Core Viewpoint - The overall macro analysis indicates a bearish outlook for equity assets, while technical analysis shows an upward trend with moderate valuation and capital outflow [1][2][8] - For bonds, the macro perspective is bullish, and the technical trend is also upward [1][2][8] - Industrial products are viewed negatively from a macro standpoint, with a downward price trend technically [1][2][8] - Gold assets are favored in the macro analysis, with an upward price trend technically [1][2][8] Macro Analysis - The macro analysis categorizes assets based on their performance under different macro indicators, indicating that equity assets are currently under pressure, while bonds and gold are favored [4][8] - The analysis employs T-tests to assess the impact of macro indicators on asset returns, revealing significant differences in average returns based on the trend of macro indicators [4][5] Technical Analysis - The technical analysis utilizes closing prices and various indicators to assess asset trends, with equity, bonds, and gold showing upward trends, while industrial products are on a downward trend [10][13] - The latest trend indicators for equity and bond assets are positive, while industrial products show a negative trend [14][13] Valuation Indicators - The equity risk premium (ERP) for the CSI 800 index is at 55.71%, indicating a moderate valuation level [17][18] - The analysis of capital flow indicates a net outflow of 102.9 billion yuan for equity assets, suggesting a negative sentiment in the market [21][22] Asset Allocation Performance Tracking - Historical performance data shows that a fixed ratio combined with macro and technical indicators yielded a return of 10.50% for 2025, with an annualized return of 12.00% since April 2006 [3][26] - Different asset allocation strategies, including volatility control and risk parity, have also been analyzed, showing varying returns and risk profiles [30][33] Summary of Views - The combined scores from macro and technical indicators suggest a bearish outlook for equity assets, a bullish stance for bonds and gold, and a negative view for industrial products [23][25]

Core Viewpoint - The quantitative convertible bond portfolio experienced a slight decline in November, with a year-to-date return of 20.14% and an excess return of 3.96% [1] Group 1: Portfolio and Performance - The quantitative convertible bond portfolio is generated based on three factor systems: fundamental factors, low-frequency price-volume factors, and high-frequency price-volume factors [5] - The portfolio's performance in November showed a return of -0.72% and an excess return of -0.03% [1] Group 2: Convertible Bond Factors - A total of 32 fundamental factors, 80 low-frequency price-volume factors, and 32 high-frequency price-volume factors are tracked for convertible bonds [2] - The report illustrates the latest data using the pricing deviation factor as an example [2] Group 3: Risk Warnings for Convertible Bonds - The report provides risk warnings for convertible bonds based on forced delisting rules and credit scoring methods, highlighting various risks including trading and financial delisting risks [3][13] Group 4: Timing for Convertible Bond Index - The report employs price-volume models, pricing deviations, and convertible bond elasticity for timing and position management of the CSI Convertible Bond Index, indicating a bullish signal for the end of November with a position recommendation of 1/3 [4][14] Group 5: Timing Signals - The timing signals for the CSI Convertible Bond Index from early November show a mix of bullish and neutral signals, with a position recommendation fluctuating between 0% and 67% throughout the month [15]

Market Performance - The Sci-Tech 50 Index increased by 3.21% and the ChiNext Index rose by 4.54% over the last five trading days, while the large-cap value index decreased by 0.21% [1] - The large-cap growth index gained 2.63%, the Shanghai 50 Index rose by 0.47%, and the small-cap index represented by the CSI 2000 increased by 4.50% [1] - The communication and electronics sectors performed well, while the oil, petrochemical, and banking sectors lagged behind [1] Valuation Levels - As of November 28, 2025, the static PE of the CSI All Share Index is at a percentile of 79%, with the Shanghai 50 and CSI 300 at 75% and 71% respectively [1] - The ChiNext Index is close to the 48th percentile, while the CSI 500 and CSI 1000 are at 60% and 57% respectively, indicating that the ChiNext Index's valuation is relatively at the historical median level [1] ETF and Fund Flows - In the last five trading days, ETF inflows amounted to 8.2 billion yuan, while margin trading decreased by approximately 19.1 billion yuan [2] - The average daily trading volume across both markets was 172.38 billion yuan [2] Thematic Investment Focus - The latest thematic investment focus includes traditional Chinese medicine, banking, and high-dividend stocks, specifically targeting indices such as the CSI Traditional Chinese Medicine Index, CSI Banking Index, and the Shanghai State-Owned Enterprises Dividend Index [2][3] Long-term Market Sentiment - The report includes observations on the proportion of stocks above the 200-day moving average, indicating long-term market sentiment [13] Risk Preference Tracking - The report tracks the risk preference between equity and bond assets, providing insights into market behavior [14] Financing Balance - The report discusses the financing balance, which is crucial for understanding market liquidity and investor sentiment [16] Individual Stock Performance - A statistical distribution of individual stock performance year-to-date based on return intervals is provided, highlighting the performance landscape [18] Oversold Indices - The report notes instances of oversold conditions in certain indices, which may present potential buying opportunities [20]

