Group 1 - The quantitative convertible bond portfolio achieved an excess return of 1.39% in the first quarter, with a total return of 25.94% since 2025, outperforming the CSI Convertible Bond Index by 8.64% [1][3] - In March 2026, the portfolio recorded a return of -6.60%, with an excess return of 0.81% [3] - The portfolio is generated based on three factor systems: fundamental factors, low-frequency price-volume factors, and high-frequency price-volume factors, with monthly rebalancing [9][10] Group 2 - A total of 32 fundamental factors, 80 low-frequency price-volume factors, and 32 high-frequency price-volume factors are tracked for the convertible bonds [3][10] - The report highlights the latest data on pricing deviation factors, showcasing the differences between market prices and theoretical pricing [17][18] Group 3 - The report provides risk warnings for convertible bonds based on delisting and risk warning rules from exchanges, as well as event-based and credit scoring methods [28][30] - Specific convertible bonds are flagged for mandatory delisting risks, financial delisting risks, and credit risks, including names like Lingang Convertible Bond and Shengxun Convertible Bond [30] Group 4 - The timing strategy for the CSI Convertible Bond Index is based on price-volume models, pricing deviations, and bond elasticity, with the latest view indicating a zero position as no bullish signals were present [31][32] - The timing model signals for March 2026 show a consistent zero position by the end of the month, indicating a cautious approach [34]

- The report includes a quantitative timing model that generates signals for various financial instruments and indices, such as government bonds, stock indices, and commodities. The model uses original signals and a 5-day moving average (MA5) to determine market views like "bullish," "adjustment," or "neutral" [2][6][7] - The construction process of the model involves calculating original signals and MA5 values for each asset or index. For example, the original signal for the 30-year government bond is 86.06%, while its MA5 is 78.05%. These values are used to derive the model's view, which is "adjustment" in this case. Similar calculations are applied to other assets like the 2-year government bond (original signal: 8.02%, MA5: 14.70, view: "bullish") and COMEX gold (original signal: 39.61%, MA5: 42.29, view: "neutral") [2][6][7] - The model's evaluation is based on its ability to provide actionable market views for different financial instruments. It categorizes assets into "bullish," "adjustment," or "neutral" based on signal persistence and alignment with market trends. For example, the 2-year government bond has maintained a "bullish" signal for over 10 trading days, indicating strong consistency [2][6][7] - The backtesting results of the model include specific signal values for each asset or index. For instance: - 30-year government bond: Original signal 86.06%, MA5 78.05%, view "adjustment" [2][6] - 2-year government bond: Original signal 8.02%, MA5 14.70%, view "bullish" [2][6] - COMEX gold: Original signal 39.61%, MA5 42.29%, view "neutral" [2][7] - IPE crude oil: Original signal 46.07%, MA5 40.68%, view "neutral" [2][7] - Various stock indices like the CSI Red Dividend Total Return Index and the Hang Seng Technology Index also have their respective signal values and views [2][6][7]

Group 1: Dividend Statistics of Broad-based Index Constituents - In 2026, the dividend progress for broad-based index constituents shows that among the CSI 300 constituents, 1 company is in the implementation stage, while the SSE 50, CSI 500, and CSI 1000 each have 1 company in the implementation or proposal stage [11][12]. - The total dividends for the year 2026 for the CSI 300 and SSE 50 are both 1.251 billion, while the CSI 500 has 0.13 billion, and the CSI 1000 is still in the proposal stage [12][11]. - The dividend yield for 2026 has been compared to 2025, indicating a slight increase in yields across the indices [13][14]. Group 2: Dividend Statistics of Industry Index Constituents - The dividend progress for industry index constituents shows that in the pharmaceutical sector, 2 companies are in the implementation stage, while the utilities, machinery, coal, and oil sectors each have 1 company in the implementation stage [15][17]. - The total dividends for the pharmaceutical sector amount to 0.066 billion, utilities 1.251 billion, machinery 0.063 billion, coal 0.13 billion, and oil 0.014 billion [17][15]. - The comparison of dividend yields for 2026 against 2025 shows variations across different sectors, with some sectors experiencing increases [18][19]. Group 3: Index Futures Basis - The annualized basis rates for index futures considering dividends are as follows: CSI 300 near-month 7.15%, far-month 5.10%, near-quarter 3.95%, and far-quarter 3.73%; SSE 50 near-month 1.24%, far-month 0.51%, near-quarter -0.59%, and far-quarter 0.03%; CSI 500 near-month 10.25%, far-month 8.74%, near-quarter 5.96%, and far-quarter 6.66%; CSI 1000 near-month 12.25%, far-month 12.21%, near-quarter 10.95%, and far-quarter 10.81% [6][25]. - The basis data reflects the impact of dividends on the futures contracts, with specific figures for each contract's closing price and basis [25][21]. - Historical basis data for various contracts shows trends in the basis rates over time, indicating fluctuations influenced by dividend announcements [30][31][32][33][36][37].

