Quantitative Models and Construction Methods 1. Model Name: Multivariate CNN-LSTM - **Model Construction Idea**: The model leverages the advantages of CNN and LSTM in different scenarios to predict multiple parallel financial time series by considering the correlation between them[12][14]. - **Detailed Construction Process**: - **General Structure**: The model consists of an input layer, a one-dimensional convolutional layer, a pooling layer, an LSTM hidden layer, and a fully connected layer to produce the final prediction results[14]. - **Formula**: $$ {\hat{x}}_{k,t+h}=f_{k}(x_{1,t},\dots,x_{k,t},\dots,x_{1,t-1},\dots,x_{k,t-1},\dots) $$ This formula indicates that each variable depends not only on its past values but also on the past values of other variables[11]. - **Hyperparameters**: - Number of convolution filters: 64 - Convolution kernel size: 2 - Use of padding: Yes - Pooling layer window size: (2,2) - Number of hidden units in the first LSTM layer: 128 - Number of hidden units in the second LSTM layer: 128 - Activation method between LSTM layers: ReLU - Time series look-back window: 10 - Number of training epochs: 100[20] - **Evaluation Metric**: Root Mean Square Error (RMSE) $$ RMSE={\sqrt{\frac{1}{n}\sum_{i}({\hat{y_{i}}}-y_{i}\,)^{2}}} $$ where \( y_i \) represents the standardized index price, and \( \hat{y_i} \) represents the CNN-LSTM prediction value[21]. - **Model Evaluation**: The model achieved good tracking and high accuracy in predicting multiple parallel financial time series, similar to the performance in predicting stock indices in the Asia-Pacific market[14][17]. 2. Model Name: Grouped Multivariate CNN-LSTM - **Model Construction Idea**: To improve prediction accuracy, the industry indices are grouped based on investment attributes, and a separate prediction model is constructed for each group[26][27]. - **Detailed Construction Process**: - **Grouping**: The industry indices are divided into six groups: Consumer and Medicine, Upstream Resources and Materials, High-end Manufacturing, Real Estate and Infrastructure, Big Tech, and Big Finance[27]. - **Model Structure**: Each group of industry indices is predicted using a separate CNN-LSTM model, as shown in the general structure diagram[28]. - **Evaluation Metric**: The prediction accuracy is evaluated using RMSE, similar to the original model[33]. - **Model Evaluation**: Grouping and training different CNN-LSTM sub-models for each industry group improved the prediction accuracy, especially for industries with previously low prediction accuracy[30][32]. Model Backtesting Results 1. Multivariate CNN-LSTM Model - **Prediction Error (Training Phase)**: 1.52% to 3.18%[23] - **Prediction Error (Testing Phase)**: 1.56% to 3.30%[23][25] 2. Grouped Multivariate CNN-LSTM Model - **Prediction Error (Training Phase)**: 1.49% to 2.60%[33] - **Prediction Error (Testing Phase)**: 1.61% to 2.82%[33] Quantitative Factors and Construction Methods 1. Factor Name: Weekly Industry Rotation Signal - **Factor Construction Idea**: Use the predicted values from the multivariate CNN-LSTM model to estimate the future weekly returns of industry indices and select the top five industries with the highest expected returns for equal-weight allocation[3]. - **Detailed Construction Process**: - **Prediction**: Predict the future weekly returns of industry indices using the multivariate CNN-LSTM model[34]. - **Allocation**: Every five trading days, select the top five industries with the highest expected returns for equal-weight allocation[35]. - **Training**: Retrain the model at the beginning of each quarter using an extended window of historical data from March 2014 to the training point[35]. - **Factor Evaluation**: The annualized return of the industry rotation portfolio reached 15.6%, with an annualized excess return of approximately 11.6%, and the risk-return characteristics significantly improved compared to the benchmark[3][35]. Factor Backtesting Results 1. Weekly Industry Rotation Signal - **Annualized Return**: 15.6%[38] - **Annualized Volatility**: 25.6%[38] - **Maximum Drawdown**: -27.1%[38] - **Sharpe Ratio**: 0.7[38] - **Longest Drawdown Recovery Time**: 248 days[38]

