- **Tracking indicators and their calculation** The report uses two indicators: the proportion of large buy order transaction amount and the proportion of net active buy transaction amount. The large buy order transaction amount proportion reflects the buying behavior of large funds. It is calculated by restoring transaction data into buy and sell orders based on transaction sequence numbers, filtering large orders by transaction volume, and computing the proportion of large buy order transaction amounts to the total daily transaction amount. The net active buy transaction amount proportion reflects investors' active buying behavior. It is calculated by identifying whether each transaction is an active buy or sell based on transaction markers, subtracting active sell amounts from active buy amounts, and computing the proportion of net active buy amounts to the total daily transaction amount[7] - **Individual stock tracking** The report provides rankings of stocks based on the 5-day average proportion of large buy order transaction amounts and net active buy transaction amounts. For example, the top-ranked stock for large buy order transaction amount proportion is "海陆重工" with a value of 93.0% and a time-series percentile of 100.0%. Similarly, the top-ranked stock for net active buy transaction amount proportion is "力聚热能" with a value of 21.2% and a time-series percentile of 100.0%[9][10] - **Broad-based index tracking** The report calculates the 5-day average proportions of large buy order transaction amounts and net active buy transaction amounts for major broad-based indices. For instance, the "上证指数" has a large buy order transaction amount proportion of 73.6% (percentile: 66.0%) and a net active buy transaction amount proportion of -4.1% (percentile: 99.6%)[11][12] - **Sector tracking** The report calculates the 5-day average proportions of large buy order transaction amounts and net active buy transaction amounts for various sectors. For example, the "石油石化" sector has a large buy order transaction amount proportion of 78.3% (percentile: 100.0%) and a net active buy transaction amount proportion of 5.0% (percentile: 27.0%)[13] - **ETF tracking** The report ranks ETFs based on the 5-day average proportions of large buy order transaction amounts and net active buy transaction amounts. For example, the top-ranked ETF for large buy order transaction amount proportion is "国泰上证 10 年期国债 ETF" with a value of 93.7% and a time-series percentile of 100.0%. Similarly, the top-ranked ETF for net active buy transaction amount proportion is "国泰上证 10 年期国债 ETF" with a value of 24.7% and a time-series percentile of 84.4%[15][16]

Quantitative Factors and Construction Methods - **Factor Name**: Large Buy Order Transaction Amount Ratio **Construction Idea**: This factor captures the buying behavior of large funds by analyzing the proportion of large buy orders in the total transaction amount for a given day[8] **Construction Process**: 1. Utilize tick-by-tick transaction data to identify buy and sell orders based on bid and ask sequence numbers[8] 2. Filter transactions by volume to identify large orders[8] 