Core Insights - The report highlights the strong performance of gold assets, with significant price increases in both international and domestic markets, leading to a historic high in China's gold ETF scale [1][4][30] - China's gold reserves have been steadily increasing, reaching 2,068 tons by the end of September 2023, ranking fourth globally, which is crucial for financial security and optimizing foreign exchange reserves [1][9][10] - The domestic gold ETF market has rapidly developed due to rising investment demand, with a total of 63 ETFs and a market size exceeding 1,200 billion yuan, marking a year-on-year increase of 63.1% [1][34] - The report discusses the complex pricing factors of gold, emphasizing the inverse relationship between gold prices and the real yield of U.S. Treasury bonds, as well as gold's role as a safe-haven asset during market volatility [1][36] - A quantitative strategy for gold ETF timing is proposed, showing annualized returns of 12.3% since 2020, with a maximum drawdown of -12.5% [1] Domestic Gold Asset Development Status - China's gold reserves have shown a steady growth trend, with the People's Bank of China continuously increasing its holdings, reflecting the importance of gold in stabilizing financial security and enhancing international payment capabilities [9][10] - In the first half of 2023, domestic gold production was 158.5 tons, with a slight year-on-year decrease of 1.1%, while gold imports increased by 12.3% [10][12] Global Gold Demand Trends - Global gold demand reached a record high in the third quarter of 2023, with total demand of 1,200 tons, a year-on-year increase of 12%, driven primarily by investment demand [12][14] - The demand for gold jewelry has decreased in weight but increased in value due to rising prices, with global gold jewelry consumption dropping 6% to 400 tons in the third quarter of 2023 [13][24] Gold Pricing Factors - The report identifies U.S. Treasury real yields as a primary pricing factor for gold, with gold prices inversely related to these yields, influenced by U.S. economic data [36] - Gold's safe-haven attribute is highlighted, as demand increases during market turmoil, with supply and demand dynamics also playing a critical role in gold pricing [1][36] Gold Price Time Series Model - A time series prediction model for gold ETF logarithmic returns has been constructed, aiding investment decision-making [1] Huashan Gold ETF Timing Strategy - The Huashan Gold ETF timing strategy has provided specific operational guidance for investors, achieving an annualized return of 12.3% since 2020 [1]

Group 1 - The public fund report for Q3 2025 shows over 300 bond funds have disclosed their performance, with 157 funds achieving net value growth [1] - The top-performing fund, Taixin Huiying Bond A, recorded a net value growth rate of 28.01%, while its C share only achieved 7.99%, indicating a significant performance disparity [1] - The bond market experienced notable adjustments in Q3 due to factors such as improved risk appetite among investors, stable macroeconomic conditions, and low bond yields reducing the attractiveness of fixed-income products [1] Group 2 - Several convertible bond funds achieved high net value growth rates, with five out of the top seven funds being convertible bond funds, including Rongtong Convertible Bond A and Jianxin Convertible Bond A [2] - The market's risk appetite has significantly increased, leading to a "see-saw effect" between stocks and bonds, with convertible bonds benefiting from the rising stock market, particularly in the technology sector [2] - Different fund managers have varied strategies; for instance, Rongtong Convertible Bond actively increased its positions in AI and innovative pharmaceutical sectors, while Changsheng Convertible Bond optimized its industry allocation based on market conditions [2] Group 3 - Wanji Convertible Bond has shifted to a "dual low convertible bond" strategy, maintaining a bond position between 85% and 90%, with plans to increase positions if the market corrects [3] - The bond market is seen as a low-risk option for investors, with recent trends indicating a recovery phase, particularly in the long-term bond segment [3] - Future bond market performance is expected to depend on monetary and fiscal policy combinations, with potential for downward adjustments in interest rates and opportunities in long-term bonds and green bonds [3]

