Core Viewpoint - The article discusses the rise of quantitative stock selection strategies in the private equity sector in China, driven by the increasing availability of financial data and advancements in AI technology, as well as significant market volatility in recent years [2]. Group 1: Advantages of Quantitative Stock Selection - Quantitative stock selection is based on mathematical models and algorithms that systematically analyze vast amounts of data to select stocks, contrasting with traditional subjective selection methods that rely on analysts' judgments [7]. - The core advantages of quantitative stock selection include efficiency, as computers can process multidimensional data across thousands of stocks in seconds, and diversification, as these strategies typically involve holding hundreds of stocks to mitigate individual stock risk [14]. Group 2: Main Strategies in Quantitative Stock Selection - The three main methods of quantitative stock selection are: 1. Multi-factor models, which use various factors to explain future stock returns, with extensive historical backtesting to identify effective factor combinations [9]. 2. Statistical arbitrage, which captures pricing discrepancies based on mean reversion principles among related assets [11]. 3. Event-driven strategies, which monitor real-time events affecting stock prices and generate trading signals based on quantifiable impacts [12]. Group 3: Performance Comparison - Over the past five years, quantitative stock selection has shown lower drawdowns, higher returns, and better Sharpe ratios compared to subjective stock selection, except in 2024, a transitional year [15][20]. - The median returns of quantitative strategies outperformed subjective strategies in all years except 2024, with average returns consistently favoring quantitative methods [15]. Group 4: Differences Between Quantitative Stock Selection and Index Enhancement - Quantitative stock selection and index enhancement both utilize quantitative models but differ in their investment approach; the former seeks absolute returns without being tethered to a specific index, while the latter aims to enhance returns relative to a benchmark index [27]. Group 5: Considerations for Ordinary Investors - Ordinary investors should evaluate quantitative stock selection strategies based on the stability of excess returns across market cycles, risk control capabilities such as maximum drawdown and Sharpe ratio, and the research team's expertise in factor discovery and model iteration [28].

1. Report Industry Investment Rating No information provided in the content. 2. Core Viewpoints of the Report - The bond market is at a transformative moment, and the "Future is Here" series of reports focuses on exploring cutting - edge technologies affecting the bond market to empower investment research [3]. - Fixed - income quantification is an inevitable product of financial industrialization and the answer of the bond market in the AI - empowered era. It has evolved from subjective to systematic, model - based, and data - driven, and has moved from the edge to the core of the trading desk [3]. - Compared with relatively mature equity quantification, fixed - income quantification has unique characteristics, including more complex tools and market structures, stronger policy and institutional factors, and more prominent liquidity and data quality issues [3]. - The report aims to answer four questions: the main schools of fixed - income quantification and their basic logics, the applicable market environments for these quantification technologies, the problems that quantification technologies cannot solve or may amplify risks, and the future prospects and optimization spaces of fixed - income quantification [3]. 3. Summary According to the Table of Contents 3.1 Global Fixed - Income Quantification: Four Schools and Basic Logics 3.1.1 Fundamental Quant - Focuses on using economic logic, macro data, and fundamental factors to predict market directions or asset values. It tries to "model" the logic that traditional macro research relies on analysts' experience for [8]. - The process includes data input (such as GDP, CPI, and PMI), building models (e.g., a two - factor model of "growth" and "inflation" or a multi - dimensional macro - factor system), and formulating trading logics (e.g., going long on interest - rate bonds in the "loose money + tight credit" cycle) [8]. - With the development of data technology, it uses high - frequency data for nowcasting to capture economic temperature changes. However, it faces challenges such as "overfitting" risk, structural breaks, and the risk of "fundamental desensitization" and model failure [8][9][10]. 3.1.2 Technical Quant - Focuses on using market volume and price data to capture trading opportunities from trends, reversals, or micro - structures without relying on macro - economic explanations [11]. - Trend - tracking and CTA fixed - income strategies use time - series momentum trading on interest rates and bond prices, which has significant long - term trend premiums and is important in multi - asset CTA strategies. The strategies are applied through unified momentum/trend rules on multiple products and can be part of a cross - asset trend strategy [11][12][15]. - Market - making and micro - structure quantification focuses on using quantification technology to improve pricing and inventory management in aspects such as order - book modeling, quoting strategies, and execution algorithms [18][20]. 