Key Points - The article discusses the construction and application of chip structure in stock selection, emphasizing the importance of understanding investor behavior and the distribution of funds at different price levels [2][6][19]. - It highlights the dynamic nature of chip costs, which are influenced by both buying and selling behaviors, and the necessity of detailed micro-trading data for accurate calculations [4][7]. - The analysis of chip structure allows for the identification of potential support and resistance levels in stock prices, providing a richer set of information compared to traditional volume-price factors [6][18]. Chip Average Cost Construction and Application - The construction of chip average cost involves calculating the weighted average cost of historical chips to depict the current market's average holding cost [8][9]. - A higher indicator value indicates that the historical average cost is above the current stock price, suggesting a floating loss for the market, while a lower value indicates a floating profit [9][10]. Improvement of Chip Structure Based on Fund Classification - Traditional methods of chip construction do not differentiate between different types of investors, which can obscure important behavioral information [19][20]. - By introducing fund flow data from institutions and retail investors, the article proposes a more nuanced approach to constructing chip distribution and weighted cost indicators for different investor types [19][21]. - The method shows slight improvements in factor IC and monotonicity of grouped returns, although the overall enhancement is limited [20][22]. Factor Synthesis and Performance Analysis - The article discusses the complementary relationship between the institutional-retail chip cost difference factor and traditional chip cost factors, suggesting that one contributes to predictive strength while the other enhances ranking stability [31][32]. - The synthesized chip factor achieves an IC mean of 4.35%, outperforming traditional volume-based chip factors and showing improved stability in multi-group performance [32][35]. - The performance of the synthesized factor varies significantly across different market capitalization segments, with better results observed in small to mid-cap stocks compared to large-cap stocks [41][42]. Market Capitalization Domain Results - The synthesized factor demonstrates stronger predictive capabilities in small-cap stocks, where investor behavior is more aligned with trading strategies that involve high turnover and profit-taking [45][46]. - Adjustments to the factor application in the mid-cap segment have led to improved performance, indicating the importance of tailoring strategies to specific market conditions [43][44].

证券研究报告 基于资金流数据的筹码结构因子构建 ——投资者分层视角下的信息增量 证券分析师：方思齐 A0230525090002 邓虎 A0230520070003 2026.3.17 主要内容 2 1. 筹码平均成本的构建与应用 2. 基于资金分类的筹码结构改进 3. 因子合成与表现分析 4. 风险收益提示 1.1 筹码结构在选股上的构建与应用 ◼ 筹码结构反映了投资者在不同价格水平上的资金持仓分布。 ◼ 然而，在实际研究中，筹码结构的精确测算需要基于一定的理想化假设完成。 • 筹码成本的动态变化同时受到买入与卖出行为的影响，每日资金的流入与流出都会改变整体筹码分布。但在现实数 据条件下如果想要计算精确筹码结构，不仅需要市场中逐笔层面的微观交易数据，同时也需要获取逐笔卖出单的持 有价数据。 www.swsresearch.com 证券研究报告 3 资料来源：Wind，申万宏源金工 1.1 筹码结构在选股上的构建与应用 $$C h i p_{T-k}^{T}=\,A m t_{T-k}\,{}^{*}\prod_{i=T-k+1}^{T}\,(1-T u r n o v e r_{i})\,,\qquad k ...