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10月30日龙虎榜,机构青睐这9股
Zheng Quan Shi Bao Wang·2025-10-30 13:17

Core Points - On October 30, the Shanghai Composite Index fell by 0.73%, with institutional investors appearing on the trading lists of 29 stocks, net buying 9 and net selling 20 [1][2] - The total net selling amount by institutional special seats was 818 million yuan, while the stocks with the highest net buying included Foxit Software, Yongxing Materials, and Tianji Co., with significant price increases [1][3] Institutional Net Buying - Foxit Software had the highest net buying amount of 76.23 million yuan, closing up by 15.69% with a turnover rate of 12.83% and a total transaction volume of 1.043 billion yuan [2][5] - Yongxing Materials reached the daily limit, with a net buying of 59.89 million yuan, a closing increase of 10.01%, and a turnover rate of 10.15% [2][5] - Tianji Co. also hit the daily limit, with a net buying of 59.21 million yuan, a closing increase of 10.00%, and a turnover rate of 27.98% [3][5] Institutional Net Selling - The stock with the highest net selling was Keda Guokuan, with a net selling amount of 358.81 million yuan, and a turnover rate of 41.42% [4][6] - Daway Co. had a net selling of 140.98 million yuan, with a turnover rate of 43.07% [4][6] - Samsung Medical saw a net selling of 112.19 million yuan, with a daily decline of 10.01% [4][6] Market Performance - The average increase of stocks with net buying by institutions was 5.61%, outperforming the Shanghai Composite Index [3] - Among the stocks with net buying, 9 had reported their Q3 earnings, with JN Holdings showing the highest net profit growth of 138.02% year-on-year [3] Stock Connect Activity - On October 30, 18 stocks on the trading list had either Shanghai or Shenzhen Stock Connect participation, with net buying amounts for Guodun Quantum and Penghui Energy being 406.26 million yuan and 217.60 million yuan respectively [7][8] - Stocks like Samsung Medical and Yingxin Development had significant net selling amounts of 1.03 billion yuan and 37.14 million yuan respectively [7][9]