1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - The convertible bond market showed an incremental increase today, with valuations rising compared to the previous period. The trading sentiment in the convertible bond market heated up, and the mid - cap growth style was relatively dominant [1]. - The central price of convertible bonds increased, and the proportion of high - priced bonds rose. The valuation of convertible bonds also increased [2]. - In the industry performance, more than half of the underlying stock industry indices rose. Different industries in the A - share and convertible bond markets had different trends in terms of rise and fall [3]. 3. Summaries According to Relevant Catalogs Market Overview - Index performance: The CSI Convertible Bond Index rose 0.90% month - on - month, the Shanghai Composite Index rose 0.08%, the Shenzhen Component Index rose 0.70%, the ChiNext Index rose 0.54%, the SSE 50 Index fell 0.11%, and the CSI 1000 Index rose 0.79% [1]. - Market style: Mid - cap growth was relatively dominant. Large - cap growth rose 0.59%, large - cap value fell 1.23%, mid - cap growth rose 1.59%, mid - cap value rose 0.09%, small - cap growth rose 0.66%, and small - cap value rose 0.44% [1]. - Fund performance: The trading sentiment in the convertible bond market heated up. The trading volume of the convertible bond market was 88.992 billion yuan, a 4.46% month - on - month increase; the total trading volume of the Wind All A was 2.623747 trillion yuan, a 6.44% month - on - month decrease; the net inflow of the main funds in the Shanghai and Shenzhen stock markets was 5.608 billion yuan, and the yield of the 10 - year treasury bond decreased by 0.14bp to 1.83% [1]. Convertible Bond Price and Valuation - Convertible bond price: The weighted average closing price of convertible bonds was 141.97 yuan, a 0.81% increase from the previous day. The closing price of stock - biased convertible bonds was 205.66 yuan, a 1.64% increase; the closing price of bond - biased convertible bonds was 122.18 yuan, a 0.07% decrease; the closing price of balanced convertible bonds was 133.34 yuan, a 0.80% increase. The proportion of high - priced bonds above 130 yuan was 74.34%, a 1.06pct increase from the previous day. The price median was 139.37 yuan, a 0.66% increase from the previous day [2]. - Convertible bond valuation: The fitted conversion premium rate of 100 - yuan par value was 37.22%, a 0.75pct increase from the previous day; the overall weighted par value was 105.68 yuan, a 0.17% increase from the previous day. The premium rate of stock - biased convertible bonds was 17.73%, a 0.03pct increase; the premium rate of bond - biased convertible bonds was 87.63%, a 2.12pct decrease; the premium rate of balanced convertible bonds was 29.68%, a 0.96pct increase [2]. Industry Performance - Underlying stock industry: Among the A - share markets, the top three rising industries were non - ferrous metals (+2.79%), electronics (+2.62%), and machinery and equipment (+1.50%); the top three falling industries were banks (-1.58%), coal (-1.57%), and food and beverage (-1.53%) [3]. - Convertible bond market: A total of 26 industries in the convertible bond market rose, with the top three rising industries being steel (+4.16%), automobile (+2.85%), and electronics (+2.57%); only two industries fell, namely food and beverage (-2.12%) and non - bank finance (-0.14%) [3]. - Other indicators by industry category: (1) Closing price: The large - cycle increased by 1.25%, manufacturing increased by 1.83%, technology increased by 1.66%, large - consumption increased by 0.33%, and large - finance decreased by 0.06%. (2) Conversion premium rate: The large - cycle increased by 0.46pct, manufacturing increased by 0.036pct, technology increased by 2.8pct, large - consumption decreased by 0.037pct, and large - finance increased by 0.19pct. (3) Conversion value: The large - cycle increased by 0.89%, manufacturing increased by 1.98%, technology decreased by 0.04%, large - consumption decreased by 0.37%, and large - finance decreased by 0.65% [3].

