Core Viewpoint - The securities industry is undergoing a significant transformation, with increasing competition prompting firms to seek high-quality development and adapt to new market conditions through leadership changes and talent acquisition [1][3]. Group 1: Executive Changes - Recent months have seen a wave of executive turnover in the securities industry, with several high-profile resignations due to job adjustments and retirements [1][2]. - Notable departures include executives from firms such as Xinda Securities, First Capital Securities, and Changcheng Securities, with many citing personal reasons or retirement age as factors [1]. - The trend of executive turnover reflects a broader shift in the industry as firms seek to adapt to changing market dynamics and operational challenges [1][3]. Group 2: New Leadership - The industry is also welcoming new executives with diverse backgrounds, indicating a shift towards hiring leaders with cross-industry experience [2][4]. - Recent appointments include Ying Chaohui as the new general manager of Caitong Securities and Wang Suwang as the chairman of Guotou Securities, both of whom bring extensive experience from various financial institutions [2]. - The new leadership is expected to leverage their insights and experiences to drive innovation and transformation within their respective firms [3][4]. Group 3: Market-oriented Recruitment - Market-oriented recruitment has become a prominent trend in the securities industry, allowing firms to attract talent with specialized skills and innovative thinking [3][4]. - Companies like Chengtong Securities and Guosheng Financial Holdings have publicly advertised executive positions, reflecting a shift from traditional internal selection methods [3][4]. - This approach is believed to enhance the firms' ability to adapt to market changes and meet diverse client needs through innovative business models and service offerings [4].

8月11日消息，黄金股早盘走低，截至发稿，、跌超4%，跌超3%，跌近1%。 编辑/melody 消息面上，8月7日，中国人民银行数据显示，7月末黄金储备达7396万盎司，环比增加6万盎司，已连续 9个月增持。世界黄金协会发布的数据显示，2025年6月，全球官方黄金储备净增22吨，已连续第三个月 环比小幅上升。2025年上半年全球央行净购金量达123吨，较2024年同期小幅下降。 招商证券8月初发布研报称，金价短期上涨动力减弱。 虽然黄金具备配置价值，但在短期维度，上涨动 能不足，需警惕局部过热风险。该机构建议投资者，关注结构性机会，而非全面押注黄金上涨。东证期 货指出，市场对未来联储局减息的预期增加，但也基本反映在了金价中。 ...

Group 1 - The announcement states that from August 11, 2025, Guotai Haitong Securities will begin selling certain funds managed by the company [1] - The applicable fund range is subject to compliance with the fund contract, prospectus, and related business announcements [1][4] - The announcement includes a list of various sales institutions that will also start selling the company's funds from the same date [5][6][7] Group 2 - Investors can consult details through the websites and customer service numbers provided for each sales institution [4][5][6][7] - The company emphasizes that the specific dates, times, processes, and any fee discount activities for fund sales will be determined by the sales institutions [1][4]

Core Insights - The new policy for technology innovation bonds has led to a significant issuance of 883.16 billion yuan in just three months, with financial institutions accounting for nearly 36% of this total [1][2] - The issuance of technology innovation bonds has expanded to include more small and private enterprises, alongside the traditional dominance of central and state-owned enterprises [1][2] - The average coupon rate for newly issued technology innovation bonds is 1.9282%, with some bonds having rates as low as 0.01% [1][3] Issuance Details - From May 7 to August 10, a total of 700 technology innovation bonds were issued, amounting to 883.16 billion yuan, compared to 197 bonds totaling 208.11 billion yuan in the same period last year, indicating a significant policy impact [1][2] - Financial institutions have issued 314.27 billion yuan of the total, with banks leading at 230.3 billion yuan across 32 banks, including major players like Agricultural Bank of China and Industrial and Commercial Bank of China [2] - Securities companies have collectively issued 54.1 billion yuan, with the largest issuers being China Merchants Securities and CITIC Securities [2] Characteristics of New Bonds - The newly issued technology innovation bonds predominantly have longer maturities, with 76.23% of the total issuance being bonds with maturities of three years or more [3] - The majority of the bonds were issued by central and local state-owned enterprises, with 203 bonds from central SOEs and 369 from local SOEs, while private enterprises issued 94 bonds [3]

