- The report focuses on tracking large buy orders and net active buy orders using transaction detail data[1][2] - Two key indicators are used: the proportion of large buy order transaction amounts and the proportion of net active buy order amounts[7] - The proportion of large buy order transaction amounts reflects the buying behavior of large funds[7] - The proportion of net active buy order amounts reflects investors' active buying behavior[7] - The report provides rankings for stocks, industries, and ETFs based on these indicators over the past 5 trading days (20260309-20260313)[4][6] Quantitative Models and Construction Methods 1. **Model Name**: Large Buy Order Transaction Amount Proportion - **Construction Idea**: To track the buying behavior of large funds[7] - **Construction Process**: - Restore transaction data to buy and sell order data using the buy and sell sequence numbers in the transaction detail data - Filter out large orders based on transaction volume - Calculate the proportion of large buy order transaction amounts to the total transaction amount of the day[7] - **Evaluation**: This indicator effectively captures the buying behavior of large funds[7] 2. **Model Name**: Net Active Buy Order Amount Proportion - **Construction Idea**: To track investors' active buying behavior[7] - **Construction Process**: - Identify each transaction as either an active buy or an active sell using the buy and sell markers in the transaction detail data - Subtract the transaction amounts of active sells from active buys to get the net active buy amount - Calculate the proportion of net active buy amounts to the total transaction amount of the day[7] - **Evaluation**: This indicator effectively captures investors' active buying behavior[7] Model Backtest Results 1. **Large Buy Order Transaction Amount Proportion** - **Top 5 Stocks**: - Jiugang Hongxing: 87.2%, 90.5%[9] - Wentou Holdings: 86.6%, 97.1%[9] - Jinbin Development: 86.3%, 86.4%[9] - Ningbo Construction: 85.6%, 98.8%[9] - Xining Special Steel: 85.3%, 97.9%[9] - **Top 5 Industries**: - Banking: 81.3%, 61.3%[13] - Real Estate: 79.8%, 51.0%[13] - Construction: 78.5%, 88.9%[13] - Comprehensive: 77.9%, 46.1%[13] - Steel: 77.7%, 35.4%[13] - **Top 5 ETFs**: - Guotai SSE 10-Year Treasury Bond ETF: 95.4%, 99.6%[15] - Huatai-PineBridge MSCI China A50 Interconnection ETF: 94.0%, 93.4%[15] - Huatai-PineBridge CSI A500 ETF: 93.2%, 90.9%[15] - Guotai CSI A500 ETF: 92.5%, 53.9%[15] - Huaxia CSI A500 ETF: 92.0%, 97.5%[15] 2. **Net Active Buy Order Amount Proportion** - **Top 5 Stocks**: - Minsheng Bank: 22.2%, 98.8%[10] - SDIC Power: 21.8%, 97.1%[10] - Everbright Bank: 19.5%, 99.6%[10] - Zhejiang Bank: 19.2%, 96.3%[10] - Shangtai Technology: 18.9%, 100.0%[10] - **Top 5 Industries**: - Banking: 10.5%, 64.2%[13] - Food & Beverage: 4.7%, 56.0%[13] - Real Estate: 2.5%, 50.2%[13] - Construction: 0.4%, 72.4%[13] - Basic Chemicals: -0.9%, 75.7%[13] - **Top 5 ETFs**: - Harvest CSI Green Power ETF: 35.4%, 98.4%[16] - E Fund CSI Dividend Low Volatility ETF: 21.6%, 97.9%[16] - Huatai-PineBridge CSI All Index Power Utilities ETF: 18.7%, 97.9%[16] - Southern S&P China A-Share Large Cap Dividend Low Volatility 50 ETF: 15.7%, 96.7%[16] - GF GEM ETF: 13.8%, 90.9%[16]