Quantitative Models and Construction Methods Genetic Programming Industry Rotation Model - **Model Name**: Genetic Programming Industry Rotation Model - **Model Construction Idea**: Directly extract factors from industry index data such as volume, price, and valuation, and update the factor library at the end of each quarter[30] - **Model Construction Process**: The model adopts weekly frequency rebalancing, selecting the top five industries with the highest composite multi-factor scores for equal-weight allocation every weekend[30] - **Model Evaluation**: The model has achieved an absolute return of 28.79% this year, outperforming the industry equal-weight benchmark by 17.68 percentage points[30] - **Model Testing Results**: - Annualized Return: 31.39% - Annualized Volatility: 18.12% - Sharpe Ratio: 1.73 - Maximum Drawdown: -19.63% - Calmar Ratio: 1.60 - Last Week Performance: 3.15% - Year-to-Date (YTD): 28.79%[32] Absolute Return ETF Simulation Portfolio - **Model Name**: Absolute Return ETF Simulation Portfolio - **Model Construction Idea**: The asset allocation weights are mainly calculated based on the recent trends of various assets, with stronger trend assets assigned higher weights. The internal equity asset allocation weights directly adopt the monthly views of the monthly frequency industry rotation model[34] - **Model Construction Process**: The model's latest holdings include dividend style ETFs and ETFs related to pharmaceuticals, non-ferrous metals, media, steel, and energy chemicals[36] - **Model Evaluation**: The model has risen by 0.34% last week and has accumulated a 5.69% return this year[34] - **Model Testing Results**: - Annualized Return: 6.52% - Annualized Volatility: 3.81% - Maximum Drawdown: 4.65% - Sharpe Ratio: 1.71 - Calmar Ratio: 1.40 - Year-to-Date (YTD): 5.69% - Last Week Performance: 0.34%[39] Global Asset Allocation Simulation Portfolio - **Model Name**: Global Asset Allocation Simulation Portfolio - **Model Construction Idea**: Predict future returns of global major assets using a cycle three-factor pricing model, and construct the portfolio using a "momentum selects assets, cycle adjusts weights" risk budgeting framework[40] - **Model Construction Process**: The strategy currently overweights bonds and foreign exchange, with higher risk budgets assigned to assets such as Chinese bonds and US bonds[40] - **Model Evaluation**: The strategy has achieved an annualized return of 7.22% in the backtest period, with a Sharpe ratio of 1.50[40] - **Model Testing Results**: - Annualized Return: 7.22% - Annualized Volatility: 4.82% - Maximum Drawdown: -6.44% - Sharpe Ratio: 1.50 - Calmar Ratio: 1.12 - Year-to-Date (YTD): -3.04% - Last Week Performance: 0.61%[41] Quantitative Factors and Construction Methods Sentiment Indicators - **Factor Name**: Sentiment Indicators - **Factor Construction Idea**: Construct sentiment indicators from the perspectives of the put-call ratio, implied volatility, and basis in the options and futures markets[2] - **Factor Construction Process**: - **Put-Call Ratio**: Observe the ratio of the trading volume of call options to put options in the 50ETF and 500ETF options markets[17] - **Implied Volatility**: Construct the implied volatility ratio series of call and put options[20] - **Basis**: Construct the annualized basis rate weighted by the open interest for the four major stock index futures products[26] - **Factor Evaluation**: The sentiment indicators show that the previous overheating sentiment in the A-share market has continued to ease[2] Factor Backtesting Results Sentiment Indicators - **Put-Call Ratio**: The ratio has significantly fallen from the high levels observed on July 23, indicating a more rational market sentiment[17] - **Implied Volatility Ratio**: Despite the stock market rebound last week, the implied volatility ratio of call options to put options has been trending downward, further reflecting rational investor sentiment[20] - **Annualized Basis Rate**: The basis rate has been fluctuating downward, indicating rational sentiment in the futures market[26]

