Report Industry Investment Rating - The operation rating for CITIC Five Styles - Finance is ★☆☆, indicating a bullish bias but with limited operability in the market [3][4]. Core Viewpoints - As of the week ending October 24, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index were 3.42%, -0.03%, and 0.94% respectively. In the public - fund market, enhanced index strategies led the gains with a weekly return of 3.89%. Neutral strategies had more gains than losses. Among commodities, precious - metal ETFs pulled back, while soybean - meal and non - ferrous - metal ETFs had a slight rebound, and energy - chemical ETFs stabilized [4]. - All CITIC five styles closed up last Friday, with the growth style leading in returns. The style rotation chart showed that the cyclical and consumer styles weakened compared to the previous period, and the growth style had a significant increase in the indicator momentum. In the public - fund pool, financial and cyclical style funds had better excess performance in the past week. The deviation of products from the consumer style increased marginally, and the overall market congestion indicator continued to rise this week, with the growth and financial styles in a historically high - congestion range [4]. - In the neutral strategy, the stock - index basis showed a marginal recovery trend during the week. The IC contract recovered to around 0.5 times the standard deviation above the three - month average. The average premium rates of the spot - index ETFs corresponding to IC and IM were relatively high, in the top 80% quantile range of the past three months [4]. - Among Barra factors, the medium - and long - term momentum factor had a better return performance this week, with a weekly excess return of 1.70%. The residual volatility and ALPHA factors retreated, and the winning rates of the dividend and leverage factors improved. The cross - section rotation speed of factors continued to increase this week, currently in the top 80% quantile range of the past year [4]. - According to the latest scoring results of the style timing model, the growth and financial styles recovered marginally this week, while the cyclical and stable styles declined. The current signal favors the financial style. The return of the style timing strategy last week was 1.45%, and the excess return compared to the benchmark balanced allocation was - 0.98% [4]. Summary by Related Catalogs Fund Market Review Recent Market Returns - The weekly, monthly, quarterly, and semi - annual returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond (net), and Nanhua Commodity are presented in a chart [6]. - The maximum drawdowns of the main public - fund strategy indices in the past three months and their weekly returns are also shown in charts [6]. CITIC Style Index - The net - value trends of CITIC style indices (finance, cycle, consumption, growth, stability) from September 24 to October 23, 2025, are presented in a chart [8][9]. - The relative rotation chart of CITIC style indices shows the relative strength and relative - strength momentum of different styles in different time periods (recent week, last week, recent month, recent three months, recent six months, recent year) [10][11]. - The excess - return performance of fund style indices in different time periods is provided in a table [12]. - The fund - style congestion chart shows the congestion levels of cycle, growth, consumption, and finance styles from September 28 to October 26, 2025 [13]. Barra Factors - The style preference of Barra single factors is within the range of 0 - 1, with a higher value indicating a stronger preference. The excess - return performance of Barra single - factor style strategies and the net - value trends of Barra single - factor style excess since this year are presented in charts [14][16][18].

Core Viewpoint - Carvana Co. is facing significant challenges due to its controversial financial history, governance issues, and increasing regulatory scrutiny, which could lead to substantial downside risks for investors [2][4]. Financial Performance - As of October 9th, Carvana's share price was $360.03, with trailing and forward P/E ratios of 98.19 and 60.61 respectively [1]. - The company has over $4.5 billion in debt maturing over the next decade, indicating potential financial strain [3]. Governance and Management - The Garcia family's history of financial misconduct raises concerns about the governance of Carvana, particularly with the audit committee chairman having longstanding ties to the Garcias [3][4]. - The aggressive financial strategies employed by the company, including subprime auto lending and complex related-party transactions, have been criticized for inflating reported sales and earnings [2][3]. Regulatory Environment - Carvana is under increasing regulatory scrutiny, with the SEC issuing a subpoena and investors pursuing lawsuits related to alleged pump-and-dump schemes [4]. - The combination of aggressive financial engineering and governance risks suggests that Carvana is vulnerable to market and regulatory pressures [4]. Market Position and Strategy - Despite the challenges, Carvana's vertically integrated e-commerce platform and operational efficiencies have been highlighted as strengths, contributing to a 12.4% appreciation in stock price since previous bullish coverage [5]. - The company's reliance on the volatile subprime lending market continues to underpin its profitability, but this also exposes it to significant risks [4].