Timing Outlook - The overall A-share market is expected to continue a slow bull recovery in 2026 from a macro perspective of credit inflation, observing valuation, risk premium, and sentiment from a micro perspective [3][42] - The current risk premium is in a balanced area, with the ChiNext index's style valuation at a relative historical median level [42] - The proportion of stocks above the 200-day moving average reflects market heat, currently in a balanced area [42] - The latest thematic allocation focuses on energy and high-dividend sectors using convolutional neural networks to model price and volume data [3][39] Style and Industry Allocation Outlook - The macroeconomic environment is expected to improve, with small-cap growth styles performing significantly better, particularly in sectors like social services, beauty care, power equipment, pharmaceutical biology, and electronics [4][12] - Industries with relatively low valuations and high expected earnings growth for 2026 include agriculture, social services, home appliances, food and beverage, automotive, and non-ferrous metals [4][50] - The inflow of northbound funds is concentrated in sectors such as electronics, power equipment, non-ferrous metals, machinery, and communications [4][55] - The best allocation period for small-cap growth is February, with a focus on technology in February and May, and consumer sectors in April and year-end [4][61] 2025 Market Review - The A-share market showed strong performance in 2025, with the ChiNext index rising by 36.4% year-to-date as of November 21 [7] - The small-cap growth style, represented by the CSI 1000 and small-cap growth indices, performed well, with increases of 6.7% and 4.5% respectively in the first half of the year [12] - The non-ferrous metals sector led the industry gains, with an increase of 65.7% year-to-date [15] 2026 Macro Environment Outlook - The macroeconomic environment is expected to improve, with inflation trends likely to rise, particularly as PPI shows signs of recovery after three years of low fluctuations [44][47] - Historical PPI recovery phases indicate that small-cap and growth sectors tend to outperform during these periods [47] Valuation and Earnings Expectations - The current valuation levels indicate that the ChiNext index and other indices still have cost-effectiveness for allocation, particularly in consumption and cyclical sectors [50][52] - Key industries to focus on for long-term investment opportunities include agriculture, social services, home appliances, food and beverage, automotive, and non-ferrous metals, based on relative valuation and expected earnings growth for 2026 [50][54]

Research Background - The stock market exhibits overnight correlation characteristics, where daily returns can be decomposed into overnight and intraday returns. This report characterizes the correlation features of similar stocks based on recent academic findings [1][9]. Overnight Price Change Correlation Research - The study separates long and short signals from trading execution to capture cross-stock information effects. A correlation matrix is constructed based on overnight and intraday returns, identifying leading (Leader) and lagging (Lagger) groups. Trading strategies are developed to generate signals only from the leading group and trade within the lagging group [2][10][16]. Empirical Research - The analysis shows that the leading-lagging effect in A-shares presents a reversal effect, where a bullish signal from the leading group results in stronger performance from the short positions, and vice versa. The strategy is particularly applicable to small-cap stocks [2][35][44]. Factor Research - Weekly and monthly stock selection factors are constructed based on overnight correlation information. The introduction of conventional correlation improves the distinction of stock selection, with the combined factor showing a monthly RANK_IC of 8.13% and an annualized return of 18.2% [2][57][79]. Correlation Analysis - The internal correlation among factors is relatively low, indicating that the correlation factors provide marginal incremental value. The correlation factor shows some similarity with style factors, such as residual volatility [2][90]. Group Identification - The report attempts to identify groups within the A-share market, including the CSI 300 and the CSI 1000. The results indicate that the method of classifying leading and lagging groups based on correlation matrix features yields stable results [30][34]. Portfolio Construction Process - The portfolio construction framework separates signal generation from execution, capturing cross-stock information effects. The process includes constructing a correlation matrix, identifying leading and lagging groups, and extracting trading signals based on the leading group's average impact score [27][35]. Factor Construction and Backtesting - The report explores the performance of factors based on overnight correlation, with results indicating that conventional correlation factors outperform overnight correlation factors in terms of predictive effectiveness [57][72]. Performance Metrics - The backtesting results show that the strategy can achieve an annualized return of approximately 10.51% when focusing on small-cap stocks, while the distinction between long and short groups is less pronounced in large-cap stocks [44][72].

