Overview - The report highlights that the convertible bond market experienced significant growth in 2025, with the China Convertible Bond Index rising by 17.87%, driven primarily by price parity and valuation support [2][12] - The current environment presents challenges for convertible bonds, with overall cost-effectiveness declining and valuation at historical highs, leading to increased investment difficulty [2][12] Section 1: 2025 Convertible Bond Review - The convertible bond market saw a strong performance in early 2025 due to ample liquidity and moderate economic recovery, with price parity being the main driver [12] - The market faced a pullback in March-April due to negative CPI and external disturbances, but recovered from May to September as fundamental expectations improved [12] - The overall market for convertible bonds is now in a "deep water zone," with a significant decline in supply and an increase in the median price to 132 yuan, indicating a high premium environment [2][12][27] Section 2: 2026 Stock Market Outlook - The report anticipates a turning point in the stock market, with corporate earnings expected to recover and long-term capital inflows continuing to support the equity market [41][45] - The M1 money supply has shown a significant turning point since September 2024, indicating improved liquidity conditions that are expected to benefit the stock market [46][52] Section 3: 2026 Convertible Bond Outlook and Allocation Strategy - The report suggests a dynamic adjustment of positions in convertible bonds based on market cycles, emphasizing a focus on index-based allocations [2][56] - The strategy indicates that the best accumulation window for convertible bonds is during the latter half of a market downturn and the early half of an uptrend [2][56] - The report highlights the importance of sector rotation, suggesting that constructing an equal-weighted index can effectively capture rotation opportunities [2][67]

Group 1 - The A-share market opened with mixed performance, with the Shanghai Composite Index down 0.07%, the Shenzhen Component Index up 0.1%, and the ChiNext Index up 0.55% [1] - Sectors such as automotive disassembly, fiberglass, and HBM saw significant gains, while sectors like non-ferrous metals, forestry, and iron ore experienced declines [1] Group 2 - Dongwu Securities suggests that the market may exhibit a balanced characteristic with a focus on mid-cap blue chips, while small-cap growth stocks may show weakness [1] - The firm emphasizes selecting sectors with improving marginal prosperity, particularly those benefiting from global supply reshaping, policy stimulus, and structural upgrades in consumption [1] - Huachuang Securities notes a recovery in industry rotation intensity, with the technology sector expanding towards dividend and "anti-involution" assets [2] - The firm highlights that the Producer Price Index (PPI) has improved from a low of -3.6% to -2.1% in October, indicating a potential benefit for cyclical assets with high weight in dividend assets [2]

Group 1 - Silver prices have surged, with the London spot price increasing by 5.8% on November 28, reaching $56.52 per ounce, more than double the price at the beginning of the year [2] - The rise in precious metals, including gold, is attributed to worsening investor sentiment and expectations of interest rate cuts by the Federal Reserve [2][9] - The supply shortage of silver in London has eased but remains a concern, with market valuations still considered high [4][5] Group 2 - Gold prices also reached a near one-month high, with the London spot price hitting $4264.29 on December 1 [8] - Predictions suggest that silver prices could reach around $70 per ounce by 2026, with potential for even higher prices under certain conditions [5] - The market is witnessing a shift of funds from overvalued AI stocks to precious metals, as investors adopt a risk-averse stance [10][12] Group 3 - The expectation of interest rate cuts by the Federal Reserve has increased, with nearly 90% of the market anticipating a rate cut during the upcoming FOMC meeting [9] - The trend of funds moving from AI stocks to precious metals is seen as a response to the volatility in AI-related investments [10][12] - The perception of gold and silver as "safe assets" is driving increased buying activity, particularly in the context of market uncertainty [12]

Core Viewpoint - The Q4 style rotation model indicates signals for small-cap and growth stocks, with a focus on sectors such as electric equipment and renewable energy for December [1][2]. Style Rotation Model - The Q4 style rotation model has issued signals favoring small-cap stocks, with a comprehensive score of -1 for the dual-driven rotation strategy as of September 30, 2025 [3]. - The value-growth style rotation model shows a comprehensive score of -3 for the dual-driven rotation strategy, indicating a preference for growth stocks [4]. Industry Rotation Insights - In November, the composite factor strategy yielded an excess return of -0.58%, while the single-factor long strategy had an excess return of -0.83% [4]. - For December, the single-factor long strategy recommends bullish sectors including banking, construction, non-bank financials, and electric equipment and renewable energy. The composite factor strategy suggests bullish sectors such as telecommunications, comprehensive finance, computer technology, electric equipment and renewable energy, and utilities [4].