Core Insights - OpenAI's collaboration with Broadcom has led to a significant increase in Broadcom's stock price, rising by 11% following the announcement of a multi-year agreement to deploy 10 gigawatts of AI data center capacity [1] - OpenAI is also in discussions with Arm, a semiconductor design giant owned by SoftBank, to incorporate Arm's CPU designs into AI server chips, which has resulted in an over 11% increase in Arm's stock price [4][6] - SoftBank, as a major shareholder of OpenAI, has committed substantial investments to support OpenAI's data center initiatives, further enhancing the commercial value for both companies [6] Collaboration and Strategy - OpenAI's chip strategy involves distinct roles: collaboration with Broadcom focuses on AI chips for inference, while discussions with Arm center on CPUs for AI server chips [10] - The partnership with Broadcom aims to produce chips for AI inference, expected to be operational by the end of next year, significantly increasing OpenAI's data center capacity [11] - OpenAI's collaboration with TSMC for chip manufacturing is crucial, as TSMC is a key supplier for major AI chip companies [12] Financial Implications - OpenAI's ambitious plans require substantial financial backing, with estimates suggesting over $1 trillion in costs for building data center capacity of 26 gigawatts [13] - The financing strategy involves deep integration with suppliers, creating a scenario where the financial burden shifts to the suppliers, thereby ensuring OpenAI's operational continuity [13] - Despite generating approximately $13 billion in revenue this year, OpenAI is projected to burn through $115 billion in cash by 2029, indicating a heavy reliance on external financing [13]

- The report does not contain any quantitative models or factors for analysis[1][3][8] - The report primarily focuses on convertible bond market performance, individual bond performance, valuation analysis, and clause tracking[1][3][8] - No quantitative models or factors are mentioned for construction, testing, or evaluation[1][3][8]

Report Summary 1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints - In the week ending September 26, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index were 0.21%, -0.27%, and 0.43% respectively [3]. - In the public - fund market, passive index products had strong performance in the past week, neutral strategy products mostly declined, convertible bonds outperformed pure bonds, and precious - metal ETFs continued to strengthen, with silver ETF rising 5.72% and soybean meal ETF continuing to decline [3]. - Among the CITIC five - style indices, growth and cyclical styles rose last week, while the others fell. The style timing model signals a preference for the growth style this week [3]. - The short - term momentum factor in Barra factors had a good performance last week, with a weekly excess return of 1.85%. The style timing strategy had a return of 1.58% last week, with an excess return of 1.85% compared to the benchmark balanced allocation [3]. 3. Summary by Related Catalogs Recent Market Returns - Market indices: Tonglian All A (Shanghai, Shenzhen, Beijing) rose 0.21%, ChinaBond Composite Bond declined 0.27%, and Nanhua Commodity Index rose 0.43% in the week ending September 26, 2025 [3]. - Public - fund market: Passive index products performed strongly, neutral strategy products mostly declined, convertible bonds outperformed pure bonds, precious - metal ETFs strengthened (silver ETF +5.72%, soybean meal ETF declined), and consumer - style funds had an excess return of 0.91% in the past week [3]. CITIC Style Index - Performance: Growth and cyclical styles rose last week, while the others fell. The cyclical style weakened marginally in relative strength, and the stable and financial styles slightly recovered in indicator momentum [3]. - Style timing: According to the style timing model, consumer and cyclical styles declined marginally this week, while stable and financial styles slightly recovered, with a signal favoring the growth style. The style timing strategy had a return of 1.58% last week, with an excess return of 1.85% compared to the benchmark balanced allocation [3]. Barra Factors - Factor performance: The short - term momentum factor had a weekly excess return of 1.85%, and the cash - flow and growth factors' returns recovered marginally. The residual momentum factor's win - rate improved [3]. - Factor rotation: The factor cross - section rotation speed increased this week, reaching the medium historical quantile range [3].

