Summary of Key Points Core Viewpoint - As of July 23, the market's latest financing balance reached 1,922.27 billion yuan, reflecting an increase of 2.66 billion yuan from the previous trading day, with 20 out of 31 industries showing an increase in financing balance [1] Industry Financing Balance Changes - The machinery equipment industry saw the largest increase in financing balance, rising by 0.93% to 1,011.95 billion yuan, with an increase of 9.34 billion yuan [1] - Other industries with notable increases include: - Construction decoration: increased by 9.29 billion yuan, with a growth rate of 2.84% - Public utilities: increased by 8.26 billion yuan, with a growth rate of 1.84% - Communication: increased by 5.78 billion yuan, with a growth rate of 0.86% [1] - Conversely, 11 industries experienced a decrease in financing balance, with the electronics industry seeing the largest drop of 12.94 billion yuan, a decrease of 0.59% [2] - The oil and petrochemical industry decreased by 4.74 billion yuan, a decline of 1.89%, while the agriculture, forestry, animal husbandry, and fishery sector decreased by 4.06 billion yuan, a decline of 1.58% [1][2] Financing Balance by Industry - The following industries had significant financing balance changes: - Steel: 149.96 billion yuan, increased by 5.16 billion yuan, growth rate of 3.57% - Construction decoration: 337.07 billion yuan, increased by 9.29 billion yuan, growth rate of 2.84% - Public utilities: 458.25 billion yuan, increased by 8.26 billion yuan, growth rate of 1.84% - Electronics: 2,190.50 billion yuan, decreased by 12.94 billion yuan, decline rate of 0.59% [1][2]

Core Insights - The electronic industry saw the highest net inflow of funds today, amounting to 7.437 billion yuan, with a price increase of 2.27% and a trading volume increase of 27.09% compared to the previous trading day [1][2] - The public utilities sector experienced the largest net outflow of funds, totaling -2.297 billion yuan, with a price decrease of -0.37% and a trading volume decrease of -1.77% compared to the previous trading day [1][2] Industry Summary - **Electronic**: - Trading volume: 8.246 billion shares - Trading volume change: +27.09% - Turnover rate: 2.99% - Price change: +2.27% - Net inflow: 7.437 billion yuan [1] - **Electric Equipment**: - Trading volume: 8.720 billion shares - Trading volume change: +26.87% - Turnover rate: 3.53% - Price change: +2.30% - Net inflow: 3.678 billion yuan [1] - **Computer**: - Trading volume: 7.146 billion shares - Trading volume change: +12.84% - Turnover rate: 4.02% - Price change: +1.73% - Net inflow: 3.540 billion yuan [1] - **Public Utilities**: - Trading volume: 6.850 billion shares - Trading volume change: -1.77% - Turnover rate: 1.73% - Price change: -0.37% - Net outflow: -2.297 billion yuan [1][2] - **Healthcare**: - Trading volume: 6.303 billion shares - Trading volume change: +6.24% - Turnover rate: 2.31% - Price change: +0.31% - Net outflow: -2.285 billion yuan [2]

Core Investment Strategies and Asset Allocation - The report recommends an overweight position in global equities, particularly in Asian markets (excluding Japan), due to expected earnings growth, policy support, and attractive valuations [2][19] - Non-USD bonds are to be increased, with emerging market local currency bonds being upgraded to overweight due to the anticipated weakening of the USD and significant room for central bank rate cuts [2][19] - Gold is positioned as a core asset, benefiting from de-dollarization, central bank purchases, and inflation hedging, with a 3-month target price of $3,400 [2][19] Macroeconomic Outlook and Risks - The core scenario anticipates a soft landing for the US economy, supported by trade truce, fiscal stimulus, and a projected 75 basis points rate cut by the Federal Reserve in the second half of the year [3][17] - Key risks include the potential end of the tariff suspension in July, Middle Eastern conflicts possibly driving oil prices above $100, and the implications of the proposed Section 899 tax on multinational investments [3][27] Asset Class Views - The USD is expected to weaken over the next 6-12 months, benefiting the Euro, Yen, and Pound, with specific targets set for currency pairs [4][20] - Gold is projected to have upward potential, with a 12-month target of $3,500, while oil prices are expected to stabilize around $65 per barrel, although geopolitical tensions could cause short-term spikes [4][27] - The stock-bond model has shifted to neutral, indicating a mixed outlook for equities, with emerging market local currency bonds requiring caution due to potential short-term reversals [4][24] Key Events and Outlook - Important upcoming events include tariff negotiations in July, central bank meetings in Europe and the US, and the IMF annual meeting in October [5][17] - The report emphasizes the importance of long-term investment principles, diversification, and balancing liquidity, growth, and protection needs in the context of the dollar's transition [5][19]