Core Viewpoint - The new policy for technology innovation bonds (科创债) has led to a significant increase in issuance, with a total of 883.16 billion yuan in new bonds over the past three months, indicating a strong market response to regulatory support [1][2]. Group 1: Issuance Scale and Participants - The total issuance scale of technology innovation bonds reached 883.16 billion yuan, with financial institutions accounting for nearly 36% of this amount [1][2]. - Among the financial institutions, banks led the issuance with 230.3 billion yuan, followed by 38 securities companies that collectively issued 54.1 billion yuan [2]. - The participation of small and medium-sized institutions and private enterprises has increased, with various smaller banks and private equity firms also issuing technology innovation bonds [2]. Group 2: Characteristics of New Bonds - The average coupon rate for newly issued technology innovation bonds was 1.9282%, which is notably low compared to other credit bonds of similar ratings [3]. - A significant portion of the new bonds has a maturity of over three years, with 76.23% of the total issuance (673.22 billion yuan) falling into this category [3]. - The majority of the issuers are central and local state-owned enterprises, with 203 bonds issued by central state-owned enterprises and 369 by local state-owned enterprises [3].

Quantitative Models and Construction Methods 1. Model Name: Volume Timing Signal - **Model Construction Idea**: The core idea is that "the decline in a shrinking volume market is significantly greater than the rise in a shrinking volume market, so avoiding shrinking volume signals can achieve higher trading odds"[3][22][24] - **Model Construction Process**: 1. Calculate the rolling 60-day average and standard deviation of the turnover and turnover rate of the index or market[23] 2. Standardize the daily turnover data: - If the turnover is within ±2 standard deviations, map the score to -1~+1 - If the turnover exceeds ±2 standard deviations, assign a score of +1/-1 3. Combine the scores of turnover and turnover rate equally[23] 4. Generate signals based on the combined score: - Method 1: Go long if the score > 0, stay out if the score < 0 - Method 2: Use the rolling 5-year or 3-year percentile of the score; go long if above the 50th percentile, stay out if below[23] 5. The report adopts the simpler method of directly judging whether the score is greater than 0[23] - **Model Evaluation**: The model is not a high-win-rate strategy but achieves relatively high odds by avoiding significant market adjustments during shrinking volume periods[24] 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of growth and value styles based on macroeconomic cycles, valuation differences, and market sentiment[52][54] - **Model Construction Process**: 1. **Fundamentals**: - Growth is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Value is favored under the opposite conditions[52] 2. **Valuation**: - Growth is favored when the PE and PB valuation differences between growth and value are in the lower percentiles and mean-reverting upward[52] 3. **Sentiment**: - Growth is favored when turnover and volatility differences between growth and value are low[52] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between growth and value[52] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[53][55] 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of small-cap and large-cap styles based on macroeconomic cycles, valuation differences, and market sentiment[56][58] - **Model Construction Process**: 1. **Fundamentals**: - Small-cap is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Large-cap is favored under the opposite conditions[56] 2. **Valuation**: - Large-cap is favored when the PE and PB valuation differences between small-cap and large-cap are in the higher percentiles and mean-reverting downward[56] 3. **Sentiment**: - Small-cap is favored when turnover differences are high - Large-cap is favored when volatility differences are mean-reverting downward[56] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between small-cap and large-cap[56] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[57][60] 4. Model Name: Four-Style Rotation Model - **Model Construction Idea**: Combines the conclusions of the growth-value and small-cap-large-cap rotation models to allocate across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[61][63] - **Model Construction Process**: 1. Use the growth-value model to determine the allocation between growth and value 2. Use the small-cap-large-cap model to determine the allocation between small-cap and large-cap 3. Combine the two models to allocate across the four styles[61] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance, with consistent outperformance in most years[61][63] --- Model Backtest Results 1. Volume Timing Signal - **Win Rate**: 47.34%[24] - **Odds**: 1.75[24] - **Annualized Excess Return**: 6.87% (based on next-day open price)[34] - **Maximum Drawdown**: 31.40%[34] - **Return-to-Drawdown Ratio**: 0.4634[34] 2. Growth-Value Style Rotation Model - **Annualized Return**: 11.76%[55] - **Annualized Volatility**: 20.77%[55] - **Maximum Drawdown**: 43.07%[55] - **Sharpe Ratio**: 0.5438[55] - **Return-to-Drawdown Ratio**: 0.2731[55] 3. Small-Cap vs. Large-Cap Style Rotation Model - **Annualized Return**: 12.45%[60] - **Annualized Volatility**: 22.65%[60] - **Maximum Drawdown**: 50.65%[60] - **Sharpe Ratio**: 0.5441[60] - **Return-to-Drawdown Ratio**: 0.2459[60] 4. Four-Style Rotation Model - **Annualized Return**: 13.37%[63] - **Annualized Volatility**: 21.51%[63] - **Maximum Drawdown**: 47.91%[63] - **Sharpe Ratio**: 0.5988[63] - **Return-to-Drawdown Ratio**: 0.2790[63]

