Group 1 - The overall growth score has decreased, indicating a balanced style with economic indicators showing weakness, liquidity slightly loose, and credit indicators weakening [1][6][21] - The asset allocation view suggests a slight decrease in gold positions, with bonds improving and U.S. stock positions increasing [1][23] - Economic leading indicators indicate that the downward cycle is nearing its end, with expectations of slight fluctuations in the next three months [11][14] Group 2 - Credit indicators show a stable price and structure, but the total credit volume has weakened significantly, leading to a further decline in comprehensive credit indicators [2][21] - The market focus remains on PPI, which has become the most watched variable, surpassing economic indicators in attention [2][24] - Industry selection remains consistent with previous periods, focusing on sectors that are insensitive to economic changes but sensitive to liquidity and credit [26][28] Group 3 - The liquidity environment is maintained at a slightly loose level, with short-term rates stable and long-term rates slightly declining [17][20] - The comprehensive credit indicators reflect a weak credit environment, with both credit volume and structure remaining low [21][22] - The asset allocation weights indicate a neutral stance on A-shares and a slight increase in bond positions, while gold positions have decreased [23]

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].

1. Report Industry Investment Rating - Not provided in the given content 2. Core Viewpoints of the Report - The report focuses on constructing multiple ETF portfolios, including macro industry, macro + momentum industry, core - satellite, and trinity style rotation portfolios, aiming to find better investment opportunities by combining macro factors, momentum factors, and style rotation [4][5]. - Different industries have different sensitivities to economic, liquidity, and credit factors. For example, traditional cycle industries are sensitive to the economy, TMT is sensitive to liquidity, and consumption is sensitive to credit [4]. 3. Summary According to Relevant Catalogs 3.1 ETF Portfolio Construction Methods 3.1.1 Based on Macro Method - Calculate macro - sensitivities of broad - based, industry - themed, and Smart Beta ETFs based on economic, liquidity, and credit variables. Combine with momentum indicators for complementary analysis [4]. - Traditional cycle industries are suitable for economic up - periods, TMT for weak - economy but loose - liquidity periods, and consumption for credit - expansion periods. State - owned enterprises and ESG - related themes have low sensitivities to liquidity and credit [4]. - Construct three ETF portfolios (macro industry, macro + momentum industry, and core - satellite) and adjust positions monthly [4]. 3.1.2 Trinity Style Rotation ETF Portfolio Construction - Build a medium - to - long - term style rotation model centered on macro - liquidity, and compare it with the CSI 300 index [5]. - Construct three types of models (growth/value rotation, market - cap, and quality models) by screening macro, fundamental, and market - sentiment factors. The model has 8 style - preference results [5]. - Select ETFs with high exposure to the target style, control industry exposure, and set allocation limits to get the final ETF allocation model [5]. 3.2 Macro Industry Portfolio - Select industry - themed ETFs with over 1 - year establishment and over 200 million current scale. Calculate sensitivity scores of economic, liquidity, and credit factors monthly, adjust scores according to the latest indicators, and sum them up. If liquidity and credit deviate significantly, remove the liquidity score. Select the top 6 industry - themed indices and corresponding largest - scale ETFs for equal - weight allocation [6][7]. - Currently, with falling economic leading indicators, loose liquidity, and tightened credit, the portfolio is biased towards TMT and consumption. The February positions are shown in Table 1 [8]. - The portfolio has large fluctuations and outperformed the benchmark significantly in January [11]. 3.3 Macro + Momentum Industry Portfolio - Combine macro and momentum methods to address the left - side nature of macro - based strategies (low win - rate but high odds). Use clustering to group industry - themed indices and select the highest - rising product in each group in the past 6 months for equal - weight allocation [12]. - The momentum - selected industries still have a high proportion of cyclical industries. The February positions are shown in Table 3 [16]. - The portfolio has performed well this year and outperformed the CSI 300 significantly in January [17]. 3.4 Core - Satellite Portfolio - Design a "core - satellite" portfolio with the CSI 300 as the core to address the high volatility and rapid industry rotation of industry - themed ETFs [19]. - Calculate macro - sensitivities for broad - based, industry - themed, and Smart Beta ETFs, construct three stock portfolios, and weight them at 50%, 30%, and 20% respectively [19]. - The current allocation of broad - based ETFs is biased towards the Sci - tech Innovation Board and the ChiNext. The portfolio has performed stably, outperforming the benchmark in most months except December, and had significant excess returns in January 2026 [23][24]. 3.5 Trinity Style Rotation ETF Portfolio - The model currently favors the small - cap growth - high - quality segment. The factor exposures and historical performance are shown in Table 7 [26]. - The February positions are shown in Table 9 [31]. - The portfolio has achieved certain excess returns, especially in some months such as August 2025 and January 2026 [29].

