Quantitative Models and Construction Methods 1. Model Name: IPCA Factor Model - **Model Construction Idea**: The IPCA factor model is designed to explain the returns of 46 option strategies, aiming to capture 80% of their returns while minimizing abnormal monthly returns to near zero[22] - **Model Construction Process**: The model integrates factors such as transaction costs and heterogeneous risk aversion to optimize derivative pricing. It also addresses the absence of reliable credit or liquidity premiums in pre-WWI corporate bond returns[25] - **Model Evaluation**: The model demonstrates strong explanatory power for option strategy returns and highlights the role of transaction costs in driving return volatility[22][25] 2. Model Name: Neural Functionally Generated Portfolios (NFGP) - **Model Construction Idea**: NFGP combines Transformer and diffusion models to enhance probabilistic time-series forecasting accuracy and improve decision reliability[35] - **Model Construction Process**: The model reduces forecasting errors by 42% compared to benchmarks and introduces dual uncertainty indicators to optimize portfolio decisions[35] - **Model Evaluation**: The model outperforms traditional approaches in terms of predictive accuracy and robustness in decision-making[35] --- Model Backtesting Results 1. IPCA Factor Model - **Explanatory Power**: 80% of option strategy returns explained[22] - **Abnormal Monthly Returns**: Approaching zero[22] 2. Neural Functionally Generated Portfolios (NFGP) - **Forecasting Error Reduction**: 42% compared to benchmarks[35] --- Quantitative Factors and Construction Methods 1. Factor Name: "Betting Against (Bad) Beta" (BABB) - **Factor Construction Idea**: The BABB factor improves the "Betting Against Beta" (BAB) strategy by managing transaction costs and isolating bad beta components[15] - **Factor Construction Process**: The factor is constructed using double sorting to isolate bad beta components. It achieves an annualized alpha exceeding 6%, independent of traditional sentiment indicators[15] - **Factor Evaluation**: The factor demonstrates strong performance in low-risk investment strategies, with significant alpha generation[15] 2. Factor Name: High-Speed Rail Network Centrality - **Factor Construction Idea**: This factor captures the impact of high-speed rail network centrality on corporate bond spreads by improving the information environment and regional trust[25] - **Factor Construction Process**: The factor is derived from the centrality of high-speed rail networks, showing a significant reduction in corporate bond spreads, particularly for non-state-owned enterprises and non-central cities[25] - **Factor Evaluation**: The factor effectively highlights the role of infrastructure in reducing financing costs and improving capital allocation efficiency[25] 3. Factor Name: Residual-Based Structural Change Detection - **Factor Construction Idea**: This factor robustly detects structural changes in factor models, accommodating over-specified factor numbers and error correlations[17] - **Factor Construction Process**: The factor employs residual-based tests to identify smooth or abrupt structural changes in factor models, enhancing robustness in model evaluation[17] - **Factor Evaluation**: The factor is highly effective in detecting structural changes and improving the robustness of factor model evaluations[17] --- Factor Backtesting Results 1. "Betting Against (Bad) Beta" (BABB) - **Annualized Alpha**: >6%[15] 2. High-Speed Rail Network Centrality - **Corporate Bond Spread Reduction**: Significant, especially for non-state-owned enterprises and non-central cities[25] 3. Residual-Based Structural Change Detection - **Robustness**: Effective in detecting both smooth and abrupt structural changes[17]