Core Insights - The article discusses the 2026 Bloomberg Private Equity Investment Strategy Conference, highlighting the balance investors seek between macroeconomic uncertainty and structural opportunities as the market anticipates the end of the Federal Reserve's interest rate cut cycle [1] - It emphasizes the impact of U.S. fiscal policy, geopolitical factors, and the re-globalization of supply chains on risk appetite, while noting that China's market is attracting both domestic and foreign capital due to supportive growth policies and ongoing capital market reforms [1] - The rapid evolution of artificial intelligence and computational infrastructure is reshaping investment models, with a focus on equity market style rotation and the maturation of derivative tools, making quantitative investment strategies a core force for industry breakthroughs and risk management [1] Event Details - The conference will take place in multiple cities including Beijing, Shanghai, Shenzhen, and Hangzhou, featuring industry leaders and Bloomberg economists discussing global macro trends, stock market strategies, and the empowerment of investment through quantitative methods [1][5] - The Beijing session will focus on finding certainty in a changing macro environment, specifically through the lens of equity and quantitative strategies to reshape asset allocation frameworks [1] Speakers - Notable speakers include Song Xuetao, Chief Economist at Guojin Securities; Zhan Siyun, Quantitative Fund Manager at Lingjun Investment; Wei Hao, CIO at Shengshi Capital; and Cheng Yaman, Chief Macro Strategist at China Galaxy [3] Agenda Highlights - The agenda includes a keynote speech on macro outlook for equity markets, an integrated research solution from Bloomberg, a roundtable forum exploring macro, equity, cross-border derivatives, and new explorations in quantitative strategies, followed by interactive discussions [4]

1. Report Industry Investment Rating There is no information about the report industry investment rating in the given content. 2. Core Viewpoints of the Report - The heavy use of factors with a reversal - like logic causes quantitative portfolios to under - allocate high - volatility stocks, leading to a deviation in the volatility distribution compared to the benchmark. When high - volatility stocks rise rapidly, the portfolio may underperform the index [4][6][9]. - Using volatility grouping to achieve "volatility neutrality" can control the volatility distribution risk but may result in a loss of excess returns. It is a risk - prevention measure [4][14][16]. - The recommended approach is to stratify individual stocks by volatility and apply different individual stock deviation constraints. Relaxing the constraints on high - volatility stocks can increase excess returns and reduce the maximum drawdown [4][19]. - The quantitative portfolio can obtain more excess returns in high - volatility stocks. Relaxing the constraints is beneficial for the portfolio to perform better [4][21]. - The volatility stratification constraint cannot achieve complete "volatility neutrality", but it can increase the allocation of high - volatility stocks. A more lenient definition of high - volatility stocks is conducive to improving the portfolio's excess returns [4][52][56]. - The volatility stratification constraint can be extended to other indices, generally increasing excess returns and reducing the maximum drawdown [4][58]. 3. Summary According to the Directory 3.1 How to Solve the Problem of the Biased Volatility Distribution in Quantitative Portfolios - **Quantitative portfolios often have a biased volatility distribution**: Four multi - factor portfolios are constructed with the CSI 500 index as the benchmark. Most portfolios under - allocate high - volatility stocks, and the heavy use of factors with a reversal - like logic is the main reason [7][8][9]. - **Using volatility grouping to achieve "volatility neutrality"**: This method can correct the portfolio's volatility distribution, mainly controlling the maximum drawdown. However, it may lead to a decline in excess returns and is more suitable for special periods [14][16][18]. - **Volatility stratification for individual stock deviation constraints is a more recommended solution**: By stratifying stocks into high - volatility and low - volatility groups and applying different deviation constraints, it can increase excess returns and reduce the maximum drawdown. Tightening constraints on high - volatility stocks has the opposite effect [19][20][21]. 