Group 1: Asset Allocation Strategy - The asset allocation strategy indicates a differentiation in low volatility and medium volatility strategies, with a recommendation to reduce gold and increase fixed income in low volatility, while increasing equities and reducing fixed income in medium volatility [4][46]. - The dynamic all-weather strategy has achieved an annualized return of 6.7% with a Calmar ratio of 4.7, while the medium-low volatility strategy has an annualized return of 9% with a Calmar ratio of 3.7 [4][10]. - The active asset allocation model is based on "return prediction-risk penalty," enhancing returns while managing concentration risk [15][22]. Group 2: Industry Rotation Strategy - The industry rotation strategy recommends sectors such as non-ferrous metals, chemicals, agriculture, and telecommunications for December, based on the analysis of market conditions [4][29]. - The strategy has outperformed benchmarks with an annualized return of 36%, surpassing the average return of mixed equity funds by 28.3% [31][32]. - The underlying logic of the industry rotation strategy is based on the behavior of active market funds under different market conditions, categorized into four states: strong equity-weak bonds, weak equity-strong bonds, strong equity-strong bonds, and weak equity-weak bonds [29][34]. Group 3: ETF Strategy - The ETF strategy for December includes recommendations for ETFs in sectors such as non-ferrous metals, aquaculture, chemicals, and telecommunications, aligning with the industry rotation strategy [41][42]. - The ETF industry rotation strategy has shown an annualized return of 33%, outperforming benchmarks like the CSI 800 and mixed equity funds [36][37]. - The asset allocation strategy using ETFs suggests increasing bond ETFs in low volatility and equities in medium volatility, reflecting the overall asset allocation strategy [42][43].

- The multi-asset allocation model is constructed based on macro state recognition, incorporating growth/inflation factors, liquidity, and gold factors to create a dynamic risk budget portfolio [4][33][34] - The growth factor includes PMI, industrial added value, retail sales, fixed asset investment, and export data, while the inflation factor uses CPI and PPI. Liquidity factor is measured by M1 year-on-year growth [34][35] - Equity market characteristics are monitored using ERP (Equity Risk Premium), EP (Earnings Yield), and BP (Book-to-Price ratio) to construct stock-bond cost-effectiveness factors [34][35] - Gold investment factors are constructed using the dollar index, central bank gold purchases, and exchange rates to assess dynamic allocation value [34][35] - The model employs a multi-objective optimization approach, integrating asset momentum into traditional risk parity and risk budget frameworks. ETFs are used for portfolio construction, with dynamic adjustments based on macro signals [37][38] - The industry rotation model incorporates six dimensions: macro, financial, analyst expectations, ETF share changes, public fund/selected fund position momentum, and event momentum [39][41] - The industry rotation model has achieved an annualized return of 28% since 2012, with an annualized excess return of 18.1% over industry equal weight and a monthly excess win rate of 70% [42][43] - The industry rotation ETF strategy employs a five-layer recursive solution method to enhance portfolio performance, achieving an annualized return improvement of over 12% [77][78] - The "Accompanying Style Enhanced FOF" uses a dynamic multi-factor model focusing on Alpha and crowding factors, with quarterly adjustments to optimize fund selection and portfolio construction [46][47] - The "Accompanying Broad-based Enhanced FOF" employs a relative benchmark strategy to control tracking error while maximizing composite factor scores, using a dynamic multi-factor model [53][54] - The "Long-term Capability Factor FOF" combines Brinson model-based decomposition with TM and H-M models for timing and selection capabilities, incorporating style factors for enhanced fund selection [64][66] - The "KF-Alpha+ Trading FOF" uses quarterly data and Kalman filter-based industry estimation to construct Alpha factors, focusing on industry-specific stock selection capabilities [70][73] - The industry rotation ETF portfolio achieved a monthly excess return of 1.5% during the reporting period, with a full-period annualized excess return of 17.79% and an IR of 1.72 [78][79][87]

