Wind金融终端数据显示，截至12月11日，红利低波ETF天弘（159549）近20个交易日有18日获资金净流 入，累计"吸金"近5亿元。 此外，截至收盘，港股通央企红利ETF天弘（159281）成交额超3000万元，成分股中，第一拖拉机股 份、中国船舶租赁、中国银河等跟涨。 Wind金融终端数据显示，截至12月11日，港股通央企红利ETF天弘（159281）近20个交易日有17日获资 金净流入，累计"吸金"超1亿元。 红利低波ETF天弘（159549）紧密跟踪中证红利低波动100指数，该指数从沪深A股中选取100只流动性 好、连续分红、股息率高且波动率低的股票作为指数样本股，采用股息率/波动率加权，以反映A股市场 股息率高且波动率低的股票整体表现。 12月12日，市场探底回升，三大指数全线收红。 热门ETF中，截至收盘，红利低波ETF天弘（159549）净申购5000万份。成分股中，特变电工、陕鼓动 力、苏泊尔等跟涨。 港股通央企红利ETF天弘（159281）紧密跟踪港股通央企红利指数（931233），该指数从港股通范围内 选取中央企业实际控制的分红水平稳定且股息率较高的上市公司证券作为指数样本，以反映港股 ...

Quantitative Models and Construction Methods 1. Model Name: Style Rotation Model - **Model Construction Idea**: The model is built from basic style factors such as valuation, market capitalization, volatility, and momentum, gradually constructing a style timing and scoring system[4][9] - **Model Construction Process**: 1. Construct 640 micro features based on 80 basic micro indicators[9] 2. Use common indices as style stock pools to replace the absolute proportion division of style factors, constructing new style returns as labels[4][9] 3. Use a random forest model for style timing and obtain the current score for each style[4][9] 4. Integrate the timing results and scoring results to construct a monthly frequency style rotation model[4][9] - **Model Evaluation**: The model effectively avoids overfitting risks through rolling training of the random forest model and constructs a comprehensive framework from style timing to style scoring and from style scoring to actual investment[9] Model Backtesting Results 1. **Style Rotation Model**: - Annualized Return: 16.18%[10][11] - Volatility: 20.28%[10][11] - Information Ratio (IR): 0.80[10][11] - Win Rate: 59.43%[10][11] - Maximum Drawdown: 25.20%[11] 2. **Market Benchmark (Hedged)**: - Annualized Return: 10.36%[10][11] - Volatility: 10.85%[10][11] - Information Ratio (IR): 0.95[10][11] - Win Rate: 54.72%[10][11] - Maximum Drawdown: 8.53%[11]

Core Viewpoint - The performance of low-volatility dividend ETFs has shown a positive trend in the first half of 2025, with over 70% of the products experiencing growth in scale and overall net inflow of funds [1][3]. Group 1: ETF Performance - Among 14 major low-volatility dividend ETFs, the total scale reached 38.883 billion yuan, with a net increase of 6.846 billion yuan in the first half of the year [3]. - The Huatai-PB Low Volatility Dividend ETF (512890.SH) saw its scale surge to 18.741 billion yuan, contributing 73% of the total market growth for this type of ETF [3]. - Other notable performers include E Fund, Tianhong, and Harvest, with scale increases of 1.102 billion, 0.609 billion, and 0.480 billion yuan respectively [4]. Group 2: Market Dynamics - The expansion and differentiation of low-volatility dividend ETFs are primarily driven by investors' demand for yield certainty and risk control in a volatile market environment [7]. - Regulatory guidance on dividend ratios has reinforced the logic behind dividend strategies, making low-valuation assets with stable dividends more attractive [7]. - The concentration of funds towards well-recognized and larger-cap products is evident, as smaller or newer products struggle to attract significant investment [5][6]. Group 3: Product Differentiation - Despite being categorized under "low-volatility dividend," the specific tracking indices lead to varied fund flows, with mainstream broad-based indices seeing rapid growth while niche indices experience moderate inflows or even outflows [5][6]. - The Invesco Low Volatility Dividend ETF (515100.SH) faced a significant net redemption of 1.191 billion yuan, marking it as the most severely shrinking product in the market [6]. - The Huatai-PB Low Volatility Dividend ETF is noted as the first hundred-billion-level low-volatility dividend theme ETF, with a holder count exceeding 829,800, making it a standout in the market [7].