Group 1 - The core viewpoint of the article is that the strong performance of equity funds in the past year has returned, with 85 funds achieving over 100% returns in 2025, including 74 active equity funds and 11 index funds and ETFs [4][5] - The article emphasizes that historical performance is not indicative of future results, and the focus should be on the sustainability of such high returns [6][24] - The best-performing funds in 2025 concentrated their investments in the top-performing sectors, particularly telecommunications and metals, which saw returns exceeding 80% [10][11] Group 2 - The article discusses the "champion curse" in mutual funds, indicating that historically, top-performing funds often struggle to maintain their performance in subsequent years [25][28] - Data shows that very few funds that ranked in the top quartile in one year continue to do so over the next five years, highlighting the difficulty of sustaining high performance [27][30] - The analysis includes both active and passive funds, revealing that even top-performing ETFs and index funds face challenges in maintaining their leading positions over time [33][36] Group 3 - The article presents statistical evidence that the probability of a fund maintaining its top quartile ranking from one year to the next is around 30%, which is only slightly better than random chance [39][41] - It notes that poor past performance is often a predictor of continued underperformance, while good past performance does not guarantee future success [43][46] - The article concludes that the concentrated investment strategies that lead to extreme performance are risky, as they rely on accurately predicting market trends, which is challenging even for professional investors [49][51]

Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the vague concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of timing signals from 10 selected indicators[9][14][15] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions (e.g., 20-day Bollinger Bands, 20-day price deviation rate, 60-day turnover rate volatility, etc.)[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score[9][14] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Dividend Style Timing Model - **Model Construction Idea**: The model times the dividend style by analyzing the relative performance of the CSI Dividend Index against the CSI All Share Index, using three indicators: relative momentum, 10Y-1Y term spread, and interbank pledged repo trading volume[16][19] - **Model Construction Process**: 1. Generate daily signals (0, +1, -1) for each indicator, representing neutral, bullish, and bearish views, respectively 2. Aggregate the scores to determine the overall long/short view on the dividend style 3. When bullish, fully allocate to the CSI Dividend Index; when bearish, fully allocate to the CSI All Share Index[16][19] - **Model Evaluation**: The model has consistently maintained a bearish view on the dividend style this year, favoring growth style instead[16] 3. Model Name: Large-Cap vs. Small-Cap Style Timing Model - **Model Construction Idea**: The model evaluates the crowding level of large-cap and small-cap styles based on momentum and trading volume differences, adjusting the strategy based on whether the market is in a high or low crowding state[20][22][24] - **Model Construction Process**: 1. Calculate momentum differences and trading volume ratios between the Wind Micro-Cap Index and the CSI 300 Index over multiple time windows 2. Derive crowding scores for both large-cap and small-cap styles based on percentile rankings of the calculated metrics 3. Use a dual moving average model with smaller parameters in high crowding states and larger parameters in low crowding states to determine trends[20][22][24] - **Model Evaluation**: The model effectively captures the medium- to long-term trends in low crowding states and reacts to potential reversals in high crowding states[22] 4. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model employs genetic programming to directly extract factors from industry index data (e.g., price, volume, valuation) without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[27][30][31] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| and NDCG@5 2. Combine multiple factors with weak collinearity into industry scores using greedy strategies and variance inflation factors 3. Select the top five industries with the highest composite scores for equal-weighted allocation[30][33][37] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks[30][33] 5. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro factor risk parity framework, emphasizing diversification across underlying macro risk sources (growth and inflation surprises) rather than asset classes[38][41] - **Model Construction Process**: 1. Divide macroeconomic scenarios into four quadrants based on growth and inflation surprises 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, actively overweighting favorable quadrants[41][42] - **Model Evaluation**: The strategy achieves enhanced performance by actively allocating based on macroeconomic expectations[38][41] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.67% - Annualized Volatility: 17.33% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.87[15] 2. Dividend Style Timing Model - Annualized Return: 16.65% - Maximum Drawdown: -25.52% - Sharpe Ratio: 0.91 - Calmar Ratio: 0.65 - YTD Return: 5.78%[17] 3. Large-Cap vs. Small-Cap Style Timing Model - Annualized Return: 27.79% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.16 - Calmar Ratio: 0.87 - YTD Return: 6.27%[25] 4. Industry Rotation Model (Genetic Programming) - Annualized Return: 31.95% - Annualized Volatility: 17.44% - Maximum Drawdown: -19.62% - Sharpe Ratio: 1.83 - Calmar Ratio: 1.63 - YTD Return: 3.31%[30] 5. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.82% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.91 - Calmar Ratio: 1.88 - YTD Return: 2.02%[42]