3. Calculate the proportion of large buy order transaction amounts to the total transaction amount for the day[8] **Evaluation**: This factor effectively reflects the behavior of large funds in the market[8] - **Factor Name**: Net Active Buy Transaction Amount Ratio **Construction Idea**: This factor measures the active buying behavior of investors by calculating the net active buy transaction amount as a proportion of the total transaction amount for a given day[8] **Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on the buy/sell flag[8] 2. Subtract the active sell transaction amount from the active buy transaction amount to obtain the net active buy transaction amount[8] 3. Calculate the proportion of net active buy transaction amount to the total transaction amount for the day[8] **Evaluation**: This factor effectively captures the active buying behavior of investors in the market[8] --- Factor Backtesting Results Large Buy Order Transaction Amount Ratio - **Top 10 Stocks (20251020-20251024)**: 1. Stone Machinery (000852.SZ): 88.4%, 99.2% time-series percentile[10] 2. ShenKai Shares (002278.SZ): 87.0%, 100.0% time-series percentile[10] 3. Oriental Garden (002310.SZ): 86.4%, 96.7% time-series percentile[10] 4. Wuhan Holdings (600168.SH): 86.1%, 97.1% time-series percentile[10] 5. Guangtian Group (002482.SZ): 85.5%, 91.4% time-series percentile[10] 6. Zhengbang Technology (002157.SZ): 85.4%, 99.2% time-series percentile[10] 7. Oriental Electric Heating (300217.SZ): 85.4%, 97.5% time-series percentile[10] 8. Nengte Technology (002102.SZ): 85.3%, 83.6% time-series percentile[10] 9. Xianfeng Holdings (002141.SZ): 85.3%, 97.5% time-series percentile[10] 10. Qingsong Jianhua (600425.SH): 85.1%, 93.4% time-series percentile[10] Net Active Buy Transaction Amount Ratio - **Top 10 Stocks (20251020-20251024)**: 1. Tangshan Port (601000.SH): 20.7%, 97.1% time-series percentile[11] 2. Changqing Shares (603768.SH): 17.0%, 100.0% time-series percentile[11] 3. Shuangyuan Technology (688623.SH): 16.3%, 99.6% time-series percentile[11] 4. Guotou Power (600886.SH): 16.3%, 98.0% time-series percentile[11] 5. Fenglong Shares (002931.SZ): 16.0%, 100.0% time-series percentile[11] 6. Gongdong Medical (605369.SH): 14.9%, 99.2% time-series percentile[11] 7. Zhaoxun Media (301102.SZ): 14.8%, 100.0% time-series percentile[11] 8. Fantuo Digital Creation (301313.SZ): 14.6%, 100.0% time-series percentile[11] 9. Huali Group (300979.SZ): 14.6%, 99.6% time-series percentile[11] --- Broad Index Backtesting Results - **Large Buy Order Transaction Amount Ratio (20251020-20251024)**: 1. Shanghai Composite Index: 75.2%, 61.5% time-series percentile[13] 2. SSE 50: 73.9%, 23.0% time-series percentile[13] 3. CSI 300: 75.5%, 77.9% time-series percentile[13] 4. CSI 500: 76.0%, 68.4% time-series percentile[13] 5. ChiNext Index: 75.2%, 76.6% time-series percentile[13] - **Net Active Buy Transaction Amount Ratio (20251020-20251024)**: 1. Shanghai Composite Index: -0.8%, 78.7% time-series percentile[13] 2. SSE 50: 3.3%, 96.3% time-series percentile[13] 3. CSI 300: 2.3%, 95.1% time-series percentile[13] 4. CSI 500: 0.8%, 86.9% time-series