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 - By the third quarter of 2025, Chinese technology assets are leading among major asset classes, reflecting the trend of China's economic upgrade and the realization of the "engineer dividend" advantage [1][9][10]. Dimension Summaries Dimension 1: Macro Risk Clearance - High-growth narratives require a risk clearance opportunity where capital is willing to invest. The study tested six potential variables, revealing that timing signals based on the MOVE index and monthly nominal GDP down volatility yield significant excess returns. The strategies based on these signals since 2006 have shown cumulative returns of 1176.91% and 1227.15%, respectively [2][13][16]. Dimension 2: Nominal Growth Rate Central Level - If the nominal growth rate is below the historical average, asset returns are constrained. A strategy of increasing allocation to technology assets when nominal GDP is low has yielded cumulative returns of 1184.04% since 2006. The best macro scenario for high-growth sectors is when nominal growth is at a low level with marginal improvement [3][17][19]. Dimension 3: High-Yield Asset Scarcity - The essence of asset scarcity is not a lack of assets but a mismatch between changing asset returns and rigid capital return expectations. A strategy based on high-yield asset scarcity has yielded cumulative returns of 258.06% since 2014, indicating that negative carry conditions favor technology assets [4][21][23]. Dimension 4: Internal and External Liquidity Conditions - The analysis considers both domestic and international liquidity conditions. A strategy based on low SHIBOR rates and a narrowing yield spread has shown cumulative returns of 433.01% since 2012, indicating favorable conditions for technology stocks [5][25][27]. Dimension 5: Existence of Industry Narratives - The study quantifies the impact of long-term narratives on short-term pricing by examining the difference between price-to-sales (P/S) and price-to-earnings (P/E) ratios. A strategy based on the presence of industry narratives has yielded cumulative returns of 518.05% since 2009, suggesting that new industry information can catalyze long-term narratives [6][30][32]. Dimension 6: High-Growth Odds Perspective - The analysis focuses on market breadth and concentration within technology stocks. A strategy based on market width has yielded cumulative returns of 264.87% since 2013, indicating that a healthy market breadth is essential for sustaining high-growth narratives [7][34][35]. Composite Summary - A "5+1" timing strategy has been constructed, integrating five winning dimensions and one odds dimension. The composite signal has shown cumulative returns of 1147.47% since 2006, indicating a robust framework for understanding high-growth asset pricing [8][36][38].

Group 1 - The core viewpoint of the article emphasizes that the influx of incremental funds has become a dominant variable in the short-term A-share market [1] - The market showed a rebound last week, with the CSI 300 index rising by 1.95%, the Shanghai Composite Index by 1.91%, and the CSI 500 index by 3.98% [3] - The sudden shift in the Middle East situation from conflict to peace has led to a significant impact on market dynamics, with a notable influx of funds supporting the A-share market [3][4] Group 2 - The domestic industrial profit data released last week indicated a continued decline, which aligns with expectations, reflecting the objective state of the Chinese economy [3] - The unexpected ceasefire in the Middle East led to a rapid revaluation of global risk assets, causing a sharp drop in oil prices and a rebound in both Chinese and U.S. stock markets [3][4] - The technical indicators showed multiple models triggering buy signals, indicating a strong upward momentum in the market [4] Group 3 - The main board is recommended to maintain a high position, following the model signals that turned bullish after last Tuesday's close [5] - The small and medium-sized stocks are also suggested to adopt a high position, benefiting from liquidity support and showing greater elasticity in the current market environment [5] - The overall market sentiment is characterized by a "dual bull" trend in both stocks and bonds, driven by the active participation of incremental funds [4]

Group 1 - The current bond market is undergoing significant changes, with risk-free yields continuing to decline and traditional bond investment returns sharply compressed. Many private bond products have seen yields drop below 1.8% in the first five months of this year, contrasting with an average return of 7.91% for the entire previous year. The era of "lying win" is over [1] Group 2 - In response to the reality of significantly reduced yield space, private institutions are upgrading their bond investment strategies. Many are shifting focus towards cross-border composite products to capture cross-market spreads or increase trading frequency to enhance returns. The traditional credit spread has compressed to historical lows, prompting institutions to increase allocations to dim sum bonds and domestic city investment bonds for base returns while controlling product drawdowns [3] Group 3 - The ability to trade effectively is crucial for enhancing returns in a low-interest-rate environment. Both private bond strategy products and public "fixed income +" products require strict drawdown control. The difficulty of active timing and asset switching has increased significantly, making precise timing and asset rotation essential. A disciplined investment strategy with clear risk budgeting and position control frameworks is necessary [4] Group 4 - To improve trading success rates, institutions need to enhance market monitoring and information collection. Keeping a close watch on bond price movements, fund flows, and new bond issuances has become a daily priority. The current bond market lacks trending opportunities and is highly uncertain, often affected by sudden events. Given the unattractive absolute yield levels, institutions must maintain competitive advantages through refined operations and strategic innovations within limited yield spaces [4]