3.1.3 Relative Value Quant - Focuses on cross - sectional comparison or finding pricing deviations to earn mean - reversion returns or risk premiums, often involving long - short hedging or factor - based bond selection [18]. - Interest - rate term structure and curve trading use various interest - rate term structure models to factorize the yield curve and conduct relative - value trading based on the deviation between the theoretical and actual curves [18][23]. - Carry/Roll - down strategies aim to earn the "time value" of the interest - rate curve and bonds. It is effective in stable or downward - trending interest - rate environments and is often incorporated into the factor - investment framework [26][28]. - Credit and spread factor strategies map bond characteristics into a series of credit and style factors to construct long - short or over -/under - weighted portfolios to earn factor premiums [33]. - Relative value and basis arbitrage focus on price "dislocation" between different tools with the same or similar risk exposures and use methods like PCA, mean - reversion modeling, and high -/medium - frequency data mining to construct statistical arbitrage strategies [38]. 3.1.4 Multi - Factor Models - Aims to systematically integrate excess returns from different sources. The core logic is to decompose the expected return of bonds into a linear combination of several risk factors to build a portfolio with a higher Sharpe ratio and smaller drawdowns [39]. - It is related to the three previous schools. It uses a large amount of fundamental data, includes momentum factors from the technical school, and is mainly used for "bond selection" similar to the relative - value school [43][45]. 3.2 Market Environments Suitable for Quantitative Technologies - **Liquidity and Trading Systems**: High - liquidity, low - transaction - cost markets are suitable for curve trading, relative - value, CTA trend, market - making, and high - frequency strategies; medium - liquidity markets are suitable for term - structure models, carry, and some relative - value and factor strategies; low - liquidity, OTC - dominated credit markets are suitable for medium - to - low - frequency factor strategies, duration/barbell allocation, and some structured - product pricing [48][49]. - **Interest - Rate Levels and Volatility Environments**: When the interest - rate center is declining with mild fluctuations, carry and roll - down strategies perform well, and term - structure strategies can profit from the "loose - neutral" switch. When interest rates rise rapidly or policies change suddenly, term - structure and carry strategies are prone to net - value drawdowns, while CTA trend and duration - hedging strategies can provide some protection [50]. - **Credit Environments and Macroeconomic Cycles**: In a low - default - rate, credit - expansion period, credit factors and credit - sinking strategies have high "tailwind returns". In a credit - contraction and high - default period, quantitative models may underestimate tail risks and are difficult to capture sudden "black - swan events" [51][53]. 3.3 Five Limitations of Fixed - Income Quantification - Policy and institutional inflection points are "unquantifiable" because central - bank monetary policies and regulatory reforms often show "discrete" and "mutant" characteristics, and historical - data - trained models may fail when regime shifts occur [55]. - "Liquidity black holes" and "out - of - model" risks exist because most models assume a "frictionless market", but the credit - bond market often faces liquidity shortages, which can lead to the failure of traditional strategies [56]. - Credit defaults have "small - sample" and jump risks. Bond defaults are sparse events, resulting in model overfitting or non - convergence, and the non - standardized information in the default process is difficult to cover [57]. - Complex terms and game behaviors are non - modelable. Many fixed - income products have complex option terms, and their triggering depends on issuers' subjective will, causing the deviation between the theoretical option value calculated by quantitative models and the market price [58]. - Crowded trading and endogenous instability occur when quantitative strategies are highly homogeneous. Once the market fluctuates in the opposite direction, the concentrated stop - loss orders can cause a stampede and more severe fluctuations [59][60]. 3.4 Outlook: The Future Landscape of Fixed - Income Quantification - **Quantamental (Quantitative + Fundamental)**: Quantitative analysis will empower fundamental analysis. Future mainstream models include "quantitative support with subjective decision - making" or "subjective logic with quantitative verification", applicable in macro - asset allocation and credit screening [62]. - **In - Depth Penetration of Alternative Data and AI Technologies**: With the development of large - language models, non - structured data can be processed, providing new sources of alpha. Applications include semantic and sentiment analysis of text data and using satellite and geographical data for investment analysis [63]. - **Algorithmic and Automated Trade Execution**: The increasing proportion of electronic trading in the Chinese bond market provides a foundation for algorithmic trading. Intelligent order - splitting algorithms can reduce impact costs, and machine - learning - based market - making strategies can adjust quotes and control inventory risks [64][66].

