increasing 0. 73 per cent and 0.56 per cent.
Advertisement
Bank shares led by ICICI Bank and HDFC Bank rose sharply as Nifty Bank of NSE ended the day at record high of 26,069.20 (+320.15) points, increasing 1.24 per cent.
In Sensex, ten shares advanced, 19 declined and two remained unchanged. For Nifty, the ratio was 20:30. HDFC Group shares such as HDFC Bank, Housing Development Finance Corporation and HDFC Standard Life increased in tandem between 2 per cent and 6.5 per cent on buying by domestic and foreign funds. The rise in these shares was on account of HDFC board’s decision to raise up to`13,000 crore through Qualified Institutional Placement (QIP) in preferential shares.
Analysts say it would be worth watching whether the BSE and NSE’s prime indices surpass 35,000 and 11,000 marks ahead of the budget presentation. Foreign funds have returned bourses adding to liquidity glut. In the first ten days of January FPIs are buyers in stocks worth `2,172 crore, according to data with NSDL.
Advertisement
