Core Viewpoint - The article emphasizes the rapid development of AI and quantitative technology in the investment sector, highlighting the importance of continuous strategy evolution for the long-term success of quantitative private equity firms like Tianyan Capital, which was founded in 2014 and has a strong focus on innovation and adaptation [2]. Company Overview - Tianyan Capital was co-founded by Xie Xiaoyang and Zhang Sen, both of whom have over ten years of industry experience. The company’s name reflects its commitment to change and deep insights into the essence of investment [2]. - The firm has received multiple industry awards, including the "Golden Changjiang Award" and "Yinghua Award," and ranks among the top ten quantitative private equity firms in terms of performance [3][4]. Performance Metrics - As of September 2025, Tianyan Capital's products have achieved impressive returns, with an average return of ***% over the past three years, placing it first in the industry [3][4]. - The firm manages approximately 2.1 billion yuan across 11 products that meet ranking criteria, showcasing its strong long-term performance [3]. Investment Strategy - The core strategy of Tianyan Capital is centered around a multi-factor model for stock selection, which allows for higher alpha returns at a lower cost [8]. - The flagship product, "Tianyan Saineng," has been operational since May 2016 and has demonstrated significant returns, with a focus on maintaining model autonomy and stability in risk control [10][11]. Team and Culture - The investment research team at Tianyan Capital consists of over half PhD holders from prestigious institutions, fostering a culture of free exploration and innovation [12]. - The company emphasizes long-termism in its operations, avoiding arbitrary changes to risk parameters and maintaining a stable risk control model [10][11]. Market Position and Future Outlook - Tianyan Capital has strategically positioned itself to balance scale and performance, understanding that growth in assets under management should align with long-term performance and research capabilities [14]. - The firm has also obtained a Hong Kong license to enhance its global asset allocation capabilities, focusing on capturing unique alpha opportunities in the Chinese market while catering to international investors [16].

- The short-term trading volume model is neutral for A-shares[2][12] - The low volatility model is neutral for A-shares[2][12] - The characteristic institutional model is bearish for A-shares[2][12] - The characteristic trading volume model is bearish for A-shares[2][12] - The intelligent algorithm model for the CSI 300 is bearish for A-shares[2][12] - The intelligent algorithm model for the CSI 500 is bearish for A-shares[2][12] - The mid-term limit-up and limit-down model is neutral for A-shares[2][13] - The mid-term calendar effect model is neutral for A-shares[2][13] - The long-term momentum model is bullish for A-shares[2][14] - The comprehensive A-share model V3 is bearish[2][15] - The comprehensive A-share model for the CSI 2000 is bearish[2][15] - The mid-term trading volume to volatility model is bearish for Hong Kong stocks[2][16]

Fund Overview - The fund is named "Suxin Ruijian Quantitative Stock Selection Equity Investment Fund" and has been approved for fundraising by the China Securities Regulatory Commission [1] - The fund will be publicly offered from October 15, 2025, to October 31, 2025, with a maximum fundraising period of three months [2][30] - The fund is categorized as an open-ended equity fund with an indefinite duration and aims to achieve stable asset appreciation while controlling risks [21] Fund Structure - The fund offers two classes of shares: Class A and Class C, with different fee structures for subscription and redemption [1][36] - Class A shares have a code of 025404 and charge subscription fees, while Class C shares (code 025405) do not charge subscription fees [1][21] Investment Strategy - The fund's investment range includes liquid financial instruments such as stocks, depositary receipts, bonds, asset-backed securities, and stock index futures [22][24] - The fund will invest 80%-95% of its assets in stocks and depositary receipts, with a minimum of 5% in cash or short-term government bonds [24] Subscription Details - The minimum initial subscription amount is RMB 10,000, and subsequent subscriptions can be as low as RMB 1,000 [3][40] - Investors can make multiple subscriptions during the fundraising period, but once a subscription application is accepted, it cannot be revoked [4][41] Fund Management - The fund is managed by Suxin Fund Management Co., Ltd., which is responsible for the fund's operations and investment decisions [64] - The fund's custodian is Shanghai Pudong Development Bank Co., Ltd., ensuring the safekeeping of the fund's assets [65] Regulatory Compliance - The fund must meet specific conditions to complete its fundraising, including raising at least 200 million shares and having a minimum of 200 investors [6][31] - If the fundraising conditions are not met by the end of the period, the fundraising will be deemed unsuccessful, and the management company will return the funds to investors [33][63]

