超56亿，“跑了”

Core Viewpoint - On November 13, the A-share market experienced a net outflow of over 5.6 billion yuan in stock ETFs, despite the overall market rising, indicating a trend of profit-taking among investors [2][3][4]. Fund Flow Summary - The stock ETF market saw a net outflow of over 5.6 billion yuan on November 13, with significant inflows into industry-themed ETFs such as artificial intelligence, dividends, food and beverage, and Hong Kong financials [3][5]. - Since the beginning of November, the total net inflow into stock ETFs has exceeded 20 billion yuan, with Hong Kong technology, innovative pharmaceuticals, securities insurance, and electric grid equipment being the main beneficiaries [3][10]. - On the same day, 19 stock ETFs recorded net inflows of over 100 million yuan, with the top three being the Southern Entrepreneurial AI ETF, the Huatai-PB Hong Kong Innovative Pharmaceutical ETF, and the GF Hong Kong Non-Bank ETF, each with inflows exceeding 400 million yuan [5][6]. ETF Performance - The total scale of the stock ETF market reached 4.69 trillion yuan as of November 13, with 31 ETFs experiencing net outflows exceeding 1 billion yuan [5][10]. - The top three ETFs with the highest net inflows were the Southern Entrepreneurial AI ETF (5.45 million yuan), the Huatai-PB Hong Kong Innovative Pharmaceutical ETF (5.04 million yuan), and the GF Hong Kong Non-Bank ETF (4.68 million yuan) [8]. - Conversely, the top outflow ETFs included the Entrepreneurial ETF, which saw a net outflow of 1.35 billion yuan, and the Non-Ferrous Metals ETF with a net outflow of 580 million yuan [12]. Market Outlook - Analysts suggest that the current market is in a transitional phase between policy and earnings disclosures, with upcoming important meetings expected to clarify economic and policy directions for the following year [10][11]. - The overall sentiment remains positive, supported by economic resilience, clearer policies, and favorable liquidity conditions, indicating that the A-share market may continue its strong performance [11].