3.2 Why Relax Constraints within High - Volatility Stocks? - **Some factors perform better in the high - volatility group**: Factors such as low - volatility, dividend, and growth factors have higher IC values in the high - volatility group. The growth factor shows better offensive performance in the high - volatility group, while the low - volatility factor has a stronger long - term excess return contribution [22][23][30]. - **The dispersion of some factors is higher in the high - volatility group**: The dispersion of factors such as reversal, low - liquidity, momentum, and growth is higher in the high - volatility group, which supports the better performance of the quantitative portfolio in this group [32][33]. - **The real contribution of stratification constraints in the high - volatility group**: After the volatility stratification constraint, the return contribution of over - allocated stocks in the high - volatility group increases significantly, while that of under - allocated stocks remains unchanged or decreases slightly, improving the overall performance of the portfolio [34][35][37]. 3.3 Portfolio Performance under Volatility Stratification Constraints - Overall, the volatility stratification constraint can improve the long - term performance of the portfolio, but the improvement varies by year. It may lead to a decline in excess returns in years when low - volatility factors perform well, and an increase in other years [38][47][48]. 3.4 Some Supplementary Conclusions - **Can volatility stratification constraints achieve "volatility neutrality?"**: Volatility stratification constraints cannot achieve complete "volatility neutrality", but they can increase the allocation of high - volatility stocks compared to the original portfolio [52]. - **Does the division of the high - volatility group have an impact?**: Narrowing the high - volatility group can reduce the maximum drawdown to some extent, but it also leads to a decline in monthly average excess returns. A more lenient definition of high - volatility stocks is beneficial for increasing excess returns [56][57]. - **Can it be applied to other indices?**: The volatility stratification constraint can be extended to other indices such as the SSE 300 and CSI 1000, generally increasing excess returns and reducing the maximum drawdown [58][59][60].

Investment Rating - The report suggests a cautious investment approach, focusing on defensive factors and identifying opportunities in performance increment information [1][12]. Core Insights - The current market sentiment is dominated by caution, with defensive factors showing overall superiority, although there has been a marginal decline in the short term [1][12]. - Geopolitical conflicts remain a core concern, with potential risks evolving, impacting market dynamics significantly [20][21]. - The upcoming peak period for annual report disclosures is expected to shift market focus from macro narratives to micro fundamentals, making performance expectations a critical variable [22][25]. Summary by Sections Market Sentiment and Performance - Cautious sentiment prevails in March, with defensive factors like valuation, volatility, and turnover rates performing well, while market turnover has decreased to below 2 trillion [12][16]. - Structural changes are emerging, with defensive factors showing marginal declines while growth styles are attempting a rebound in large and mid-cap stocks [16][19]. Geopolitical Risks - Ongoing geopolitical tensions, particularly in the Middle East, are influencing global risk appetite, with significant implications for energy prices and supply chains [20][21]. - Two potential scenarios are outlined: continued conflict leading to sustained high oil prices and supply chain disruptions, or a de-escalation that could enhance market performance through improved earnings expectations [21]. Earnings Reports and Market Dynamics - The first peak of annual report disclosures is approaching, with performance expectations likely to become a key market driver [22][25]. - The report emphasizes the importance of identifying stocks with significant performance discrepancies relative to market expectations, particularly in undervalued segments [25]. Factor Performance Tracking - The report tracks the effectiveness of various factors such as valuation, growth, and profitability across different stock pools, highlighting their performance metrics [26][27][28][29].