Core Insights - The 2025 Autumn Strategy Conference by CITIC Futures focused on the theme "Tides Surge, Breakthroughs and Innovations," analyzing investment opportunities across various sectors for Q4 and 2026 [1] Macro and Precious Metals Forum - The macroeconomic outlook for Q4 is characterized by a "steady progress" approach, with policies aimed at stabilizing growth through 500 billion yuan in financial tools and potential interest rate cuts [2] - Gold is expected to show a strong oscillation in Q4, with long-term strategic allocation opportunities due to the anticipated decline in real interest rates and ongoing geopolitical tensions [2] Financial Forum - Equity assets are projected to perform positively in Q4, driven by new capital inflows and policy expectations, with a focus on IM long positions and strategies to capitalize on market movements [3] - The bond market may shift from a weak stance, with a potential recovery in bullish sentiment, although the 10-year government bond yield is expected to fluctuate between 1.65% and 1.95% [3] Energy and Chemical Forum - The energy and chemical sectors are facing slightly weak supply and demand dynamics in Q4, with oil prices influenced by geopolitical factors and supply disruptions [4] - The chemical industry is under pressure from increasing production capacities, particularly in PVC and styrene, which may hinder demand growth without supportive consumption policies [4] Non-Ferrous Metals Forum - The non-ferrous metals sector is expected to see a positive shift in Q4, with copper, aluminum, and tin being highlighted as potential bullish opportunities due to supply disruptions and macroeconomic support from interest rate cuts [5][6] - Industrial silicon and lithium carbonate may face downward pressure, while polysilicon is expected to benefit from supply-side contraction policies [6] Agricultural Forum - Agricultural products are in a transitional phase between old and new crops, with inventory dynamics and international trade relations significantly impacting market conditions [7] - The soybean market is expected to remain stable, while palm oil may see bullish opportunities due to seasonal production declines [7] Black Metals Forum - The black metals market is anticipated to experience a mixed trend, with short-term price support from a favorable macro environment, but potential long-term weakness due to inventory pressures [8] - Iron ore prices are expected to fluctuate widely, while coal and coke prices may initially rise before facing downward pressure [8] Innovation Forum - The energy sector is under pressure from oversupply, with fossil fuels facing challenges, while the demand for new energy sources is expected to grow steadily [9] - The shipping market is projected to perform strongly due to production increases and sanctions, with coal supply tightening expected to support prices [9]