Group 1: Market Overview - The domestic equity market continues to rise, with various fund indices achieving positive returns, particularly in the healthcare sector, which saw the highest net value increase among thematic funds [3] - Stock ETFs experienced a net outflow of 20.817 billion yuan, while Hong Kong stock ETFs saw a significant inflow of 7.821 billion yuan [3] Group 2: Financial Data Insights - In June, the expected increase in RMB loans is projected to be between 2.3 to 2.5 trillion yuan, with a year-on-year increase of 200 to 400 billion yuan [4] - Social financing is expected to remain stable, supported by steady credit and government bond issuance, leading to an anticipated increase in social financing growth rate [4] Group 3: Industry Developments - The Central Economic Committee emphasized the need to strengthen market mechanisms to eliminate inefficient production capacity and prevent "involution" in competition, which may optimize the photovoltaic materials industry [5] - The National Medical Products Administration announced measures to support the innovation and commercialization of high-end medical devices, which is expected to benefit leading companies with strong R&D capabilities and international strategies [6] Group 4: Company Performance - China Hongqiao is expected to see a 35% increase in net profit for the first half of 2025, reaching approximately 12.36 billion yuan, supported by lower costs and stable aluminum prices [6] - Yanjing Beer anticipates a net profit of 1.062 to 1.137 billion yuan for the first half of 2025, reflecting a year-on-year growth of 40% to 50%, driven by cost reduction and efficiency improvements [6]

证券研究报告 | 金融工程 2025 年 7 月 6 日 利率择时信号维持中性偏空 ——利率市场趋势定量跟踪 20250704 利率市场结构变化 - 10 年期国债到期收益率录得 1.64%，相对上周下降 0.29BP。当前 利率水平、期限和凸性结构读数分别为 1.51%、0.31%、0.01%， 从均值回归视角看，目前处于水平结构点位较低、期限结构点位 偏低、凸性结构点位偏低的状态。 利率价量周期择时信号：中性偏空 - 利率数据的多周期择时信号为：长周期向上突破、中周期向下突破、 短周期向上突破。综合来看，当前合计下行突破 1 票、上行突破 2 票，由于 3 种周期下的同向突破票数总和未达到 2/3，最终信号的 综合评分结果为中性。 公募债基行为跟踪：久期提升、分歧微降 利率价量多周期择时策略表现 - 自 2023 年底以来，策略的短期年化收益率为 7.24%，最大回撤为 1.55%，收益回撤比为 6.21，相对业绩基准的超额收益率为 2.14%。 过去的 18 年内，策略逐年绝对收益大于 0 的胜率为 100%，逐年超 额收益大于 0 的胜率为 100%。 风险提示：本报告基于对历史数据的分析，当市场环境 ...

Quantitative Models and Construction Methods Model Name: "Clock + Turning Point Improvement Method" Large Asset Rotation Model - **Model Construction Idea**: The model combines the investment clock theory with turning point improvement methods to optimize asset rotation strategies[5][23] - **Model Construction Process**: 1. Assume that the macroeconomic factors will continue their current state into the next month[23] 2. Calculate the total score of each asset based on the current state of macroeconomic risk factors[24] 3. Introduce a risk budget model with initial risk ratios for each asset: large-cap stocks: small-cap stocks: bonds: commodities: gold = 1:1:1:0.5:0.5. Adjust the risk ratios based on the total score, doubling the risk ratio for each positive score and halving it for each negative score[24] 4. Backtesting period: January 2011 - December 2023[25] - **Model Evaluation**: The model performs excellently in terms of returns, risk control, and drawdown management, achieving nearly 10% annualized returns while controlling high-risk asset positions[27] Quantitative Factors and Construction Methods Factor Name: Macroeconomic Risk Factors - **Factor Construction Idea**: Utilize macroeconomic data and asset portfolios to construct six macroeconomic risk factors: economic growth, inflation, interest rates, exchange rates, credit, and term spreads[8] - **Factor Construction Process**: - **Economic Growth**: Use industrial added value year-on-year (M0000545), PMI (M0017126), and social retail sales year-on-year (M0001428). Apply HP filtering and volatility inverse weighting[8] - **Inflation**: Use PPI year-on-year (M0001227) and CPI year-on-year (M0000612). Apply HP filtering and volatility inverse weighting[8] - **Interest Rates**: Construct an equal-weighted investment portfolio using the ChinaBond Treasury Wealth Index (1-3 years) (CBA00621.CS) and the CSI Money Market Fund Index (H11025.CSI), and calculate net value year-on-year returns[8] - **Exchange Rates**: Construct an equal-weighted long-short investment portfolio using Shanghai Gold (AU9999.SGE) and London Gold Spot (SPTAUUSDOZ.IDC), and calculate net value year-on-year returns[8] - **Credit**: Construct a duration-neutral investment portfolio using the ChinaBond Corporate Bond AAA Index (CBA04231.CS) and the ChinaBond Treasury Wealth Index (CBA00631.CS), and calculate net value year-on-year returns[8] - **Term Spreads**: Construct