Core Insights - The recent surge in new fund issuances, with 51 new funds launched and 82% being equity products, signals a potential market recovery, but underlying patterns suggest caution [1][11][12] Fund Issuance Overview - A total of 51 new funds were launched in the first trading week after the National Day holiday, with 31 funds starting their fundraising on the same day [1] - Equity products accounted for 82% of the new fund offerings, indicating a strong interest in this asset class [1][11] Market Behavior and Trends - Historical parallels are drawn to the 2007 bull market, where similar patterns of fund issuance were observed, suggesting that current enthusiasm may not be sustainable [1][12] - The market has seen a strong recovery since April, with the banking sector rising over 12% and micro-cap stocks increasing by 35% [3] Investment Strategies and Risks - The current earnings season is highlighted as a critical period where stock performance becomes closely tied to earnings reports, but this can also lead to misleading market movements [5][6] - The concept of "fake drops" and "real declines" is introduced, emphasizing the need for investors to be wary of market manipulations that can mislead retail investors [5][6] Data-Driven Investment Approach - The importance of using quantitative data to analyze market trends and fund performance is stressed, as it can reveal underlying truths that are not apparent through traditional analysis [11][12] - Investors are advised to remain calm amidst market excitement and focus on data-driven decision-making rather than emotional reactions [12][13]

Core Insights - The article highlights the impressive annualized return of 194.49% from Yongying Technology, while cautioning about the underlying risks that may be overlooked due to the allure of such high returns [1] - The current AI hype is compared to the "Internet Plus" boom in 2015, suggesting that the market may be at a critical juncture with both speculative trading and institutional repositioning occurring simultaneously [3][5] - The article emphasizes the importance of understanding market dynamics beyond surface-level trends, advocating for a deeper analysis of quantitative data and investment strategies [5][9] Market Dynamics - The intertwining of "speculative trading" and "institutional repositioning" is noted as a frequent occurrence in the third quarter, coinciding with the adjustment of 42 doubling funds [5] - The article suggests that while many investors are captivated by technology stocks, there is a need to recognize the potential of energy-related investments, as indicated by the unusual movements in uranium stocks during a market downturn [5] - The research on rare earth elements by Morgan Stanley is mentioned, highlighting the often-overlooked components in the electric vehicle supply chain, such as neodymium-iron-boron magnets, which are crucial for connecting technology narratives with resource logic [7] Investment Strategies - The article advocates for a strategy of identifying low-entry opportunities following institutional repositioning, likening it to finding hidden culinary gems in a bustling city [9] - It compares the market to a symphony, where different sectors play distinct roles, emphasizing the need for a holistic understanding of market rhythms rather than focusing on individual stocks [9] - The article concludes that successful fund managers are more adept at decoding market behavior than merely timing the market, suggesting that the true advantage lies in recognizing moments of consensus among investors [9]