Report Industry Investment Rating - Not mentioned in the provided content Core Viewpoints of the Report - The study explores the effectiveness of the NASDAQ Golden Dragon China Index in predicting the short - term trends of domestic stock indices. The quantitative timing strategy based on the previous night's performance of the NASDAQ Golden Dragon China Index can significantly outperform the corresponding benchmark indices, with a smoother net - value curve, enhancing returns and reducing the maximum historical drawdown, especially for the CSI 500 and CSI 1000 indices [4][37]. - This research expands investors' strategy toolkits and helps futures and options intraday traders optimize trading decisions and improve trading win - rates [4][37]. Summary by Relevant Catalogs 1. NASDAQ Golden Dragon China Index Introduction - It is a stock index compiled by the NASDAQ to track the stock price performance of Chinese companies listed in the US, regarded as a "barometer" of Chinese new - economy enterprises in US stocks. As of September 23, 2025, it has 73 constituent stocks, including Alibaba and Baidu, covering new - economy sectors such as the Internet, new energy, and consumer services. In terms of the number of constituent stocks, the optional consumer and information technology sectors have relatively large shares [9]. 2. Correlation Analysis between NASDAQ Golden Dragon China Index and Domestic Stock Indices - There is a significant positive correlation (correlation coefficients above 0.65) between the NASDAQ Golden Dragon China Index and the Shanghai 50, SSE 300, CSI 500, and CSI 1000 indices in the past three years, indicating that the previous night's movement of the NASDAQ Golden Dragon China Index affects the next - day movement of domestic stock indices [12][13]. - The Granger causality test on the NASDAQ Golden Dragon China Index and the SSE 300 and CSI 1000 indices shows that the lagged first - order NASDAQ Golden Dragon China Index has a certain predictive effect on domestic stock indices, and it can be used to predict the next - day movement of domestic stock indices statistically [16]. 3. Quantitative Timing Strategy Based on NASDAQ Golden Dragon China Index 3.1 Strategy Basic Logic - When (closing price - opening price)/opening price of the NASDAQ Golden Dragon China Index on the previous day is greater than X%, indicating that the K - line entity is at least a medium - sized positive line, go long on domestic stock indices at the opening price the next day and hold until closing [17]. 3.2 Historical Back - test Performance - **Shanghai 50 Index Timing Strategy**: Since 2018, the strategy has significantly outperformed the Shanghai 50 Index, with a compound annualized return of 7.63% (compared to 0.22% of the Shanghai 50 Index), and the maximum drawdown has decreased from - 44.43% to - 13.21% [19][22]. - **SSE 300 Index Timing Strategy**: The compound annualized return of the strategy is 8.42% (compared to 1.28% of the SSE 300 Index), and the maximum drawdown has decreased from - 45.6% to - 10.07% [23][24]. - **CSI 500 Index Timing Strategy**: The compound annualized return of the strategy can reach 11.05% (compared to 1.65% of the CSI 500 Index), and the maximum drawdown has decreased from - 41.68% to - 9.44% [28][29]. - **CSI 1000 Index Timing Strategy**: The compound annualized return of the strategy can reach 12.74% (compared to 0.63% of the CSI 1000 Index), and the maximum drawdown has decreased from - 45.38% to - 10.51% [33][36]. 4. Conclusion - The strategy based on the NASDAQ Golden Dragon China Index can significantly outperform the corresponding benchmark indices in the past seven - plus years, with a smoother net - value curve, enhancing returns and reducing the maximum historical drawdown, especially effective for the CSI 500 and CSI 1000 indices [37]. - The research expands investors' strategy toolkits and helps futures and options intraday traders optimize trading decisions and improve trading win - rates [37].

Group 1 - The report emphasizes the importance of identifying the current market state as either "trend" or "range," which aids in timing and stock selection strategies [4][8][83] - A two-phase, layered diagnostic algorithm is employed to define the index state, utilizing a "zig-zag" algorithm combined with breakpoint correction to distinguish historical performance [4][10][11] - Six feature variables are constructed from price, volume, and volatility dimensions, and machine learning models such as logistic regression and decision trees are used for state prediction, achieving over 80% accuracy in future market state predictions after smoothing [4][31][32] Group 2 - A dynamic position management strategy is designed based on model predictions, switching between "momentum" logic in trend states and "mean-reversion" logic in range states, with weekly adjustments to balance risk and return [4][55][83] - The decision tree model-driven strategy yielded a total return of 77.26% and a Sharpe ratio of 1.12 during the backtesting period from 2020 to 2025, significantly outperforming the buy-and-hold benchmark return of 14.68% [4][55][78] - The report concludes that the decision tree model's strategy not only outperforms the benchmark but also demonstrates lower volatility and maximum drawdown, achieving a Sharpe ratio of 1.12, indicating robust performance [4][83]