Quantitative Models and Factors Summary Quantitative Models and Construction Methods - **Model Name**: Rotation Model - **Model Construction Idea**: The model uses relative valuation and momentum indicators to determine allocation between micro-cap stocks and "Mao Index" (a proxy for large-cap stocks) to capture style rotation opportunities[18][25] - **Model Construction Process**: 1. **Rotation Indicators**: - Calculate the relative net value of micro-cap stocks to the Mao Index - Compare the relative net value to its 243-day moving average. If above, favor micro-cap stocks; otherwise, favor the Mao Index - Use the 20-day closing price slope of both indices. If one slope is positive and the other is negative, allocate to the index with a positive slope[18][25] 2. **Timing Indicators**: - Use the 10-year government bond yield (threshold: 0.3%) and micro-cap stock volatility crowding degree (threshold: 0.55). If either indicator hits its threshold, issue a closing signal[25] - **Model Evaluation**: The model currently signals a balanced allocation between micro-cap stocks and the Mao Index, with no systemic risk triggers observed in the medium term[18][19] Quantitative Factors and Construction Methods - **Factor Name**: Growth Factor - **Factor Construction Idea**: Measures the growth potential of stocks based on financial metrics like revenue and profit growth[65] - **Factor Construction Process**: - Key metrics include: - **OperatingIncome_SQ_Chg1Y**: Year-over-year growth in quarterly operating income - **Revenues_SQ_Chg1Y**: Year-over-year growth in quarterly revenue - **ROE_FTTM**: Forward 12-month return on equity based on consensus estimates[65] - **Factor Evaluation**: The growth factor performed well in the past week, driven by market sentiment favoring growth-oriented stocks[54][56] - **Factor Name**: Consensus Expectation Factor - **Factor Construction Idea**: Captures market sentiment and analyst expectations through forward-looking metrics[65] - **Factor Construction Process**: - Key metrics include: - **ROE_FTTM_Chg3M**: 3-month change in forward 12-month ROE estimates - **TargetReturn_180D**: Expected return based on consensus target price over the next 180 days - **Volume_Mean_20D_240D**: Ratio of 20-day average trading volume to 240-day average trading volume[65] - **Factor Evaluation**: This factor exhibited strong performance last week, reflecting improved market sentiment and positive analyst revisions[54][56] - **Factor Name**: Volatility Factor - **Factor Construction Idea**: Measures the risk and defensive characteristics of stocks based on historical price volatility[65] - **Factor Construction Process**: - Key metrics include: - **IV_CAPM**: Residual volatility from the CAPM model - **IV_FF**: Residual volatility from the Fama-French three-factor model - **Volatility_60D**: Standard deviation of 60-day returns[65] - **Factor Evaluation**: The volatility factor weakened last week due to a decline in risk-averse sentiment as geopolitical tensions eased[54][56] - **Factor Name**: Reversal Factor - **Factor Construction Idea**: Exploits mean-reversion tendencies in stock prices over different time horizons[65] - **Factor Construction Process**: - Key metrics include: - **Price_Chg60D**: 60-day return - **Price_Chg120D**: 120-day return[65] - **Factor Evaluation**: The reversal factor underperformed last week, reflecting a market preference for momentum and growth[54][56] - **Factor Name**: Convertible Bond Selection Factors - **Factor Construction Idea**: Constructs factors based on the relationship between convertible bonds and their underlying stocks, as well as valuation metrics[59] - **Factor Construction Process**: - Key metrics include: - **Equity Consensus Expectation**: Derived from the underlying stock's consensus estimates - **Equity Growth**: Based on the growth metrics of the underlying stock - **Equity Financial Quality**: Evaluates the financial health of the underlying stock - **Equity Valuation**: Assesses the valuation of the underlying stock - **Convertible Bond Valuation**: Uses metrics like parity and premium rate[59][65] - **Factor Evaluation**: The equity consensus expectation factor achieved the highest IC mean among convertible bond factors last week[59][63] Backtest Results of Models and Factors - **Rotation Model**: - Relative net value of micro-cap stocks to Mao Index: 2.45 (above the 243-day moving average of 2.00)[18][25] - 20-day closing price slope: Micro-cap stocks -0.37%, Mao Index -0.17%[18][25] - Volatility crowding degree: 12.43% (below the risk threshold of 55%)[18][25] - 10-year government bond yield: 0.61% (below the risk threshold of 0.3%)[18][25] - **Factor Backtest Results (IC Mean)**: - **Consensus Expectation**: 7.37% (All A-shares), 0.08% (CSI 300), 6.25% (CSI 500), 2.85% (CSI 1000)[56] - **Growth**: 0.90% (All A-shares), 4.95% (CSI 300), 3.53% (CSI 500), 4.77% (CSI 1000)[56] - **Volatility**: -4.38% (All A-shares), -6.04% (CSI 300), -10.10% (CSI 500), -8.15% (CSI 1000)[56] - **Reversal**: -12.58% (All A-shares), -4.57% (CSI 300), -2.55% (CSI 500), -8.97% (CSI 1000)[56] - **Convertible Bond Factors (IC Mean)**: - Equity Consensus Expectation: Highest IC mean among all convertible bond factors[59][63]

Core Viewpoint - In an era of information overload and accelerated style rotation, capturing excess returns in investment has become increasingly challenging, prompting a search for effective strategies [1] Group 1: Fund Performance - The fund managed by Chen Cong, Xingquan Hong Kong-Shenzhen Two-Year Holding Mixed Fund, has achieved a net value growth of 39.13% over the past year, surpassing its performance benchmark by 17.67 percentage points, ranking 14th out of 43 in its category [3] - Another fund independently managed by Chen, Xingquan Hexi Mixed A, has recorded a return of 21.82% since its establishment on June 27, 2025, until March 25, 2026 [3] Group 2: Investment Philosophy - Chen Cong's investment methodology combines a strong "Xingquan imprint" with a disciplined approach derived from quantitative analysis, focusing on sectors such as the internet, innovative pharmaceuticals, technology hardware, and new consumption [3][6] - His investment framework emphasizes industry aesthetics and portfolio discipline, with a focus on company culture, organizational structure, and management cognition in light asset industries, while prioritizing performance tracking in sectors undergoing significant development [7][8] Group 3: Market Insights - Chen identifies critical points in industry trends, focusing on capturing the upward Beta before uncovering undervalued Alpha opportunities, particularly in sectors like AI and storage [10][11] - His approach to investing in the storage sector involved recognizing supply tightness early and identifying a leading storage company with significant advantages, leading to a strong investment decision [11] Group 4: Risk Management - The investment strategy incorporates risk management from the outset, ensuring a balanced exposure across sub-sectors and maintaining a clear delineation of asset positioning within the portfolio [14] - Chen emphasizes the importance of adjusting positions in response to extreme macroeconomic events to mitigate tail risks, prioritizing the reduction of high Beta stocks during such times [14] Group 5: Long-term Vision - The overarching goal is to deliver attractive long-term performance, with a belief that true growth will ultimately be validated over time, despite market fluctuations [15]

- The report includes a quantitative model that provides timing signals for various financial instruments, such as government bonds, stock indices, and commodities. The model outputs probabilities representing the likelihood of short-term upward movements in yields or indices[3][7][8] - The model uses a moving average (MA5) to smooth the daily timing signals. For example, the original signal for the 30-year government bond is 82.96%, while its MA5 value is 74.29%. This approach helps identify trends and reduces noise in the data[3][7] - The model evaluates multiple instruments, including 2-year and 10-year government bonds, stock indices like the CSI All A Index and the Hang Seng Tech Index, and commodities such as COMEX gold and IPE crude oil. Each instrument is assigned a viewpoint, such as "bullish," "adjustment," or "neutral," based on the signal probabilities and thresholds[3][7][8] - The thresholds for the model's viewpoints are defined as follows: probabilities above 60% indicate a bullish stance, below 40% suggest bearishness, and values in between are considered neutral[8] - The model's performance is tracked over a 10-day period, with daily updates on signal probabilities and viewpoints for each instrument. For example, the 2-year government bond has maintained a "bullish" viewpoint for over 10 days, while the CSI All A Index has been in "adjustment" for 3 days[3][7][8] - The report provides detailed signal probabilities and MA5 values for each instrument, such as the CSI All A Index (83.02% original signal, 64.68% MA5) and COMEX gold (57.89% original signal, 42.29% MA5)[3][7][8]

