Economic Focus - The central government is emphasizing infrastructure development, "trade-in" policies for consumer goods, and foreign trade, particularly in economically strong provinces like Jiangsu[1] - Recent surveys in Jiangsu highlighted the need for high-standard project execution and the importance of Jiangsu's role in national economic contributions[1] Industrial Development - The government is focusing on technological advancements and new production capabilities, with significant attention on sectors like engineering machinery, AI, and clean energy[2] - Regulatory measures are being reinforced in the platform economy to combat issues like false advertising and unfair competition practices[2] Financial Initiatives - The establishment of an offshore financial service reform pilot in Shanghai is aimed at enhancing international trade finance capabilities[4] - A digital RMB international operation center is being set up to facilitate cross-border payments and enhance the global use of the RMB[4] International Cooperation - The recent China-Central Asia summit resulted in the signing of multiple cooperation agreements, marking a significant milestone in regional diplomatic relations[9] - The summit emphasized the importance of mutual trust and support among participating nations, with a focus on high-quality development along the Belt and Road Initiative[10] Financial Market Reforms - The China Securities Regulatory Commission (CSRC) is set to introduce a growth tier on the Sci-Tech Innovation Board to better support innovative companies[21] - Plans to expand the Qualified Foreign Institutional Investor (QFII) program to include 100 tradable futures and options products are underway, enhancing foreign investment opportunities[5]

Quantitative Models and Construction 1. Model Name: Heston Model - **Model Construction Idea**: The Heston model is used to calculate the implied volatility of near-month at-the-money options, serving as a market fear index. Implied volatility reflects market participants' expectations of future volatility [8]. - **Model Construction Process**: The Heston model is a stochastic volatility model where the variance of the asset price follows a mean-reverting square-root process. The model is defined by the following equations: $ dS_t = \mu S_t dt + \sqrt{v_t} S_t dW_t^1 $ $ dv_t = \kappa (\theta - v_t) dt + \sigma \sqrt{v_t} dW_t^2 $ Here: - \( S_t \): Asset price - \( v_t \): Variance - \( \mu \): Drift rate - \( \kappa \): Mean reversion speed - \( \theta \): Long-term variance - \( \sigma \): Volatility of variance - \( W_t^1, W_t^2 \): Correlated Wiener processes [8] 2. Model Name: Multi-Sector Timing Model (Scissor Difference Strategy) - **Model Construction Idea**: This model uses the difference in the number of bullish and bearish signals among sector constituents to construct a timing strategy. If no bullish or bearish signals are present, the scissor difference is set to zero. The model aims to outperform sector indices [16]. - **Model Construction Process**: - Count the number of bullish and bearish signals for each sector's constituent stocks daily. - Compute the scissor difference as the difference between bullish and bearish signals. - If both counts are zero, the scissor difference is set to zero. - Construct a timing strategy based on the scissor difference ratio [16]. - **Model Evaluation**: The model historically outperformed all sector indices, demonstrating excellent backtesting performance [16]. --- Model Backtesting Results 1. Heston Model - **Implied Volatility Results**: - SSE 50: 11.85% (down 0.88% WoW) - SSE 500: 14.35% (down 1.59% WoW) - CSI 1000: 18.06% (down 0.42% WoW) - CSI 300: 12.64% (down 0.73% WoW) [10] 2. Multi-Sector Timing Model - **Sector Outperformance**: The model outperformed all sector indices, achieving a 100% success rate in backtesting [16]. --- Quantitative Factors and Construction 1. Factor Name: Shape-Based Timing Signals - **Factor Construction Idea**: Shape-based signals are derived from historical K-line patterns, including bullish patterns (e.g., "Golden Needle Bottom," "Rocket Launch," "Manjianghong") and bearish patterns (e.g., "Hanging Line," "Paradise Line," "Dark Cloud Cover"). These patterns indicate potential price reversals [24]. - **Factor Construction Process**: - Identify specific K-line patterns based on predefined criteria. - Evaluate the historical performance of these patterns in predicting price movements. - Use the patterns to generate timing signals for individual stocks [24]. - **Factor Evaluation**: Bullish patterns like "Golden Needle Bottom" and "Rocket Launch" demonstrated strong positive predictive power [24]. --- Factor Backtesting Results 1. Shape-Based Timing Signals - **Signal Statistics**: - Positive signals: 2,699 occurrences, with an average future high-point success rate of 28.25% - Negative signals: 3,525 occurrences, with an average future low-point success rate of 71.88% [13] 2. Sector Timing Signals - **Bullish Sectors**: Home Appliances, Comprehensive, Communication, Textile & Apparel, Consumer Services, Transportation, Petrochemicals [19] 3. Stock-Specific Signals - **Consecutive Bullish Signals**: - 5-day signals: Stocks like Kailong Co. and Shipu Testing [21] - 4-day signals: Stocks like Jiangnan Chemical, Beijing-Shanghai High-Speed Railway, and Nandu Property [22][23] - **Special Bullish Patterns**: - Stocks like Retired Longyu ("Arrow on the String") and Suotong Development ("Manjianghong") [25][26] 4. Broker Gold Stock Signals - **Highlighted Stocks**: BYD, Feilihua, Wancheng Group, Sichuan Road & Bridge, Wolong Electric Drive, Lansheng Co., PetroChina, Dongpeng Beverage [29][33]