Core Viewpoint - The article highlights the significance of small to medium-sized private equity firms in the industry, focusing on Guangzhou Tianzhanhan as a representative example of a successful quantitative investment firm [3][4]. Company Overview - Guangzhou Tianzhanhan Private Fund Management Co., Ltd. was established in March 2017 and is located in Tianhe District, Guangzhou. The firm specializes in quantitative investment, particularly in timing strategies for selected targets, aiming for absolute returns through low buying and high selling [4][6]. - As of August 2025, Tianzhanhan's products achieved an average return of ***% from January to August, ranking in the top 10 among quantitative private equity firms in Guangdong [4]. Investment Model - The firm operates with a management scale of 0-5 billion and focuses on quantitative strategies [6]. - The development timeline includes key milestones such as the issuance of representative products and securing seed investments from major financial institutions [6]. Core Team - The core team at Guangzhou Tianzhanhan has extensive experience in quantitative investment, with expertise in computer science, mathematics, and finance. The team has been stable and cohesive for over 10 years, ensuring strong continuity in product strategies [8][20]. Investment Philosophy & Representative Strategies - The core investment philosophy revolves around exploiting market volatility driven by human emotions, using mathematical models to identify price imbalances. The goal is to achieve absolute returns through strategic timing in buying and selling [11][24]. - The firm employs two main strategies: quantitative individual stock timing and quantitative index timing, utilizing proprietary models to predict future trends [12][16]. Strategy Logic - The investment decision-making process combines macro and micro timing analyses, focusing solely on the target assets rather than market indices. This approach aims to maximize profits through precise timing in buying low and selling high [13][21]. Core Advantages - The company has a clear ownership structure and stable operations since its inception in 2017. The focus on individual stock timing rather than market exposure allows for lower correlation with mainstream strategies, reducing competition [20][22]. - The firm emphasizes direct analysis of timing effectiveness, aiming for high win rates and strategy validity, which is relatively rare in the market [23][25].

Core Viewpoint - The article discusses the limitations of traditional market sentiment indicators and proposes a new approach to measure market sentiment through structural indicators, aiming to provide more detailed insights for market timing decisions. Group 1: Market Sentiment Measurement - The existing market sentiment indicators lack sensitivity and are not effective in signaling market reversals, as they are influenced heavily by a limited number of metrics [1][3][9] - The proposed sentiment temperature model consists of five indicators: total turnover rate, trading volume, northbound capital inflow, and volatility indices for options [1][3] - The methodology for constructing the sentiment temperature involves averaging the VIX percentiles and smoothing the data over a five-day period [1] Group 2: Structural Indicators - The article emphasizes the need for structural indicators to better capture market trading characteristics, especially in weak trend environments where investment hotspots shift rapidly [9][10] - Key structural indicators include: - **Industry Turnover Rate Consistency**: Measures the degree of consensus among funds regarding industry sectors, indicating whether market trading behavior is consistent or shifting [11][14] - **Industry Concentration**: Reflects the degree of trading activity concentration in specific sectors, with higher values indicating a lack of diversification in fund preferences [18][20] - **Industry Performance and Turnover Consistency**: Assesses whether the performance of leading sectors aligns with their trading volumes, indicating market sentiment stability [21][24] - **Growth Board Activity**: Indicates risk appetite among investors, with higher activity in the growth sector suggesting bullish sentiment [25][28] Group 3: Financing Data - The financing balance to free float market value ratio serves as a long-term sentiment indicator, with increases suggesting bullish sentiment and decreases indicating bearish sentiment [29][32] - The article also discusses the use of the Relative Strength Index (RSI) as a sentiment indicator, where values above 50 indicate strong buying power [33][34] Group 4: Timing Strategy - The sentiment structure indicators have been tested for their effectiveness in timing strategies, with daily strategies outperforming weekly ones in terms of annualized returns and risk management [91][92] - The backtesting results show that the sentiment indicators can provide significant excess returns compared to the benchmark index, with a notable reduction in drawdown and volatility [91][92]

Group 1 - The article discusses the relationship between the depreciation of the US dollar and the performance of the stock market, suggesting that a weaker dollar can lead to better stock market performance [1] - It raises the question of whether the exchange rate of the Chinese yuan against the US dollar can influence the A-share market [1] - The article explores the potential for constructing quantitative timing strategies based on macroeconomic indicators and whether these strategies can outperform the market [1]

Core Viewpoint - The article discusses a quantitative timing research framework for style indices, focusing on the identification of market bottoms and tops through valuation, liquidity, and trading congestion models, highlighting their effectiveness in generating returns since 2011 [1][2][3]. Group 1: Style Index Quantitative Timing Research Framework - The style index includes large-cap, small-cap, value, growth, and dividend indices, with a focus on their valuation and market liquidity characteristics [1]. - The average annualized return for the long positions in the style index valuation model since 2011 is 10.38%, with an average excess annualized return of 8.30% [1]. Group 2: Market Liquidity Model - The market liquidity factors include buy and sell impact costs, as well as liquidity indices for rising and falling markets, with bottom timing showing more significant accuracy compared to top timing [2]. - The average rebound return from liquidity factor bottom timing is 6.86%, and the average annualized return for the long positions in the liquidity model since 2011 is 12.38%, with an average excess annualized return of 10.30% [2]. Group 3: Trading Congestion Model - Trading congestion is identified as a top-timing risk factor, effectively complementing the valuation and liquidity models [2]. - The excess annualized return for the congestion composite model since 2011 is 4.87% [2]. Group 4: Application of Quantitative Timing Models - The combined application of valuation, liquidity, and congestion models has accurately captured style index bottoms and tops, while effectively mitigating risks from trading congestion [3]. - The average annualized return for the long positions in the timing model since 2011 is 18.54%, with an average excess annualized return of 16.46% and a SHARP ratio of 1.06, achieving an excess return win rate of 87% [3].

