Core Viewpoint - The collaboration between Nasdaq and Quant360 aims to provide high-quality data services for quantitative private equity firms in China, addressing challenges in the U.S. stock market data quality and depth [8][14][15]. Industry Overview - The quantitative private equity industry in China has seen rapid growth, with 45 firms reaching over 10 billion yuan in assets by August 2025, accounting for nearly half of the total number of such firms in the country [8]. - The number of registered quantitative products has doubled year-on-year, indicating a strong development trend in quantitative strategies [8]. Challenges Faced - Chinese quantitative private equity firms face two main challenges in the U.S. stock market data: 1. Data quality is inconsistent, making it difficult to ensure accuracy and completeness [9][14]. 2. Ordinary Level-2 data does not provide sufficient market depth information, which affects strategy precision [15]. Solution Provided - Nasdaq TotalView offers a comprehensive view of market supply and demand by disclosing detailed information on all buy and sell orders, helping investors assess market dynamics [14]. - Quant360 enhances this by providing high-quality, efficient, standardized, and customizable data technology services [14]. - The partnership will facilitate access to Nasdaq TotalView data through various integration methods, including API connections and Excel exports, supported by a dedicated technical support team [15]. Future Outlook - Nasdaq and Quant360 plan to deepen their collaboration to continuously provide quality data services for quantitative investment firms, promoting the growth of the quantitative investment industry in the global market [15].

Core Insights - The recent financing news about Suzhou Xien Technology securing hundreds of millions in Pre-A round funding highlights the growing interest in companies with core technologies in the smart manufacturing sector [1][3]. Company Overview - Xien Technology's "Huashan No. 1" micro servo driver has achieved 100% localization, a significant milestone that reflects the reality of domestic substitution in the market [3]. - The company has successfully developed high-efficiency driving chips and high-precision control algorithms, allowing its products to meet international standards in key performance indicators such as power density [3]. Market Dynamics - The investment community's interest in Xien Technology indicates a shift in the servo driver market, which was previously dominated by Japanese and German companies, showcasing the potential for domestic players to capture market share [3]. - The development trajectory of Xien Technology illustrates the typical lifecycle of a potential stock, moving from obscurity to attracting capital attention and entering a rapid growth phase [3][11]. Investment Behavior - Understanding trading behavior data is crucial for investors, as it can reveal underlying market dynamics and help distinguish between noise and genuine signals [5][12]. - The presence of specific trading behaviors, such as the combination of buying momentum and institutional activity, can signal opportunities in the market [5][7]. Key Success Factors - Xien Technology's success can be attributed to three critical factors: breakthrough in core technology, precise market positioning, and strong capital support [12][13].

Core Insights - The Japanese Financial Services Agency is considering allowing banks to directly invest in cryptocurrencies like Bitcoin, which has led to a rebound in Bitcoin prices, surpassing the $110,000 mark [1] - Historical data shows that retail investors often suffer significant losses during bull markets, with an average loss of -60% during the 2015 bull market [3] - Behavioral finance explains why investors continue to take risks despite knowing the potential downsides, highlighting phenomena such as herd mentality, loss aversion, and confirmation bias [7] Market Analysis - The 2007 bull market lasted 553 days, with 207 days showing declines, while the 2015 bull market had 212 down days out of 495 trading days, indicating that downturns are common even in rising markets [3][5] - The adjustment in bull markets can be severe, as evidenced by a 22% drop in just six days during the 2007 market [5] - The data from the 2015 market shows a starting price of 4272.11 and an ending price of 3767.10, reflecting an 11.8% decline [6] Institutional Insights - Institutional investors hold the pricing power in the market, and understanding their behavior is crucial for making informed investment decisions [7][10] - A comparison of two stocks illustrates the importance of recognizing institutional activity; one stock showed resilience despite fluctuations, while the other faced continued declines despite apparent rebounds [10][13] - The rise of digital assets is underscored by a 3.5-fold increase in cryptocurrency accounts over five years, indicating a shift in the financial landscape [13] Strategic Takeaways - The evolution of financial markets necessitates that investors adapt and utilize tools that provide clarity on market dynamics [14] - Recognizing the importance of data over expert opinions can lead to better investment decisions in an era of information overload [13][14] - The current situation in Japan, with a debt-to-GDP ratio of 240%, highlights the challenges facing traditional financial systems and the growing significance of digital assets [13]