Quantitative Factors and Construction Methods 1. Factor Name: Large Buy Order Transaction Amount Ratio - **Construction Idea**: This factor captures the buying behavior of large capital by measuring the proportion of large buy orders in the total transaction amount for a given day[7] - **Construction Process**: - Utilize tick-by-tick transaction data to identify buy and sell orders based on bid and ask sequence numbers - Filter transactions by order size to isolate large orders - Calculate the proportion of large buy order transaction amounts relative to the total transaction amount for the day - Formula: $ \text{Large Buy Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Amount}}{\text{Total Transaction Amount}} $ where "Large Buy Order Amount" represents the sum of transaction amounts for large buy orders, and "Total Transaction Amount" is the aggregate transaction amount for the day[7] - **Evaluation**: This factor effectively reflects the behavior of large-scale investors and their influence on market dynamics[7] 2. 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[7] - **Construction Process**: - Use tick-by-tick transaction data to classify each transaction as either an active buy or an active sell based on the buy/sell indicator - Compute the net active buy transaction amount by subtracting the active sell amount from the active buy amount - Calculate the proportion of the net active buy transaction amount relative to the total transaction amount - Formula: $ \text{Net Active Buy Transaction Amount Ratio} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Total Transaction Amount}} $ where "Active Buy Amount" and "Active Sell Amount" represent the transaction amounts for active buy and sell orders, respectively, and "Total Transaction Amount" is the aggregate transaction amount for the day[7] - **Evaluation**: This factor provides insights into the level of active buying interest in the market, which can be indicative of investor sentiment[7] --- Factor Backtesting Results 1. Large Buy Order Transaction Amount Ratio - **Top 10 Stocks by 5-Day Average**: - Highest value: 89.4% (Zhouji Oil & Gas, 600759.SH)[9] - Lowest value in the top 10: 85.0% (Wentou Holdings, 600715.SH)[9] - Time-series percentile range: 81.1% to 100.0%[9] 2. Net Active Buy Transaction Amount Ratio - **Top 10 Stocks by 5-Day Average**: - Highest value: 14.2% (China Construction Bank, 601939.SH)[10] - Lowest value in the top 10: 11.5% (Zhende Medical, 603301.SH)[10] - Time-series percentile range: 93.8% to 100.0%[10] 3. Broad Market Indices - **Large Buy Order Transaction Amount Ratio (5-Day Average)**: - Highest: 73.7% (CSI 300 Index)[12] - Lowest: 70.4% (ChiNext Index)[12] - **Net Active Buy Transaction Amount Ratio (5-Day Average)**: - Highest: 2.0% (CSI 500 Index)[12] - Lowest: -0.4% (SSE 50 Index)[12] 4. Industry-Level Analysis - **Large Buy Order Transaction Amount Ratio (5-Day Average)**: - Highest: 79.2% (Non-Banking Financials)[13] - Lowest: 69.3% (Telecommunications)[13] - **Net Active Buy Transaction Amount Ratio (5-Day Average)**: - Highest: 12.8% (Non-Banking Financials)[13] - Lowest: -5.2% (Oil & Petrochemicals)[13] 5. ETFs - **Large Buy Order Transaction Amount Ratio (5-Day Average)**: - Highest: 94.3% (Huatai-PineBridge CSI A500 ETF, 563360.SH)[15] - Lowest in the top 10: 89.6% (Fuguo CSI Tourism Theme ETF, 159766.SZ)[15] - **Net Active Buy Transaction Amount Ratio (5-Day Average)**: - Highest: 33.1% (HFT SSE Urban Investment Bond ETF, 511220.SH)[16] - Lowest in the top 10: 9.8% (Harvest CSI Rare Earth Industry ETF, 516150.SH)[16]

Core Viewpoint - The Tianjin state-owned enterprise reform sector experienced a slight increase of 0.45% on February 25, with Jinbin Development leading the gains [1] Market Performance - The Shanghai Composite Index closed at 4147.23, up by 0.72% - The Shenzhen Component Index closed at 14475.86, up by 1.29% [1] Capital Flow - The main funds in the Tianjin state-owned enterprise reform sector saw a net outflow of 66.08 million yuan - Speculative funds experienced a net outflow of 23.82 million yuan - Retail investors contributed a net inflow of 89.89 million yuan [1]

Core Viewpoint - Tianjin Bin Development (000897.SZ) forecasts a net loss of 35 million to 65 million yuan for the fiscal year 2025, a significant decline from a profit of 508 million yuan in the same period last year [1] Financial Performance - The net profit attributable to shareholders is expected to be a loss of 35 million to 65 million yuan, compared to a profit of 508 million yuan in the previous year [1] - The net profit after deducting non-recurring gains and losses is projected to be a loss of 32 million to 62 million yuan, down from a profit of 509 million yuan in the prior year [1] Reasons for Performance Decline - The primary reason for the decline in performance is that most of the company's projects are still under construction, with no new projects delivered during the reporting period [1] - Revenue from real estate project settlements is derived solely from the remaining unsold properties [1] - The company anticipates recognizing investment losses on significant equity-method projects [1]