Core Viewpoint - Guangdong Provincial Academy of Building Research Group Co., Ltd. (hereinafter referred to as "Guangdong Jian Ke") is preparing for its initial public offering (IPO) on the ChiNext board, with a focus on providing high-tech services in the construction engineering field, particularly in inspection and testing [1][2]. Group 1: Company Overview - The registered capital of Guangdong Jian Ke is 31.39 million yuan, and it was established on December 25, 2013, with its shares incorporated on December 16, 2014 [1]. - The company is recognized as a large-scale construction technology service provider in Guangdong Province and has received multiple national honors, including "National High-tech Enterprise" and "National Model Enterprise for Scientific and Technological Reform" [2][3]. Group 2: Main Business and Services - Guangdong Jian Ke's primary business involves inspection and testing technology services in the construction engineering sector, which is a key area supported by the national government [3][4]. - The company has developed over 4,100 recognized testing standards and holds various qualifications, including comprehensive Class A qualifications for highway engineering and all five categories of Class A qualifications for water conservancy engineering [3][4]. Group 3: Technological Capabilities - The company emphasizes technological research and development, having established multiple provincial and national-level innovation platforms, including the only national green building quality inspection and testing center in South China [5][6]. - Guangdong Jian Ke has participated in over 20 national scientific and technological projects and has received 89 national and provincial-level scientific and technological awards [6][7]. Group 4: Industry Impact and Recognition - The company has provided inspection and testing services for major projects such as the Hong Kong-Zhuhai-Macao Bridge and Guangzhou Baiyun International Airport, contributing to significant economic and social benefits [6][7]. - Guangdong Jian Ke has been awarded titles such as "National AAA Credit Enterprise in the Construction Industry" and "National Green Building Pioneer Award," reflecting its strong reputation in the industry [6][7].

Core Viewpoint - The stability of information systems in the securities industry is essential for ensuring the safe operation of financial markets, prompting the China Securities Association to seek industry feedback on the "Stability Assurance System Standard for the Securities Industry" [1][2]. Group 1: Background and Purpose - The initiative aims to integrate best practices from securities firms to create a practical stability assurance framework, promoting the digital and standardized development of technical capabilities across the industry [1]. - The project for drafting the standard began in November 2023, with participation from 14 securities firms, including major players like GF Securities and CITIC Securities [1]. Group 2: Current Challenges - There are four main challenges identified: 1. Lack of resilience design in system development, leading to high operational risk prevention costs due to insufficient monitoring and automation capabilities [2]. 2. Predominantly reactive risk perception during operations, lacking proactive data-driven risk identification capabilities [2]. 3. Emergency response relies heavily on individual expert experience, lacking data-driven human-machine collaborative capabilities [2]. 4. Insufficient depth of intelligent technology application, resulting in a gap between abnormal response efficiency and real-time business requirements [2]. Group 3: Proposed Framework - The "Stability Assurance System Standard" proposes a "three-in-one" framework for stability assurance, focusing on organizational, institutional, and process guarantees [3]. - Organizational guarantees include defining the structure, personnel competency requirements, and management objectives [3]. - Institutional guarantees encompass regulations, technical support, operational procedures, and timelines to ensure management requirements are actionable and traceable [3]. - Process guarantees focus on ten core processes related to stability management, including monitoring, alerting, and fault management, with mechanisms for evaluation and key activities [3]. - The standard emphasizes a shift towards proactive operations management to meet non-functional requirements like resilience and maintainability, utilizing digital methods to enhance defense capabilities [3].