Core Insights - Inno Asset has been awarded the "Golden Bull Private Fund Management Company (Three-Year Managed Futures Strategy)" for the fifth time, showcasing its robust capabilities in quantitative investment [1] - The founder and CEO Xu Shunan emphasized that AI is an extension of quantitative methodologies rather than a revolutionary force, enhancing the ability to identify and represent trading patterns in complex data environments [1][3] Group 1: AI and Quantitative Investment - AI is viewed as a powerful statistical tool that enhances the capabilities of quantitative investment, which is fundamentally based on mathematics and statistics [3] - Inno Asset has systematically applied AI across various strategies, including Alpha, CTA, and algorithmic trading, leading to improved model recognition, response speed, and iterability [3] - The application of AI is expected to expand further as data becomes richer and foundational engineering is solidified, providing more "methodological dividends" for strategy evolution [3] Group 2: Efficiency and Creativity - AI is seen as an efficiency amplifier, taking over repetitive tasks such as data cleaning and feature construction, allowing teams to focus on more creative aspects like problem definition and risk control [4] - The integration of AI throughout the data-model-engine-trading chain aims to standardize processes and enhance execution paths while ensuring compliance with regulations [4][9] - The company believes that while AI enhances efficiency and precision, it cannot replace the human element in defining problems and constructing logic [7] Group 3: Methodology and Results - The source of good strategies is not solely dependent on the use of AI but rather on clear problem definitions, reliable data, and robust testing [8] - Inno Asset maintains a principle of methodological neutrality and results orientation, using AI to optimize strategy performance when appropriate, but also valuing traditional methods [8] - AI signals and traditional factors are developed in parallel, calibrated, and combined to create low-correlation multi-source Alpha, evaluated against stability, transaction costs, and capacity constraints [8] Group 4: Future Directions - Inno Asset aims to embed AI into multi-strategy and full-chain processes, focusing on building a solid foundation in local markets while seeking low-correlation opportunities across multiple assets and markets [9] - The company emphasizes the importance of maintaining a balance between innovation and compliance within a risk management framework, ensuring that creativity flourishes within defined boundaries [9] - The direction and pace of AI integration will continue to be guided by human judgment, reinforcing the commitment to delivering verifiable long-term performance to clients and the market [9]

Market Performance - Major indices have shown significant gains this year, with the Shanghai Composite Index up by 16%, CSI 300 up by 23%, ChiNext Index up by 60%, and the Hang Seng Tech Index up by 64% as of the end of Q3 [1] - Domestic fund products have also performed well, with overall returns exceeding 20%, although QDII funds have yielded the highest returns [1] Fund Performance - Various fund indices have reported strong year-to-date performance, with the top-performing QDII mixed fund index showing a return of 34.71% [2] - Many actively managed funds have achieved returns exceeding 100%, while some funds still reported negative returns by the end of Q3 [3][4] Underperforming Funds - The Minsheng Jianyin Preferred Fund, managed by Liu Hao, has reported a year-to-date return of -7.39%, ranking last among 976 stock funds [4] - This fund has consistently underperformed over the past five years, with only four years of positive returns since its inception in 2014 [5][9] Sector Analysis - The Minsheng Jianyin Preferred Fund's holdings primarily consist of home appliance and manufacturing stocks, which have not performed well this year [6] - The fund's top holdings include Haier, BYD, and Midea, but it has failed to capitalize on market trends [7][8] Other Underperforming Funds - The Qianhai Kaiyuan Traditional Chinese Medicine Research Fund has underperformed its benchmark by over 10%, with a return of -6% this year [10][13] - The Qianhai Kaiyuan Artificial Intelligence Fund has also reported a loss of 4.38%, despite the underlying index gaining nearly 70% [19][20] Quantitative Strategy Issues - The Fuguo Large Cap Value Fund has reported negative returns, despite the average performance of quantitative funds being significantly positive [25][27] - The Silver Hua Wealth Theme Fund has underperformed for five consecutive years, with a year-to-date return of -1.5% [29][32] Concentrated Investment Risks - The Wan Jia Selected Fund, heavily invested in coal stocks, has reported a year-to-date return of -2.5%, significantly underperforming the market [34][35] - The fund manager's strategy of focusing solely on coal has raised concerns about the sustainability of this investment approach [35]

Group 1 - The A-share market is experiencing fluctuations, with 79 stocks having net inflows of financing for five consecutive trading days, indicating potential investment interest [1] - Notable companies like Mindray Medical and BOE Technology Group are among those attracting financing, suggesting market recognition of fundamentally strong firms [3] - The phenomenon of "stronger getting stronger" is evident during market volatility, where news amplifies stock price movements rather than guiding them [3] Group 2 - The reflexivity theory and mean reversion theory explain market behaviors, highlighting the interaction between stock prices and news, as well as the eventual return to value [3] - Historical examples, such as the performance of Cai Bai Co. during gold price surges, illustrate how institutional behavior can impact stock prices despite seemingly attractive fundamentals [3][5] - The case of Tianyi Co., linked to Huawei's HiSilicon, shows that institutional involvement can precede stock price increases, even when initial reactions to news are negative [7] Group 3 - The 79 stocks with net financing inflows should be approached with caution, as financing balance changes are merely one indicator of market sentiment [9] - Distinguishing between genuine institutional behavior and leveraged financing activities is crucial for making informed investment decisions [9] - Establishing a quantitative analysis framework is essential in an era of information overload, where reliable data is more valuable than sensational stories [9]