percentile[13] 5. ChiNext Index: 5.3%, 100.0% time-series percentile[13] --- Industry Backtesting Results - **Large Buy Order Transaction Amount Ratio (20251020-20251024)**: 1. Banking: 80.7%, 91.0% time-series percentile[14] 2. Steel: 79.5%, 3.3% time-series percentile[14] 3. Non-Banking Finance: 79.2%, 33.2% time-series percentile[14] 4. Comprehensive: 79.1%, 35.7% time-series percentile[14] 5. Real Estate: 78.7%, 34.0% time-series percentile[14] - **Net Active Buy Transaction Amount Ratio (20251020-20251024)**: 1. Electronics: 8.0%, 74.2% time-series percentile[14] 2. Communication: 7.4%, 96.3% time-series percentile[14] 3. National Defense and Military Industry: 3.5%, 35.7% time-series percentile[14] 4. Computers: 2.6%, 89.3% time-series percentile[14] 5. Automobiles: 2.6%, 60.2% time-series percentile[14] --- ETF Backtesting Results - **Large Buy Order Transaction Amount Ratio (20251020-20251024)**: 1. Bosera China Education ETF: 91.2%, 100.0% time-series percentile[16] 2. Huaxia Growth ETF: 90.5%, 97.1% time-series percentile[16] 3. Fortune Shanghai Composite ETF: 90.0%, 94.7% time-series percentile[16] 4. Fortune Tourism Theme ETF: 89.6%, 97.5% time-series percentile[16] 5. Guotai Shanghai Composite ETF: 89.3%, 92.2% time-series percentile[16] - **Net Active Buy Transaction Amount Ratio (20251020-20251024)**: 1. Bosera Chip ETF: 15.6%, 93.0% time-series percentile[17] 2. E Fund Dividend ETF: 15.2%, 94.3% time-series percentile[17] 3. Huatai-PineBridge 2000 ETF: 15.0%, 100.0% time-series percentile[17] 4. Tianhong Growth ETF: 13.7%, 82.4% time-series percentile[17] 5. Huaxia Sci-Tech ETF: 13.6%, 91.4% time-series percentile[17]

Group 1 - The report focuses on tracking large purchases and net active buying through transaction data to identify potential investment opportunities [1][2] - The top five stocks with the highest large purchase amounts over the last five trading days (October 13 to October 17, 2025) are: Asia-Pacific Pharmaceutical, Guosheng Technology, Anke Technology, Huayuan Holdings, and Delixi [5][8] - The top five stocks with the highest net active buying amounts during the same period are: Hu Nong Commercial Bank, Youngor, Guotou Power, Shandong Highway, and Jiangyin Bank [5][10] Group 2 - The top five industries with the highest large purchase amounts are: Comprehensive, Banking, Steel, Coal, and Transportation [5][13] - The top five industries with the highest net active buying amounts are: Banking, Steel, Coal, Transportation, and Agriculture, Forestry, Animal Husbandry, and Fishery [5][13] Group 3 - The top five ETFs with the highest large purchase amounts are: Guotai Shanghai Stock Exchange State-owned Enterprise Dividend ETF, Fortune CSI Tourism Theme ETF, Huatai-PB Shanghai Stock Exchange Dividend ETF, Huaxia CSI Tourism Theme ETF, and Huaxia CSI 1000 ETF [5][15] - The top five ETFs with the highest net active buying amounts are: Huatai-PB Dividend Low Volatility ETF, Huabao CSI Banking ETF, Guotai CSI Animal Husbandry ETF, Huitianfu CSI Major Consumption ETF, and Guotai Shanghai Stock Exchange State-owned Enterprise Dividend ETF [5][16]