a duration-neutral investment portfolio using the ChinaBond Medium-Short Term Bond Wealth Index (CBA00701.CS) and the ChinaBond Long Term Bond Wealth Index (CBA00801.CS), and calculate net value year-on-year returns[8] - **Factor Evaluation**: The factors provide a comprehensive risk perspective by capturing multiple aspects of the macroeconomic environment[8] Model Backtesting Results "Clock + Turning Point Improvement Method" Large Asset Rotation Model - **Total Return**: 242.45%[27] - **Annualized Return**: 9.93%[27] - **Annualized Volatility**: 6.83%[27] - **Annualized Sharpe Ratio**: 1.45[27] - **Maximum Drawdown**: 6.31%[27] - **Win Rate**: 73.08%[27] Factor Backtesting Results Macroeconomic Risk Factors - **Economic Growth**: Upward[36] - **Inflation**: Downward[36] - **Interest Rates**: Downward[36] - **Credit**: Downward[36] - **Exchange Rates**: Downward[36] - **Term Spreads**: Downward[36]

Core Viewpoint - The article emphasizes the importance of understanding accounts receivable in the property management industry, particularly in the context of cash flow management and dividend sustainability. It highlights the significant changes in accounts receivable due to recent industry downturns and the need for a balanced development model focusing on scale, quality, and profit [3][4]. Accounts Receivable Analysis - The total accounts receivable for 30 tracked listed property companies increased from 29.18 billion to 75.37 billion from 2020 to 2024, with growth rates of +42.6%, +65.6%, +41.4%, +8.7%, and +1.5% respectively. Notably, from 2023 onwards, the growth rate of accounts receivable is lower than that of operating income, indicating a significant slowdown [3]. - The proportion of accounts receivable from related parties has decreased from 47% to 39% over the past five years, while third-party receivables have increased from 53% to 61%. This trend suggests a gradual reduction in related party risks as the industry stabilizes [4]. - The aging of accounts receivable has worsened, with the proportion of receivables due within one year dropping from 89% in 2019 to 58% in 2024. Consequently, the provision for bad debts has risen sharply from 4% to 26% during the same period, reflecting increased collection difficulties [4]. Investment Recommendations - Companies that demonstrate independent business competitiveness and can effectively reduce related party transactions are deemed favorable. Additionally, firms with strong parent company backgrounds and high rankings in property sales are likely to provide performance support while mitigating related party risks [5]. - Property management companies with natural advantages in merchant payment collection, low long-term arrears, controlled accounts receivable growth, adequate provisions, healthy aging structures, and high collection rates are recommended for investment [5].

Group 1: Market Overview and Strategy - The report emphasizes the importance of actively participating in market breakthroughs and focusing on performance pricing cues, particularly in sectors like consumer goods, precious metals, and engineering machinery [2] - In June, despite increased geopolitical tensions, risk appetite improved overall, leading to a recovery in equity assets, with major indices in A-shares experiencing upward breakthroughs [2] - The report suggests increasing trading positions to capitalize on strong breakout directions while prioritizing performance certainty in investment allocations [2] Group 2: Financial Engineering and Investment Value - The report discusses the use of DeepSeek to assist fund managers in reducing tracking errors relative to benchmarks, highlighting its capabilities in strategy implementation and code generation [3][4] - The analysis of the 华夏中证生物科技主题 ETF indicates that the demand for innovative drugs is driven by structural upgrades and policy support, with a significant increase in the number of innovative drugs included in the national medical insurance directory [6][7] - The 中证生物科技主题指数 reflects the overall performance of biotechnology stocks, with a focus on sectors like biopharmaceuticals and medical devices, indicating a high concentration of leading companies [8] Group 