- Model Name: Four-Wheel Drive Industry Rotation Model; Model Construction Idea: The model suggests focusing on specific sectors based on their recent performance and potential opportunities; Model Construction Process: The model tracks the trading volume and performance of various sectors, identifying potential opportunities based on significant changes in trading volume and performance metrics. The model specifically suggests focusing on sectors like media, retail, agriculture, communication, non-ferrous metals, machinery, and computers[2][9][15]; Model Evaluation: The model is effective in identifying sectors with potential for rotation and growth[2][9][15] - Model Backtesting Results: - Four-Wheel Drive Industry Rotation Model, Sharpe Ratio for Agriculture sector: 19[15]

Core Insights - The report highlights the performance tracking of Guosen Securities' active quantitative strategies, indicating that the excellent fund performance enhancement portfolio achieved an absolute return of 3.70% this week and 17.22% year-to-date, ranking in the 49.35th percentile among active equity funds [1][12][23] - The report emphasizes the strong performance of the expected selection portfolio, which recorded an absolute return of 3.78% this week and 34.72% year-to-date, ranking in the 12.68th percentile among active equity funds [1][12][33] - The brokerage gold stock performance enhancement portfolio achieved an absolute return of 4.00% this week and 23.05% year-to-date, ranking in the 33.58th percentile among active equity funds [1][12][39] - The growth and stability portfolio reported an absolute return of 3.65% this week and 40.87% year-to-date, ranking in the 8.30th percentile among active equity funds [1][12][46] Excellent Fund Performance Enhancement Portfolio - The strategy aims to benchmark against the median return of active equity funds, utilizing a quantitative approach to enhance performance based on the holdings of top-performing funds [3][17][52] - The portfolio's year-to-date performance shows a relative underperformance of -3.26% compared to the mixed equity fund index [16][23] Expected Selection Portfolio - This strategy selects stocks based on expected performance and analyst profit upgrades, focusing on both fundamental and technical criteria to build a robust portfolio [4][24][58] - The portfolio has outperformed the mixed equity fund index by 14.24% year-to-date [16][33] Brokerage Gold Stock Performance Enhancement Portfolio - The strategy utilizes a selection from the brokerage gold stock pool, optimizing the portfolio to minimize deviations from the benchmark [5][34][63] - Year-to-date, this portfolio has outperformed the mixed equity fund index by 2.57% [16][39] Growth and Stability Portfolio - The strategy employs a two-dimensional evaluation system for growth stocks, prioritizing those with upcoming earnings announcements to capture potential excess returns [6][40][68] - The portfolio has achieved a year-to-date return of 40.87%, significantly outperforming the mixed equity fund index by 20.39% [16][46]

Core Insights - The report highlights the significant performance of the A-share market driven by passive investment and leveraged funds, with the total margin financing and securities lending balance exceeding 1.99 trillion as of August 4, 2025, marking a historical high since 2024 [5][8][6] - The automotive sector, particularly the company North Car Blue Valley (600733.SH), has launched a "Three-Year Leap Plan" aimed at enhancing profitability through sales growth, structural optimization, cost control, and expanding its profit ecosystem [4][16] - The company reported a 151% year-on-year increase in revenue for Q1 2025, with a gross margin improvement of 4.1 percentage points, and a reduction in net loss by 60 million [4][16] Industry Overview - The automotive industry is focusing on high-end market penetration, with North Car Blue Valley collaborating with Huawei to enhance its brand image and product offerings, particularly in the high-end vehicle segment [18][17] - The report indicates a notable increase in sales for the "Extreme Fox" brand due to comprehensive adjustments in product positioning, marketing strategies, and channel expansion [17] - The "Enjoy" brand, under the Huawei partnership, aims to redefine high-end sedans with innovative features and improved range, which is expected to boost sales significantly [18] Market Dynamics - The report discusses the microstructure of the market, emphasizing the importance of early trading concentration and the dynamics between institutional and retail investors [9][10][12] - It notes that the market's profitability effect has increased retail participation, contrasting with the trend of rising institutional ownership since 2017 [6][8] - The report tracks high-frequency factors, indicating strong performance in various trading strategies, with notable returns from specific factors such as the high-dimensional memory factor yielding 29.3% since 2023 [14]

