Quantitative Models and Construction Methods - **Model Name**: Multi-cycle timing model for domestic interest rate price-volume trends **Model Construction Idea**: The model uses kernel regression algorithms to capture interest rate trend patterns, identifying support and resistance lines based on the shape of interest rate movements across different investment cycles [10][24] **Model Construction Process**: 1. **Data Input**: Utilize 5-year, 10-year, and 30-year government bond YTM data as the basis for analysis [10][24] 2. **Cycle Classification**: Divide the investment horizon into long-term (monthly frequency), medium-term (bi-weekly frequency), and short-term (weekly frequency) cycles [10][24] 3. **Signal Identification**: Detect upward or downward breakthroughs of support and resistance lines for each cycle [10][24] 4. **Composite Scoring**: Aggregate signals across cycles, assigning scores based on the number of consistent breakthroughs (e.g., 2/3 consistent signals lead to a "buy" or "sell" recommendation) [10][24] **Model Evaluation**: The model effectively captures multi-cycle resonance in interest rate trends, providing actionable timing signals for bond trading strategies [10][24] - **Model Name**: Multi-cycle timing model for U.S. interest rate price-volume trends **Model Construction Idea**: Apply the domestic interest rate price-volume timing model to the U.S. Treasury market [21] **Model Construction Process**: 1. **Data Input**: Use 10-year U.S. Treasury YTM data for analysis [21] 2. **Cycle Classification**: Similar to the domestic model, divide the investment horizon into long-term, medium-term, and short-term cycles [21] 3. **Signal Identification**: Detect upward or downward breakthroughs of support and resistance lines for each cycle [21] 4. **Composite Scoring**: Aggregate signals across cycles, assigning scores based on the number of consistent breakthroughs [21] **Model Evaluation**: The model provides a neutral-to-bullish outlook for U.S. Treasury yields, indicating its adaptability to international markets [21] Model Backtesting Results - **Domestic Multi-cycle Timing Model**: - **5-year YTM**: - Long-term annualized return: 5.5% - Maximum drawdown: 2.88% - Return-to-drawdown ratio: 1.91 - Short-term annualized return (since 2024): 1.86% - Maximum drawdown: 0.59% - Return-to-drawdown ratio: 3.16 - Long-term excess return: 1.07% - Short-term excess return: 0.85% [25][27] - **10-year YTM**: - Long-term annualized return: 6.09% - Maximum drawdown: 2.74% - Return-to-drawdown ratio: 2.22 - Short-term annualized return (since 2024): 2.42% - Maximum drawdown: 0.58% - Return-to-drawdown ratio: 4.19 - Long-term excess return: 1.66% - Short-term excess return: 1.55% [28][32] - **30-year YTM**: - Long-term annualized return: 7.38% - Maximum drawdown: 4.27% - Return-to-drawdown ratio: 1.73 - Short-term annualized return (since 2024): 3.11% - Maximum drawdown: 0.92% - Return-to-drawdown ratio: 3.39 - Long-term excess return: 2.42% - Short-term excess return: 2.87% [33][35] - **U.S. Multi-cycle Timing Model**: - **10-year YTM**: - Current signal: Neutral-to-bullish - Long-term annualized return: Not provided - Maximum drawdown: Not provided - Return-to-drawdown ratio: Not provided [21][23] Quantitative Factors and Construction Methods - **Factor Name**: Interest rate structure indicators (level, term, convexity) **Factor Construction Idea**: Transform YTM data into structural indicators to analyze the interest rate market from a mean-reversion perspective [7] **Factor Construction Process**: 1. **Level Structure**: Calculate the average YTM across maturities (1-10 years) 2. **Term Structure**: Measure the slope between short-term and long-term YTM 3. **Convexity Structure**: Assess the curvature of the yield curve [7] **Factor Evaluation**: The indicators effectively capture the current state of the interest rate market, highlighting deviations from historical averages [7] Factor Backtesting Results - **Interest Rate Structure Indicators**: - **Level Structure**: Current reading: 1.64%, historical 10-year percentile: 7% - **Term Structure**: Current reading: 0.38%, historical 10-year percentile: 16% - **Convexity Structure**: Current reading: -0.09%, historical 10-year percentile: 1% [7]