3: Company-Specific Insights - The report on 容知日新 (688768.SH) highlights its strong gross margin above 60% and net margin near 20%, driven by predictive maintenance solutions that align with industry trends [12] - Revenue projections for 容知日新 are set at 7.91 billion, 10.23 billion, and 12.88 billion CNY for 2025-2027, with corresponding net profits of 1.44 billion, 1.94 billion, and 2.56 billion CNY, indicating robust growth potential [12] - The report on the domestic optical module market suggests that companies like 华工科技 and 中际旭创 are well-positioned to benefit from high demand and supply shortages in the optical communication sector [16] Group 4: Emerging Trends and Innovations - The report notes that the pain relief market is evolving with a shift towards non-opioid medications, driven by innovations in pain management mechanisms [16] - The entry of Robinhood into the tokenized U.S. stock market is expected to accelerate the development of this sector, potentially leading to significant regulatory advancements [14][15] - The analysis of the biotechnology sector indicates a strong long-term growth outlook due to aging demographics and increasing healthcare spending, with innovative drugs gaining traction in the market [7][8]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is designed to monitor the critical points of trend changes in the diffusion index, which reflects the breadth of stock price movements within the micro-cap index constituents[5][40]. - **Model Construction Process**: The diffusion index is calculated based on the relative price changes of constituent stocks over a specific time window. For example, if all stocks in the micro-cap index drop by 5% after 5 days, the diffusion index value is 0.13. The formula is as follows: $ DI = \frac{\text{Number of stocks meeting criteria}}{\text{Total number of stocks}} $ The model uses three methods to generate signals: - **Initial Threshold Method (Left-Side Trading)**: Triggered a sell signal on May 8, 2025, when the index reached 0.9850[43]. - **Delayed Threshold Method (Right-Side Trading)**: Triggered a sell signal on May 15, 2025, at 0.8975[47]. - **Dual Moving Average Method (Adaptive Trading)**: Triggered a sell signal on June 11, 2025[48]. - **Model Evaluation**: The model effectively identifies overbought conditions and provides timely sell signals, but its predictive power may be limited by the dynamic updates of index constituents[40][5]. --- Quantitative Factors and Construction Methods 1. Factor Name: Illiquidity Factor - **Factor Construction Idea**: Measures the illiquidity of stocks, which is inversely related to trading volume and price impact[4][35]. - **Factor Construction Process**: The factor is calculated as the ratio of absolute daily returns to trading volume over a specific period. $ Illiquidity = \frac{|R_t|}{Volume_t} $ - $R_t$: Daily return - $Volume_t$: Daily trading volume - **Factor Evaluation**: This factor showed the highest rank IC this week (0.174), significantly outperforming its historical average (0.038), indicating strong predictive power in the current market environment[4][35]. 2. Factor Name: 1-Year Volatility Factor - **Factor Construction Idea**: Captures the historical price volatility of stocks over the past year[4][35]. - **Factor Construction Process**: The factor is calculated as the standard deviation of daily returns over the past 252 trading days. $ Volatility = \sqrt{\frac{\sum_{i=1}^{252}(R_i - \bar{R})^2}{252}} $ - $R_i$: Daily return - $\bar{R}$: Average daily return - **Factor Evaluation**: This factor ranked second in rank IC this week (0.155), a significant improvement from its historical average (-0.033), suggesting its relevance in identifying outperforming stocks[4][35]. 3. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market returns[4][35]. - **Factor Construction Process**: The factor is calculated using regression analysis: $ R_i = \alpha + \beta R_m + \epsilon $ - $R_i$: Stock return - $R_m$: Market return - $\beta$: Beta coefficient - **Factor Evaluation**: This factor ranked third in rank IC this week (0.146), outperforming its historical average (0.004), indicating its effectiveness in the current market[4][35]. 