Core Viewpoint - The article discusses the performance of equity funds, highlighting that while some funds have achieved impressive returns, the sustainability of such performance is questionable. Investors should be cautious about chasing high-performing funds without understanding the underlying factors driving their success [2][15][31]. Group 1: Fund Performance Analysis - In 2025, 85 funds achieved over 100% annual returns, including 74 active equity funds and 11 index funds and ETFs [2]. - The best-performing sectors in 2025 were telecommunications and non-ferrous metals, with returns exceeding 80% [5][14]. - Concentrated investments in high-performing sectors were key to achieving significant returns, as seen in funds like Yongying Technology Select, which allocated over 90% of its assets to telecommunications and electronics [6][11]. Group 2: Historical Performance and Sustainability - Historical data shows that funds with outstanding performance in previous bull markets often fail to maintain their top rankings in subsequent years [17][18]. - For instance, in the 2021 bull market, very few funds that ranked in the top quartile were able to sustain that performance over the following five years [17][18]. - Similar trends were observed in bear markets, indicating that high performance is not consistently repeatable across different market conditions [19][20]. Group 3: Randomness of Performance - The probability of a fund maintaining its top quartile ranking from one year to the next is around 30%, which is only slightly better than random chance [24][28]. - Both active and passive funds exhibit similar performance randomness, with last quartile funds having a higher likelihood of remaining in the bottom tier the following year [26][28]. Group 4: Challenges in Maintaining Performance - The article emphasizes that the short-term performance of funds is often random, making it difficult to predict future success based on past results [31][35]. - The concentration in specific sectors that leads to high returns can be risky, as market conditions change and may not favor the same sectors in the future [35][36]. - Investors should be cautious about blindly following past high performers without a solid understanding of market dynamics and sector rotations [36][39].

Core Viewpoint - The current market is experiencing a split trend, with certain sectors like real estate rebounding while others like semiconductors are facing pullbacks. The overall sentiment suggests a cautious approach as the market prepares for potential index movements [1][3][5]. Group 1: Market Trends - The market has seen 3,731 stocks rise, with 111 hitting the daily limit, indicating a favorable short-term trading environment despite the challenges faced by large-cap stocks [1]. - There is an expectation of a significant rebound in the market before the upcoming holiday, with a potential surge in trading volume if the securities sector continues to perform well [3][5]. - The current market dynamics are characterized by rapid sector rotation, which may lead to increased volatility and potential losses for investors who are not careful [5][7]. Group 2: Sector Performance - Real estate is showing signs of recovery, while the semiconductor sector has experienced a pullback, highlighting the uneven performance across different industries [1]. - The banking sector has seen a two-day correction, which is viewed as a preparatory phase for a stronger upward movement [5]. - Continued performance in sectors like real estate, securities, and technology is crucial for maintaining upward momentum in the index, particularly as it approaches the 4,100-point mark [5]. Group 3: Investor Sentiment - Many investors are feeling uncertain about the market, which could lead to impulsive trading decisions such as chasing gains or cutting losses prematurely [5][7]. - The sentiment suggests that patience is key, as the market may present opportunities for gains despite current fluctuations [7]. - The overall message emphasizes the importance of having a clear strategy and understanding market rhythms to avoid being caught off guard by sudden movements [7].