Quantitative Models and Construction Methods DTW Timing Model - **Model Name**: DTW Timing Model - **Model Construction Idea**: The model is based on the principle of similarity and the DTW algorithm, focusing on price and volume timing[1][5][14] - **Model Construction Process**: - The model examines the similarity between current index trends and historical trends, selecting several historical segments with high similarity as references[25] - It calculates the weighted average future price change and weighted standard deviation of the selected historical segments (weights are the inverse of the distance)[25] - Based on the average future price change and standard deviation, trading signals are generated[25] - The model uses the DTW distance algorithm instead of the Euclidean distance for similarity measurement, as DTW distance can better handle time series mismatches[27] - Improved DTW algorithms such as Sakoe-Chiba and Itakura Parallelogram are introduced to overcome the "over-bending" issue in traditional DTW algorithms[29][30][35] - **Model Evaluation**: The model has shown stable excess returns in general market conditions, although it faced some drawdowns during periods of sudden macroeconomic policy changes[16] Foreign Capital Timing Model - **Model Name**: Foreign Capital Timing Model - **Model Construction Idea**: The model is based on the divergence between foreign and domestic related assets[1][14] - **Model Construction Process**: - The model uses two foreign-listed assets related to A-shares: FTSE China A50 Index Futures (Singapore market) and Southern A50 ETF (Hong Kong market)[34] - It constructs two indicators from FTSE China A50 Index Futures: premium and price divergence, forming the FTSE China A50 Index Futures timing signal[34] - It constructs a price divergence indicator from Southern A50 ETF, forming the Southern A50 ETF timing signal[34] - The timing signals from both assets are combined to form the foreign capital timing signal[34] - **Model Evaluation**: The model has shown good performance with high annualized returns and low maximum drawdowns[20][23] Model Backtest Results DTW Timing Model - **Absolute Return**: 25.79% since November 2022[5][16] - **Excess Return**: 16.83% relative to CSI 300[5][16] - **Maximum Drawdown**: 21.32%[5][16] - **Absolute Return (2024)**: 23.98% on CSI 300[18] - **Excess Return (2024)**: 2.76%[18] - **Maximum Drawdown (2024)**: 21.36%[18] - **Win Rate (2024)**: 53.85%[18] - **Profit-Loss Ratio (2024)**: 2.93[18] Foreign Capital Timing Model - **Absolute Return (2024)**: 29.11% for long strategy[5][23] - **Maximum Drawdown (2024)**: 8.32% for long strategy[5][23] - **Annualized Return (2014-2024)**: 18.96% for long-short strategy, 14.19% for long strategy[20] - **Maximum Drawdown (2014-2024)**: 25.69% for long-short strategy, 17.27% for long strategy[20] - **Daily Win Rate (2014-2024)**: Nearly 55%[20] - **Profit-Loss Ratio (2014-2024)**: Both exceed 2.5[20]

Core Viewpoint - The recent internal conflict at Jingqi Investment has escalated, involving allegations of financial misconduct and mismanagement, with the company’s founder and fund manager Fan Siqi at the center of the controversy [2][3][4]. Group 1: Company Operations and Allegations - Jingqi Investment's basic account experienced a significant abnormal fund transfer on July 2, which has severely impacted the company's financial operations [3]. - The company claims that there have been illegal uses of forged corporate seals and unauthorized actions regarding fund establishment and liquidation, implicating its custodian, China Merchants Securities, for failing to fulfill its due diligence obligations [4]. - Jingqi Investment has filed formal complaints with the China Securities Regulatory Commission and the Asset Management Association of China regarding these allegations [4]. Group 2: Management Disputes - The company is currently undergoing a change in its legal representative, with Fan Siqi allegedly using his authority to alter the management of the company's WeChat account during this transition [6]. - There are conflicting narratives between the founders, with Fan Siqi asserting that he was unfairly removed from his positions and that his actions were in defense of the company [5][6]. - The other founder, Tang Jingren, claims that Fan Siqi has not been involved in the company's daily operations for nearly three years and has been living in Japan, which has contributed to the management issues [7][8]. Group 3: Financial Management and Structure - Jingqi Investment was established on March 9, 2015, and currently manages between 1 billion to 2 billion yuan, with 56 funds under management [5]. - The company has faced scrutiny over its operational practices, particularly regarding the management of its funds and the legitimacy of its financial transactions [4][5].