Report Industry Investment Rating No relevant content provided. Core Viewpoints - Since the third quarter, the 30 - 10 spread has widened rapidly, which is related to the marginal changes in the factors compressing the spread. The macro - narrative has changed, and the tax policy adjustment in August had a more obvious impact on the 30 - year Treasury yield. Looking ahead, the 30 - 10 spread is expected to compress again. The spread has adjusted significantly, the 30 - year Treasury still has liquidity advantages, and the demand for the 30 - year Treasury will improve with the bond market rebound [2]. Summary by Related Content 1. Widening of the 30 - 10 Spread since the Third Quarter - From late July to mid - September, the 30 - 10 spread rose from 20BP to 30BP. From mid - September to October 14, it further widened by 13BP to 43BP, returning to the level in September 2022 [3]. 2. Long - term Characteristics and Historical Performance of the 30 - 10 Spread - The 30 - 10 spread shows a long - term mean - reversion trend. From 2006 - 2023, the average spread was 56BP, with an upper limit of 70 - 80BP (maximum over 90BP in early 2009) and a lower limit of 20 - 30BP (minimum less than 20BP in mid - 2007). In most cases, it moves inversely to the 10 - year Treasury yield. In 2024, it broke through the historical low, compressing to around 10BP and oscillating in the [10BP - 30BP] range from 2024 to the first half of 2025 [6][7][8]. 3. Reasons for the Extreme Compression of the 30 - 10 Spread in the Past Two Years - The increase in the trading volume and proportion of 30 - year Treasuries led to a liquidity premium, which supported the spread compression. The reasons for the increased activity of 30 - year Treasuries include investors' increased demand for long - duration bonds due to economic concerns, the preference of insurance institutions for long - duration bonds, the large - scale issuance of 30 - year Treasuries, and the issuance of 30 - year Treasury ETFs [12][13]. 4. Marginal Changes in Factors Supporting Spread Narrowing in the Third Quarter - The macro - narrative has changed, with better - than - expected economic performance, reduced deflation expectations, and a strong equity market suppressing the bond market, weakening the demand for 30 - year Treasuries. The tax policy adjustment in August had a greater impact on the 30 - year Treasury yield. The trading volume proportion of ultra - long Treasuries has declined since August [20]. 5. Outlook for the 30 - 10 Spread - In the short term, the 30 - 10 spread is expected to compress again. The spread has adjusted significantly, the 30 - year Treasury still has liquidity advantages, and the demand for the 30 - year Treasury will improve with the bond market rebound. In the long - term, the probability of the spread returning below 20BP is small as the market's economic expectations improve and 20BP is at the lower limit of historical fluctuations [21].

1. Report Industry Investment Rating - Not mentioned in the provided content 2. Core View of the Report - Since the third quarter, the 30 - 10 spread has widened rapidly, which is related to the marginal changes in the factors compressing the spread. Looking forward, the 30 - 10 spread is expected to compress again. After the previous adjustment, the spread has returned to the level of the third quarter of 2022. The 30 - year treasury bond still has liquidity advantages, and the demand for 30 - year treasury bonds will improve marginally with the bond market rebound, which is conducive to the phased compression of the 30 - 10 spread [2] 3. Summary by Relevant Catalog 3.1 Third - quarter widening of 30 - 10 spread - From July to mid - September, the 30 - 10 spread rose from 20BP to 30BP. From mid - September to October 14, it further widened to 43BP, returning to the level of September 2022 [3] 3.2 Long - term characteristics of 30 - 10 spread - The 30 - 10 spread shows a long - term mean - reversion trend. From 2006 to 2023, the 30 - 10 spread averaged 56BP, with an upper limit of 70 - 80BP and a lower limit of 20 - 30BP. In most cases, it moves in the opposite direction to the 10 - year treasury bond rate. In 2024, it broke through the historical extreme, once compressing to around 10BP and oscillating in the range of 10BP - 30BP until the first half of 2025 [6][7][10] 3.3 Reasons for the extreme compression of 30 - 10 spread in the past two years - The increase in the liquidity premium of 30 - year treasury bonds, driven by supply and demand factors, is the main reason. Factors include increased demand from fixed - income investors and insurance institutions, increased primary supply, and active trading of 30 - year treasury bond ETFs. In 2025, the weekly average trading volume of ultra - long treasury bonds reached 700 billion yuan, and the proportion in all treasury bond trading volume rose to 40% [12][13] 3.4 Marginal changes in factors supporting spread narrowing in the third quarter - The macro - narrative has changed, including better - than - expected economic performance, the dissipation of deflation expectations, and the suppression of the bond market by the stock market. The tax policy adjustment in August had a more obvious impact on the 30 - year treasury bond yield. Since August 2025, the trading volume proportion of ultra - long treasury bonds has declined [20] 3.5 Spread outlook - In the short term, the 30 - 10 spread is expected to compress again. In the medium - to - long term, the probability of the 30 - 10 spread returning below 20BP is small [21]