Content: --------- <doc id='1'>大额买入与资金流向跟踪（20250922-20250926） [Table_Authors] 郑雅斌(分析师) 本报告导读： 参考团队前期发布的专题报告选股因子系列研究（五十六、五十七），本报告旨在通 过交易明细数据构建相关指标，跟踪大额买入和净主动买入。</doc> <doc id='2'>投资要点：</doc> <doc id='3'>| | 021-23219395 | | --- | --- | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 张耿宇(分析师) | | | 021-23183109 | | | zhanggengyu@gtht.com | | 登记编号 | S0880525040078 |</doc> <doc id='4'>[Table_Report] 相关报告 量化择时和拥挤度预警周报（20250928） 2025.09.28 高频选股因子周报（20250922-20250926） 2025.09.28 低频选股因子周报（2025.09.19-2025.09.26） 2025.09.27 国内权益资产震荡，资产配置策略整体回调 2025.09.26 绝对收益产品及策略周报（250915-250919） 2025.09.24 金 融 工 程 金 融 工 程 周 报 金融工程 /[Table_Date] 2025.09.30 [Table_Summary] 个股大额买入跟踪与净主动买入跟踪。近 5 个交易日大额买入排名 前 5 的个股为：华泰股份、美凯龙、联美控股、启迪环境、酒钢宏 兴；近 5 个交易日净主动买入排名前 5 的个股为：鄂尔多斯、中信 特钢、桂冠电力、南钢股份、京泉华。（本周报中，近 5 个交易日特 指 20250922 至 20250926。） 行业大额买入与净主动买入跟踪。近 5 个交易日大额买入排名前 5 的中信一级行业为：银行、石油石化、非银行金融、钢铁、房地产； 近 5 个交易日净主动买入排名前 5 的中信一级行业为：银行、石油 石化、农林牧渔、房地产、电力及公用事业。 ETF 大额买入跟踪与 ETF 净主动买入跟踪。近 5 个交易日大额买 入排名前 5 的 ETF 为：嘉实中证 500ETF、南方标普中国 A 股大盘 红利低波 50ETF、易方达中证红利 ETF、嘉实中证稀土产业 ETF、 易方达沪深 300ETF；近 5 个交易日净主动买入排名前 5 的 ETF 为： 富国创业板 ETF、嘉实中证新能源 ETF、华夏中证内地低碳经济主 题 ETF、广发创业板 ETF、鹏华中证国防 ETF。 风险提示。因子失效风险，模型误设风险，市场系统性风险。</doc> <doc id='6'>| 1. | 跟踪指标计算说明 3 | | --- | --- | | 2. | 个股大额买入与资金流向跟踪 3 | | 3. | 宽基指数大额买入与资金流向跟踪 3 | | 4. | 行业大额买入与资金流向跟踪 4 | | 5. | ETF 大额买入与资金流向跟踪 5 | | 6. | 风险提示 5 |</doc> <doc id='7'>1. 跟踪指标计算说明 本文使用大买单成交金额占比和净主动买入金额占比，跟踪个股、宽 基指数、行业和 ETF 的大额买入与资金流向。 大买单成交金额占比刻画了大资金的买入行为。根据逐笔成交数据中 的叫买和叫卖序号，可将逐笔成交数据还原为买卖单数据，并按照每单的 成交量筛选得到大单，计算其中大买单的成交金额占当日总成交金额的比 例。 净主动买入金额占比刻画了投资者的主动买入行为。根据逐笔成交数 据中的买卖标志，可界定每笔成交属于主动买入还是主动卖出。将两者的 成交金额相减，可得净主动买入金额，并计算占当日总成交金额的比例。</doc> <doc id='8'>2. 个股大额买入与资金流向跟踪 下表为全市场大买单成交金额占比过去 5 日均值排名前 10 的股票。</doc> <doc id='9'>| | 表 1 大买单成交金额占比前 10 股票列表（20250922-20250926） | | | | | --- | --- | --- | --- | --- | | 排序 | WIND 代码 | 股票名称 | 指标值 | 时序分位数 | | 1 | 600308.SH | 华泰股份 | 89.1% | 100.0% | | 2 | 601828.SH | 美凯龙 | 88.9% | 99.2% | | 3 | 600167.SH | 联美控股 | 88.7% | 98.8% | | 4 | 000826.SZ | 启迪环境 | 88.6% | 99.6% | | 5 | 600307.SH | 酒钢宏兴 | 87.9% | 95.9% | | 6 | 601996.SH | 丰林集团 | 87.7% | 98.0% | | 7 | 601366.SH | 利群股份 | 87.7% | 99.2% | | 8 | 002453.SZ | 华软科技 | 87.7% | 99.6% | | 9 | 600935.SH | 华塑股份 | 87.5% | 97.6% | | 10 | 600606.SH | 绿地控股 | 87.3% | 99.2% | 资料来源: Wind，国泰海通证券研究 下表为部分全市场净主动买入金额占比过去 5 日均值排名前 10 的股票。</doc> <doc id='10'>| | 表 2 净主动买入金额占比前 10 股票列表（20250922-20250926） | | | | | --- | --- | --- | --- | --- | | 排序 | WIND 代码 | 股票名称 | 指标值 | 时序分位数 | | 1 | 600295.SH | 鄂尔多斯 | 17.9% | 98.4% | | 2 | 000708.SZ | 中信特钢 | 17.4% | 100.0% | | 3 | 600236.SH | 桂冠电力 | 15.2% | 98.4% | | 4 | 600282.SH | 南钢股份 | 12.7% | 95.1% | | 5 | 002885.SZ | 京泉华 | 11.6% | 100.0% | | 6 | 600323.SH | 瀚蓝环境 | 11.5% | 95.1% | | 7 | 600176.SH | 中国巨石 | 11.5% | 99.2% | | 9 | 603037.SH | 凯众股份 | 11.0% | 100.0% | | 10 | 603091.SH | 众鑫股份 | 10.9% | 99.2% | 资料来源: Wind，国泰海通证券研究</doc> <doc id='11'>3. 宽基指数大额买入与资金流向跟踪 本文使用整体法计算主要宽基指数的大买单成交金额占比和净主动买 入金额占比，列于下表。</doc> <doc id='12'>表 3 宽基指数大额买入与资金流向（20250922-20250926） | | | 大买单成交金额占比 | | 净主动买入金额占比 | | --- | --- | --- | --- | --- | | | 5 日均值 | 5 日均值分位数 | 5 日均值 | 5 日均值分位数 | | 上证指数 | 72.3% | 2.9% | -3.6% | 21.6% | | 上证 50 | 70.2% | 7.3% | 1.1% | 18.4%