Report Industry Investment Rating No relevant information provided. Core Viewpoints of the Report - T0 strategies, with low risk exposure and high return - drawdown ratios, are attracting more attention. Stock T0 strategies have an annualized return of 5% - 20% and a drawdown of around 1%, making them popular as alternative absolute - return strategies. Futures T0 strategies are more advantageous carriers, offering high liquidity, low costs, and leverage, and having lower slippage compared to commodity futures [1]. - Combining deep - learning - based medium - low - frequency momentum/reversal strategies is a viable approach for futures T0 strategies. The K - Shape algorithm is used to classify intraday trends into three types: upward, downward, and sideways. An MLP + GRU neural network is used to predict these trends, with a validation set win - rate increasing from 33% to 40%. By integrating these predictions with an intraday CTA base strategy, the strategy can achieve a post - fee annualized return of 11.19% and a drawdown of 3.62%, and over 30% annualized return on the IM contract [2][3][4]. Summary According to the Table of Contents 1. Analysis of the Characteristics of Futures T0 Strategies 1.1 From Stock T0 to Futures T0 Strategies - **Stock T0 Strategies**: T0 strategies are less affected by index trends and macro - economic conditions, and more related to turnover and intraday amplitude. They can be divided into manual and programmed T0. Their annualized returns range from 5% to 20%, with small drawdowns, and are suitable for small - scale funds or large - position strategies. There are already mature third - party algorithm providers collaborating with brokerages [9][12][13]. - **Advantages of Futures T0 Strategies**: Futures offer a T + 0 trading mechanism, high liquidity, sufficient amplitude, low trading costs, and leverage. They also have lower slippage compared to commodity futures, providing a better platform for T0 strategies [16][18]. - **Significance for Multi - asset and Multi - strategy Allocation**: Futures T0 strategies can diversify asset allocation, provide free leverage and short - side returns, and improve the performance of traditional asset portfolios. For example, adding a CTA - like strategy to a basic asset pool can increase the annualized return of a risk - parity strategy from 5.50% to 6.67% and reduce the maximum drawdown from 6.71% to 3.74% [19][21]. 1.2 Exploration of Futures T0 Strategy Paradigms - **Differences from Traditional Strategies**: T0 strategies have time - limited opening and closing positions, a narrowing decision - making space, and are highly susceptible to high - frequency information flows, requiring strict trading discipline [23]. - **Specific Implementation Logics**: - **Micro - structure Strategies Based on Order Books**: Analyze high - frequency data such as order book volume and price distribution to predict short - term price trends, with high trading frequencies [25]. - **Momentum/Reversal and Statistical Arbitrage Strategies**: Based on financial time - series statistical laws, with medium - low trading frequencies. Momentum strategies follow trends, while reversal strategies capture corrective rebounds [26]. - **Combination of Machine/Deep Learning with Traditional Paradigms**: Machine and deep learning can automatically learn complex non - linear patterns from large - scale, high - dimensional, and noisy data, and are used in the above two types of strategies [27]. 2. T0 Framework Based on Intraday Trend Pattern Prediction 2.1 Review of Time - Series Clustering Algorithms - **DTW + K - Means**: DTW can measure the similarity between time series, overcoming translation, scaling, and periodic invariance. Combined with K - Means, it can cluster intraday index trends, but is affected by outliers and has high computational complexity [33][39][40]. - **K - Shape**: A time - series clustering algorithm using shape - based distance (SBD) to measure similarity, with translation and scaling invariance. It has better computational efficiency and cluster - center representation, and is used for subsequent analysis [41]. 2.2 Clustering Performance of the K - Shape Algorithm on Stock Index Spot - The K - Shape algorithm is used to cluster the intraday trends of the Shanghai 50, CSI 300, CSI 500, and CSI 1000. Initially, 20 - category clustering is performed, and then reduced to 8 categories. The cluster centers are explicitly initialized, and the final three - category classification (upward, downward, and sideways) is used for subsequent prediction [48][51][53]. 2.3 Prediction of Trend Pattern Labels Based on Deep Learning - For medium - low - frequency T0 strategies, predicting trend types is more meaningful than predicting returns. An MLP neural network with a Softmax output layer is used, integrating cross - sectional and time - series price - volume features. The validation set win - rate can increase from 12.5% to 20.35%, and for the three - category classification, it can increase from 33% to 40%. The model is retrained quarterly to ensure stable performance [57][58][65]. 2.4 T0 Baseline Strategy: Intraday ATR Breakout - The intraday ATR breakout strategy is a trend - following strategy that uses the previous day's ATR to set trading intervals, with opening, stop - profit, and stop - loss thresholds. It is sensitive to trading fees. Under a unilateral fee rate of 0.0025%, the CSI 300, CSI 500, and CSI 1000 can achieve positive long - term returns [72][75][80]. 2.5 Futures T0 Strategy Based on Trend Pattern Prediction - By predicting intraday trends, the application and parameters of the base strategy can be adjusted. For example, on four equal - weighted contracts from January 2023 to June 2025, the annualized return can increase from 6.65% to 11.19%, and the maximum drawdown can be reduced from 7.45% to 3.62% [84][86][87]. 2.6 Summary and Outlook - Futures T0 strategies are more advantageous than stock T0 strategies, and an intraday trend pattern prediction + intraday CTA framework is used to construct the strategy. Future research can focus on improving trend prediction by adding more information and developing reversal CTA strategies [92][93][96].

证券研究报告 | 金融工程 gszqdatemark 2025 10 12 年 月 日 风格上，当前价值因子占优。从纯因子收益来看，本周有色金属、钢铁、 国防军工等行业因子相对市场市值加权组合跑出较高超额收益，消费 者服务、银行等行业因子回撤较多；风格因子中，价值因子超额收益较 高，Beta、残差波动率呈较为显著的负向超额收益。从近期因子表现来 看，高杠杆、高成长股表现优异，残差波动率、价值等因子表现不佳。 风险提示：量化周报观点全部基于历史统计与量化模型，存在历史规律与 量化模型失效的风险。 作者 分析师 刘富兵 执业证书编号：S0680518030007 邮箱：liufubing@gszq.com 分析师 林志朋 执业证书编号：S0680518100004 邮箱：linzhipeng@gszq.com 分析师 沈芷琦 执业证书编号：S0680521120005 邮箱：shenzhiqi@gszq.com 分析师 梁思涵 执业证书编号：S0680522070006 邮箱：liangsihan@gszq.com 分析师 张国安 执业证书编号：S0680524060003 量化周报 创业板、科创 50 短期内或 ...