- The report highlights the performance of the quantitative stock portfolios, including the CSI 300 enhanced portfolio, which achieved a weekly excess return of 2.16% and a cumulative excess return of 5.70% in 2026[1][15][14] - The CSI 500 enhanced portfolio recorded a weekly excess return of 0.38% and a cumulative excess return of -1.98% in 2026[15][14][17] - The CSI 1000 enhanced portfolio achieved a weekly excess return of 0.96% and a cumulative excess return of 1.56% in 2026[15][14][24] - The PB-Earnings optimized portfolio delivered a weekly excess return of 4.05% and a cumulative excess return of 3.64% in 2026[30][31][32] - The GARP portfolio achieved a weekly excess return of 5.85% and a cumulative excess return of 8.81% in 2026[33][34] - The Small-cap Value Optimized Portfolio 1 recorded a weekly excess return of -0.75% and a cumulative excess return of -1.42% in 2026[35][36] - The Small-cap Value Optimized Portfolio 2 achieved a weekly excess return of 0.70% and a cumulative excess return of 2.23% in 2026[37][38] - The Small-cap Growth Portfolio delivered a weekly excess return of -0.24% and a cumulative excess return of -0.57% in 2026[39][40] - Style factors showed that small-cap stocks outperformed large-cap stocks, and low valuation stocks outperformed high valuation stocks. The market capitalization factor achieved a weekly multi-long-short return of 2.83%, while the PB factor and PE_TTM factor achieved 1.05% and 0.71%, respectively[42][43][45] - Technical factors indicated positive contributions from turnover rate factors, while reversal and volatility factors showed negative returns. The turnover rate factor achieved a weekly multi-long-short return of 0.48%, while reversal and volatility factors recorded -2.05% and -0.98%, respectively[46][48][49] - Fundamental factors demonstrated positive returns from SUE and adjusted net profit expectation factors. The SUE factor achieved a weekly multi-long-short return of 0.82%, while adjusted net profit expectation factors recorded 0.47%. ROE factors showed a negative return of -0.67%[50][51][52]

Core Viewpoint - The article emphasizes the importance of understanding the interplay between industrial revolutions and financial theories, highlighting how advancements in the real economy drive the evolution of asset valuation and allocation methods [4][5]. Group 1: Historical Context of Wealth and Financial Theory - Approximately 2000 years ago, the widespread use of iron tools in agriculture marked the beginning of material surplus, representing humanity's initial wealth [6]. - The Talmud introduced a simplistic wealth allocation principle of "1/3 land, 1/3 business, 1/3 savings," which lacked optimization efforts and was based on experiential rules [6]. - About 100 years ago, the outcomes of two industrial revolutions led to exponential growth in production capacity, shifting wealth accumulation from aristocracy to the emerging bourgeoisie, who began to view wealth as a means to expand production capabilities [6]. Group 2: Evolution of Investment Theories - The introduction of value investing by Benjamin Graham represented a breakthrough in asset allocation methodology, moving from a zero-dimensional approach to a more sophisticated understanding of investment value [6]. - The third industrial revolution, which transitioned humanity from the electrical age to the information age, democratized wealth ownership and introduced complex asset classes, leading to the development of modern portfolio theory by Harry Markowitz and William Sharpe [7]. - This theory incorporated the concept of risk-adjusted returns, fundamentally changing how investors construct portfolios and view expected returns and risks [7]. Group 3: Contemporary Challenges and Opportunities - Recent global events, including the COVID-19 pandemic and geopolitical tensions, have prompted a reevaluation of expected returns and risk factors in investment strategies [8]. - The article notes that the historical reliance on financial returns as the sole measure of investment success is being challenged, as investors seek to understand and incorporate a broader range of risk factors into their decision-making processes [8].