Core Insights - The digital asset market is experiencing a significant shift as silver reaches its highest price in nearly 50 years, indicating a potential transition from "digital gold" to traditional assets [1][2] - Silver's market capitalization has surpassed that of Bitcoin, highlighting a divergence in the performance of these asset classes [2][3] - The current trend suggests a bear market for cryptocurrencies, particularly Bitcoin and Ethereum, as they decline while traditional assets like gold and silver rally [3][5] Group 1: Market Performance - Silver has achieved a historic peak, marking its highest level in approximately 45 years, with unprecedented demand for physical silver [1] - Bitcoin and Ethereum have seen sharp declines following the recent "Crypto Black Friday," contrasting with the rising prices of silver and gold [2][3] - The Bitcoin/silver ratio has been in decline since its peak four years ago, indicating a significant shift in market dynamics [4] Group 2: Investor Sentiment - Prominent economist Peter Schiff suggests that crypto investors may face significant losses, with many young investors likely to experience a steep learning curve [3] - Some traders have reported substantial losses, with one individual losing 80% of their portfolio value during the recent market downturn [6] - The cyclical rotation between physical and digital assets is becoming evident as investors seek traditional safe havens amid economic uncertainty [7]

Core Viewpoint - The banking sector is experiencing a slight adjustment followed by a significant rise, with dividend assets like coal and water resources showing strong performance, indicating a shift towards defensive asset allocation in response to global uncertainties [1] Group 1: Market Performance - As of 10:35, the CSI Dividend Index rose by 0.8% and the CSI Low Volatility Dividend Index increased by 1.0% [1] - Recent inflows into related ETFs include 150 million yuan into the E Fund Dividend ETF (515180) and 20 million yuan into the Low Volatility Dividend ETF (563020) [1] Group 2: Investment Insights - China Galaxy Securities suggests that increased uncertainty is driving demand for defensive asset allocation, presenting opportunities in the banking sector due to stable dividends and improved yield attractiveness after recent corrections [1] - Current style trading in the domestic market has reached historical extremes, with the rolling return difference between small-cap growth and large-cap value exceeding 50%, indicating a high probability of mean reversion and a shift towards value stocks [1] Group 3: Fund Management - E Fund is noted as the only fund company offering all dividend ETFs at a low fee rate, with management fees set at the lowest tier of 0.15% per year for its dividend ETFs, catering to diverse investor allocation needs [1]

10月以来，全球贵金属市场迎来"狂欢时刻"。10月13日，伦敦现货黄金冲破4080美元/盎司，年内涨幅超55%；现货白银历史最高价刷新至51.714美元/盎 司，年内涨幅更突破76%。金价飙升带动国内金饰价格突破1120元/克，社交媒体上"囤金囤银"热潮涌动，投资者跟风追高情绪升温。 在此背景下，建设银行、工商银行、宁波银行等多家银行密集发布贵金属风险提示，已有银行上调贵金属业务投资门槛。分析人士称，银行提高投资门槛、 密集发布风险提示，既能保护普通投资者免受高风险冲击，也能降低银行自身的声誉风险和合规风险。 一路飙升的贵金属 10月以来，全球贵金属市场迎来历史性行情，国际黄金、白银价格接连突破历史关口，涨势之猛、速度之快，超出年初市场预期，上演了一场前所未有 的"贵金属狂欢季"。 "而从白银来看，其具备强商品属性与弱货币属性，从金银比（即每盎司黄金与每盎司白银的价格比值）维度来看，当前该比值处于相对高位。"高政扬进一 步补充道，基于市场"均值回归"逻辑，在黄金价格持续上行的带动下，白银具备补涨需求，推动白银价格不断走高。同时，在中小投资者群体中，白银因单 价低于黄金、投资门槛相对较低，成为配置的重要补充选择 ...