- Model Name: Diffusion Index Model; Model Construction Idea: The model is based on the principle of price momentum; Model Construction Process: The model tracks the weekly changes in the diffusion index of various industries, ranking them based on their diffusion index values. The formula used is $ \text{Diffusion Index} = \frac{\text{Number of Stocks with Positive Momentum}}{\text{Total Number of Stocks}} $; Model Evaluation: The model captures industry trends effectively but may face challenges during market reversals[5][27][28] - Model Name: GRU Factor Model; Model Construction Idea: The model utilizes GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level price and volume data; Model Construction Process: The model ranks industries based on their GRU factor values, which are derived from the GRU network's analysis of trading information. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Model Evaluation: The model performs well in short cycles but may struggle in long cycles or extreme market conditions[6][13][33] - Diffusion Index Model, IR value 2.05%, weekly average return 0.24%, monthly excess return -1.00%, annual excess return 2.05%[25][30] - GRU Factor Model, IR value -4.52%, weekly average return 1.32%, monthly excess return 0.77%, annual excess return -4.52%[32][37] - Factor Name: GRU Industry Factor; Factor Construction Idea: The factor is derived from GRU deep learning networks analyzing minute-level trading data; Factor Construction Process: The factor values are calculated based on the GRU network's output, ranking industries accordingly. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Factor Evaluation: The factor captures short-term trading information effectively but may face challenges in long-term or extreme market conditions[6][13][33] - GRU Industry Factor, IR value -4.52%, weekly average return 1.32%, monthly excess return 0.77%, annual excess return -4.52%[32][37]

Market Overview - The A-share market saw most indices decline today, with the Shanghai Composite Index down 0.09% closing at 3454.78 points, the Shenzhen Component down 0.61% at 10412.63 points, the Sci-Tech 50 down 1.22% at 982.64 points, and the ChiNext Index down 1.13% at 2123.72 points [1] - Overall, there were 3282 stocks that fell, while 1943 stocks rose, and 192 stocks remained flat. The total trading volume in the Shanghai and Shenzhen markets was 13770 billion yuan, a decrease of 890.48 billion yuan compared to the previous trading day [1] Industry Performance - Among the Shenwan first-level industries, sectors such as steel, coal, building materials, agriculture, banking, and non-ferrous metals led the gains, while electronics, communications, defense, computing, beauty care, and biomedicine sectors experienced declines [1] Sector Highlights - The top-performing concept sectors today included aquaculture, photovoltaic rooftops, deep-sea technology, BC batteries, silicon energy, solar thermal power, HJT batteries, selected cement manufacturing, photovoltaic, central enterprise coal, selected coal mining, perovskite batteries, lithium mining, new energy, and poultry industry [1]