Core Insights - In September, A-shares indices reached new highs for the year, with the Shanghai Composite Index slightly up by 0.64%, while the Shenzhen Component Index and ChiNext Index rose by 6.54% and 12.04% respectively [2] - Approximately 80.49% of the private equity products under billion-yuan private equity firms achieved historical net value highs in September, with a total of 429 products meeting the criteria of being established for over a year and having performance displayed on the platform [2][3] - Among the products, quantitative strategies accounted for 263, while non-quantitative strategies had 166, with stock strategy products being the most prevalent at 364 [2] Private Equity Performance - Fourteen billion-yuan private equity firms had all their products reach historical highs in September, with eight being quantitative and four subjective, while twelve focused on stock strategies [3] - The top five private equity firms based on average returns over the past year include Lingjun Investment, Fusheng Asset, Kaishi Private Equity, Ningbo Huansheng Quantitative, and Chengqi Asset [4] Notable Firms and Strategies - Lingjun Investment, established in 2014, has a strong research team with over 85% of its members holding master's or doctoral degrees from prestigious universities, focusing on fundamental factor research to enhance strategy resilience [6] - Blackwing Asset, Rido Investment, Dongfang Port, JQK Investment, and Tianyan Capital all had over 90% of their products reach historical highs in September [6] - Dongfang Port, led by Dan Bin, had 74 products reach historical highs, representing 93.67% of its offerings, primarily due to a strong focus on AI-related investments [8] Top Performing Products - The threshold for the top 20 products achieving historical highs in the past year exceeded a certain percentage, with 15 being quantitative long products [9] - The leading product in terms of performance is "Duration Macro Multi-Strategy" from Duration Investment, which has shown significant returns over the past three years and five years [10][12] - Fusheng Asset's "Fusheng Positive Energy No. 2" product is noted for its outstanding performance over five years, with a significant return in the first three quarters of the year [17] Market Outlook - Fusheng Asset expressed optimism about the overall market performance, indicating a gradual economic recovery and a shift in market dynamics [17]

Core Viewpoint - The issuance of new funds has surged post the National Day holiday, with a total of 51 new funds launched during the week from October 13 to October 17, indicating a strong recovery in the fund issuance market, particularly in equity funds [2][6]. Fund Issuance Overview - A significant portion of the new funds, 31 out of 51, were launched on Monday, October 13, accounting for 60.78% of the total new funds for the week [4]. - The average subscription period for the new funds was 12.59 days, which is a noticeable decrease compared to previous periods [5]. - The longest subscription period was 21 days for the 华夏上证 180ETF 联接, while the shortest was just 2 days for two public REITs products [6]. Fund Types and Composition - Equity funds dominated the new fund landscape, with 42 out of 51 funds classified as equity funds, representing 82.35% of the total [7]. - Among the equity funds, 28 were index equity funds, making up 66.67% of the equity category [8]. - The new funds included a variety of themes, such as those tracking Hong Kong Stock Connect indices and those focused on the STAR Market and ChiNext indices [8]. Active Equity Funds - There were 14 actively managed equity funds launched, including 11 mixed funds and 3 ordinary stock funds, reflecting a diverse investment strategy among fund companies [9]. - The active equity funds featured several quantitative theme products and a range of investment styles, from technology growth to balanced value strategies [9]. Bond Funds and Market Trends - Only 3 bond funds were launched during the week, indicating a decline in interest in bond funds as equity markets show signs of recovery [9]. - The "fixed income plus" funds have gained attention, suggesting a shift in investor preference towards more flexible investment strategies [9]. - The overall sentiment in the fund issuance market is improving, with expectations for continued growth in equity fund issuance if market conditions remain favorable [9].

金融工程| 证券研究报告 —周报 2025 年 10 月 13 日 中银量化大类资产跟踪 市场整体回撤，红利指数大幅跑赢创业板指 股票市场概览 本周 A 股下跌，港股下跌，美股下跌，其他海外权益市场普遍上涨。 A 股风格与拥挤度 成长 vs 红利：成长风格拥挤度及超额净值持续处于历史低位；红利 风格拥挤度近期处于历史较低位置。 小盘 vs 大盘：小盘风格拥挤度近期上升至历史均衡位置，大盘风格 拥挤度近期上升至历史高位。 微盘股 vs 基金重仓：微盘股拥挤度下降至历史低位；基金重仓超额 累计净值持续处于历史低位，拥挤度近期上行至历史高位。 A 股行情及成交热度 本周领涨的行业为有色金属、煤炭、钢铁；领跌的行业为传媒、消费者 服务、电子。本周成交热度最高的行业为国防军工、有色金属、机械； 成交热度最低的行业为农林牧渔、综合金融、食品饮料。 A 股估值与股债性价比 A 股资金面 机构调研活跃度 当前机构调研活跃度历史分位居前的行业为有色金属、钢铁、交通运 输，居后的行业为医药、银行、机械。 利率市场 本周中国国债利率下跌，美国国债利率下跌，中美利差处于历史高位。 汇率市场 近一周在岸人民币较美元贬值，离岸人民币较美 ...