Group 1 - The article examines the application of PLS model expected factor returns in factor weighting, focusing on both single-factor multi-strategy and multi-factor single-strategy dimensions [1] - In the top 100 combinations of 20 single factors, using the PLS model for the five most volatile factor combinations resulted in an annualized return increase of approximately 4.0% compared to mean-weighted returns, and 6.6% compared to equal-weighted returns [2] - The article constructs six basic combinations including one dividend selection, one growth selection, two small-cap combinations, and two relatively balanced style combinations, achieving an annualized return increase of 3.3% over excess return mean weighting and 3.9% over equal weighting for volatile combinations [2] Group 2 - In multi-factor models, using PLS expected returns to determine factor weights can improve the expected IC and performance of top 100 combinations, although this improvement is not consistent across all cross-sections [3] - The PLS weighting method is noted to be more robust overall, but may underperform compared to mean IC weighting and ICIR weighting when factor momentum is strong, as observed in 2023 [3] - A composite quantitative fixed income + strategy using PLS expected return weighted multi-factor model for the stock side and the China Bond Short-term Index for the bond side achieved an annualized return of 8.1% with a volatility of 5.6% and a maximum drawdown of 5.4% from January 2018 to November 2025 [3]

Core Insights - The A-share market has shown a significant structural trend this year, with private equity stock quantitative long strategies achieving excess returns due to their systematic advantages [1] - As of the end of November, the average excess return rate for 833 quantitative long products in the market reached over 17%, with 91.48% of these products achieving excess returns, indicating the overall effectiveness and stability of this strategy [1] Group 1: Market Performance - The A-share market has experienced a fluctuating upward trend this year, with frequent rotations between technology sectors like AI computing and cyclical sectors [1] - The average daily trading volume has remained high, providing a favorable liquidity environment for quantitative trading [1] Group 2: Performance by Fund Size - Large and medium-sized private equity institutions have demonstrated stronger excess return capabilities, with products under management sizes between 2 billion and 5 billion yuan achieving an average excess return rate of 20.12%, the highest among all management size tiers [2] - Products from institutions managing over 10 billion yuan achieved an average excess return rate of 19.98%, with 98.13% of these products generating excess returns, reflecting the comprehensive strength of leading private equity firms in research, strategy iteration, and risk control [2] - Smaller private equity institutions showed weaker overall performance, with products under 500 million yuan achieving an average excess return rate of only 13.85%, the lowest among all tiers [2] Group 3: Sub-strategy Performance - As of the end of November, other index enhancement strategy products led with an average excess return rate of 20.13%, with 93% of these products achieving positive excess returns [3] - The mainstream strategy of quantitative stock selection (air index enhancement) had 331 products with an average excess return rate of 19.14% [3] - Among broad-based index enhancement strategies, the small and mid-cap index enhancement products performed better, with the CSI 1000 index enhancement products achieving an average excess return rate of 17.53%, significantly higher than the CSI 500 index enhancement products at 14.14% and the CSI 300 index enhancement products at 8.20% [3]

Core Viewpoint - The article emphasizes the importance of identifying companies with strong intrinsic value growth over the long term, despite short-term market volatility and valuation fluctuations [1][2][3]. Group 1: Investment Strategy - The investment strategy involves focusing on companies with strong intrinsic value growth, which is a compounding variable that can withstand market fluctuations over time [2][4]. - The article discusses the importance of distinguishing between short-term valuation changes and long-term value growth, highlighting that exceptional companies can outperform mediocre ones over extended periods [2][4]. - It suggests that finding companies with sustainable high intrinsic value growth simplifies the complex task of navigating market uncertainties [3][5]. Group 2: Characteristics of Good Businesses - Good businesses are defined by three key characteristics: high value, strong dependency, and significant growth potential [6][8]. - High value refers to a company's ability to significantly outperform industry standards, which can change over time due to various factors such as technological advancements and policy shifts [6][7]. - Strong dependency can arise from unique products or high switching costs, leading to a natural market lock-in effect [7][8]. - Significant growth potential is essential for providing high investment returns over the long term [8][9]. Group 3: Characteristics of Good Companies - Good companies exhibit traits of resilience, ambition, humility, and adaptability, which are crucial for navigating challenges and seizing opportunities [14][18]. - The article stresses the importance of a company's management in realizing its potential and effectively executing its business model [9][14]. - Companies that prioritize long-term strategies and foster a strong corporate culture are more likely to succeed [19][20]. Group 4: Market Dynamics and Valuation - The article highlights the significance of understanding market dynamics, including the impact of extreme market conditions on investment returns over different time frames [4][20]. - It discusses the importance of recognizing valuation differences and understanding stock price movements to identify investment opportunities [22][23]. - The article also emphasizes the need to adapt to macroeconomic variables and market cycles to optimize investment strategies [22][24].

- The report tracks the performance of various style factors, including market capitalization, non-linear market capitalization, profitability, momentum, volatility, and beta factors[2] - The construction process involves creating long-short portfolios at the end of each month, going long on the top 10% of stocks with the highest factor values and shorting the bottom 10% with the lowest factor values, with equal weighting[16] - The recent performance shows strong long positions in market capitalization, non-linear market capitalization, and profitability factors, while momentum, volatility, and beta factors had strong short positions[16] Factor Performance Tracking - The fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[3][4][5] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[3][4][5] - GRU factors had weak long-short performance, with the barra1d model showing some pullback, while other models had insignificant returns[3][4][5] CSI 300 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with growth and surprise growth factors performing negatively, while static financial factors performed positively[4] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[4] - GRU factors had mixed long-short performance, with the barra1d model showing significant pullback, while the barra5d and close1d models performed strongly[4] CSI 500 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[5] - Technical factors had negative long-short returns, with short-term factors showing more significant performance, favoring low volatility and low momentum stocks[5] - GRU factors had good long-short performance, with the open1d and barra1d models showing slight pullback, while the close1d and barra5d models performed strongly[5] CSI 1000 Component Stocks Factor Performance - Fundamental factors showed similar long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[6] - Technical factors had negative long-short returns, favoring low volatility and low momentum stocks[6] - GRU factors had strong long-short performance, with the barra1d model showing some pullback, while the close1d and open1d models performed strongly[6] Long-Only Portfolio Performance - The GRU long-only portfolio showed weak performance, with various models underperforming the CSI 1000 index by 0.54% to 1.12%[7] - The barra5d model performed strongly year-to-date, outperforming the CSI 1000 index by 8.55%[7] - The multi-factor portfolio showed weak performance, underperforming the CSI 1000 index by 0.47%[7] Factor Performance Metrics - Momentum factor: -1.93% (one week), -8.36% (one month), -24.78% (six months), 19.89% (year-to-date), 17.64% (three-year annualized), 17.58% (five-year annualized)[17] - Volatility factor: 1.82% (one week), -2.33% (one month), 16.17% (six months), 6.56% (year-to-date), 7.58% (three-year annualized), -11.09% (five-year annualized)[17] - Beta factor: -1.54% (one week), 5.68% (one month), 0.60% (six months), 19.29% (year-to-date), 7.50% (three-year annualized), 8.99% (five-year