Quantitative Models and Construction Methods 1. Model Name: Volume Timing Signal - **Model Construction Idea**: The core idea is that "the decline in a shrinking volume market is significantly greater than the rise in a shrinking volume market, so avoiding shrinking volume signals can achieve higher trading odds"[3][22][24] - **Model Construction Process**: 1. Calculate the rolling 60-day average and standard deviation of the turnover and turnover rate of the index or market[23] 2. Standardize the daily turnover data: - If the turnover is within ±2 standard deviations, map the score to -1~+1 - If the turnover exceeds ±2 standard deviations, assign a score of +1/-1 3. Combine the scores of turnover and turnover rate equally[23] 4. Generate signals based on the combined score: - Method 1: Go long if the score > 0, stay out if the score < 0 - Method 2: Use the rolling 5-year or 3-year percentile of the score; go long if above the 50th percentile, stay out if below[23] 5. The report adopts the simpler method of directly judging whether the score is greater than 0[23] - **Model Evaluation**: The model is not a high-win-rate strategy but achieves relatively high odds by avoiding significant market adjustments during shrinking volume periods[24] 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of growth and value styles based on macroeconomic cycles, valuation differences, and market sentiment[52][54] - **Model Construction Process**: 1. **Fundamentals**: - Growth is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Value is favored under the opposite conditions[52] 2. **Valuation**: - Growth is favored when the PE and PB valuation differences between growth and value are in the lower percentiles and mean-reverting upward[52] 3. **Sentiment**: - Growth is favored when turnover and volatility differences between growth and value are low[52] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between growth and value[52] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[53][55] 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of small-cap and large-cap styles based on macroeconomic cycles, valuation differences, and market sentiment[56][58] - **Model Construction Process**: 1. **Fundamentals**: - Small-cap is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Large-cap is favored under the opposite conditions[56] 2. **Valuation**: - Large-cap is favored when the PE and PB valuation differences between small-cap and large-cap are in the higher percentiles and mean-reverting downward[56] 3. **Sentiment**: - Small-cap is favored when turnover differences are high - Large-cap is favored when volatility differences are mean-reverting downward[56] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between small-cap and large-cap[56] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[57][60] 4. Model Name: Four-Style Rotation Model - **Model Construction Idea**: Combines the conclusions of the growth-value and small-cap-large-cap rotation models to allocate across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[61][63] - **Model Construction Process**: 1. Use the growth-value model to determine the allocation between growth and value 2. Use the small-cap-large-cap model to determine the allocation between small-cap and large-cap 3. Combine the two models to allocate across the four styles[61] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance, with consistent outperformance in most years[61][63] --- Model Backtest Results 1. Volume Timing Signal - **Win Rate**: 47.34%[24] - **Odds**: 1.75[24] - **Annualized Excess Return**: 6.87% (based on next-day open price)[34] - **Maximum Drawdown**: 31.40%[34] - **Return-to-Drawdown Ratio**: 0.4634[34] 2. Growth-Value Style Rotation Model - **Annualized Return**: 11.76%[55] - **Annualized Volatility**: 20.77%[55] - **Maximum Drawdown**: 43.07%[55] - **Sharpe Ratio**: 0.5438[55] - **Return-to-Drawdown Ratio**: 0.2731[55] 3. Small-Cap vs. Large-Cap Style Rotation Model - **Annualized Return**: 12.45%[60] - **Annualized Volatility**: 22.65%[60] - **Maximum Drawdown**: 50.65%[60] - **Sharpe Ratio**: 0.5441[60] - **Return-to-Drawdown Ratio**: 0.2459[60] 4. Four-Style Rotation Model - **Annualized Return**: 13.37%[63] - **Annualized Volatility**: 21.51%[63] - **Maximum Drawdown**: 47.91%[63] - **Sharpe Ratio**: 0.5988[63] - **Return-to-Drawdown Ratio**: 0.2790[63]