Group 1 - The company expects a negative net profit for the fiscal year 2025, covering the period from January 1, 2025, to December 31, 2025 [1] - The performance forecast has not been audited by a registered accountant, but preliminary discussions with the accounting firm indicate no significant discrepancies in the financial data [1][2] Group 2 - The main reason for the decrease in performance for the fiscal year 2025 compared to the previous year is that most projects are still under construction, with no new projects delivered, and revenue is primarily from remaining unsold properties [2] - The company anticipates recognizing investment losses on significant equity-method projects [2]

Financial Projections - The estimated net profit for 2025 is projected to be between -65 million RMB and -35 million RMB, compared to a net profit of 508.47 million RMB in the previous year[5]. - The estimated net profit after deducting non-recurring gains and losses is projected to be between -62 million RMB and -32 million RMB, compared to 508.66 million RMB in the previous year[5]. - The basic earnings per share is expected to be between -0.0402 RMB and -0.0216 RMB, compared to 0.3144 RMB in the previous year[5]. Performance Factors - The decrease in performance is primarily due to most projects being under construction with no new delivery projects, and revenue from real estate projects is derived from remaining unsold properties[9]. - The company anticipates recognizing investment losses from significant equity-method projects during the reporting period[9]. Audit and Reporting - The performance forecast has not been audited by registered accountants, and the specific financial data for 2025 will be disclosed in the annual report[10].

Quantitative Factors and Construction Methods 1. Factor Name: Large Order Transaction Amount Ratio - **Factor Construction Idea**: This factor captures the buying behavior of large funds by analyzing the proportion of large order transaction amounts relative to the total daily transaction amount[7] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to identify buy and sell orders based on bid and ask sequence numbers 2. Filter transactions by order size to identify large orders 3. Calculate the proportion of large buy order transaction amounts to the total daily transaction amount Formula: $ \text{Large Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Transaction Amount}}{\text{Total Daily Transaction Amount}} $ - **Factor Evaluation**: This factor effectively reflects the buying behavior of large funds[7] 2. Factor Name: Net Active Buy Amount Ratio - **Factor Construction Idea**: This factor measures the active buying behavior of investors by calculating the net active buy amount as a proportion of the total daily transaction amount[7] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on trade direction 2. Subtract the active sell transaction amount from the active buy transaction amount to obtain the net active buy amount 3. Calculate the proportion of the net active buy amount to the total daily transaction amount Formula: $ \text{Net Active Buy Amount Ratio} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Total Daily Transaction Amount}} $ - **Factor Evaluation**: This factor provides insights into the active buying behavior of investors[7] --- Factor Backtesting Results 1. Large Order Transaction Amount Ratio - **Top 5 Stocks by 5-Day Average**: 1. 惠博普 (92.6%, 99.6% percentile)[9] 2. 美年健康 (89.6%, 99.2% percentile)[9] 3. 志特新材 (89.2%, 99.2% percentile)[9] 4. 津滨发展 (88.4%, 99.6% percentile)[9] 5. 江南高纤 (87.7%, 98.8% percentile)[9] 2. Net Active Buy Amount Ratio - **Top 5 Stocks by 5-Day Average**: 1. 杭萧钢构 (16.7%, 99.8% percentile)[10] 2. 纬德信息 (15.4%, 100.0% percentile)[10] 3. 中科微至 (15.0%, 99.6% percentile)[10] 4. 新风光 (13.8%, 100.0% percentile)[10] 5. 联合水务 (13.3%, 97.5% percentile)[10] 3. Broad-Based Indices - **Large Order Transaction Amount Ratio (5-Day Average)**: - 上证指数: 73.8% (12.8% percentile)[12] - 上证50: 70.6% (64.2% percentile)[12] - 沪深300: 73.1% (64.2% percentile)[12] - 中证500: 73.0% (6.6% percentile)[12] - 创业板指: 71.6% (90.1% percentile)[12] - **Net Active Buy Amount Ratio (5-Day Average)**: - 上证指数: -5.8% (86.8% percentile)[12] - 上证50: -12.9% (90.5% percentile)[12] - 沪深300: -8.8% (89.3% percentile)[12] - 中证500: -3.4% (86.0% percentile)[12] - 创业板指: -4.4% (84.8% percentile)[12] 4. Industry-Level Results - **Top 5 Industries by Large Order Transaction Amount Ratio (5-Day Average)**: 1. 房地产: 79.8% (90.1% percentile)[13] 2. 煤炭: 78.5% (66.3% percentile)[13] 3. 钢铁: 78.2% (42.8% percentile)[13] 4. 建筑: 77.9% (24.3% percentile)[13] 5. 综合: 77.8% (50.6% percentile)[13] - **Top 5 Industries by Net Active Buy Amount Ratio (5-Day Average)**: 1. 房地产: -9.5% (95.1% percentile)[13] 2. 电子: 2.2% (78.6% percentile)[13] 3. 汽车: 0.9% (60.9% percentile)[13] 4. 家电: 0.1% (84.4% percentile)[13] 5. 通信: -4.7% (89.7% percentile)[13] 5. ETFs - **Top 5 ETFs by Large Order Transaction Amount Ratio (5-Day Average)**: 1. 华泰柏瑞中证A500ETF (92.9%, 96.3% percentile)[15] 2. 易方达中证A500ETF (91.6%, 100.0% percentile)[15] 3. 国泰中证A500ETF (91.5%, 15.6% percentile)[15] 4. 华泰柏瑞沪深300ETF (91.0%, 99.2% percentile)[15] 5. 易方达沪深300ETF (91.0%, 99.6% percentile)[15] - **Top 5 ETFs by Net Active Buy Amount Ratio (5-Day Average)**: 1. 东财上证科创板50成份ETF (23.4%, 100.0% percentile)[16] 2. 海富通上证城投债ETF (20.9%, 88.5% percentile)[16] 3. 国泰上证10年期国债ETF (15.6%, 61.3% percentile)[16] 4. 富国创业板人工智能ETF (14.3%, 65.9% percentile)[16] 5. 嘉实中证稀土产业ETF (14.1%, 92.6% percentile)[16]