Group 1 - The report highlights the significant role of quantitative stock selection funds in the public fund market, with a total of 277 funds managing a combined scale of 90.32 billion yuan as of the end of Q2 2025, offering broader investment scope and higher style exposure flexibility compared to standard index-enhanced funds [3][12][13] - The report focuses on five fund managers with distinctive investment frameworks in quantitative stock selection, including Feng Xixiang from Xinda Australia Fund, Gao Chongnan from Guotai Fund, Lin Jingyi from Xinda Australia Fund, Shi Yunchao from Penghua Fund, and Zhai Zijian from Western Li De Fund, providing insights into their strategies and product positioning [3][12][13] Group 2 - Feng Xixiang employs a unified framework emphasizing the effectiveness of factors and the universality of alpha models, integrating static multi-factor linear models with machine learning dynamic weighting models since 2023, achieving balanced allocation in his representative products [4][16][23] - Gao Chongnan focuses on the Calmar ratio, selecting high dividend, quality, and growth styles to enhance the stability of risk-return profiles, with a product positioning aimed at low volatility value style [4][35][36] - Lin Jingyi implements a "HI+AI" approach using an integrated research platform, employing a three-step method to replicate successful peer consensus and enhance index tracking through multiple alpha models [5][22] - Shi Yunchao's strategy combines multi-factor linear models with a higher proportion of non-linear models, focusing on short prediction cycles and higher turnover rates, while maintaining a diversified portfolio to mitigate risks [6][24] - Zhai Zijian adopts an AI quantitative investment strategy with a "core + satellite" multi-strategy balanced configuration, utilizing machine learning for long-term predictions and high-frequency data analysis [6][24] Group 3 - The report indicates that as of the end of Q2 2025, Feng Xixiang manages a total of 4.54 billion yuan across seven quantitative stock selection products, with representative products achieving cumulative returns of 40.66% and 74.91% since inception, significantly outperforming their benchmark indices [17][21] - Gao Chongnan's strategy iteration has led to improved performance, with the National Strategy Yield Fund achieving an annualized return of 28.72% in 2024, reflecting a notable enhancement in risk-adjusted returns [36][37] - The quantitative team at Xinda Australia Fund consists of experienced professionals, with a comprehensive product line that includes 11 quantitative stock selection products and 2 quantitative fixed income + strategy products, aiming to reduce volatility while seeking absolute returns [32][33]

Core Viewpoint - The financing balance of the two markets has surpassed 2.3 trillion yuan, marking a historical high since 2015, indicating a significant increase in liquidity and investor risk appetite during the current bull market [2][3]. Group 1: Exposure of Micro-Cap Stocks - There is a noticeable differentiation in the exposure of quantitative long products to micro-cap stocks this year, with micro-cap indices significantly outperforming mid and large-cap stocks [4][5]. - The weighted discount rate of IC/IM stock index futures has remained high, suggesting an increased exposure of quantitative managers to micro-cap stocks [7]. - In the first quarter, the proportion of holdings in stocks below the 2000 index was about 20-40%, which may rise to over 50% in the third quarter [8]. Group 2: Reasons and Risks of Exposure to Micro-Cap Stocks - Historically, small-cap stocks have shown higher average annualized beta returns compared to large-cap stocks, attracting speculative interest from retail investors [9]. - The lower coverage of small micro-cap stocks by large institutional investors leads to higher mispricing probabilities, providing opportunities for quantitative models to identify undervalued targets [9]. - The current market liquidity favors micro-cap stocks, pushing their prices higher, especially during periods of weak economic data [9]. Group 3: Investor Strategies to Mitigate Risks - As long as micro-cap stocks maintain a strong market position, the likelihood of high exposure in quantitative long products remains significant [10]. - New investors may have concerns, but the current bull market is relatively rare, and any adjustments are expected to manifest as fluctuations rather than sharp declines [10]. - Quantitative long strategies differ from simple micro-cap strategies, focusing on identifying strong stocks and increasing exposure based on market conditions [10].