Quantitative Models and Construction Methods 1. Model Name: Short-term Quantitative Timing Model - **Model Construction Idea**: The model integrates macroeconomic fundamentals, valuation, sentiment, and liquidity indicators to generate short-term market timing signals[24][25][26] - **Model Construction Process**: - **Macroeconomic Fundamentals**: - Manufacturing PMI: Current value at 49.70, 44.92% percentile over the past 5 years, indicating neutral sentiment[24] - Long-term RMB loan growth: 0.00% percentile, indicating weak credit growth and cautious signals[24] - M1 growth rate: 94.92% percentile, indicating strong growth and optimistic signals[24] - **Valuation**: - PE median: 43.18, 97.19% percentile, indicating high valuation and neutral signals[25] - PB median: 2.85, 86.77% percentile, indicating high valuation and neutral signals[25] - **Sentiment**: - Beta dispersion: -0.59%, 40.68% percentile, indicating neutral sentiment[25] - Volume sentiment score: 0.98, 99.59% percentile, indicating strong sentiment and optimism[25] - Volatility: 7.53% (annualized), 0.17% percentile, indicating optimism[25] - **Liquidity**: - Monetary rate: -0.10, 33.90% percentile, indicating relative ease and optimism[26] - Exchange rate expectations: -0.09%, 40.68% percentile, indicating neutrality[26] - 5-day average financing: 50.66 billion RMB, 95.53% percentile, indicating neutral leverage signals[26] - **Model Evaluation**: The model demonstrates strong performance with significant excess returns and reduced drawdowns compared to benchmarks[26][30] 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model evaluates growth and value styles based on macroeconomic fundamentals, valuation, and sentiment indicators to recommend allocation[35] - **Model Construction Process**: - **Macroeconomic Fundamentals**: - Profit cycle slope: High, favoring growth[37] - Interest rate cycle: High, favoring value[37] - Credit cycle: Weak, favoring value[37] - **Valuation**: - PE difference: 19.57% percentile, favoring growth[37] - PB difference: 38.03% percentile, favoring growth[37] - **Sentiment**: - Turnover difference: 38.13% percentile, favoring value[37] - Volatility difference: 17.73% percentile, favoring balanced allocation[37] - **Model Evaluation**: The model has historically delivered significant excess returns over benchmarks, though recent performance has been mixed[36][39] 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model assesses small-cap and large-cap styles using macroeconomic fundamentals, valuation, and sentiment indicators to suggest balanced allocation[40] - **Model Construction Process**: - **Macroeconomic Fundamentals**: - Profit cycle slope: High, favoring small-cap[42] - Interest rate cycle: High, favoring large-cap[42] - Credit cycle: Weak, favoring large-cap[42] - **Valuation**: - PE difference: 78.86% percentile, favoring large-cap[42] - PB difference: 96.59% percentile, favoring large-cap[42] - **Sentiment**: - Turnover difference: 72.56% percentile, favoring small-cap[42] - Volatility difference: 62.60% percentile, favoring large-cap[42] - **Model Evaluation**: The model has consistently outperformed benchmarks, delivering significant excess returns over time[41][44] 4. Model Name: Four-Style Rotation Model - **Model Construction Idea**: Combines insights from growth-value and small-cap-large-cap models to allocate across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[45] - **Model Construction Process**: - Allocation recommendation: Small-cap growth (12.5%), small-cap value (37.5%), large-cap growth (12.5%), large-cap value (37.5%)[45] - **Model Evaluation**: The model has historically generated significant excess returns, though recent performance has been slightly below benchmarks[45][46] --- Model Backtest Results 1. Short-term Quantitative Timing Model - Annualized return: 16.98% - Annualized volatility: 14.55% - Maximum drawdown: 27.70% - Sharpe ratio: 1.0138 - Excess return (2024 onwards): 2.26%[26][30][33] 2. Growth-Value Style Rotation Model - Annualized return: 11.82% - Annualized volatility: 20.79% - Maximum drawdown: 43.07% - Sharpe ratio: 0.5457 - Excess return (2025 YTD): -2.32%[36][39] 3. Small-Cap vs. Large-Cap Style Rotation Model - Annualized return: 12.38% - Annualized volatility: 22.69% - Maximum drawdown: 50.65% - Sharpe ratio: 0.5408 - Excess return (2025 YTD): -5.11%[41][44] 4. Four-Style Rotation Model - Annualized return: 13.29% - Annualized volatility: 21.53% - Maximum drawdown: 47.91% - Sharpe ratio: 0.6001 - Excess return (2025 YTD): -3.25%[45][46]