Investment Rating - The report indicates a cautious outlook on the market, highlighting high volatility and potential risks that could disrupt current optimism [1][3]. Core Insights - The S&P index has declined approximately 1.25%, with CTA no longer acting as buyers, indicating heightened market risk [1][3]. - The non-profitable tech sector has seen a parabolic rise driven by retail investors and a $100 billion digital impact, suggesting signs of market exuberance [1][5]. - Emerging market stocks have performed well over the past six months, reflecting diversified growth across the region rather than being solely driven by China [1][11]. Summary by Sections Market Performance - The implied volatility remains high despite a rising stock market, as investors continue to buy put options [4]. - Quality stocks have outperformed the S&P 500 index, with a basket of short-term stocks exceeding the index by approximately 100 basis points on a given day [4]. Sector Analysis - The non-profitable tech sector's recent surge is attributed to active retail investors and discussions around significant digital investments [5]. - AI-related trades are gaining attention, with concerns about private credit risks permeating various sectors [5]. Gold Market - Gold prices have surpassed $4,000, reflecting themes of inflation and government spending, establishing itself as a de facto safe-haven asset [8]. - Central bank demand is driving gold prices, indicating a non-speculative boom in the market [8]. Emerging Markets - Emerging market stocks have shown strong performance, with 70% of the broad index maintaining positive monthly returns throughout the year [11]. - The performance is not solely led by China but includes significant contributions from regions like Europe, the Middle East, and Latin America [11]. AI Development - AI technology is penetrating emerging market companies, particularly in China and North Asia, although their investment portfolios lag behind U.S. counterparts [14]. - The current state of AI in emerging markets is still in early development, with a notably lower allocation in global funds compared to benchmarks [14].

Core Insights - The article highlights the rapid advancement of AI in quantitative investment, with Minsheng Jianyin Fund as a pioneer in this "AI race" [1] - The firm has developed a "data-feature-strategy-portfolio" closed-loop system over four years, creating a unique competitive advantage in AI-driven quantitative investment [1][6] Group 1: AI Quantitative Investment Strategy - Minsheng Jianyin's AI quantitative strategy integrates market perception, engineering capabilities, and advanced algorithm applications [1] - The transition from traditional quantitative models to AI models allows for the capture of complex non-linear market relationships, enhancing predictive accuracy [5][7] - The firm emphasizes the necessity of AI in the survival of public funds, predicting a future ecosystem dominated by "AI-led quantification and tool-based index products" [10] Group 2: Market Opportunities and Performance - The National Securities 2000 Index is viewed as a valuable asset for technology upgrades and quantitative enhancement, with significant structural opportunities in AI and high-end manufacturing [2][8] - The Minsheng Jianyin National Securities 2000 Index Enhanced Fund has outperformed its benchmark, achieving returns of 17.18% and 49.66% over six months and one year, respectively [8] - The index's diverse composition and low pricing efficiency provide fertile ground for capturing alpha through quantitative strategies [8] Group 3: Challenges and Risk Management - AI models are not infallible; they rely on historical data and may face challenges during extreme market conditions, highlighting the importance of risk management [9] - The firm maintains that AI enhances human cognitive boundaries rather than replacing human judgment, allowing for the analysis of complex relationships among thousands of stocks [9]

本文首发于公众号"私募排排网"。 （点击↑↑ 上图查看详情 ） 上海佳期私募基金管理有限公司（以下简称"佳期投资"）自2014年成立以来，始终以科学、系统化的投资理念为指引，依托持续的技术创新，在充满 变数的资本市场中不断前行，致力于在严控风险的基础上为客户创造可持续的优质回报。目前，佳期投资的管理规模约400亿元。 精英汇聚，夯实人才基石 佳期投资团队是国内专注于量化投资的早期实践者。 历经十余年市场周期淬炼，团队在跨资产类别投资中积累了深厚的实战经验。目前公司团 队规模超过150人，近80%成员拥有硕士或博士学历，背景高度集中于数学、物理、统计学、计算机科学等STEM领域，主要毕业于清华、北 大、复旦、上海交大、中科大，以及哈佛、普林斯顿、斯坦福、耶鲁等海内外顶尖高校。 团队成员中不乏曾在国际权威期刊发表论文、斩获知名学术竞赛奖项的科研人才，亦有多人具备国内外顶尖金融机构或科技企业的资深从业经 验。这一高度 专业化、多元化的"全明星"阵容 ，为公司持续发展构筑了坚实的人才基础和核心竞争力。 创新驱动，构筑硬核实力 佳期投资始终将科技创新视为发展驱动力，不仅在研发算力设施上持续重投入，更在多个创新性研发方向 ...