Quantitative Models and Construction Methods 1. Model Name: Stock Index Futures Timing Model - **Model Construction Idea**: The model uses the basis of stock index futures to reflect market sentiment changes and constructs daily frequency timing signals based on this correlation[7] - **Model Construction Process**: - The model groups and tests the correlation trend between the basis of stock index futures and the index itself - Constructs daily frequency timing signals based on this correlation - As of October 17, 2025, the timing signal based on the basis of the CSI 500 stock index futures remained at 1[31] - **Model Evaluation**: The model effectively captures market sentiment changes and provides timely signals for trading decisions[7] 2. Model Name: Multi-Dimensional Timing Model - **Model Construction Idea**: The model integrates macro, micro, meso, and derivative signals to form a four-dimensional non-linear timing model[33] - **Model Construction Process**: - The A-share market is divided into nine states based on macro, micro, and meso signals, each corresponding to long and short signals to form a three-dimensional large cycle timing signal - On this basis, the derivative signal generated by the basis of stock index futures is superimposed to form a four-dimensional non-linear timing model - The latest composite multi-dimensional timing signal is long (1)[34] - **Model Evaluation**: The model provides a comprehensive view of market conditions by integrating multiple dimensions, enhancing the accuracy of timing signals[33] 3. Model Name: Style Enhancement Model - **Model Construction Idea**: The model enhances returns by adding enhancement factors to the multi-style strategy, suppressing single-style fluctuations, and achieving stable excess returns in different cycles[41] - **Model Construction Process**: - The model is based on the multi-style strategy and adds enhancement factors - It dynamically adjusts the weights of different styles to achieve stable excess returns - As of October 17, 2025, the low volatility enhancement strategy achieved an excess return of 6.05%[42] - **Model Evaluation**: The model effectively enhances returns while controlling risks, providing stable performance across different market cycles[41] Model Backtesting Results Stock Index Futures Timing Model - **Absolute Return**: Not specified - **Excess Return**: 4.33%[9] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Multi-Dimensional Timing Model - **Absolute Return**: Not specified - **Excess Return**: 4.33%[9] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Style Enhancement Model - **Absolute Return**: Not specified - **Excess Return**: 6.05%[8] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Quantitative Factors and Construction Methods 1. Factor Name: High-Frequency Factor - **Factor Construction Idea**: The factor captures market valuation and sentiment risks using high-frequency data[11] - **Factor Construction Process**: - The factor uses high-frequency data to measure market depth, spread, and price elasticity - Constructs indicators such as average depth, spread, and price elasticity to reflect market liquidity and sentiment - For example, the average depth is calculated as: $$ avg_{depth} = \frac{av1 + bv1}{2} $$ where av1 and bv1 are the sell and buy volumes at the first level of the order book[98] - **Factor Evaluation**: The factor effectively captures market liquidity and sentiment changes, providing valuable insights for trading decisions[11] Factor Backtesting Results High-Frequency Factor - **Absolute Return**: Not specified - **Excess Return**: Not specified - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Industry and ETF Rotation Strategy 1. Strategy Name: Industry Rotation Strategy - **Strategy Construction Idea**: The strategy uses quantitative fundamental drivers, quality low volatility style drivers, and distressed reversal industry discovery methods to construct an industry rotation strategy[76] - **Strategy Construction Process**: - Combines industry fundamental rotation, quality low volatility, and distressed reversal three-dimensional industry rotation strategies into an equal-weight portfolio - Selects industries from different dimensions to achieve factor and style complementarity, reducing the risk of a single strategy - As of October 17, 2025, the annualized excess return of the industry rotation strategy based on three-strategy integration was 10.59%, with a Sharpe ratio of 0.74[80] - **Strategy Evaluation**: The strategy effectively combines multiple dimensions to enhance returns while controlling risks, providing stable performance across different market cycles[76] Strategy Backtesting Results Industry Rotation Strategy - **Absolute Return**: Not specified - **Excess Return**: 14.75%[10] - **Annualized Return**: 10.59%[80] - **Sharpe Ratio**: 0.74[80]