Core Viewpoint - The article discusses the market's response to recent events, highlighting the differences in market behavior compared to previous downturns, and emphasizes the importance of understanding market dynamics and style shifts in investment strategies [5][6][9]. Market Behavior Analysis - On the recent trading day, all indices opened lower, with the A-share index dropping 3.6% initially but recovering half of that loss within 15 minutes, indicating a more balanced market sentiment compared to a previous day when indices fell sharply without recovery [5]. - The trading volume on this day decreased by 160 billion compared to the previous day, suggesting a lack of aggressive selling pressure, which was concentrated at the opening [5]. - The volatility index for the Shanghai and Shenzhen 300 rose only 20% at the opening, contrasting sharply with a previous spike of 76%, indicating a more stable market environment [5]. Style Shift and Historical Trends - Historical data shows that the strongest market styles in Q3 often do not carry over into Q4, with only a 25% probability of the leading style continuing its dominance, while a 75% probability exists for a shift in leadership [7]. - The article cites examples from past bull markets where significant style shifts occurred between Q3 and Q4, indicating a pattern that investors should be aware of [9]. Potential Catalysts for Change - Two main factors could trigger a mean reversion between high and low valuations: a narrowing Producer Price Index (PPI) indicating a potential easing of inflation, or escalating trade tensions between the U.S. and China affecting market risk appetite [8]. - The article suggests that extreme disparities between market styles can lead to increased vulnerability in strong styles, making them susceptible to shifts based on macroeconomic data or news [8]. Investment Strategy Recommendations - The focus should be on maintaining a consistent investment strategy rather than reacting to market fluctuations, emphasizing the importance of understanding quarterly trends and making informed adjustments based on established principles [9].

↑点击上面图片 加雪球核心交流群 ↑ 以下文章来源于ETF大白 ，作者ETF大白 ETF大白 . 三句话： 1.宁愿不说，只说真话。 2.ETF将是大多数散户的终极归宿。 3.投ETF，做自己的基金经理。 风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。 作者： ETF大白 来源：雪球 大家好 ， 我是(●—●) 。 01 你的投资信条是什么 ？ 市场大跌 ， 引发了本白一个有趣的思考 。 第一时间出了一道小题 ， 考考家人们 。 如图 ， 二选一 ， 看看哪个更符合你的投资理念 ： A ： 梦想还是要有的 ， 万一实现了呢 。 B ： 我知道你会来 ， 所以我会等 。 | FSID | 合类 详情 | | | --- | --- | --- | | 1 躺红利掩转债 | | 分红在手,心中元忧 | | 2 佛说ETF被影守息 | | 我等了三年,就是要等一个机会,我要争一口气、不是证明我有多了不起,而是要告诉别人、我失去的东面一定要拿回来! | | 3 红利三十年的图层 | | 用梦想(A) 去发现,用等待(B)去拥有。 | | 4 老男夜bro | B | 普通人还是选 ...