1. Report Industry Investment Rating No information provided in the report regarding the industry investment rating. 2. Core Viewpoints of the Report - On December 1, the convertible bond market showed a trend of increasing in price with decreasing trading volume, and the valuation increased compared to the previous period. The CSI Convertible Bond Index rose by 0.10%, while the Shanghai Composite Index, Shenzhen Component Index, ChiNext Index, Shanghai 50 Index, and CSI 1000 Index all increased to varying degrees [1]. - The market style favored large - cap growth stocks. Large - cap growth stocks rose by 1.42%, outperforming other styles [1]. - The trading sentiment in the convertible bond market weakened. The trading volume of the convertible bond market was 5.3624 billion yuan, a decrease of 9.67% compared to the previous day, while the total trading volume of the Wind All - A Index was 188.9449 billion yuan, an increase of 18.26% [1]. 3. Summary According to Relevant Catalogs Market Main Index Performance - The CSI Convertible Bond Index closed at 482.09, up 0.10% for the day, down 0.18% for the week, down 0.49% for the month, and up 16.29% since the beginning of 2025. Other convertible bond - related indices also showed different degrees of increase or decrease [7]. - Among the A - share indices, the Shanghai Composite Index closed at 3914.01, up 0.65% for the day; the Shenzhen Component Index closed at 13146.72, up 1.25% for the day; the ChiNext Index closed at 3092.50, up 1.31% for the day [7]. Market Capital Performance - The trading volume of the convertible bond market was 5.3624 billion yuan, a decrease of 9.67% compared to the previous day, while the total trading volume of the Wind All - A Index was 188.9449 billion yuan, an increase of 18.26% [1][8]. - The net outflow of the main funds in the Shanghai and Shenzhen stock markets was 343 million yuan, and the yield of the 10 - year Treasury bond decreased by 0.46bp to 1.84% [1][11][12]. Convertible Bond Price and Valuation - The weighted average closing price of convertible bonds increased to 132.54 yuan, up 0.08% compared to the previous day. The proportion of high - price bonds above 130 yuan increased by 0.51pct to 54.55% [2]. - The fitting conversion premium rate of 100 - yuan par value increased to 31.50%, up 0.04pct compared to the previous day. The overall weighted par value increased by 0.61% to 100.63 yuan [2][17][22]. Industry Performance - In the A - share market, the top three industries in terms of gains were non - ferrous metals (+2.85%), communication (+2.81%), and electronics (+1.58%); the top three industries in terms of losses were agriculture, forestry, animal husbandry and fishery (-0.43%), environmental protection (-0.23%), and real estate (-0.06%) [3]. - In the convertible bond market, the top three industries in terms of losses were environmental protection (-3.58%), steel (-1.37%), and household appliances (-1.01%); the top three industries in terms of gains were communication (+0.85%), electronics (+0.82%), and coal (+0.66%) [3]. ETF Share Changes - The share of Bosera Convertible Bond ETF was 4.237 billion shares, with a net decrease of 27.9 million shares [37]. - The share of Haifutong Convertible Bond ETF was 802 million shares, with a net increase of 10.1 million shares [40].

- The diffusion index model tracks industry rotation based on momentum principles, aiming to capture upward trends in industries. It has been monitored for four years, with notable performance in 2021 and stable returns in 2022. However, it faced challenges in 2023 and 2024 due to market reversals. For December 2025, recommended industries include non-ferrous metals, comprehensive, steel, banking, power equipment & new energy, and electronics[23][24][27] - The GRU factor model utilizes GRU deep learning networks to analyze minute-level volume and price data, focusing on short-cycle performance. It has achieved significant excess returns since 2021 but struggled in 2025 due to concentrated market themes. For the week ending November 28, 2025, industries ranked highest by GRU factors include comprehensive, steel, banking, comprehensive finance, retail, and agriculture[30][31][33] - Diffusion index model weekly rankings show top industries as non-ferrous metals (0.994), comprehensive (0.961), steel (0.939), banking (0.937), power equipment & new energy (0.902), and electronics (0.853). Industries with the lowest rankings include food & beverage (0.343), utilities (0.498), transportation (0.503), real estate (0.548), construction (0.563), and oil & petrochemicals (0.616)[24][25][26] - GRU factor weekly rankings highlight top industries as comprehensive (4.42), steel (3.9), banking (0.5), comprehensive finance (0.43), retail (0.18), and agriculture (-0.33). Industries ranked lowest include communication (-15.26), defense (-9.1), electronics (-8.71), pharmaceuticals (-8.44), computing (-8.11), and real estate (-7.63)[31][32][33] - Diffusion index model achieved an average weekly return of 3.53%, exceeding the equal-weighted return of CICC primary industries by 1.10%. Year-to-date excess return stands at 2.55%[27] - GRU factor model recorded an average weekly return of 1.06%, underperforming the equal-weighted return of CICC primary industries by -1.43%. Year-to-date excess return is -4.45%[33]