天津经济技术开发区第二大街62号泰达MSD-B2座306 Tel: 86-22-25326960 25326966 25326759 25326760 Fax: 86-22-25326959 PC:300457 天津市南开区水上公园北道鲁能国际中心10层1019-1020 Tel:86-22-87192100 87192101 Fax: 86-22-25326959 Http://www.tedalawfirm.com E-mail: teda@tedalawfirm.com 天津津滨发展股份有限公司 2025 年第一次临时股东会 法 律 意 见 书 致：天津津滨发展股份有限公司 根据《中华人民共和国公司法》（以下简称"公司法"）、《中华人民 共和国证券法》（以下简称"证券法"）、《深圳证券交易所上市公司自律 监管指引第 1 号——主板上市公司规范运作（2025 年修订）》、《深圳证 券交易所上市公司股东大会网络投票实施细则（2020 年修订）》（以下简 称"《股东大会网络投票实施细则》"）及深圳证券交易所《关于修改部分 业务规则和业务指南的通知》，以及《天津津滨发展股份有限公司章程》 （以下简称"公司章程" ...

证券代码：000897 公司简称：津滨发展 公告编号：2025-28 天津津滨发展股份有限公司 2025年第一次临时股东会决议暨取消监事会的公告 本公司及全体董事成员保证信息披露的内容真实、准确、完整，没有虚假记载、误导性陈述 或重大遗漏。 重要内容提示： ●本次股东会无增加、修改议案的情形；未出现否决提案的情形。 ●本次股东会无涉及变更以往股东会已通过的决议。 ●本次会议以现场投票和网络投票相结合的方式召开。 ●为尊重中小投资者利益，本次股东会所提议案均对中小投资者的表决单独计票。 一、会议召开和出席情况 1、会议召开情况 （1）现场会议时间：2025 年 12 月 26 日下午 14:00 （2）网络投票时间：2025 年 12 月 26 日。其中，通过深交所交易系统进行网络投票的 具体时间为 2025 年 12 月 26 日 9:15～9:25，9:30～11:30，13:00～15:00；通过深交所互 联网投票系统投票的具体时间为 2025 年 12 月 26 日 9:15～15:00 期间的任意时间。 （3）召开地点：天津市干部俱乐部内八号楼会议室 （4）召开方式：现场投票和网络投票相结合的方式； ...

Group 1 - The core viewpoint of the article indicates that the Tianjin state-owned enterprise reform sector experienced a decline of 1.66% on December 11, with Jinbin Development leading the drop [1] - On the same day, the Shanghai Composite Index closed at 3873.32, down by 0.7%, while the Shenzhen Component Index closed at 13147.39, down by 1.27% [1] - The main capital flow in the Tianjin state-owned enterprise reform sector showed a net outflow of 43.98 million yuan, with retail investors contributing a net inflow of 16.76 million yuan [1] Group 2 - The article provides a detailed table of individual stock performances within the Tianjin state-owned enterprise reform sector, highlighting the variations in stock prices [1] - The net inflow from speculative funds was recorded at 27.22 million yuan, indicating a mixed sentiment among different types of investors [1] - The overall market performance reflects a broader trend of decline in the stock indices, suggesting potential challenges in the market environment [1]