Group 1 - The report indicates a return of the low volatility factor, forming a resonance with macroeconomic indicators showing a weakening economy, slightly loose liquidity, and a contraction in credit [2][5][8] - The report emphasizes the selection of factors that are insensitive to economic conditions, sensitive to liquidity, and insensitive to credit, with a focus on low volatility factors in the CSI 300 and small-cap stocks in the CSI 500 [5][9] - The overall asset allocation viewpoint suggests a slight allocation to US stocks, with a neutral stance on A-shares and a positive outlook on gold despite recent declines [29][30] Group 2 - Economic leading indicators maintain a downward judgment, with the PMI and new orders showing declines, indicating the economy is in an early stage of a downward cycle since December 2025 [12][18] - Liquidity is assessed as slightly loose, with short-term interest rates declining and monetary supply showing a neutral signal, while excess reserves continue to decrease [21][26] - Credit indicators show a widening credit spread and weakening credit price indicators, with a general decline in comprehensive credit indicators [27] Group 3 - The market focus remains on PPI, which has gained attention as inflation expectations rise, particularly after September 2024, indicating a heightened concern for future demand recovery [31] - The industry selection continues to favor TMT (Technology, Media, and Telecommunications) and consumer sectors, based on macroeconomic indicators [32]

Investment Strategy Overview - The report emphasizes a fundamental-driven style factor switch, awaiting confirmation of trend movements for 2026 [1][4][8] Factor Performance - Growth factors have shown strong performance, while low volatility and momentum factors have retreated, indicating a rapid rotation among market sectors and themes this year [4][10][12] - Year-to-date performance of various factors in different indices shows growth at 37.93% in CSI 300, while low volatility and liquidity factors have negative returns [10][12] Macro Quantitative Framework - The macroeconomic cycle has shifted more frequently in the past three years, with leading indicators suggesting a downturn in the first half of 2025, followed by a recovery signal towards the end of the year [4][38][43] - The liquidity indicators have shown a weak overall trend, with market trading rates rising, indicating a correction in liquidity for the second half of 2025 [50][54][60] - Credit indicators have shown a preference for expansion in the first half of 2025, transitioning to contraction by November [65][66] 2026 Equity Quantitative Outlook - The report anticipates a fundamental-driven style switch, with a focus on economic fundamentals becoming the key driver, transitioning from liquidity concerns to economic and inflation factors [4][86][91] - Market trends indicate a shift to a consolidation phase since August, with an increasing probability of trend confirmation from late October [92][97] - Emotional indicators have shown a supportive trend since July, with overall sentiment remaining warm and moderate [102][105] Industry Rotation and Focus - The speed of industry rotation has slowed down in 2025, with potential for a main trend to form, particularly in sectors with lower crowding and emerging trends [106][112] - Key sectors to watch include electronics and computing, which have shown lower crowding and are in a trend initiation phase [113][116]

Core Insights - The article discusses the integration of macro quantification and factor momentum to identify resonant factors for investment strategies, emphasizing the importance of economic, liquidity, and credit indicators in shaping market expectations [1][3]. Group 1: Factor Scores and Market Indicators - The macroeconomic indicators show signs of recovery, with economic growth expected to improve, while liquidity is slightly weak and credit conditions are tightening [3][4]. - Value factors have seen a significant increase in scores, becoming resonant factors in the CSI 300 index, while growth factors have declined [4][6]. - The article presents a table of factor scores across different indices, indicating a preference for value and low volatility factors in the current market environment [4]. Group 2: Economic Outlook and Leading Indicators - The economic leading indicators model suggests that the economy is in a rising cycle since September 2025, with a slight upward trend expected in the coming months [6][9]. - Specific indicators such as PMI and fixed asset investment are analyzed, showing a mixed outlook with some indicators in a rising phase while others are nearing a peak [11][12]. - The article highlights the importance of monitoring leading indicators to anticipate future economic cycles and potential downturns [9][10]. Group 3: Liquidity and Credit Conditions - The liquidity environment is assessed as slightly loose despite some tightening in interest rates, with a focus on the net monetary supply and excess reserve rates [12][16]. - Credit indicators show a mixed picture, with overall credit volume and structure remaining low, but some signs of recovery are noted [17][18]. - The article suggests a cautious approach to credit-sensitive investments due to the ongoing tightening in credit conditions [17]. Group 4: Asset Allocation and Market Focus - The asset allocation strategy is adjusted to reflect a neutral to positive stance on A-shares, while reducing exposure to gold and bonds due to changing market dynamics [18]. - The focus on PPI and liquidity as key market drivers indicates a shift in investor sentiment towards these macroeconomic variables [19]. - The article emphasizes the importance of selecting industries that are sensitive to economic changes but less affected by credit conditions, with a preference for sectors like utilities and coal [21].