Quantitative Models and Construction Methods - **Model Name**: Multi-cycle timing model for domestic interest rate price-volume trends **Model Construction Idea**: The model uses kernel regression algorithms to capture interest rate trend patterns, identifying support and resistance lines based on different investment cycles. It provides composite timing signals by analyzing the shape of interest rate movements across long, medium, and short cycles[10][24][29] **Model Construction Process**: 1. **Data Input**: Use 5-year, 10-year, and 30-year government bond YTM data as the basis for analysis[10][24][29] 2. **Cycle Definition**: Define long, medium, and short cycles with average switching frequencies of monthly, bi-weekly, and weekly, respectively[10][24][29] 3. **Signal Generation**: - If at least two cycles show downward breakthroughs of the support line and the interest rate trend is not upward, allocate fully to long-duration bonds - If at least two cycles show downward breakthroughs of the support line but the interest rate trend is upward, allocate 50% to medium-duration bonds and 50% to long-duration bonds - If at least two cycles show upward breakthroughs of the resistance line and the interest rate trend is not downward, allocate fully to short-duration bonds - If at least two cycles show upward breakthroughs of the resistance line but the interest rate 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[24][29][29] **Model Evaluation**: The model demonstrates strong adaptability across different market environments and provides consistent timing signals based on multi-cycle resonance[10][24][29] - **Model Name**: Multi-cycle timing model for U.S. interest rate price-volume trends **Model Construction Idea**: The domestic price-volume timing model is applied to the U.S. interest rate market, analyzing long, medium, and short cycles to generate composite timing signals[21][23][24] **Model Construction Process**: 1. **Data Input**: Use 10-year U.S. Treasury YTM data for analysis[21][23][24] 2. **Cycle Definition**: Define long, medium, and short cycles with average switching frequencies of monthly, bi-weekly, and weekly, respectively[21][23][24] 3. **Signal Generation**: Similar to the domestic model, signals are generated based on the number of cycles showing breakthroughs of support or resistance lines and the direction of interest rate trends[21][23][24] **Model Evaluation**: The model effectively captures U.S. interest rate trends and provides reliable timing signals for investment decisions[21][23][24] Model Backtesting Results - **Domestic Multi-cycle Timing Model** - **5-year YTM**: - Long-term annualized return: 5.5% - Maximum drawdown: 2.88% - Return-to-drawdown ratio: 1.91 - Short-term annualized return (since 2024): 1.86% - Maximum drawdown: 0.59% - Return-to-drawdown ratio: 3.15 - Long-term excess return: 1.07% - Excess return-to-drawdown ratio: 0.62 - Short-term excess return: 0.86% - Excess return-to-drawdown ratio: 2.18[25][27][37] - **10-year YTM**: - Long-term annualized return: 6.09% - Maximum drawdown: 2.74% - Return-to-drawdown ratio: 2.23 - Short-term annualized return (since 2024): 2.35% - Maximum drawdown: 0.58% - Return-to-drawdown ratio: 4.07 - Long-term excess return: 1.66% - Excess return-to-drawdown ratio: 1.16 - Short-term excess return: 1.56% - Excess return-to-drawdown ratio: 3.46[28][32][37] - **30-year YTM**: - Long-term annualized return: 7.38% - Maximum drawdown: 4.27% - Return-to-drawdown ratio: 1.73 - Short-term annualized return (since 2024): 2.98% - Maximum drawdown: 0.92% - Return-to-drawdown ratio: 3.26 - Long-term excess return: 2.42% - Excess return-to-drawdown ratio: 0.87 - Short-term excess return: 2.87% - Excess return-to-drawdown ratio: 3.21[33][35][37] - **U.S. Multi-cycle Timing Model** - **10-year YTM**: - Composite signal: Long cycle upward breakthrough, medium and short cycles downward breakthrough - Final signal: Bullish[21][23][24]

Core Viewpoint - The real estate market is currently facing challenges, but misleading information and scams are distorting the perception of its health, particularly regarding price drops in major cities [1][8][21] Group 1: Market Analysis - A comprehensive analysis of short videos revealed claims of property price drops in Beijing, Shanghai, Guangzhou, and Shenzhen ranging from 40% to 73, which were found to be fabricated for views [1][8] - The actual price fluctuations for normal residential properties in these cities over the past year were much lower, with declines of 5% to 9% in Beijing, 3% to 15% in Shanghai, 8% to 12% in Guangzhou, and 2.4% to 6.7% in Shenzhen [8][21] - The National Bureau of Statistics reported minor changes in new and second-hand housing prices, indicating that the market is not experiencing a collapse as suggested by some media [8][21] Group 2: Types of Scams - Five major scams were identified, including the apartment price drop scam, which misrepresents the stability of apartment prices while highlighting exaggerated claims of price drops [1][2] - The "old and dilapidated" property price drop scam involved specific cases where properties with certain characteristics were sold at lower prices, misleading consumers about the overall market trend [4][21] - Real estate agents were found to engage in deceptive practices, such as creating false urgency and manipulating sellers into lowering prices, driven by a commission-based model [6][21] Group 3: Key Figures and Events - Wang Jianlin and Dalian Wanda Group have been in the spotlight due to financial difficulties, including restrictions on high consumption linked to economic disputes [13][15] - The group's debt situation has escalated, with over 70 billion yuan in total execution amounts across various companies, raising concerns about its financial stability [13][15] - Wang Jianlin's commitment to managing the company's debts has garnered respect, contrasting with the lifestyle of his son, Wang Sicong, who remains financially independent despite the family's challenges [17][19] Group 4: Future Outlook - The real estate market is expected to undergo further differentiation, with core areas in first and second-tier cities likely to maintain value, while suburban and third-tier cities face downward pressure [21] - The current market environment necessitates a shift in perspective for potential buyers, emphasizing the importance of assessing personal purchasing power and focusing on properties with residential value [21]