Core Conclusions - The speed of market rotation has significantly accelerated, with the rotation index dropping to 61.95% in 2025, and the duration of hot themes shortening, with most themes lasting less than or equal to 20 days [3][4] - The relationship between rotation speed and funding structure indicates that during accelerated rotation, financing balances are highly synchronized with the index, while during slower rotations, financing responses lag [3][14] - Based on the alpha158 factor, derived strategies were constructed for wind hot rotation, industry rotation, and hot index mapping leading stocks. The index rotation strategy achieved an annualized return of 20.25%, outperforming industry rotation at 16.03% [3][4] Industry Rotation Effective Factors - Quantile factors (QTLU/QTUD) are identified as effective for industry rotation, with support momentum (QTUD) being more effective in bear markets and resistance momentum (QTLU) in bull markets [3][4] - The proportion of positive volatility (SUMN) indicates stronger industry strength, while extreme value factors (RSV/MAX) are sensitive to hot themes [3][4] Hot Index Rotation Optimization - The analysis utilized 68 Wind hot indices, focusing on core factors such as quantile factors (QTLU_20_95) and residual ranking factors (RESI30, RANK20) which have shown high win rates in recent years [4][6] - The adjustment strategy involves T+1 closing for rebalancing to mitigate factor decay, with the top 5 components of hot indices yielding an annualized return of 15.79%, significantly outperforming the CSI 300 [4][6] Strategy Application - For industry rotation holdings in 2025, high-frequency positions include banking, automotive, and non-ferrous metals, with recent additions in coal and basic chemicals [4][6] - Hot index holdings for July 2025 included semiconductor, lithium mining, and energy equipment, while automotive parts and liquor indices were removed [4][6] Market Rotation Dynamics - The analysis indicates that the speed of rotation is influenced by the structure of market participation funds, with rapid rotation correlating with high retail participation and financing balance synchronization [14][18] - In contrast, slower rotation reflects a dominance of institutional funds, leading to a significant lag in financing balances compared to index gains [14][18] Performance of Hot Rotation Strategies - The report suggests that in recent years of rapid hot rotation, short-term trend strategies are more likely to achieve excess returns [21][27] - The effectiveness of the index rotation has been higher than that of industry rotation in the past three years, indicating a shift in alpha generation from broader industry to more granular segments [27][28]

Core Viewpoint - The high-end equipment sector is experiencing mixed performance, with significant movements in specific stocks and a positive long-term outlook for the military industry driven by technological advancements and increased defense spending [1][3][4]. Group 1: Market Performance - As of August 7, 2025, the CSI High-End Equipment Sub-Index decreased by 0.80%, with stocks showing varied performance; 712 led with an increase of 8.65%, while Guorui Technology saw the largest decline [1]. - The high-end equipment ETF (159638) had a turnover rate of 4.57% and a transaction volume of 54.32 million yuan, with an average daily transaction volume of 63.18 million yuan over the past week [3]. Group 2: ETF Performance - The latest scale of the high-end equipment ETF reached 1.198 billion yuan, with a net value increase of 33.28% over the past year [3]. - Since its inception, the ETF has recorded a highest single-month return of 19.30%, with the longest consecutive monthly gains being three months and a maximum increase of 21.15% [3]. Group 3: Industry Outlook - Recent reports indicate that the domestic military construction is transitioning towards "intelligent and unmanned" systems, with global military trade demand expanding, suggesting a long-term growth cycle for the military industry [3]. - The recent successful launch of the Pakistan Remote Sensing Satellite 01 demonstrates the maturity and stability of China's aerospace technology, while the successful flight of the Kuaizhou-1A rocket reinforces the high prosperity of the aerospace equipment sector [3]. Group 4: Key Stocks - As of July 31, 2025, the top ten weighted stocks in the CSI High-End Equipment Sub-Index accounted for 46.03% of the index, with notable companies including AVIC Shenyang Aircraft Company and Aero Engine Corporation of China [4]. - The performance of key stocks varied, with AVIC Shenyang Aircraft Company down by 2.36% and Aerospace Electronic Technology up by 2.08% [6]. Group 5: Investment Opportunities - Investors can consider the CSI High-End Equipment Sub-Index ETF linked fund (018028) for potential industry rotation opportunities [6].