- The report includes a global asset allocation model based on artificial intelligence, which uses machine learning to score and rank various assets for monthly equal-weighted allocation strategy[30][31] - The global asset allocation model suggests weights for May: government bond index (66.09%), Nasdaq index (17.59%), German DAX index (13.83%), and Nikkei 225 (2.49%)[30] - Historical performance of the global asset allocation model from January 2021 to April 2025 shows an annualized return of 13.76%, Sharpe ratio of 0.75, maximum drawdown of 16.53%, and excess annualized return of 9.02%[30][36] - The dynamic macro event factor-based stock-bond rotation strategy includes three different risk preference models: conservative, balanced, and aggressive[37] - The stock-bond allocation models for April show stock weights of 45% for aggressive, 13.82% for balanced, and 0% for conservative[37][39] - Historical performance of the stock-bond allocation models from January 2005 to April 2025 shows annualized returns of 19.93% for aggressive, 11.00% for balanced, and 6.06% for conservative[37][44] - The dividend timing model uses economic growth and monetary liquidity indicators to construct a timing strategy for the dividend index, showing an annualized return of 15.84%, maximum drawdown of -21.70%, and Sharpe ratio of 0.89[45][49] - The dividend timing model's recommended position for April is 0%, with most economic growth indicators showing bearish signals and cautious monetary liquidity signals[45] Model Performance Metrics - Global asset allocation model: annualized return 13.76%, Sharpe ratio 0.75, maximum drawdown 16.53%[30][36] - Stock-bond allocation models: annualized returns 19.93% (aggressive), 11.00% (balanced), 6.06% (conservative)[37][44] - Dividend timing model: annualized return 15.84%, Sharpe ratio 0.89, maximum drawdown -21.70%[45][49]

Core Viewpoint - The macroeconomic environment is experiencing significant fluctuations due to tariff impacts, with asset prices showing a "rebound" effect after initial adjustments, leading to increased volatility in global markets [1][2][3]. Group 1: Asset Performance - As of April 30, 2025, the performance ranking of major assets is as follows: Gold > Euro Stoxx > Nikkei > Chinese Bonds > Nasdaq > 0 > Sci-Tech 50 > CSI 300 > Dow Jones > Hang Seng > US Dollar > Hang Seng Tech > LME Copper > Crude Oil [1][13]. - Gold has shown a year-to-date increase of 26.5% and a monthly rise of 6%, leading in both returns and Sharpe ratio among major assets, although it faced a pullback in late April [1][17]. - The domestic stock market exhibited a "dumbbell" characteristic, with small-cap and stable dividend stocks outperforming large-cap stocks, as evidenced by a 5.0% increase in the micro-cap index [1][41]. Group 2: Macroeconomic Indicators - The April manufacturing PMI, services PMI, and construction PMI in China fell to 49.0%, 50.1%, and 51.9%, respectively, indicating initial impacts from external demand [3]. - The US economy is showing signs of negative impacts from trade tensions, with Q1 GDP growth adjusted for inflation recording a negative annualized rate, and consumer spending growth slowing to 1.8% [3]. - The Eurozone and Japan's manufacturing PMIs showed slight increases, indicating some resilience in their economies [3]. Group 3: Market Dynamics - The domestic bond market displayed a dual pricing characteristic of nominal growth and liquidity, with interest rates declining in early April due to tariff impacts and expectations of policy easing later in the month [2][4]. - The stock market is increasingly focused on "finding certainty," with a shift towards dividend-paying and stable sectors amid rising external demand concerns [2][4]. - The correlation between stocks and bonds has deepened, with the rolling 12-month correlation increasing from -0.26 to -0.30, indicating a stronger inverse relationship [28]. Group 4: Sector Performance - In April, only 4 out of 31 sectors recorded positive returns, with beauty care, agriculture, retail, and utilities leading the gains, while sectors like power equipment and telecommunications lagged due to tariff impacts [41][51]. - The real estate market showed a mixed performance, with new home sales declining while second-hand home sales maintained a high growth rate, reflecting resilience in major cities [53]. Group 5: Investment Strategies - The dividend asset timing model indicates a continued rise in dividend scores, suggesting a shift towards dividend-paying stocks as a strategy to mitigate uncertainty [6][7]. - The valuation macro deviation framework suggests that if nominal GDP growth can recover, there will be further room for reasonable valuation expansion in the market [8].

- The global asset allocation model uses machine learning to score and rank assets based on factor investment principles, constructing a monthly quantitative equal-weight strategy for global asset allocation[39][43][44] - The model's historical performance from January 2021 to March 2025 shows an annualized return of 6.45%, Sharpe ratio of 1.01, maximum drawdown of 6.66%, and excess annualized return of 1.28%, outperforming the benchmark across all dimensions[39][44][45] - The dynamic macro event factor-based stock-bond rotation strategy includes three risk preference models (conservative, balanced, aggressive), with April stock weights of 0%, 13.73%, and 25%, respectively[45][46][47] - The macro timing module and risk budget framework signal strengths for April are 50% for monetary liquidity and 0% for economic growth[45][46][48] - Historical performance of the stock-bond rotation strategy from January 2005 to March 2025 shows annualized returns of 20.02% (aggressive), 11.02% (balanced), and 6.03% (conservative), all outperforming the benchmark[45][51][47] - The dividend timing model recommends a 100% allocation to the CSI Dividend Index for April, with economic growth indicators mostly bearish and monetary liquidity signals positive[53][54][52] - The dividend timing strategy achieves an annualized return of 16.86%, maximum drawdown of -21.22%, and Sharpe ratio of 0.95, significantly improving stability compared to the CSI Dividend Total Return Index[53][54][52]