Quantitative Factors and Construction Methods 1. Factor Name: UTR (U-Turnover Rate) - **Construction Idea**: The UTR factor combines two sub-factors, "Turn20" (volume-small factor) and "STR" (volume-stable factor), using a scoring method to address the issue of "1+1<2" in factor integration. The key idea is to prioritize stocks with stable volumes while favoring higher turnover within this stable group [6] - **Construction Process**: 1. At the end of each month, calculate the "Turn20" and "STR" values for all stocks [6] 2. Rank all stocks by the "STR" factor in ascending order and assign scores from 1 to N (N is the total number of stocks), referred to as "Score 1" [6] 3. For the top 50% of stocks ranked by "STR", rank them by "Turn20" in descending order and assign scores from 1 to N/2, referred to as "Score 2". The final score for these stocks is "Score 1 + Score 2" [6] 4. For the bottom 50% of stocks ranked by "STR", rank them by "Turn20" in ascending order and assign scores from 1 to N/2, referred to as "Score 3". The final score for these stocks is "Score 1 + Score 3" [6] 5. The resulting factor is named "UTR" [6] - **Evaluation**: The UTR factor effectively integrates the two sub-factors, achieving the goal of favoring higher turnover within stable-volume stocks [6] 2. Factor Name: UTR2.0 (U-Turnover Rate 2.0) - **Construction Idea**: UTR2.0 improves upon the original UTR factor by transitioning from ordinal scale to ratio scale for factor values. Additionally, it introduces a coefficient for the "Turn20" factor, which is a function of "STR", to better capture the varying impact of "Turn20" across different stability levels. The softsign activation function from neural networks is used to model this relationship [7] - **Construction Process**: 1. Transition from ordinal scale to ratio scale for factor values to retain more information [7] 2. Define the coefficient for "Turn20" as a function of "STR", where the coefficient increases with stability (positive impact) and decreases with instability (negative impact) [7] 3. Use the softsign activation function to model the relationship: $$ \mathrm{UTR2.0} = \mathrm{STR} + \text{softsign}(\mathrm{STR}) \cdot \mathrm{Turn20} $$ $$ \text{softsign}(x) = \frac{x}{1 + |x|} $$ [7] - **Evaluation**: Compared to the original UTR factor, UTR2.0 achieves lower returns but demonstrates superior volatility, information ratio (IR), and monthly win rate, making it a more robust factor [7][9] --- Factor Backtesting Results 1. UTR2.0 Factor - **Annualized Return**: 40.36% [9] - **Annualized Volatility**: 14.97% [9] - **Information Ratio (IR)**: 2.70 [9] - **Monthly Win Rate**: 75.74% [9] - **Maximum Drawdown**: 11.03% [9] 2. July 2025 Performance (UTR2.0 Factor) - **Long Portfolio Return**: 1.29% [9] - **Short Portfolio Return**: -0.06% [9] - **Long-Short Portfolio Return**: 1.35% [9]

- The report aims to track large purchases and net active purchases using transaction detail data[1] - The indicators used are the proportion of large order transaction amounts and the proportion of net active purchase amounts[7] - The proportion of large order transaction amounts reflects the buying behavior of large funds[7] - The proportion of net active purchase amounts reflects the active buying behavior of investors[7] - The top 5 stocks with the highest average proportion of large order transaction amounts over the past 5 days are: Sobute, China Railway Industry, Tibet Tianlu, Poly United, and China Power Construction[4][9] - The top 5 stocks with the highest average proportion of net active purchase amounts over the past 5 days are: Weixing Co., HNA Holdings, Kaili Medical, Liaogang Co., and Hengyi Petrochemical[4][10] - The top 5 industries with the highest average proportion of large order transaction amounts over the past 5 days are: Banking, Real Estate, Petroleum and Petrochemical, Transportation, and Coal[4] - The top 5 industries with the highest average proportion of net active purchase amounts over the past 5 days are: Media, Textile and Apparel, Computers, Electronics, and Light Manufacturing[4] - The top 5 ETFs with the highest average proportion of large order transaction amounts over the past 5 days are: China Agricultural Theme ETF, E Fund CSI 300 Medical and Health ETF, Huabao CSI Medical ETF, Bosera SSE STAR 100 ETF, and Guotai CSI Livestock Breeding ETF[4][15] - The top 5 ETFs with the highest average proportion of net active purchase amounts over the past 5 days are: Penghua CSI Subdivision Chemical Industry Theme ETF, GF SSE STAR 50 ETF, Harvest CSI Rare Metals Theme ETF, E Fund Guozheng Robotics Industry ETF, and Harvest CSI Software Services ETF[4][16]