- Model Name: Short-term Quantitative Timing Model; Model Construction Idea: The model is based on market sentiment indicators, valuation, macro liquidity, and macro fundamentals to generate timing signals; Model Construction Process: The model uses various indicators such as manufacturing PMI, long-term loan balance growth rate, M1 growth rate, PE and PB valuation percentiles, Beta dispersion, volume sentiment score, volatility, monetary rate, exchange rate expectation, and net financing amount to generate signals. For example, the formula for the volume sentiment score is: $$ \text{Volume Sentiment Score} = \frac{\text{Current Volume} - \text{Mean Volume}}{\text{Standard Deviation of Volume}} $$ where the current volume is the trading volume of the current period, the mean volume is the average trading volume over a specified period, and the standard deviation of volume is the standard deviation of trading volumes over the same period. The model evaluates these indicators to determine the overall market sentiment and generates a timing signal accordingly[9][14][15]; Model Evaluation: The model is highly sensitive to market sentiment indicators, which can lead to frequent signal changes[9] - Model Name: Growth-Value Style Rotation Model; Model Construction Idea: The model uses economic cycle analysis to determine the allocation between growth and value styles; Model Construction Process: The model evaluates the slope of the profit cycle, the level of the interest rate cycle, and the changes in the credit cycle. For example, the formula for the profit cycle slope is: $$ \text{Profit Cycle Slope} = \frac{\text{Current Profit} - \text{Previous Profit}}{\text{Previous Profit}} $$ where the current profit is the profit of the current period, and the previous profit is the profit of the previous period. The model also considers PE and PB valuation differences and turnover and volatility differences between growth and value styles to generate allocation signals[25][26]; Model Evaluation: The model provides significant improvement over the benchmark in terms of annualized returns and Sharpe ratio[25][26] - Model Name: Small-Cap vs. Large-Cap Style Rotation Model; Model Construction Idea: The model uses economic cycle analysis to determine the allocation between small-cap and large-cap styles; Model Construction Process: The model evaluates the slope of the profit cycle, the level of the interest rate cycle, and the changes in the credit cycle. For example, the formula for the interest rate cycle level is: $$ \text{Interest Rate Cycle Level} = \frac{\text{Current Interest Rate} - \text{Mean Interest Rate}}{\text{Standard Deviation of Interest Rate}} $$ where the current interest rate is the interest rate of the current period, the mean interest rate is the average interest rate over a specified period, and the standard deviation of interest rate is the standard deviation of interest rates over the same period. The model also considers PE and PB valuation differences and turnover and volatility differences between small-cap and large-cap styles to generate allocation signals[30][31][32]; Model Evaluation: The model provides significant improvement over the benchmark in terms of annualized returns and Sharpe ratio[30][31][32] - Model Name: Four-Style Rotation Model; Model Construction Idea: The model combines the conclusions of the growth-value and small-cap vs. large-cap rotation models to determine the allocation among four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value; Model Construction Process: The model uses the signals generated by the growth-value and small-cap vs. large-cap rotation models to allocate the portfolio among the four styles. For example, if the growth-value model suggests overweighting value and the small-cap vs. large-cap model suggests overweighting large-cap, the allocation would be adjusted accordingly[33][34]; Model Evaluation: The model provides significant improvement over the benchmark in terms of annualized returns and Sharpe ratio[33][34] Model Backtest Results - Short-term Quantitative Timing Model: Annualized Return 16.24%, Annualized Volatility 14.70%, Maximum Drawdown 27.70%, Sharpe Ratio 0.9613, IR 0.5862, Monthly Win Rate 68.21%, Quarterly Win Rate 68.63%, Annual Win Rate 85.71%[16][19][22] - Growth-Value Style Rotation Model: Annualized Return 11.51%, Annualized Volatility 20.85%, Maximum Drawdown 43.07%, Sharpe Ratio 0.5316, IR 0.2672, Monthly Win Rate 58.00%, Quarterly Win Rate 60.00%, Annual Win Rate 85.71%[27][29] - Small-Cap vs. Large-Cap Style Rotation Model: Annualized Return 11.92%, Annualized Volatility 22.75%, Maximum Drawdown 50.65%, Sharpe Ratio 0.5283, IR 0.2386, Monthly Win Rate 60.67%, Quarterly Win Rate 56.00%, Annual Win Rate 85.71%[32] - Four-Style Rotation Model: Annualized Return 13.03%, Annualized Volatility 21.60%, Maximum Drawdown 47.91%, Sharpe Ratio 0.5834, IR 0.2719, Monthly Win Rate 59.33%, Quarterly Win Rate 62.00%, Annual Win Rate 85.71%[34][35]

Core Insights - The electronic industry experienced the highest net inflow of main funds, amounting to 4.346 billion yuan, with a price change of 1.50% and a turnover rate of 2.52% [1][2] - The pharmaceutical and biological industry faced the largest net outflow of main funds, totaling -3.729 billion yuan, with a price change of -0.85% and a turnover rate of 2.10% [1][2] Industry Summary - **Electronic**: - Trading volume: 6.941 billion shares - Change in trading volume: +28.91% - Net inflow: 4.346 billion yuan - **Banking**: - Trading volume: 3.791 billion shares - Change in trading volume: +22.07% - Net inflow: 1.314 billion yuan - **Communication**: - Trading volume: 2.926 billion shares - Change in trading volume: +3.33% - Net inflow: 1.088 billion yuan - **Pharmaceutical and Biological**: - Trading volume: 5.722 billion shares - Change in trading volume: -20.00% - Net outflow: -3.729 billion yuan - **Steel**: - Trading volume: 1.171 billion shares - Change in trading volume: -9.34% - Net outflow: -0.169 billion yuan [1][2]