Core Viewpoint - The article emphasizes that the year 2025 marks the beginning of high-quality development for China's public fund industry, highlighting the importance of excess returns for actively managed equity funds. Only those funds that can consistently outperform their benchmarks are deemed valuable, while others should consider lower-cost, more transparent ETF index funds [1]. Performance Analysis - Zhang Xu, managing the Huaan Event-Driven Quantitative Mixed Fund since May 18, 2020, has outperformed both the CSI 300 and the Wind Equity Fund Index for six consecutive years, despite only half of those years favoring actively managed equity funds [1][2]. - The performance data from 2020 to 2025 shows that the Huaan Event-Driven Mixed Fund achieved returns of 59.19%, 30.84%, -17.86%, -8.63%, 21.82%, and 38.06% respectively, while the CSI 300 and Wind Equity Fund Index had varying performances [2]. Manager Recognition and Growth - Zhang Xu was relatively unknown until late 2024, but his fund's assets surged from approximately 200 million to 4.722 billion by Q3 2025, indicating a significant recognition from institutional investors, with 87.99% of A-class and 98.19% of C-class shares held by institutions [4][5]. Investment Strategy - Zhang Xu's investment strategy is characterized by a systematic and scientific approach, utilizing a quantitative framework that allows for effective industry rotation and risk management. This approach has led to high adaptability and consistent performance across different market conditions [9][15][16]. - His ability to rotate industries effectively has been demonstrated through various market phases, where he adjusted his portfolio to capitalize on emerging trends, such as focusing on healthcare and consumer electronics in 2020, and shifting to the renewable energy sector in 2021 [10][11][12]. Future Outlook - The article suggests that as institutional investors increase their share in the market, the competition for excess returns will intensify. Zhang Xu's evolving investment framework is expected to maintain its competitive edge, making him a valuable asset for both institutional and individual investors [18][19].

- The report tracks the prosperity signals of various industries, including coal, livestock farming, steel, structural materials, and fuel refining, based on recent industry operating indicators[9] - For the coal industry, the model uses price factors and capacity factors to estimate monthly revenue and profit growth rates[10] - The livestock farming model employs the "slaughter coefficient method" to calculate the supply-demand gap for pigs six months in advance, based on the relationship between piglet birth cycles and sow pregnancy arrangements. The formula is as follows: $ \text{Slaughter Coefficient} = \frac{\text{Quarterly Pig Slaughter}}{\text{Breeding Sow Inventory (Lag 6 months)}} $ $ \text{Potential Capacity (6 months later)} = \text{Breeding Sow Inventory (t)} \times \text{Slaughter Coefficient (t+6 months)} $[15][16] - For the steel industry, the model integrates comprehensive steel prices and cost indicators (e.g., iron ore, coke, pulverized coal, scrap steel) to predict monthly profit growth rates and calculate per-ton profitability[18] - The structural materials and construction engineering model tracks profitability changes in the glass and cement manufacturing industries using price and cost indicators. It also incorporates manufacturing PMI and real estate sales data to analyze potential investor expectations for infrastructure support[25] - The fuel refining and oil services model calculates industry profit growth rates and cracking spreads based on changes in fuel prices and crude oil prices. It also designs allocation signals using oil prices, cracking spreads, and new drilling activity[27] - Backtesting results for the coal industry show that profit growth signals do not indicate significant improvement, maintaining a neutral allocation view[14] - The slaughter coefficient method effectively identifies pig price upward cycles, with the Q2 2026 potential pig supply estimated at 167.73 million heads, slightly tight compared to the Q2 2025 demand of 171.43 million heads[16][17] - For the steel industry, December 2025 profit growth is predicted to be negative, with PMI rolling averages remaining unchanged, leading to a neutral allocation view[22] - The glass industry continues to show negative profit growth as of December 2025, while the cement industry also exhibits declining profits with no positive signals in new housing starts, maintaining a neutral view for both[26] - The fuel refining industry is predicted to have flat profit growth in December 2025, with oil prices continuing to decline and new drilling activity showing minimal change, resulting in a neutral allocation view for both refining and oil services[33][34]

