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].

Group 1 - The current market does not expect significant short-term interest rate cuts, making it difficult for long-term government bond rates and short-term deposit rates to decline significantly [1] - The year-end focus should be on institutional allocation willingness and equity market performance, which could impact the government bond spread [1] - Two investment strategies are suggested: 1) opt for slightly lower duration for defense and wait for a 5 basis point rate adjustment before considering longer duration opportunities; 2) maintain a market-neutral or slightly longer duration stance, focusing on active bonds where spreads may compress [1] Group 2 - The Q3 monetary policy report indicates a cautious approach to significant rate cuts or reserve requirement reductions, emphasizing stable growth as the primary goal of monetary policy [2] - The current duration measurement is 4.5 years, with a focus on the absolute yield and credit spread compression opportunities in the 3-5 year credit bond market [2] - The credit bond market is expected to follow the trends of government bonds, with a recommendation to focus on mid-term government bonds for short-term capital gains [2] Group 3 - Convertible bonds in sectors like electronics, TMT, and automotive are significantly higher than other industries, indicating investor expectations for stock price increases and volatility [3] - The proportion of high premium convertible bonds in the market is higher than in previous years, suggesting that if stock market expectations remain stable, high premium convertible bonds will continue to thrive [3] - The valuation of convertible bonds is rising, but the sustainability of this increase depends on stock market expectations [3] Group 4 - As of November 14, 2025, the 5-10 year government bond ETF index has seen a slight decline of 0.01%, while the ETF itself has increased by 0.01% [5] - The 5-10 year government bond ETF has shown a 3.15% increase over the past year, with active trading and a recent scale of 1.656 billion yuan [5] - The ETF has a historical profitability rate of 100% over three years, with a monthly profitability probability of 71.06% [5] Group 5 - The maximum drawdown for the 5-10 year government bond ETF in the past six months is 1.09%, with a management fee of 0.15% and a custody fee of 0.05% [6] - The ETF closely tracks the index of active government bonds with maturities of 5, 7, and 10 years, reflecting the overall performance of these bonds [6]

Quantitative Models and Construction Methods - **Model Name**: Multi-period interest rate timing strategy **Model Construction Idea**: The model uses multi-period resonance strategies to capture interest rate trends and generate timing signals based on shape recognition algorithms[10][22] **Model Construction Process**: 1. **Signal Generation**: Utilize kernel regression algorithms to identify support and resistance lines of interest rate data. Analyze the breakthrough patterns of interest rate trends across long, medium, and short cycles[10][22] 2. **Portfolio Construction**: - If at least two cycles show downward breakthroughs and the trend is not upward, allocate fully to long-duration bonds - If at least two cycles show downward breakthroughs but the trend is upward, allocate 50% to medium-duration bonds and 50% to long-duration bonds - If at least two cycles show upward breakthroughs and the trend is not downward, allocate fully to short-duration bonds - If at least two cycles show upward breakthroughs but the trend is downward, allocate 50% to medium-duration bonds and 50% to short-duration bonds - In other cases, allocate equally across short, medium, and long durations - Stop-loss mechanism: Adjust holdings to equal-weighted allocation if daily excess returns fall below -0.5%[22] **Model Evaluation**: The strategy demonstrates strong performance with consistent positive returns and high excess return ratios over the long term[22][23] Model Backtesting Results - **Multi-period interest rate timing strategy**: - **Short-term annualized return**: 7.27%[4][22] - **Short-term maximum drawdown**: 1.56%[4][22] - **Short-term return-to-drawdown ratio**: 6.23[4][22] - **Short-term excess return**: 2.2%[4][23] - **Long-term annualized return**: 6.17%[22] - **Long-term maximum drawdown**: 1.52%[22] - **Long-term return-to-drawdown ratio**: 2.26[22] - **Long-term excess return**: 1.66%[22] - **Excess return-to-drawdown ratio**: 1.18[22] - **Annual absolute return win rate**: 100%[23] - **Annual excess return win rate**: 100%[23] Quantitative Factors and Construction Methods - **Factor Name**: Interest rate structure indicators (level, term, convexity) **Factor Construction Idea**: Transform yield-to-maturity (YTM) data of 1-10 year government bonds into structural indicators to analyze market trends from a mean-reversion perspective[7][9] **Factor Construction Process**: 1. Calculate the level structure indicator as the average YTM across maturities 2. Compute the term structure indicator as the difference between long-term and short-term YTM 3. Derive the convexity structure indicator based on the curvature of the yield curve[7][9] **Factor Evaluation**: The indicators provide insights into the current state of the interest rate market, showing low levels across all three structures[7][9] - **Factor Name**: Multi-period interest rate timing signals **Factor Construction Idea**: Use kernel regression algorithms to identify interest rate trends and generate timing signals based on breakthroughs across long, medium, and short cycles[10] **Factor Construction Process**: 1. Apply kernel regression to identify support and resistance lines for interest rate data 2. Analyze breakthrough patterns across different cycles (monthly for long-term, bi-weekly for medium-term, weekly for short-term)[10] **Factor Evaluation**: The signals are effective in capturing market trends, with the latest signals indicating a neutral-to-bearish stance[10] Factor Backtesting Results - **Interest rate structure indicators**: - **Level structure**: Current reading is 1.51%, positioned at 6%, 4%, and 2% percentiles for 3, 5, and 10-year historical perspectives, respectively[9] - **Term structure**: Current reading is 0.3%, positioned at 13%, 8%, and 10% percentiles for 3, 5, and 10-year historical perspectives, respectively[9] - **Convexity structure**: Current reading is 0.02%, positioned at 18%, 11%, and 11% percentiles for 3, 5, and 10-year historical perspectives, respectively[9] - **Multi-period interest rate timing signals**: - **Long-term signal**: Upward breakthrough[10] - **Medium-term signal**: No signal[10] - **Short-term signal**: Downward breakthrough[10] - **Overall signal**: Neutral-to-bearish[10]