Quantitative Models and Construction Methods 1. Model Name: Broad-based Index Heat Rotation Strategy - **Model Construction Idea**: The strategy is based on the weekly heat change rate (MA2) of broad-based indices. By identifying the index with the highest heat change rate, the strategy aims to capture short-term market sentiment shifts[7][13][16] - **Model Construction Process**: 1. Group all A-share stocks into five categories: CSI 300, CSI 500, CSI 1000, CSI 2000, and "Others" 2. Calculate the total heat indicator for each group by summing the heat indicators of constituent stocks 3. Compute the weekly heat change rate for each group and apply a 2-week moving average (MA2) for smoothing 4. At the end of each week, invest in the index with the highest heat change rate (MA2). If the "Others" group has the highest rate, remain in cash[8][11][13] - **Model Evaluation**: The strategy demonstrates the ability to capture short-term sentiment-driven opportunities in the market[13] --- Backtesting Results of Models 1. Broad-based Index Heat Rotation Strategy - **Annualized Return**: 8.74% since 2017[16] - **Maximum Drawdown**: 23.5%[16] - **Cumulative Return (2025)**: 9.6%[16] --- Quantitative Factors and Construction Methods 1. Factor Name: Total Heat Indicator - **Factor Construction Idea**: The total heat indicator is used as a proxy for market sentiment, aggregating the attention metrics of individual stocks[7] - **Factor Construction Process**: 1. Define the total heat indicator for individual stocks as the sum of browsing, watchlist additions, and click counts 2. Normalize the indicator by dividing it by the total market value on the same day 3. Multiply the normalized value by 10,000 to scale the indicator within the range [0, 10,000][7] - **Factor Evaluation**: The factor effectively captures market sentiment at the stock level and can be aggregated to broader categories like indices, industries, or concepts[7] 2. Factor Name: Weekly Heat Change Rate (MA2) - **Factor Construction Idea**: This factor measures the short-term change in market sentiment by tracking the weekly variation in the total heat indicator[13] - **Factor Construction Process**: 1. Calculate the weekly change rate of the total heat indicator for each stock or group 2. Apply a 2-week moving average (MA2) to smooth out short-term fluctuations[13][20] - **Factor Evaluation**: The MA2 smoothing enhances the stability of the factor, making it suitable for identifying trends in market sentiment[13] 3. Factor Name: Concept Heat Ranking - **Factor Construction Idea**: This factor identifies the hottest and coldest concepts based on their weekly heat change rates, enabling the construction of sentiment-driven portfolios[31] - **Factor Construction Process**: 1. Rank concepts by their weekly heat change rates 2. Select the top 5 concepts with the highest heat change rates 3. Construct two portfolios: - **TOP Portfolio**: Select the top 10 stocks with the highest total heat within each concept - **BOTTOM Portfolio**: Select the bottom 10 stocks with the lowest total heat within each concept[31] - **Factor Evaluation**: The factor highlights the potential for excess returns by exploiting sentiment-driven mispricing in concept stocks[31] --- Backtesting Results of Factors 1. Total Heat Indicator - **No specific backtesting results provided** 2. Weekly Heat Change Rate (MA2) - **No specific backtesting results provided** 3. Concept Heat Ranking - **BOTTOM Portfolio Annualized Return**: 15.71%[33] - **BOTTOM Portfolio Maximum Drawdown**: 28.89%[33] - **BOTTOM Portfolio Cumulative Return (2025)**: 18%[33]