Market Performance - The ChiNext 50 Index fell by 5.54% and the ChiNext Index dropped by 6.15% over the last five trading days, while the large-cap indices showed a decline of 1.73% for the large-cap value and 4.25% for the large-cap growth [1] - The Shanghai Composite Index decreased by 3.90%, and the small-cap index represented by the CSI 2000 fell by 6.24%, with banks and media sectors performing relatively well, while power equipment and conglomerates lagged behind [1] Valuation Levels - As of November 21, 2025, the static PE ratio of the CSI All Share Index is at the 76th percentile, with the Shanghai 50 and CSI 300 at 76% and 71% respectively, indicating that the ChiNext Index is close to the 46th percentile, while the CSI 500 and CSI 1000 are at 58% and 51% respectively [1] ETF Fund Flows - In the last five trading days, ETF inflows amounted to 40.2 billion yuan, while margin trading decreased by approximately 13.6 billion yuan, with an average daily trading volume of 1.8473 trillion yuan across the two markets [2] Thematic Indexes - The latest thematic allocations include energy and high dividend strategies, specifically focusing on the CSI Energy Index, CSI Select High Dividend Strategy Index, and CSI Tourism Theme Index [2][3] Market Sentiment - The report includes observations on the proportion of stocks above the 200-day moving average, indicating market sentiment trends [11] Risk Preference Tracking - The report tracks the risk preferences between equity and bond assets, providing insights into investor behavior [12] Financing Balance - The report discusses the changes in financing balances, which reflect market liquidity and investor sentiment [14]

Group 1 - The core viewpoint of the article emphasizes the characteristics and trading behaviors of convertible bonds, particularly focusing on the phenomenon of abnormal trading in this market segment [1][7]. - Abnormal convertible bonds are influenced by factors such as T+0 trading, relaxed price limits, and lower transaction costs, making them more susceptible to speculative trading [8][10]. - The article categorizes abnormal trading in convertible bonds based on special clause triggers, significant price fluctuations, and high turnover rates [2][12]. Group 2 - The performance statistics after significant price fluctuations indicate that if a convertible bond experiences a daily price swing exceeding 10% and closes up by more than 5%, its future performance tends to be weak unless it is in a redemption counting period [3][28]. - Conversely, if a convertible bond closes down by more than 5% after a significant price drop, it shows potential for excess returns, especially if it is in a down-adjustment or repurchase counting period [4][37]. Group 3 - The article outlines event-driven strategies, suggesting a sell strategy for convertible bonds that experience significant price increases after abnormal trading, which has yielded excess returns of 69.5% since 2017 [5][56]. - A buy strategy is proposed for convertible bonds that decline significantly after abnormal trading, particularly those in down-adjustment counting periods, although caution is advised due to high concentration risks [6][61]. Group 4 - The characteristics of abnormal convertible bonds include small market capitalization, low ratings, high valuations, and strong stock characteristics [7][73]. - The analysis reveals that abnormal trading convertible bonds tend to have lower average remaining scales and ratings compared to the overall sample, indicating a distinct profile for these securities [69][70].

Core Viewpoint - The article discusses the "Leading Stock Diffusion Effect" as a mechanism driving sector trends, emphasizing the importance of stock selection to enhance returns from industry rotation strategies. The report presents various stock selection strategies and their performance metrics, highlighting the effectiveness of the "Alpha Dual-Drive Preferred Combination" strategy, which has achieved an annualized return of 33.6% since 2013, outperforming the CSI 500 index by 28.3% [1][68]. Group 1: Research Background - The demand for industry-level beta timing has increased with the development of flexible allocation funds and FOF products, making industry rotation a core asset allocation need [3]. - The article notes that the return dispersion between industries is often greater than that among individual stocks within the same industry, indicating that selecting the right industry is more beneficial than selecting individual stocks [3]. - Challenges in extracting industry rotation factors include limited sample sizes and the heterogeneous nature of industries, which complicates the universality of factor logic [3][4]. Group 2: Mechanism of the Leading Stock Diffusion Effect - The diffusion effect is described as the process where stock price increases in leading stocks spread to related stocks, leading to a broader industry uptrend [12]. - The process includes several stages: policy triggers leading to the activation of leading stocks, active capital inflow driving sector resonance, and cognitive dissemination leading to widespread price increases across related stocks [12][13]. - The article outlines different migration methods of capital during the diffusion process, including vertical and horizontal diffusion, market capitalization descent, and valuation arbitrage [15]. Group 3: Stock Selection Strategies - The report evaluates various stock selection strategies to replicate or enhance industry rotation returns, including full replication, half-weighted combinations, and top 10 equal-weighted combinations [30][31]. - The full replication strategy achieved an annualized return of 24.9% since 2013, while the half-weighted and top 10 equal-weighted strategies yielded returns of 24.5% and 23.5%, respectively, with reduced trading complexity [34][46]. - The "Alpha Dual-Drive Preferred Combination" strategy, which selects stocks based on both industry and individual stock factors, has shown superior performance with an annualized return of 33.6% [52][59]. Group 4: Performance Metrics - The "Alpha Dual-Drive Preferred Combination" strategy has an information ratio (IR) of 2.07 and a maximum drawdown of 27.8%, indicating strong risk-adjusted performance [68]. - The article provides detailed annual performance data for the preferred industry combination, showing significant absolute and excess returns across various years [29][66]. - The report emphasizes that the improved SUE and active large order factors contribute to the strong performance of the preferred industry combination, achieving annualized excess returns of 8.3% and 10.1%, respectively [18][23].