10月15日，由中国证券报主办的"2025私募基金高质量发展大会"暨"国信证券杯·第十六届私募金牛 奖"颁奖典礼在深圳举行。 凭借出色的策略表现与持续优异的长期回报，因诺资产荣获 "金牛私募管理公司（三年期管理期货策 略）" 奖项。这是因诺资产自成立以来第五次斩获金牛奖，充分体现了公司在量化投资领域的稳健实力 与专业深耕。 在AI热潮席卷资管、市场风格加速切换的背景下，因诺资产创始人、总经理兼投资总监徐书楠在大会 的"AI引领变革 量化投资的崛起与未来"圆桌对话环节分享了他的判断。他认为，AI不是外来革命，而 是量化方法论的自然延展。量化立基于数学与统计，目标是在嘈杂、非平稳的数据中，以可验证、可复 现的方式提炼可交易规律；AI，尤其机器学习与大模型，本质上是"更有特点、更具优势的统计学"，在 高维、非线性、弱信号场景中显著提升识别与表征能力，让模型"看得更远、摸得更细"。 因诺资产创始人、总经理兼投资总监徐书楠 从方法到落地，边界与分工更关键。AI能提高"看见"的概率，不能替代"怎么做"的判断；策略能否可交 易，仍受执行约束、流动性与风险预算所限；能否稳健，还要经受样本外与极端情景压力测试。在因诺 资产，A ...

Core Insights - The latest data indicates a slight decline in the margin financing balance of the Sci-Tech Innovation Board, yet stocks like Cambrian are experiencing increased interest from investors [1][3] - A total of 41 stocks on the Sci-Tech Innovation Board saw net purchases exceeding 10 million yuan, with Cambrian leading at 368 million yuan, while 75 stocks experienced a decrease in financing balance of over 10 million yuan [3] - The market is characterized by significant stock performance divergence, highlighting that not all stocks will rise in a bull market [4] Market Behavior - The professor's remark on behavioral finance emphasizes that the greatest risk in the market is not volatility but cognitive biases among investors [4] - The performance of various sectors before April 2025 shows that few sectors can maintain consistent performance, with the electronics sector being the only exception, yet it still faced declines in four months [4] - The stock market operates on a principle of survival of the fittest, where large funds hold significant pricing power [7] Quantitative Analysis - Quantitative tools reveal discrepancies in stock performance, indicating that institutional holdings do not equate to constant trading activity [10][12] - The analysis of institutional fund activity shows that stocks with sustained institutional support tend to perform better, as seen in the financing data for Cambrian and other electronic sector stocks [12] Investment Insights - The data suggests that market performance should not mislead investors, as stock differentiation is a common occurrence [13] - A rebound in stock prices does not necessarily indicate an investment opportunity; the focus should be on the sustainability of fund support [13] - The technology sectors, particularly electronics and semiconductors, remain focal points for investor interest [13]

Core Insights - The A-share market has shown a significant divergence in the third quarter, with a strong structural performance in the technology sector providing opportunities for subjective investments to outperform [2] - Quantitative long products achieved average returns of 17.41% in Q3 and 2.55% in the last month, while subjective long products had average returns of 20.43% in Q3 and 4.21% in the last month, indicating a clear advantage for subjective strategies [2] - The number of private equity firms managing over 10 billion yuan has increased, with 42 firms now classified as "billion-yuan private equity" as of September 2025 [3] Group 1: Performance of Billion-Yuan Private Equity - As of September 2025, there are 42 billion-yuan private equity firms, with notable new entrants and returning firms, such as Kaishi Private Equity and Hongchou Investment [3] - The expansion in the number of billion-yuan private equity firms is attributed to the recovery of the A-share market, which has boosted returns on equity assets and increased investor trust in leading private equity firms [3] - Among the 42 firms, 28 are located in Shanghai and Beijing, with only 9 firms having more than 50 employees, indicating a concentration of resources in major cities [3] Group 2: Top Performers in Various Scales - In the billion-yuan category, the top three firms are Fusheng Asset, Kaishi Private Equity, and Jiuqi Investment, with Fusheng Asset achieving an average return of ***% [11][12] - In the 50-100 billion category, the top three firms are Tongben Investment, Shengqi Asset, and Wangzheng Asset, with the average return threshold for the top performers being ***% [12][16] - In the 20-50 billion category, Beijing Xiyue Private Equity leads with an average return of ***%, reflecting a significant increase in its management scale [20] - In the 10-20 billion category, Luyuan Private Equity and Rongshu Investment are the top performers, with Luyuan achieving an average return of ***% [21][24] - In the 5-10 billion category, Fuyuan Capital ranks first with an average return of ***%, emphasizing its commitment to long-term value investment [26][29]