4. Factor Name: Free Float Ratio Factor - **Factor Construction Idea**: Represents the proportion of shares available for public trading relative to total shares outstanding[4][35]. - **Factor Construction Process**: $ Free\ Float\ Ratio = \frac{\text{Free Float Shares}}{\text{Total Shares Outstanding}} $ - **Factor Evaluation**: This factor ranked fourth in rank IC this week (0.113), significantly better than its historical average (-0.011), highlighting its predictive strength in the current market[4][35]. 5. Factor Name: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of a company, indicating its debt level relative to equity[4][35]. - **Factor Construction Process**: $ Leverage = \frac{\text{Total Debt}}{\text{Total Equity}} $ - **Factor Evaluation**: This factor ranked fifth in rank IC this week (0.031), slightly above its historical average (-0.005), showing moderate predictive power[4][35]. --- Backtesting Results of Factors Weekly Rank IC Performance 1. **Illiquidity Factor**: Weekly rank IC = 0.174, Historical average = 0.038[4][35] 2. **1-Year Volatility Factor**: Weekly rank IC = 0.155, Historical average = -0.033[4][35] 3. **Beta Factor**: Weekly rank IC = 0.146, Historical average = 0.004[4][35] 4. **Free Float Ratio Factor**: Weekly rank IC = 0.113, Historical average = -0.011[4][35] 5. **Leverage Factor**: Weekly rank IC = 0.031, Historical average = -0.005[4][35] Weekly Rank IC Underperformance 1. **Nonlinear Market Cap Factor**: Weekly rank IC = -0.285, Historical average = -0.033[4][35] 2. **Log Market Cap Factor**: Weekly rank IC = -0.285, Historical average = -0.033[4][35] 3. **10-Day Return Factor**: Weekly rank IC = -0.205, Historical average = -0.061[4][35] 4. **10-Day Free Float Turnover Factor**: Weekly rank IC = -0.183, Historical average = -0.06[4][35] 5. **10-Day Total Market Cap Turnover Factor**: Weekly rank IC = -0.16, Historical average = -0.059[4][35] --- Strategy Performance Small-Cap Low-Volatility 50 Strategy - **Strategy Description**: Selects 50 stocks with the smallest market capitalization and lowest volatility from the micro-cap index constituents, rebalanced biweekly[19][37]. - **Performance**: - 2024 Return: 7.07%, Excess Return: -2.93% - 2025 YTD Return: 53.93%, Weekly Excess Return: 0.38% - Benchmark: Wind Micro-Cap Index (8841431.WI) - Transaction Cost: 0.3% per side[19][37]

Core Viewpoint - The article discusses various sectors in the market, highlighting trends and potential investment opportunities, particularly in the context of recent geopolitical developments and market dynamics. Financial Market Overview - A-shares have shown strong growth, with the North China 50 index rising by 6.84% weekly, leading major broad-based indices. Market sentiment is positive, with trading volume steadily increasing, indicating a shift towards bullish signals for most indices, except for the North China 50 which remains cautious [3]. Oil and Gas Sector - Geopolitical risks have eased, with reports of a ceasefire agreement between Israel and Iran, which may lead to a restart of consolidation among overseas oil and gas giants. As of June 27, Brent and WTI crude oil prices were reported at $66.34 and $65.07 per barrel, reflecting declines of 12.5% and 12.1% respectively from the previous week [4]. Agriculture Sector - In the pig farming sector, the industry capacity cycle has reached a bottom, but high inventory levels continue to impact market dynamics. Recent policy initiatives are accelerating the process of reducing inventory, which is expected to realign supply and demand. A long-term perspective suggests that after inventory reduction, the sector may enter a prolonged period of profitability [6]. Coal Mining Sector - There are signs of a turning point in coking coal inventories, with a reported decrease in both raw and refined coal stocks for the first time since May. As of the week of June 23-29, the inventory of raw coal was 683.5 million tons, down by 17.9 million tons, and refined coal was 463.1 million tons, down by 36.1 million tons. Additionally, the average price of thermal coal at Qinhuangdao port increased by 7 yuan to 616 yuan per ton, indicating the start of a seasonal price rise [7].