宏观研究 证 券 研 究 报 告 【每周经济观察】第 25 期 四问专项债清欠 一、此前政策如何部署？ 2024 年清欠已有进展，去年 10 月财政部在发布会上提及"2024 年以来…财政 部已经安排了1.2万亿元债务限额支持地方化解存量隐性债务和消化政府拖欠 企业账款"。从实际效果看，云南昭通市、陕西咸阳市已实现拖欠款清零。 2025 年政策明确使用新增专项债清欠，今年政府工作报告中提及"拟安排地 方政府专项债券 4.4 万亿元…重点用于投资建设、土地收储和收购存量商品 房、消化地方政府拖欠企业账款等…" 二、近期有何进展？ 随着财政部下达各地的全年债务限额，近期多地发布预算调整报告，部分省份 披露了用于清欠的专项债额度，包括：1）云南省，与预算调整前相比，新增 356 亿专项债额度用于清欠、调增用于补充政府性基金财力的额度（主要是化 债）169 亿元；调减用于项目建设的额度 270 亿元（占全年新增专项债的比重 降至 24%）。2）湖南省，第二批债务中明确用于清欠的专项债资金为 200 亿 元，占全年专项债限额约 14%。3）广西省，"今年新增债务限额共 495 亿元， 部分用于'清欠'"。 三、全年体量 ...

行业研究 证 券 研 究 报 告 推荐（维持） 以伊冲突升级，本周油价上涨 基础化工 2025 年 06 月 23 日 华创证券研究所 证券分析师：杨晖 邮箱：yanghui@hcyjs.com 执业编号：S0360522050001 证券分析师：吴宇 邮箱：wuyu1@hcyjs.com 执业编号：S0360524010002 证券分析师：陈俊新 邮箱：chenjunxin@hcyjs.com 执业编号：S0360525040001 能源周报（20250616-20250622） 行业基本数据 | | | 占比% | | --- | --- | --- | | 股票家数(只) | 490 | 0.06 | | 总市值(亿元) | 42,481.40 | 4.23 | | 流通市值(亿元) | 37,730.76 | 4.79 | 相对指数表现 | % | 1M | 6M | 12M | | --- | --- | --- | --- | | 绝对表现 | -1.1% | -1.2% | 11.1% | | 相对表现 | 0.3% | 0.9% | 1.3% | -12% 0% 11% 23% 24/06 2 ...

Investment Rating - The report maintains a "Recommendation" rating for the computer industry, expecting the industry index to rise more than 5% over the next 3-6 months compared to the benchmark index [3][38]. Core Insights - The computer sector experienced a decline in the week of June 16-20, with the CITIC computer index dropping by 1.87%, the ChiNext index down by 1.66%, and the Shanghai Composite index falling by 0.51% [9][16]. - The report highlights the rapid advancements in AI, particularly in multimodal models, with significant developments from Google and Meta, indicating a promising future for the industry [9][20][23]. - The report notes that the market's focus is shifting from technology themes to performance realization and rapid industrial transformation as companies begin to disclose their earnings [9][12]. Summary by Sections Industry Weekly Viewpoint - The report discusses the performance of the computer sector, noting the top gainers and losers during the week, with notable increases in stocks like Chutianlong (up 36.59%) and Sifang Jingchuang (up 29.21%) [9][16]. - It emphasizes the ongoing innovations in AI, particularly the introduction of multimodal products that integrate visual and audio elements, which are gaining traction in the market [9][12]. Weekly Market Review - The report provides a detailed review of the market performance for the week, highlighting the overall decline in major indices and the net outflow of funds from the computer sector [16][19]. Funding Situation Review - The report indicates a total net outflow of 270 billion from all A-shares, with the computer sector experiencing a net outflow of 21 billion [19]. Progress in Multimodal Models - The report details the advancements in AI models, particularly Google's Veo 3 and Meta's Llama 4, which have achieved significant breakthroughs in video generation and multimodal capabilities [20][23]. - Veo 3 is noted for its ability to synchronize audio and visual elements seamlessly, while Llama 4 boasts a massive parameter scale and enhanced multimodal integration [20][23]. Investment Recommendations and Beneficiary Targets - The report suggests focusing on AI enterprise services and application scenarios, listing specific companies across various sectors such as office software, finance, education, and healthcare that are expected to benefit from these advancements [12][25].