Group 1 - The core viewpoint of the articles is that the current market exhibits characteristics of a "water buffalo" market, defined as a divergence between fundamentals and liquidity [1] - The first report from CITIC Securities reviews historical instances of such divergence since 2010, noting that significant macro policies or liquidity improvements typically drive short-lived rallies lasting no more than four months [1] - The second report from GF Securities analyzes historical liquidity-driven bull markets, categorizing them into rapid rotation periods and sustained mainline periods [1][3] Group 2 - During the rapid rotation period, various styles can lead, but the sustainability is weak, with financial and cyclical sectors often initiating the rally due to their low valuations and sensitivity to policy changes [3] - In the sustained mainline period, despite no overall improvement in fundamentals, certain sectors may see enhanced expectations due to policy support or industry cycles, becoming strong market leaders [4] - The current A-share market is characterized by rapid sector rotation, with various themes emerging quickly, making it challenging for investors to capture opportunities effectively [4] Group 3 - The Shanghai Composite Index has surpassed 3600 points, yet many investors remain uncertain about stable investment choices [5] - In this environment, broad-based indices are recommended for investment as they cover a wide range of sectors, reducing the risk of missing out on market gains [5] - The introduction of the CSI A500 index provides a new option for core portfolio allocation, offering a more balanced industry distribution compared to the CSI 300 index [5][7] Group 4 - The CSI A500 index has a higher content of new productive forces, with a reduced weight in traditional sectors like non-bank financials and food & beverage, allowing for greater growth potential [7] - Historical data shows that the CSI A500 index has outperformed the CSI 300 index in various market conditions, demonstrating its adaptability [9] - Long-term holding of the CSI A500 index is expected to yield higher returns compared to short-term holding, with a reported increase of 363.05% since its inception [11]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industry performance[26][39] - **Model Construction Process**: The diffusion index is calculated for each industry, reflecting the proportion of stocks within the industry that exhibit positive momentum. The index ranges from 0 to 1, where higher values indicate stronger momentum. The model selects industries with the highest diffusion indices for allocation. For example, as of August 1, 2025, the top-ranked industries included Steel (1.0), Comprehensive Finance (1.0), and Non-Banking Finance (0.999)[27][28] - **Model Evaluation**: The model has shown mixed performance over the years. While it achieved significant excess returns in 2021 (up to 25% before September), it experienced notable drawdowns in 2023 (-4.58%) and 2024 (-5.82%) due to its inability to adjust to market reversals[26] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency volume and price data, aiming to identify industry rotation opportunities[40] - **Model Construction Process**: The GRU network is trained on historical minute-level data to predict industry factor rankings. The model then allocates to industries with the highest predicted rankings. As of August 1, 2025, the top-ranked industries included Non-Banking Finance (-1.15), Steel (0.7), and Base Metals (0.5)[34][38] - **Model Evaluation**: The model has demonstrated strong adaptability in short-term scenarios but struggles in long-term or extreme market conditions. Its performance in 2025 has been hindered by concentrated market themes, resulting in difficulty capturing inter-industry excess returns[33][40] --- Backtesting Results of Models 1. Diffusion Index Model - **Weekly Average Return**: -1.67%[30] - **Excess Return (August)**: -0.44%[30] - **Excess Return (2025 YTD)**: -0.40%[25][30] 2. GRU Factor Model - **Weekly Average Return**: 0.00%[38] - **Excess Return (August)**: 0.16%[38] - **Excess Return (2025 YTD)**: -2.35%[33][38] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the breadth of positive momentum within an industry[27] - **Factor Construction Process**: The diffusion index is calculated as the proportion of stocks in an industry with positive momentum. For example, as of August 1, 2025, the diffusion index for Steel was 1.0, while for Coal it was 0.23[27][28] - **Factor Evaluation**: The factor effectively identifies industries with strong upward trends but may underperform during market reversals[26] 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: Utilizes GRU deep learning to rank industries based on high-frequency trading data[40] - **Factor Construction Process**: The GRU network processes minute-level volume and price data to generate factor rankings. For instance, as of August 1, 2025, the GRU factor for Non-Banking Finance was -1.15, while for Steel it was 0.7[34][38] - **Factor Evaluation**: The factor is effective in capturing short-term trends but struggles in long-term or highly volatile markets[33][40] --- Backtesting Results of Factors 1. Diffusion Index Factor - **Top Industries (August 1, 2025)**: Steel (1.0), Comprehensive Finance (1.0), Non-Banking Finance (0.999)[27][28] - **Weekly Average Return**: -1.67%[30] - **Excess Return (August)**: -0.44%[30] - **Excess Return (2025 YTD)**: -0.40%[25][30] 2. GRU Industry Factor - **Top Industries (August 1, 2025)**: Non-Banking Finance (-1.15), Steel (0.7), Base Metals (0.5)[34][38] - **Weekly Average Return**: 0.00%[38] - **Excess Return (August)**: 0.16%[38] - **Excess Return (2025 YTD)**: -2.35%[33][38]