Group 1: Fixed Income and Economic Outlook - The Federal Open Market Committee (FOMC) maintained the federal funds rate target range at 4.25% to 4.5%, marking the fourth consecutive meeting without a rate change, aligning with market expectations [2] - The statement indicated a slight reduction in uncertainty regarding the economic outlook, although it remains at a high level [2] Group 2: Energy Transition and High-Temperature Superconductors - Shanghai Superconductor, a leading company in high-temperature superconducting materials, reported projected revenues of CNY 0.83 billion and CNY 2.40 billion for 2023 and 2024, respectively, reflecting year-on-year growth of 133% and 187% [2] - The company's gross profit margins are expected to improve to 55.77% and 60.52% in 2023 and 2024, respectively, with a significant increase in net profit to CNY 0.73 billion in 2024, indicating a turnaround [2] - The report anticipates that the demand for controllable nuclear fusion will drive down costs and expand application scenarios for high-temperature superconductors [2] Group 3: E-commerce and Retail Growth - The 2025 "618" e-commerce promotion is expected to see stable double-digit growth, driven by a slightly extended activity cycle, government subsidies, and increased user engagement through instant retail scenarios [4] - Major e-commerce platforms are expected to continue competing on improving merchant operations and enhancing user stickiness through multi-channel marketing [4] - Recommended stocks include Alibaba (BABA US/9988 HK) and JD.com (JD US/9618 HK) due to their strong brand support and potential for cross-selling in instant retail [4] Group 4: Utilities and Environmental Sector Performance - The report anticipates a mixed performance among major thermal power companies in Q2 2025, with coal prices expected to decline month-on-month [5] - Hydropower generation is projected to decline year-on-year, while nuclear power operations remain stable [5] - Key focus areas for green power operators include electricity pricing mechanisms and cash flow management for environmental companies [5] Group 5: New Energy and Technology Trends - The report highlights optimism in the profitability of battery and structural components in the electric vehicle sector, alongside advancements in solid-state battery technology [8] - Wind power demand is expected to remain robust, with profitability recovery driven by offshore wind projects [8] - Recommended stocks include CATL, EVE Energy, and others involved in emerging technology sectors such as humanoid robots and AIDC [8] Group 6: U.S. Treasury Market Demand - The report analyzes the structural characteristics of U.S. Treasury investors, noting that international investors, broad-based mutual funds, and the Federal Reserve account for over 60% of the market [7] - Different investor types exhibit distinct motivations for purchasing Treasuries, with expectations for continued demand from commercial banks and pension funds in the second half of 2025 [7]

Market Overview - The market experienced fluctuations this week, with only the ChiNext index showing an increase. The ETF market continued to see net outflows, primarily from large-cap ETFs. The market is transitioning from wide fluctuations to narrower ones, with increased trading volume during this process, indicating potential consolidation in a weak market [4]. Copper Industry - In May, domestic waste copper production was 92,000 tons, a year-on-year decrease of 20% but a month-on-month increase of 5%. The negative impact of trade conflicts on the economy has not fully materialized, which continues to suppress copper price increases. Supply-side disturbances in copper mining have increased, while demand is weakening due to reduced export stocking effects and the domestic off-season [5]. Metal Prices - The price of London gold has reached a historical high. Sunac China’s offshore debt-to-equity swap plan received support from 82% of bondholders. In May, Sunac's total sales amounted to 4.9 billion yuan, a year-on-year increase of 128%, indicating strong performance [6]. Chemical Industry - Recent safety incidents in chemical parks have led to stricter approval and production regulations for high-risk chemical reactions. Leading companies in the chemical industry, with better safety management and advanced production technologies, are expected to benefit from stable production amid limited growth in high-risk products [7]. Construction Materials - The market performance showed a decline, with the CITIC building materials index down 2.16% and the CITIC construction index down 1.27%. The average price of PO42.5 cement was 365.70 yuan/ton, a slight increase, while glass prices decreased by 20 yuan/ton [8]. Agriculture and Livestock - In the pig farming sector, the industry capacity cycle has bottomed out, but high inventory levels continue to impact market dynamics. Recent policy-driven efforts are accelerating the reduction of inventory, which may lead to a rebalancing of supply and demand. Long-term, the end of inventory reduction could signal the start of a prolonged profit upcycle for the sector [9]. Renewable Energy - The nuclear fusion sector, while far from full commercialization, is seeing increased investment and research due to global military competition. Recent data from May indicates a downward trend in overall renewable energy prices, highlighting ongoing pressures in power supply and demand. Wind power, virtual power plants, and energy storage are identified as promising investment opportunities [10].