Group 1 - The report highlights the significant role of quantitative stock selection funds in the public fund market, with a total of 277 funds managing a combined scale of 90.32 billion yuan as of the end of Q2 2025, offering broader investment scope and higher style exposure flexibility compared to standard index-enhanced funds [3][12][13] - The report focuses on five fund managers with distinctive investment frameworks in quantitative stock selection, including Feng Xixiang from Xinda Australia Fund, Gao Chongnan from Guotai Fund, Lin Jingyi from Xinda Australia Fund, Shi Yunchao from Penghua Fund, and Zhai Zijian from Western Li De Fund, providing insights into their strategies and product positioning [3][12][13] Group 2 - Feng Xixiang employs a unified framework emphasizing the effectiveness of factors and the universality of alpha models, integrating static multi-factor linear models with machine learning dynamic weighting models since 2023, achieving balanced allocation in his representative products [4][16][23] - Gao Chongnan focuses on the Calmar ratio, selecting high dividend, quality, and growth styles to enhance the stability of risk-return profiles, with a product positioning aimed at low volatility value style [4][35][36] - Lin Jingyi implements a "HI+AI" approach using an integrated research platform, employing a three-step method to replicate successful peer consensus and enhance index tracking through multiple alpha models [5][22] - Shi Yunchao's strategy combines multi-factor linear models with a higher proportion of non-linear models, focusing on short prediction cycles and higher turnover rates, while maintaining a diversified portfolio to mitigate risks [6][24] - Zhai Zijian adopts an AI quantitative investment strategy with a "core + satellite" multi-strategy balanced configuration, utilizing machine learning for long-term predictions and high-frequency data analysis [6][24] Group 3 - The report indicates that as of the end of Q2 2025, Feng Xixiang manages a total of 4.54 billion yuan across seven quantitative stock selection products, with representative products achieving cumulative returns of 40.66% and 74.91% since inception, significantly outperforming their benchmark indices [17][21] - Gao Chongnan's strategy iteration has led to improved performance, with the National Strategy Yield Fund achieving an annualized return of 28.72% in 2024, reflecting a notable enhancement in risk-adjusted returns [36][37] - The quantitative team at Xinda Australia Fund consists of experienced professionals, with a comprehensive product line that includes 11 quantitative stock selection products and 2 quantitative fixed income + strategy products, aiming to reduce volatility while seeking absolute returns [32][33]

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - Economic growth shows a deep decline in the container shipping freight index at a low level, with power plant daily consumption experiencing a seasonal decline, and inflation marked by a further drop in pork prices [1][2]. 3. Summary by Related Catalogs 3.1 Economic Growth: Deep Decline in Container Shipping Freight Index at a Low Level 3.1.1 Production: Seasonal Decline in Power Plant Daily Consumption - Power plant daily consumption has declined seasonally. On September 23, the average daily consumption of 6 major power - generating groups was 864,000 tons, a 4.0% decrease from September 16. On September 17, the daily consumption of power plants in eight southern provinces was 2.262 million tons, a 0.4% decrease from September 10 [4]. - The blast furnace operating rate has continued to rise. On September 19, the national blast furnace operating rate was 84.0%, a 0.2 - percentage - point increase from September 12, and the capacity utilization rate was 90.4%, also a 0.2 - percentage - point increase. In Tangshan, the blast furnace operating rate of steel mills was 93.0% on September 19, a 2.4 - percentage - point increase from September 12 [4]. - The tire operating rate has rebounded moderately. On September 18, the operating rate of truck all - steel tires was 65.7%, a 0.1 - percentage - point increase from September 11, and that of passenger car semi - steel tires was 73.7%, a 0.2 - percentage - point increase [4]. - The operating rate of looms in the Jiangsu and Zhejiang regions has slightly declined. On September 18, the operating rate of polyester filament in the Jiangsu and Zhejiang regions was 91.5%, a 0.1 - percentage - point increase from September 11, while the operating rate of downstream looms was 62.2%, a 0.2 - percentage - point decrease [4]. 3.1.2 Demand: New Home Sales in 30 Cities Exceeded the Same Period Last Year - From September 1 - 23, the average daily sales area of commercial housing in 30 large and medium - sized cities was 210,000 square meters, an 8.8% increase from the same period in August, a 13.7% increase from September last year, and a 30.6% decrease from September 2023 [4]. - The retail trend in the auto market has been stable. In September, retail sales increased by 1% year - on - year, and wholesale sales increased by 0% year - on - year [4]. - Steel prices have been weak. On September 23, the prices of rebar, wire rod, hot - rolled coil, and cold - rolled coil decreased by 0.9%, 1.1%, 2.0%, and increased by 0.03% respectively compared to September 16 [4]. - Cement prices have weakly rebounded. On September 23, the national cement price index increased by 2.3% compared to September 16, with prices in the East China and Yangtze River regions rising by 3.5% and 4.8% respectively [4]. - Glass prices have fluctuated widely. On September 23, the active futures contract price of glass was 1,181 yuan/ton, a 4.4% decrease from September 16 [4]. - The container shipping freight index has deeply declined at a low level. On September 19, the CCFI index decreased by 0.5% compared to September 12, and the SCFI index dropped by 14.3% [4]. 3.2 Inflation: Further Drop in Pork Prices 3.2.1 CPI: Further Drop in Pork Prices - Pork prices have further declined. On September 23, the average wholesale price of pork was 19.6 yuan/kg, a 1.6% decrease from September 16 [4]. - The agricultural product price index has moderately rebounded. On September 23, the agricultural product wholesale price index increased by 0.2% compared to September 16. By variety, eggs (up 4.4%) > fruits (up 1.3%) > mutton (up 0.6%) > chicken (up 0.5%) > beef (up 0.4%) > vegetables (up 0.2%) > pork (down 1.6%) [4]. 3.2.2 PPI: Weak Oscillation in Oil Prices - Oil prices have shown a weak oscillation. On September 23, the spot prices of Brent and WTI crude oil were $68.6 and $63.4 per barrel respectively, a 0.03% and 1.7% decrease from September 16 [4]. - Copper and aluminum prices have turned down. On September 23, the prices of LME 3 - month copper and aluminum decreased by 1.7% and 2.9% respectively compared to September 16 [4]. - The decline in the domestic commodity index has widened on a month - on - month basis. On September 23, the Nanhua Industrial Products Index decreased by 2.1% compared to September 16, and the CRB index decreased by 1.3% [4].