Group 1 - The article emphasizes the importance of combining macro quantification with factor momentum to select resonant factors, particularly focusing on growth factors while considering market conditions [1][4] - Current macro indicators show economic decline, slightly loose liquidity, and improving credit indicators, leading to a correction in the direction of economic downturn and tight liquidity [3][4] - The article identifies that the stock pools are still biased towards growth factors, especially in the CSI 300 and CSI 1000 indices, while the CSI 500 leans more towards fundamental factors [4][5] Group 2 - Economic leading indicators suggest a potential slight increase after reaching a short-term bottom in August 2025, despite recent declines in PMI and new orders [6][8] - Various leading indicators are analyzed, indicating that many are in a downward cycle, with expectations for some to reach their bottom by early 2026 [9][10] - The liquidity environment is assessed as slightly loose, with interest rates remaining stable and monetary supply indicators suggesting a continuation of this trend [12][14] Group 3 - Credit indicators are generally weak, but the overall credit environment remains positive, with some signs of recovery in recent months [15][16] - The article recommends increasing stock allocations due to improving equity trends, while reducing allocations in other asset classes [16][17] - The focus remains on liquidity as the most significant variable affecting market dynamics, with credit and inflation also being monitored [18][20] Group 4 - The article suggests industry selection based on economic sensitivity and credit sensitivity, highlighting sectors that are less sensitive to economic downturns but more responsive to credit conditions [20][21] - Industries identified as having high growth potential include electronics, media, and beauty care, which are less affected by economic fluctuations [20][21]

Group 1 - The report indicates a weakening of profit expectation indicators, leading to a preference for growth-oriented asset allocation. The economic recovery is noted, but the micro mapping shows a shift towards weaker profit expectations, resulting in a focus on factors that are less sensitive to economic changes and more sensitive to credit conditions [4][7][9] - The economic outlook is positioned at the late stage of an upward trend, with expectations of reaching a peak in June 2025 and entering a downward cycle thereafter. Recent PMI data shows a decline, indicating a potential slowdown [11][14] - Liquidity is maintained at a slightly tight level, with short-term interest rates showing a slight decline while long-term rates have decreased more significantly. Overall liquidity indicators remain neutral to slightly tight [24][27] Group 2 - The report suggests reducing commodity positions in the asset allocation strategy, with a slight increase in A-share positions and a minor recovery in US stock positions. The commodity allocation has been reduced to zero [31] - Market focus has shifted towards liquidity, which has become a significant variable influencing market performance, especially following the recent upward trends in September [32] - In terms of industry selection, the report emphasizes choosing sectors that are less sensitive to economic fluctuations but more sensitive to credit conditions, highlighting industries with growth attributes [33]

Group 1 - The core viewpoint emphasizes the continued significance of low volatility factors as resonance factors in investment strategies, integrating macroeconomic quantitative insights with factor momentum [1][2] - The analysis indicates that the economic recovery is ongoing, liquidity is returning to a neutral-tight state, and credit indicators are improving, with no need for adjustments based on micro mappings [1][2] - The stock pool configurations for various indices such as CSI 300 and CSI 1000 show a consistent preference for low volatility and growth factors, with value factors also being selected in the CSI 500 index [2] Group 2 - Economic leading indicators are positioned in the late stage of an upward trend, with expectations of reaching a peak by June 2025 and entering a downward cycle by December 2025 [3][8] - Specific indicators such as PMI and fixed asset investment are showing positive trends, suggesting continued economic growth in the near term [3][9] - The liquidity environment is tightening, with short-term interest rates rising above their moving averages, indicating a shift towards a tighter monetary policy [11][15] Group 3 - Credit indicators have shown improvement, with social financing stock increasing for two consecutive months, reflecting a more favorable credit environment [16][18] - The asset allocation strategy suggests reducing bond and US stock positions while increasing allocations in A-shares and commodities, reflecting a bullish outlook on domestic markets [18][22] - The focus on liquidity as a key variable driving market performance indicates that fluctuations in liquidity will significantly impact stock volatility and overall market dynamics [19][22]