Global Core Risks - Geopolitical conflicts, such as the Russia-Ukraine war and tensions in the Middle East, are significant risks affecting global markets, leading to increased oil and gold prices and higher supply chain costs [2][4] - The Federal Reserve's policies and the dollar cycle have profound impacts on global financial markets; prolonged high interest rates could suppress valuations of A-share growth stocks, while fluctuations in the dollar affect foreign capital inflows [2][4] - The strength of China's economic recovery is crucial for the A-share market, with weak real estate and consumption potentially pressuring cyclical stocks, while emerging industries like renewable energy and AI may present structural opportunities [4] Safe Asset Allocation - Gold is highlighted as a key safe-haven asset, with investors encouraged to consider gold ETFs for liquidity and gold stocks for potential upside, particularly during geopolitical crises or currency devaluation [5] - High-dividend assets serve as a defensive tool against market volatility, with banks and utilities providing stable cash flows and low valuations, making them suitable for conservative investors [6] - Government bonds and interest rate bonds are considered low-risk havens, with options for short-term liquidity management through reverse repos and long-term holdings via bond ETFs [7] - Essential consumer goods and pharmaceuticals are identified as defensive sectors with strong demand characteristics, benefiting from brand loyalty and demographic trends [8] A-share Adaptation Strategies - A core-satellite strategy is recommended for portfolio construction, with a core allocation of 60% in high-dividend assets, gold ETFs, and government bonds for stability, while 40% can be flexibly allocated based on market conditions [9] - Investors should focus on policy-driven industry rotations, with potential benefits for sectors like machinery and consumer goods from government incentives, while avoiding high-debt real estate and export-dependent sectors [10] - Dynamic rebalancing of the investment portfolio is advised, adjusting allocations based on market movements, such as increasing high-dividend assets during market downturns [11] Summary and Practical Recommendations - In the context of global risks, geopolitical conflicts and Federal Reserve policies are critical external variables that require ongoing monitoring [12] - A suggested asset allocation includes 20% in gold, 30% in high-dividend assets, and 10% in government bonds to create a safety net against market risks [12] - Conservative investors are encouraged to focus on sectors like electricity, coal, and utilities, while aggressive investors may consider technology and resource sectors during market corrections [12] - Flexibility in response to market changes is essential, with adjustments based on Federal Reserve actions and inflation trends to optimize asset allocation [12][13]

Core Viewpoint - Public fund institutions are optimistic about the market outlook and advocate for balanced allocation to respond to industry rotation [1] Group 1 - Public fund institutions believe that the market will continue to show positive trends in the near future [1] - The strategy of balanced allocation is recommended to mitigate risks associated with industry rotation [1] - There is an emphasis on the importance of adapting investment strategies in response to changing market conditions [1]

Quantitative Models and Construction Methods Model: Duration Timing Model - **Construction Idea**: Predict the yield curve and map the expected returns of bonds with different durations[20] - **Construction Process**: - Use the improved Diebold2006 model to predict the instantaneous yield curve - Predict level, slope, and curvature factors - Level factor prediction based on macro variables and policy rate following - Slope and curvature factors prediction based on AR(1) model[20] - **Evaluation**: The model effectively predicts the yield curve and provides actionable insights for bond duration management[20] - **Test Results**: - July return: 6.6bp - Benchmark return: -25.8bp - Strategy excess return: 32.4bp[21] Model: Gold Timing Model - **Construction Idea**: Relate the forward real returns of gold and US TIPS to construct the expected return