Quantitative Models and Construction Methods - **Model Name**: Three-dimensional Timing Framework **Model Construction Idea**: The model integrates liquidity, divergence, and prosperity indicators to assess market timing[6][13][15] **Model Construction Process**: 1. Liquidity Index: Measures market liquidity trends[24] 2. Divergence Index: Captures market disagreement levels[19] 3. Prosperity Index: Reflects economic activity and market sentiment[22] These three dimensions are combined to form a comprehensive timing framework[13][15] **Model Evaluation**: The framework indicates a downward market trend with limited short-term rebound potential[6][13] - **Model Name**: All-weather Strategy **Model Construction Idea**: Focuses on risk diversification and avoids reliance on predictions for stable returns[42][53] **Model Construction Process**: 1. Asset Selection: Diversified across equities, bonds, and commodities[55] 2. Risk Adjustment: Balances risk exposure through structured layers[46][48] 3. Structural Hedging: Implements cyclic hedging to smooth volatility[42][47][48] **Model Evaluation**: High-wave version achieves higher returns with moderate risk, while low-wave version prioritizes stability[53] - **Model Name**: Hotspot Trend ETF Strategy **Model Construction Idea**: Identifies ETFs with strong upward trends and high market attention[29][32] **Model Construction Process**: 1. Select ETFs with simultaneous upward trends in highest and lowest prices[29] 2. Construct support-resistance factors based on 20-day regression slopes[29] 3. Choose top ETFs with the highest turnover ratios in the past 5 and 20 days[29] **Model Evaluation**: The strategy outperformed the CSI 300 index with a 56.47% return since 2025[29][30] Model Backtesting Results - **Three-dimensional Timing Framework**: No specific numerical backtesting results provided - **All-weather Strategy**: - High-wave version: Annualized return 11.8%, max drawdown 3.6%, Sharpe ratio 1.9 (2025)[53] - Low-wave version: Annualized return 6.7%, max drawdown 2.0%, Sharpe ratio 2.4 (2025)[53] - 2026 YTD: High-wave return 1.8%, low-wave return 1.2%[53] - **Hotspot Trend ETF Strategy**: - Return since 2025: 56.47% - Excess return over CSI 300: 38.62%[29][30] Quantitative Factors and Construction Methods - **Factor Name**: Volatility Factor **Factor Construction Idea**: Captures stocks with high price fluctuations[56] **Factor Construction Process**: Measures weekly returns of high-volatility stocks[56] **Factor Evaluation**: Positive weekly return of 1.95%, indicating market preference for high-volatility stocks[56][57] - **Factor Name**: Momentum Factor **Factor Construction Idea**: Identifies stocks with strong upward price trends[56] **Factor Construction Process**: Calculates weekly returns of high-momentum stocks[56] **Factor Evaluation**: Positive weekly return of 1.58%, reflecting market interest in momentum stocks[56][57] - **Factor Name**: Leverage Factor **Factor Construction Idea**: Targets stocks with high financial leverage[56] **Factor Construction Process**: Measures weekly returns of high-leverage stocks[56] **Factor Evaluation**: Positive weekly return of 0.96%, showing market favor for leveraged stocks[56][57] - **Factor Name**: Twelve-month Residual Momentum **Factor Construction Idea**: Tracks residual momentum over a 12-month period[61] **Factor Construction Process**: $ specific\_mom12 = residual\_momentum\_12months $ Measures excess returns of stocks with strong residual momentum[61][62] **Factor Evaluation**: Weekly excess return of 0.87%, monthly excess return of 0.46%[61][62] - **Factor Name**: 1-year-1-month Return Factor **Factor Construction Idea**: Compares returns between 1-year and 1-month periods[61] **Factor Construction Process**: $ mom\_1y\_1m = (return\_1year - return\_1month) $ Calculates excess returns based on the difference between long-term and short-term returns[61][62] **Factor Evaluation**: Weekly excess return of 0.79%, monthly excess return of -0.03%[61][62] Factor Backtesting Results - **Volatility Factor**: Weekly return 1.95%[56][57] - **Momentum Factor**: Weekly return 1.58%[56][57] - **Leverage Factor**: Weekly return 0.96%[56][57] - **Twelve-month Residual Momentum**: Weekly excess return 0.87%, monthly excess return 0.46%[61][62] - **1-year-1-month Return Factor**: Weekly excess return 0.79%, monthly excess return -0.03%[61][62]