1. Report Industry Investment Rating No information provided in the content. 2. Core Viewpoints of the Report - In the second quarter, the growth rate of fixed - asset investment slowed down. Given the external uncertainties in the second half of the year, the necessity of stabilizing investment has increased. The new policy - based financial instruments proposed in the April Politburo meeting have attracted high market attention. There is a high probability that the instruments will be implemented in the short term, and the third quarter may be the period for the implementation of broad - credit policies, which will affect the bond market sentiment [1][24]. - The risk of crossing the half - year is controllable, but the institutional fund arrangement is relatively slow. The large - bank net lending scale has significantly increased, and the inter - bank cross - quarter fund progress is relatively slow [3]. - Since June, the bond market's spread exploration has shifted from the mid - term to the ultra - long - term. However, investors should flexibly stop losses and realize profits [4]. - In the bond market this week, the LuJiaZui Conference did not meet expectations, and the yield fluctuated within a narrow range. The central bank's OMO continued net withdrawals, and the capital market was balanced and loose. The net financing of national bonds and policy - based financial bonds decreased, while that of local bonds and inter - bank certificates of deposit increased. The term spread of national bonds widened, and that of China Development Bank bonds narrowed [10][54]. 3. Summary According to the Directory 3.1 New Policy - based Financial Instruments: Progress - The necessity of stabilizing investment has increased due to the slowdown of fixed - asset investment growth in the second quarter and external uncertainties in the second half of the year. Since May, local governments have accelerated the project reserve of policy - based financial instruments, with a possible quota of 500 billion yuan, and the support for science and technology and consumption infrastructure construction may be prioritized [1][14]. - Referring to the experience in 2022, it took about two months from the release of the instrument quota to the completion of the investment, with a relatively fast pace. The policy - based financial supply in 2022 had a leverage ratio of over 4.7 times for credit and over 10 times for infrastructure investment [17]. - The third quarter may be the period for the implementation of broad - credit policies, and attention should be paid to the impact on the bond market sentiment [24]. 3.2 Cross - half - year Risk is Controllable, and Institutional Fund Arrangement is Slow - This week, the tax period passed smoothly. After the tax period, the capital sentiment tightened briefly, which may be related to the increase in the bond market leverage level. The central bank's operations remained relatively active, and the reverse - repurchase balance was at a seasonal high [3]. - The large - bank net lending scale has significantly increased, with the single - day net lending scale reaching 5.3 trillion yuan, a record high for the same period. As of June 20, the inter - bank cross - quarter fund progress was 12%, lower than the previous level. The cross - half - year risk is expected to be relatively controllable [28]. 3.3 Bond Market Strategy: Spread Exploration Shifts from the Mid - term to the Ultra - long - term, but Flexibly Stop Losses and Realize Profits - From April to May, the bond market fully explored the spreads of mid - term interest - rate varieties. The spread quantile of mid - term varieties decreased from 75% - 96% to 44% at the end of May, with limited room for further compression [34]. - In June, the spread exploration shifted to the ultra - long - term. The best - performing maturities since April have experienced two rounds of "short - term → mid - term → ultra - long - term" rotation. The recent ultra - long - term market, mainly driven by trading desks such as funds and other products, is due to the strong demand for capital gains in a low - interest - rate environment and the expected opening of the bond - allocation space for insurance companies in the third quarter [4][37]. - Investors should continue to pay attention to the cost - effectiveness of the ultra - long - term bonds and stop losses and realize profits at appropriate times, such as when the ultra - long - term spreads are fully explored, if the central bank announces bond purchases at the end of June, and in July [5]. 3.4 Bond Market Review: The LuJiaZui Conference did not Meet Expectations, and the Yield Fluctuated within a Narrow Range - **Funding**: The central bank's OMO continued net withdrawals, and the capital market was balanced and loose. The weighted price of DR001 fell back to around 1.37%, and the issuance price of 1 - year national - share bank certificates of deposit dropped to 1.63% [11]. - **Primary Issuance**: The net financing of national bonds and policy - based financial bonds decreased, while that of local bonds and inter - bank certificates of deposit increased [59]. - **Benchmark Changes**: The term spread of national bonds widened, and that of China Development Bank bonds narrowed. The short - term performance of national bonds was better than that of the long - term, while the long - term performance of China Development Bank bonds was better than that of the short - term [54].