Summary of Conference Call Notes Industry or Company Involved - The discussion primarily revolves around the A-share market and investment strategies for 2026, focusing on various asset classes and sectors. Core Points and Arguments 1. **Market Style and Sentiment** - In January 2026, the growth value dimension maintains a preference for value style but slightly leans towards growth style. Investor sentiment favors value style, while market momentum shows a slight advantage for growth style [3][4][5] 2. **Asset Allocation** - The outlook for domestic stock assets is relatively positive, with a neutral stance on commodities and a cautious approach towards bonds. The macroeconomic indicators suggest a cautious view on stocks and commodities, while being neutral on bonds [3][5][8] 3. **Industry Rotation Model** - The current state is characterized by rapid rotation, with December's model showing a 0.7% underperformance against the benchmark. Recommended sectors for January 2026 include banking, building materials, computers, comprehensive finance, and coal [3][5] 4. **Quantitative Strategy Performance** - The aggressive growth strategy, particularly the growth trend resonance stock selection strategy, achieved a 46.4% annual return, outperforming by 13 percentage points. The small-cap mining strategy yielded the highest returns at 86% [6][9] 5. **Market Conditions and Strategy Implications** - The current market is experiencing high differentiation, which typically benefits value and dividend strategies. January is noted for the dense disclosure of annual reports, presenting potential investment opportunities if earnings forecasts exceed expectations [9] 6. **Machine Learning and Derivative Models** - The quantitative strategy team has developed various models based on reinforcement learning and deep learning, achieving stable performance. The option timing model has a high success rate, with the sentiment indicator yielding a cumulative return of 37.47% since its launch [10] 7. **Technical Analysis and Market Signals** - Technical indicators show mixed signals, with four bearish and one bullish signal among major indices, suggesting potential resistance in the current market [8] 8. **Sector Recommendations** - The recommended sectors for January 2026 include banking, building materials, computers, comprehensive finance, and coal, with a shift away from previously favored sectors like non-ferrous metals [5] Other Important but Possibly Overlooked Content - The quantitative strategy team is actively engaging clients with subscription services for their sentiment indicators, indicating a focus on client engagement and market responsiveness [10]

- The report mentions an A-share timing model, specifically the "wave model," which turned bullish on November 14, 2025, and has maintained a high position since then. This model is used to determine optimal market entry and exit points based on market trends and signals[1][29] - Another timing model, the "short-term model," is highlighted for its bullish signal on the CSI 1000 index, while the bullish signals for the CSI 300 and CSI 500 indices have ended. This model focuses on short-term market movements and provides directional signals for specific indices[1][29] - The report also discusses a "Hong Kong stock timing model," which indicates a high certainty of a liquidity-driven bullish trend post-New Year. This model is used to assess market conditions and timing for Hong Kong stocks, with a focus on buy-side activity confirmation[4][29] - A "gold timing model" is mentioned, which has been adjusted to a higher position. This model evaluates the market conditions for gold investments, considering factors like the U.S. dollar index and short-term trading opportunities[5][29] - The report includes a "small-cap A-share timing model," which suggests a bullish outlook for small-cap stocks in January 2026. This model is used to analyze and predict trends in small-cap segments of the A-share market[6][29] - The "dividend growth A-share timing model" is also highlighted, which has been adjusted to favor growth stocks in January 2026. This model focuses on identifying opportunities in dividend-paying growth stocks within the A-share market[6][29]

Group 1 - The report emphasizes a focus on equity products and mid-cap blue-chip industries for asset allocation in January 2026, indicating a positive outlook for A-shares and commodities, while maintaining a neutral stance on U.S. stocks and bonds [2][61] - The report highlights that A-shares are expected to experience slight upward movement with limited odds but a relatively high win rate historically in January, while the overall sentiment remains neutral to slightly bullish [11][61] - The performance of various asset allocation strategies since 2025 is noted, with low-volatility strategies yielding an annualized return of 6.2%, medium-low volatility strategies at 11.7%, and medium-high volatility strategies at 17.6% [7][62] Group 2 - The industry rotation strategy for January recommends focusing on sectors such as non-ferrous metals, chemicals, new energy, telecommunications, electronics, and media, driven by a return of risk appetite [42][48] - The report indicates that the industry rotation strategy has outperformed benchmarks, achieving an annualized return of 40% since 2025, significantly surpassing the performance of the CSI 800 and mixed equity funds [44][45] - The ETF strategy for January includes recommendations for ETFs in sectors like non-ferrous metals, chemicals, new energy, telecommunications, information technology, and gaming, aligning with the broader asset allocation strategy [50][55]