annualized)[17] - Liquidity factor: 0.91% (one week), 42.89% (one month), 9.98% (six months), 12.24% (year-to-date), -20.32% (three-year annualized), -24.87% (five-year annualized)[17] - Valuation factor: 0.82% (one week), 0.46% (one month), 0.14% (six months), 3.77% (year-to-date), 14.92% (three-year annualized), 5.46% (five-year annualized)[17] - Growth factor: 0.71% (one week), 2.28% (one month), 2.34% (six months), 3.16% (year-to-date), 49.33% (three-year annualized), -4.78% (five-year annualized)[17] - Leverage factor: 0.35% (one week), 2.37% (one month), 3.68% (six months), 15.17% (year-to-date), 6.40% (three-year annualized), 1.98% (five-year annualized)[17] - Profitability factor: 0.49% (one week), -0.64% (one month), 7.01% (six months), 14.10% (year-to-date), 3.12% (three-year annualized), 0.51% (five-year annualized)[17] - Non-linear market capitalization factor: 4.22% (one week), 0.44% (one month), 3.16% (six months), -32.83% (year-to-date), -38.38% (three-year annualized), -30.29% (five-year annualized)[17] - Market capitalization factor: 5.39% (one week), 0.59% (one month), 2.18% (six months), -37.92% (year-to-date), -40.48% (three-year annualized), -34.25% (five-year annualized)[17]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods 1. Model Name: Broker Gold Stock Performance Enhancement Portfolio - **Model Construction Idea**: The model aims to optimize the selection from the broker gold stock pool to outperform the benchmark index of equity-biased hybrid funds[12][39] - **Model Construction Process**: - The model uses the broker gold stock pool as the stock selection space and constraint benchmark - It employs portfolio optimization to control deviations in individual stocks and styles from the broker gold stock pool - The industry allocation is based on the industry distribution of all public funds - The portfolio is adjusted at the closing price on the first day of each month[12][39][42] - **Model Evaluation**: The model has shown stable performance historically, consistently outperforming the equity-biased hybrid fund index annually from 2018 to 2022[12][39][42] Model Backtest Results Broker Gold Stock Performance Enhancement Portfolio - **Absolute Return (Monthly)**: -0.77% (20251009-20251031)[41] - **Excess Return Relative to Equity-biased Hybrid Fund Index (Monthly)**: 1.37% (20251009-20251031)[41] - **Absolute Return (Year-to-date)**: 35.08% (20250102-20251031)[41] - **Excess Return Relative to Equity-biased Hybrid Fund Index (Year-to-date)**: 2.61% (20250102-20251031)[41] - **Ranking in Active Equity Funds (Year-to-date)**: 40.13% percentile (412/3469)[41] Quantitative Factors and Construction Methods 1. Factor Name: Total Market Value - **Factor Construction Idea**: This factor measures the total market capitalization of a stock, which is often used to capture the size effect in stock returns[3][28] - **Factor Construction Process**: - The total market value is calculated as the product of the stock's current price and the total number of outstanding shares[3][28] - **Factor Evaluation**: The total market value factor has shown good performance in the recent month and year-to-date periods[3][28] 2. Factor Name: Single Quarter Revenue Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of a company's revenue in a single quarter, indicating its short-term growth potential[3][28] - **Factor Construction Process**: - The single quarter revenue growth rate is calculated as the percentage change in revenue from the previous quarter to the current quarter[3][28] - **Factor Evaluation**: The single quarter revenue growth rate factor has shown good performance year-to-date[3][28] 3. Factor Name: Analyst Net Upward Revision - **Factor Construction Idea**: This factor measures the net number of upward revisions by analysts, reflecting positive changes in analyst sentiment[3][28] - **Factor Construction Process**: - The analyst net upward revision is calculated as the difference between the number of upward revisions and the number of downward revisions over a specific period[3][28] - **Factor Evaluation**: The analyst net upward revision factor has shown good performance year-to-date[3][28] Factor Backtest Results Total Market Value Factor - **Recent Month Performance**: Good[3][28] - **Year-to-date Performance**: Good[3][28] Single Quarter Revenue Growth Rate Factor - **Recent Month Performance**: Not specified - **Year-to-date Performance**: Good[3][28] Analyst Net Upward Revision Factor - **Recent Month Performance**: Not specified - **Year-to-date Performance**: Good[3][28]

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].