Market Overview - The A-share market showed mixed performance today, with the Shanghai Composite Index rising by 0.01% to close at 3402.66 points, while the Shenzhen Component Index fell by 0.11% to 10234.33 points. The ChiNext Index decreased by 0.30% to 977.97 points, and the Growth Enterprise Market Index increased by 0.26% to 2067.15 points. Overall, there were more decliners than gainers, with 2864 stocks declining and 2325 stocks rising across the market. The total trading volume in the Shanghai and Shenzhen markets reached 12718 billion, an increase of 163.10 billion compared to the previous trading day [1][2]. Industry Performance - Among the primary industries, sectors such as non-ferrous metals, media, beauty care, pharmaceutical biology, telecommunications, and comprehensive services led the gains, while industries like home appliances, coal, food and beverage, agriculture, forestry, animal husbandry, fishery, real estate, and national defense and military industry experienced declines [1][2]. Sector Highlights - The top-performing concept sectors today included optical modules (CPO), gold and jewelry, contract research organizations (CRO), innovative drugs, optical communications, partnerships with Pinduoduo, short drama games, millet economy, weight loss drugs, quantum technology, rare earth permanent magnets, generic drugs, high-speed copper connections, Chinese language corpus, and virtual humans [2].

Core Viewpoint - China emphasizes open scientific cooperation to benefit humanity, establishing partnerships with over 160 countries and regions, and signing 119 intergovernmental agreements on scientific collaboration [2][5]. Group 1: International Cooperation Platforms - The SKA project, known as the "Earth's Giant Eye," is being developed with support from China and over ten other countries, aiming to create the world's largest radio telescope [3]. - China plays a significant role in the SKA project, contributing to key technology development and infrastructure construction [3]. - The FAST telescope, known as "China's Eye," has an open data sharing platform that serves users from dozens of countries, showcasing international collaboration in scientific research [3]. Group 2: Equal International Dialogue - Chinese and German academic institutions have innovated cooperation models, fostering a new generation of young academic leaders through various collaborative initiatives [6]. - The "Tengchong Scientist Forum" in Yunnan has become a platform for advanced academic exchanges and international cooperation in science and technology [7]. Group 3: Effective Innovation Networks - China has established over 70 "Belt and Road" joint laboratories in various fields, enhancing international scientific collaboration [8]. - The "China-Uruguay Joint Laboratory" focuses on soybean genetics research and aims to become an international scientific innovation platform [9]. Group 4: Nurturing a Cooperative Ecosystem - The "Xishuangbanna Biodiversity Platform" was launched to provide a new window for exploring tropical biodiversity, showcasing collaborative efforts in ecological research [11]. - The Chinese government is committed to creating a conducive environment for scientific innovation and cooperation, focusing on practical and efficient funding mechanisms [12].

Market Overview - As of 10:28 AM, the Shanghai Composite Index increased by 0.09%, with a trading volume of 485.53 million shares and a transaction value of 592.605 billion yuan, representing a decrease of 6.99% compared to the previous trading day [1] Industry Performance - The banking sector showed the highest increase at 1.10%, with a transaction value of 162.50 billion yuan, up by 26.80% from the previous day, led by Minsheng Bank which rose by 2.83% [1] - The oil and petrochemical industry rose by 0.73%, with a transaction value of 35.64 billion yuan, down by 14.10%, with Renji Co. leading at 5.40% [1] - The comprehensive sector increased by 0.68%, with a transaction value of 20.07 billion yuan, up by 117.47%, led by Yuegui Co. which rose by 5.00% [1] - The public utilities sector increased by 0.46%, with a transaction value of 99.63 billion yuan, up by 8.07%, led by Leshan Electric Power which rose by 10.01% [1] - The real estate sector increased by 0.44%, with a transaction value of 65.97 billion yuan, up by 27.69%, led by Zhujiang Co. which rose by 9.25% [1] Declining Industries - The defense and military industry experienced the largest decline at 1.66%, with a transaction value of 224.39 billion yuan, down by 27.80%, led by Jieqiang Equipment which fell by 14.00% [2] - The computer sector declined by 1.25%, with a transaction value of 574.06 billion yuan, down by 1.37%, led by the delisted Longyu which fell by 39.06% [2] - The telecommunications sector decreased by 0.91%, with a transaction value of 287.09 billion yuan, down by 23.11%, led by the delisted Pengbo which fell by 59.68% [2]