Investment Rating - The report maintains a "Recommended" investment rating for the banking sector, expecting the industry index to outperform the benchmark index by over 5% in the next 3-6 months [24]. Core Insights - The new market risk regulations enhance operational efficiency for banks and establish a solid foundation for future business development [4]. - The regulations clarify the definition of market risk, separating it from interest rate risk, and emphasize the need for improved governance structures and responsibilities among the board, supervisors, and senior management [2][3]. - The report highlights the importance of the new regulations in strengthening banks' risk management capabilities amid increasing complexity in domestic and global trade [4]. Summary by Sections Market Risk Definition and Governance - The new regulations define market risk more rigorously, focusing on risks from adverse changes in interest rates, exchange rates, stock prices, and commodity prices [2]. - The governance structure is refined, specifying the responsibilities of the board, supervisors, and senior management in market risk management [3]. Market Risk Management Requirements - The report details enhanced requirements for risk identification, measurement, monitoring, control, and reporting, aligning with current market practices [3]. - Stricter requirements for the segregation of trading and non-trading activities are introduced, ensuring clearer roles and responsibilities [3]. Investment Opportunities - The report suggests that the banking sector is poised for increased capital inflows due to medium to long-term investments and public fund reforms, recommending a diversified investment strategy focusing on state-owned banks and quality regional banks [9]. - It emphasizes the potential for absolute returns in banks with high dividend yields and strong asset quality, particularly recommending banks like China Merchants Bank and CITIC Bank [9][10].

Investment Rating - The report maintains a "Recommended" rating for the mechanical industry, particularly focusing on AI-related machinery and consumables [1]. Core Insights - The demand for machinery and consumables is expected to resonate positively due to the rapid iteration of AI models and smart hardware applications, leading to a significant increase in the demand for high-performance servers, GPUs, and advanced PCBs [6]. - The report emphasizes three key directions for robotics: marginal changes in the main chain, the evolution from products to customers and scenarios, and the importance of precision and efficiency in processing equipment [6]. - Investment suggestions highlight the potential for a new recovery cycle in the equipment industry, driven by monetary and fiscal policies, with a focus on companies in various sectors such as industrial control, robotics, machine tools, and laser industries [6]. Summary by Sections Industry and Company Investment Views - The report includes in-depth research on companies like Bozhong Precision Engineering, which is a leading player in 3C automation equipment, and is expanding into emerging fields such as new energy and semiconductor sectors [21][22]. - Another highlighted company, Sikan Technology, specializes in 3D visual digital solutions and has shown rapid revenue growth, indicating a strong market position and potential for future expansion [24][25]. Key Data Tracking - The mechanical industry comprises 633 listed companies with a total market capitalization of approximately 48,991.09 billion [3]. - The report tracks various macroeconomic indicators, including manufacturing PMI and fixed asset investment growth rates, which are crucial for understanding the industry's performance [28][32].

Investment Rating - The report maintains a "Buy" recommendation for the non-ferrous metals sector, particularly highlighting the potential reversal in cobalt prices due to the extension of the export ban from the Democratic Republic of Congo [2]. Core Insights - The report emphasizes the positive outlook for precious metals, copper, and aluminum sectors, driven by inventory reductions and market dynamics [3][9]. - The extension of the cobalt export ban in the Democratic Republic of Congo is expected to support cobalt prices, impacting supply chains and pricing strategies for Chinese companies [9]. - The report also notes the increasing dividend distribution from Yun Aluminum Co., indicating a strong commitment to shareholder returns [3]. Industry Overview - The non-ferrous metals sector includes 125 listed companies with a total market capitalization of approximately 295.59 billion yuan and a circulating market value of about 257.00 billion yuan [5]. - Recent performance metrics show a 2.9% increase over the past month, a 9.0% increase over six months, and a 12.4% increase over the past year, indicating a positive trend in the sector [6]. Aluminum Sector Insights - Domestic aluminum ingot inventory has decreased to 449,000 tons, down 1.1% from the previous week and 30.7% year-on-year, suggesting a tightening supply that supports aluminum prices [8]. - The report indicates that while aluminum ingot inventories are low, the recent increase in aluminum rod inventories suggests a potential stabilization in prices [8]. Cobalt Market Dynamics - The report highlights that the extension of the cobalt export ban is aimed at managing high inventory levels and is expected to provide price support for cobalt in the near term [9]. - Chinese cobalt refining companies are likely to face short-term supply pressures due to the ban, which may lead to increased prices as they rely on existing stocks [9]. Company-Specific Developments - Yun Aluminum Co. announced a cash dividend of 1.8 yuan per share, totaling 624 million yuan, reflecting a dividend payout ratio of 32.23% for 2024, showcasing the company's strong financial health and commitment to shareholders [3]. - Xiamen Tungsten Co. has successfully launched a new production line for high-performance neodymium-iron-boron magnetic materials, which is expected to enhance its competitive position in the market [10].