Quantitative Models and Construction - **Model Name**: Volume Model **Construction Idea**: This model uses trading volume as a key indicator to predict market trends in the short term[12][65] **Construction Process**: The model evaluates the trading volume of broad-based indices to generate buy or sell signals. A higher trading volume relative to historical averages indicates a "bullish" signal, while lower volumes may indicate neutrality or bearishness[12][65] **Evaluation**: The model is effective in capturing short-term market momentum and is widely applicable across broad indices[12][65] - **Model Name**: Low Volatility Model **Construction Idea**: This model focuses on the volatility of indices to assess market stability and predict trends[12][65] **Construction Process**: The model calculates the historical volatility of indices over a defined period. If the volatility is low, the model remains neutral, indicating a stable market environment[12][65] **Evaluation**: The model is useful for identifying periods of market stability but may lack predictive power during high-volatility phases[12][65] - **Model Name**: Institutional Feature Model (Top Trader) **Construction Idea**: This model analyzes institutional trading patterns to predict market movements[12][65] **Construction Process**: The model tracks the trading activity of institutional investors, particularly their buying and selling patterns. A high level of institutional selling generates a "bearish" signal[12][65] **Evaluation**: The model provides insights into institutional sentiment but may be less effective in retail-dominated markets[12][65] - **Model Name**: Momentum Model **Construction Idea**: This model leverages price momentum to predict long-term market trends[14][67] **Construction Process**: The model calculates the rate of price change over a long-term horizon. Positive momentum generates a "bullish" signal, while negative momentum indicates bearishness[14][67] **Evaluation**: The model is effective in identifying long-term trends but may lag during sudden market reversals[14][67] - **Model Name**: A-Share Comprehensive Weapon V3 Model **Construction Idea**: This is a composite model that integrates multiple signals across different time horizons[15][68] **Construction Process**: The model combines short-term, medium-term, and long-term signals from various sub-models (e.g., volume, momentum, institutional features) to generate an overall market outlook[15][68] **Evaluation**: The model balances short-term and long-term perspectives, making it robust for comprehensive market analysis[15][68] - **Model Name**: Hang Seng Turnover-to-Volatility Model **Construction Idea**: This model uses the ratio of turnover to volatility to predict medium-term trends in the Hong Kong market[16][69] **Construction Process**: The model calculates the turnover-to-volatility ratio for the Hang Seng Index. A higher ratio indicates a "bullish" signal, suggesting strong market participation relative to risk[16][69] **Evaluation**: The model is effective in capturing medium-term trends but may be less responsive to short-term fluctuations[16][69] Model Backtesting Results - **Volume Model**: All broad-based indices showed "bullish" signals in the short term[12][65] - **Low Volatility Model**: Neutral signals were observed, indicating stable market conditions[12][65] - **Institutional Feature Model**: Bearish signals were generated due to high institutional selling activity[12][65] - **Momentum Model**: Long-term "bullish" signals were observed, indicating positive price momentum[14][67] - **A-Share Comprehensive Weapon V3 Model**: Overall "bullish" signals were generated, reflecting a positive market outlook[15][68] - **Hang Seng Turnover-to-Volatility Model**: "Bullish" signals were observed, suggesting optimism in the Hong Kong market[16][69]

Quantitative Models and Construction Methods - **Model Name**: GAN_GRU **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for processing volume-price time-series features and Gated Recurrent Unit (GRU) for encoding time-series features to create a stock selection factor[4][13][41] **Model Construction Process**: 1. **GRU Component**: - Input features include 18 volume-price features such as closing price, opening price, turnover, and turnover rate[14][17][19] - Training data consists of the past 400 days of these features, sampled every 5 trading days, forming a 40x18 matrix to predict cumulative returns over the next 20 trading days[18] - Data preprocessing includes outlier removal and normalization at both time-series and cross-sectional levels[18] - Model architecture: Two GRU layers (128, 128) followed by an MLP (256, 64, 64), with the final output being the predicted return (pRet), which serves as the stock selection factor[22] - Training method: Semi-annual rolling training, with training conducted on June 30 and December 31 each year[18] - Optimization: Adam optimizer, learning rate of 1e-4, IC loss function, early stopping after 10 epochs, and a maximum of 50 training epochs[18] 2. **GAN Component**: - GAN consists of a generator (G) and a discriminator (D)[23] - Generator: Uses LSTM to preserve the time-series nature of the input features, transforming random noise into realistic data samples[33][37] - Loss function: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( z \) represents random noise, \( G(z) \) is the generated data, and \( D(G(z)) \) is the discriminator's output probability[24][25] - Discriminator: Uses CNN to process the two-dimensional volume-price time-series features, distinguishing between real and generated data[33][37] - Loss function: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( x \) is real data, \( D(x) \) is the discriminator's output for real data, and \( D(G(z)) \) is the output for generated data[27][29] - Training: Alternating updates of the generator and discriminator parameters until convergence[30] **Model Evaluation**: The GAN_GRU model effectively captures both time-series and cross-sectional features, leveraging the strengths of GAN and GRU for stock selection[4][13][41] --- Model Backtesting Results - **GAN_GRU Model**: - **IC Mean**: 11.36%[41][42] - **ICIR (Non-Annualized)**: 0.88[42] - **Turnover Rate**: 0.83[42] - **Recent IC**: -2.56%[41][42] - **1-Year IC Mean**: 8.94%[41][42] - **Annualized Return**: 38.09%[42] - **Annualized Volatility**: 23.68%[42] - **IR**: 1.61[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 23.52%[41][42] --- Quantitative Factors and Construction Methods - **Factor Name**: GAN_GRU Factor **Factor Construction Idea**: Derived from the GAN_GRU model, this factor encodes volume-price time-series features to predict stock returns[4][13][41] **Factor Construction Process**: - The factor is generated using the output of the GAN_GRU model, which combines GAN-based feature generation and GRU-based time-series encoding[4][13][41] - The factor undergoes industry and market capitalization neutralization, as well as standardization, before being used for testing[22] **Factor Evaluation**: The GAN_GRU factor demonstrates strong predictive power across various industries, with consistent outperformance in recent years[4][13][41] --- Factor Backtesting Results - **GAN_GRU Factor**: - **IC Mean**: 11.36%[41][42] - **ICIR (Non-Annualized)**: 0.88[42] - **Turnover Rate**: 0.83[42] - **Recent IC**: -2.56%[41][42] - **1-Year IC Mean**: 8.94%[41][42] - **Annualized Return**: 38.09%[42] - **Annualized Volatility**: 23.68%[42] - **IR**: 1.61[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 23.52%[41][42]