Core Viewpoint - The article discusses the construction of an ETF grid trading strategy based on the Average True Range (ATR), aiming to capture profits from price fluctuations by buying low and selling high [1][10][13]. Group 1: ETF Market Development - Since Q4 2018, the number of equity ETFs in the A-share market has increased from 133 to 1,043 by Q3 2025, with total assets rising from 0.27 trillion yuan to 3.71 trillion yuan [8]. Group 2: Single Index Grid Trading Strategy - A single index grid trading strategy is constructed using ATR, focusing on 28 large-scale sample equity indices. Historically, the strategy has shown limited returns during bull markets while effectively controlling drawdowns in bear markets [2][16]. - The ATR indicator reflects the true volatility of an index, with historical ATR values showing significant fluctuations during different market periods [18][20]. Group 3: Incorporating Timing Signals - The article introduces timing signals based on ATR trends to enhance the grid trading strategy's performance, particularly in bull markets, while reducing drawdowns in bear markets [34][35]. - Backtesting results indicate that incorporating timing signals significantly improves both returns and drawdowns compared to the original grid trading strategy [38][45]. Group 4: ETF Grid Trading Strategy Combination - A robust ETF grid trading strategy combination is constructed using a selection of liquid ETFs, with a historical annualized return of 12.57% and a maximum drawdown of 6.80% from December 31, 2018, to September 30, 2025 [54][57]. - The strategy consistently outperformed a fixed 30% equity allocation ETF combination across various years, demonstrating positive excess returns [63][64]. Group 5: Sensitivity Analysis of Parameters - The performance of the ETF grid trading strategy combination shows low sensitivity to the frequency of updating the selection pool, with minimal differences in returns across various update cycles [67]. - Shorter rebalancing periods (1-2 days) yield higher cumulative returns compared to longer periods, indicating a preference for more frequent adjustments [71]. - Increasing the upper limit of equity allocation enhances both returns and drawdown characteristics, providing different risk-return profiles [77]. Group 6: Broad-based Index ETF Strategy - A broad-based index ETF grid trading strategy, utilizing common indices, achieved an annualized return of 11.78% and a maximum drawdown of 11.50% over the same period [79][82].

Market Performance - The Sci-Tech 50 Index decreased by 3.85% over the last five trading days, while the ChiNext Index fell by 3.01%. In contrast, the large-cap value stocks rose by 1.44%, and large-cap growth stocks declined by 1.64%. The Shanghai Stock Exchange 50 Index saw a minimal increase of 0.003%, and the small-cap index represented by the CSI 2000 dropped by 0.53%. The comprehensive and textile apparel sectors performed well, whereas the communication and electronics sectors lagged behind [1]. Valuation Levels - As of November 14, 2025, the static PE of the CSI All Share Index is at a percentile rank of 81%. The Shanghai Stock Exchange 50 and CSI 300 indices are at 77% and 73%, respectively. The ChiNext Index is close to the 50th percentile, while the CSI 500 and CSI 1000 indices are at 62% and 61%, respectively. The valuation of the ChiNext Index is relatively at the historical median level [1]. Risk Premium - The risk premium, calculated as the inverse of the static PE of the CSI All Share Index minus the yield of ten-year government bonds, stands at 2.78% as of November 14, 2025. The two-standard deviation boundary is at 4.74% [1]. ETF Fund Flows - In the last five trading days, ETF inflows amounted to 12.2 billion yuan, while the margin trading balance increased by approximately 7.7 billion yuan. The average daily trading volume across both markets was 20.226 billion yuan [2]. Thematic Indexes - The latest thematic allocations include energy and high dividend strategies, specifically focusing on the CSI Energy Index, CSI High Dividend Strategy Index, and CSI Tourism Theme Index among others [2][3].