- The report tracks industry prosperity signals using quantitative models and indicators, focusing on coal, livestock, steel, structural materials, and fuel refining industries[9] - For the coal industry, the model uses price factors and capacity factors to estimate monthly revenue and profit growth rates. The formula is based on the monthly price index of thermal coal, which determines the sales price for the following month[10][14] - In the livestock industry, the "slaughter coefficient method" is applied to predict the supply-demand gap for pigs six months ahead. The formula is: $ \text{Slaughter Coefficient} = \frac{\text{Quarterly Pig Slaughter}}{\text{Breeding Sow Inventory (lagged 6 months)}} $ This method effectively identifies pig price upcycles based on historical data[15][16] - For the steel industry, the model incorporates comprehensive steel prices and cost indicators (e.g., iron ore, coke, coal, and scrap steel) to predict monthly profit growth and calculate per-ton profit[18][21] - In the structural materials sector, profitability changes in glass and cement manufacturing are tracked using price and cost indicators. These changes are used to design allocation signals. Additionally, manufacturing PMI and real estate sales data are analyzed to assess potential infrastructure investment expectations[24][26] - For the fuel refining and oil services industry, the model uses changes in fuel prices, crude oil prices, and cracking spreads to estimate profit growth and design allocation signals. The model also considers changes in new drilling activities[27][34][35]

Core Insights - The report suggests focusing on the financial, non-ferrous metals, electronics, and machinery sectors for December [1] - The value-growth rotation strategy has a composite score of 5, indicating a higher score for growth style as of November 30, 2025 [3][6] - The small-cap style has a higher score in the size rotation strategy, with a composite score of 4 [8] Style Rotation Insights - The large-cap stocks are more sensitive to economic prosperity, while growth stocks benefit more from liquidity easing [3][6] - The value-growth rotation strategy yielded a growth index return of -2.85% and a value index return of 0.35% in November 2025 [6] - The size rotation strategy showed a return of -2.46% for the CSI 300 and -2.30% for the CSI 1000 in November 2025 [8] Industry Rotation Insights - The report constructs a four-dimensional engine with macro, fundamental, technical, and crowding indicators for industry index rotation [11] - The top five industries for December based on the industry rotation composite score are banking, electronics, machinery, non-ferrous metals, and non-bank financials [3][23] - The bottom five industries are coal, real estate, construction, oil and petrochemicals, and textiles and apparel [3][23] Macro Indicators - The macroeconomic growth dimension is in the "expansion strengthening/recession alleviation" phase, while the liquidity dimension is in the "easing intensification/tightening slowdown" phase as of November 30, 2025 [13] - The report recommends allocating to the large financial and midstream manufacturing sectors based on these macro indicators [13] Fundamental Indicators - The top five industries based on fundamental indicators are non-bank financials, non-ferrous metals, electronics, telecommunications, and electric equipment and new energy [17] - The bottom five industries based on fundamental indicators are real estate, coal, construction, agriculture, forestry, animal husbandry, and textiles and apparel [17] Technical Indicators - The top five industries based on technical indicators are electronics, banking, telecommunications, non-ferrous metals, and machinery [18] - The bottom five industries based on technical indicators are coal, construction, food and beverage, oil and petrochemicals, and real estate [18] Crowding Indicators - The industries with high crowding indicators include basic chemicals, electric equipment and new energy, agriculture, real estate, and textiles and apparel [22] - The industries with low crowding indicators are machinery, non-bank financials, automobiles, computers, and food and beverage [22]