- The short-term volume model indicates a bullish outlook for most broad-based indices[2][12][70] - The low volatility model is neutral in the short term[2][12][70] - The institutional model based on the characteristics of the Dragon and Tiger list is bearish in the short term[2][12][70] - The characteristic volume model is bullish in the short term[2][12][70] - The intelligent algorithm models for the CSI 300 and CSI 500 indices are bullish in the short term[2][12][70] - The mid-term limit-up and limit-down model is bullish[2][13][71] - The mid-term calendar effect model is neutral[2][13][71] - The long-term momentum model is bullish[2][14][72] - The comprehensive A-share models, including the A-share Comprehensive Weapon V3 model and the A-share Comprehensive Guozheng 2000 model, are bullish[2][15][73] - The mid-term turnover rate inverse volatility model for Hong Kong stocks is bullish[2][16][74]

Core Viewpoint - The Southern Asset Management has launched the Southern CSI A500 Index Enhanced Fund, aiming to provide investors with a quality tool for allocating core A-share assets while striving for excess returns based on the new generation of broad-based indices, the CSI A500 Index [1] Group 1: Fund Overview - The Southern CSI A500 Index Enhanced Fund is anchored to the CSI A500 Index and leverages the quantitative investment team's expertise to achieve excess returns while tracking the index's beta returns [1] - The fund's management fee is set at 0.80% per year, with a custody fee of 0.10% per year and a sales service fee of 0.40% for Class C shares, combining low costs and high transparency typical of index funds with the management capabilities of the fund manager [5] Group 2: Team and Strategy - The quantitative investment team consists of 13 members with an average of over 8 years of industry experience, focusing on a platform-based work model to enhance transparency and collaboration in investment research [2] - The investment strategy employs a "multi-strategy overlay" approach, utilizing a diverse set of sub-strategies to achieve stable enhancement of the target benchmark, incorporating models such as multi-factor, fundamental quantitative, style rotation, and deep learning [4] Group 3: Market Positioning - The CSI A500 Index covers 107 industries in the A-share market, including sectors like semiconductors, healthcare, banking, and liquor, representing a "condensed version" of the entire market, which allows the fund to capture opportunities in new productivity sectors while balancing traditional industry values [5] - Southern Asset Management has validated its strength in broad-based management, with multiple index products exceeding 100 billion in scale, indicating a robust track record in managing large-scale index funds [5]