model for gold[32] - **Construction Process**: - Use the formula: $E[Real\_Return^{gold}]=k\times E[Real\_Return^{Tips}]$ - Estimate parameter k using OLS with an extended window - Use the Fed's long-term inflation target of 2% as a proxy[32] - **Evaluation**: The model provides a robust framework for predicting gold returns based on TIPS yields[32] - **Test Results**: - Expected return for the next year: 22.4% - Past year absolute return: 39.77%[33][35] Model: Active Risk Budget Model - **Construction Idea**: Combine the risk parity model with active signals to construct an active risk budget model for optimal stock and bond allocation[37] - **Construction Process**: - Use the Fed model to define equity risk premium (ERP): $ERP={\frac{1}{PE_{ttm}}}-YTM_{TB}^{10Y}$ - Adjust asset weights dynamically based on ERP, stock valuation percentiles, and market liquidity (M2-M1 spread) - Convert equity asset signal scores into risk budget weights using the softmax function: $softmax(x)={\frac{\exp(\lambda x)}{\exp(\lambda x)+\exp(-\lambda x)}}$[39][47] - **Evaluation**: The model dynamically adjusts asset weights based on multiple dimensions, providing a balanced risk-return profile[37] - **Test Results**: - July stock position: 18.72% - Bond position: 81.28% - July portfolio return: 0.84% - August stock position: 7.44% - Bond position: 92.56%[51] Model Backtest Results 1. **Duration Timing Model** - July return: 6.6bp - Benchmark return: -25.8bp - Strategy excess return: 32.4bp[21] 2. **Gold Timing Model** - Expected return for the next year: 22.4% - Past year absolute return: 39.77%[33][35] 3. **Active Risk Budget Model** - July stock position: 18.72% - Bond position: 81.28% - July portfolio return: 0.84% - August stock position: 7.44% - Bond position: 92.56%[51] Quantitative Factors and Construction Methods Factor: High-Frequency Macroeconomic Factors - **Construction Idea**: Use asset portfolio simulation to construct a high-frequency macro factor system to observe market macro expectations[12] - **Construction Process**: - Combine real macro indicators to form low-frequency macro factors - Select assets leading low-frequency macro factors - Use rolling multiple leading regression to determine asset weights and simulate macro factor trends[12] - **Evaluation**: High-frequency macro factors provide leading indicators for market expectations, offering valuable insights for asset allocation[12] Factor: Convertible Bond Valuation Factors - **Construction Idea**: Compare the relative valuation of convertible bonds and stocks, and between convertible bonds and credit bonds[25] - **Construction Process**: - Construct the "100-yuan conversion premium rate" to compare the valuation of convertible bonds and stocks - Use the "modified YTM - credit bond YTM" median to compare the valuation of debt-biased convertible bonds and credit bonds - Construct style rotation portfolios based on market sentiment indicators like 20-day momentum and volatility deviation[25][27] - **Evaluation**: The factors effectively capture the relative valuation and style characteristics of convertible bonds, aiding in portfolio construction[25][27] - **Test Results**: - "100-yuan conversion premium rate": 33.71% - "Modified YTM - credit bond YTM" median: -2.06% - Style rotation annualized return: 24.54% - Maximum drawdown: 15.89% - IR: 1.47 - Monthly win rate: 65.17% - 2025 return: 35.17%[26][29] Factor Backtest Results 1. **High-Frequency Macroeconomic Factors** - High-frequency economic growth: Upward trend - High-frequency consumer inflation: Downward trend - High-frequency producer inflation: Upward trend[17] 2. **Convertible Bond Valuation Factors** - "100-yuan conversion premium rate": 33.71% - "Modified YTM - credit bond YTM" median: -2.06% - Style rotation annualized return: 24.54% - Maximum drawdown: 15.89% - IR: 1.47 - Monthly win rate: 65.17% - 2025 return: 35.17%[26][29]

Group 1 - The core viewpoint is that the Hong Kong stock market is experiencing significant sector rotation, with the consumer goods sector currently undervalued and having potential for rebound [1] - Since the beginning of the year, the entertainment, accessories, and cosmetics sectors within the Hong Kong Stock Connect have shown significant gains [1] - The pharmaceutical industry is expected to rebound first by 2025, followed by a potential revaluation of consumer goods driven by policy catalysts [1] Group 2 - The Hong Kong Dividend ETF (159331) tracks the Hong Kong Stock Connect High Dividend Index (930914), which selects listed companies with stable high dividend characteristics from the Hong Kong Stock Connect universe [1] - This index covers traditional high dividend sectors such as finance, industry, and energy, aiming to reflect the overall performance of quality high dividend securities available through the Hong Kong Stock Connect mechanism [1] - Investors without stock accounts can consider the Cathay CSI Hong Kong Stock Connect High Dividend Investment ETF Initiated Link A (022274) and Link C (022275) [1]