Investment Rating - The report assigns a "Buy" rating to the company, with a target price of 6.83 CNY per share based on a projected PE of 30x for 2026 [5]. Core Insights - The company is positioned as a representative of the commercialization of Eastern aesthetic brands, focusing on diverse consumer sectors, with a significant emphasis on jewelry and cultural dining [2][15]. - The expansion of the commercial area, particularly the second and third phases of the Yuyuan project, is expected to significantly enhance the company's rental and management income [3][29]. - The company is actively pursuing international expansion of its dining brands, with the first overseas store of Songhe Lou set to open in London, which is anticipated to exceed growth expectations [3][41]. Summary by Sections Company Overview - The company operates primarily in three business segments: consumption industry operations (76.1% of revenue), commercial comprehensive operations and property services (17.2%), and real estate development and sales (6.7%) [2][15]. - Jewelry accounts for 88% of the consumption industry operations revenue, with additional segments including dining, health, and alcohol [2][19]. Investment Logic - The company is undergoing significant changes in its commercial area, with the second and third phases of the Yuyuan project expected to double the area of the first phase, enhancing rental income [3][34]. - The dining business is expanding internationally, with a focus on traditional brands, which is expected to create new growth opportunities [3][41]. Financial Performance - The jewelry segment has faced challenges but is expected to recover due to store adjustments and product innovation, with a revenue of 12.79 billion CNY in the first half of 2025 [4][22]. - The real estate segment is gradually improving as inventory is cleared, with a revenue increase of 4.84% year-on-year in the first half of 2025 [4][22]. Profit Forecast and Valuation - Revenue projections for 2025-2027 are estimated at 467.21 billion CNY, 489.30 billion CNY, and 508.30 billion CNY, respectively, with net profits expected to rise significantly [5][9]. - The company is expected to benefit from a cultural export boom, leveraging its rich IP resources [5][54]. Brand and Cultural Assets - The company has a robust portfolio of traditional brands and cultural heritage, which supports its commercial activities and enhances brand visibility [54][56]. - The introduction of new brands like "Dongjia Gold" aims to tap into the high-end market, further diversifying the company's offerings [52][54].

Investment Rating - The report provides a positive investment outlook for the AI sector, highlighting significant growth potential and commercial viability of AI applications and products. Core Insights - The AI industry is experiencing rapid growth, with domestic AI product access rates outpacing global counterparts. Notably, the revenue share of AI in some listed companies has increased to 10-30% by mid-2025 [3][42]. - Major players in the AI market are focusing on commercializing their products, with a notable increase in bidding for large AI models, indicating a strong demand for AI technology in various sectors [3][42]. - The report emphasizes the importance of user engagement and product stickiness, suggesting that products with strong user bases and integration into daily workflows are less likely to be replaced by emerging AI models [3][42]. Summary by Sections 1. Investment Logic - The report discusses the ongoing recruitment of AI talent by major domestic companies, which is expected to enhance the commercialization of AI products. The growth in AI product access rates is significant, with domestic AI products showing a month-on-month increase of 11.9% compared to a global increase of 3.5% [3][8]. - By mid-2025, some computer companies have seen their AI revenue share rise to between 10-30% [3][42]. 2. AI Product User Engagement - The top 20 AI products globally are dominated by leading internet companies and AI model developers, with ChatGPT consistently ranking first in user access [8][10]. - The report highlights that the competitive landscape for AI products is intensifying, particularly among mid-tier applications, while top-tier products maintain a stable market position [10][19]. 3. AI Product Monetization - The report identifies that the top AI products by annual recurring revenue (ARR) are primarily from leading tech companies, with ChatGPT leading at $14.279 billion, followed by Claude at $5 billion [35][38]. - In the domestic market, the top AI products also show strong revenue performance, with PictureThis leading at $143 million [38][39]. 4. Domestic AI Model Bidding Demand - The report notes a significant increase in the number of domestic AI model bidding projects, with a year-on-year growth rate of 1190% in January 2025, indicating a rapid acceptance and implementation of AI technologies in the market [42][43].

Report Industry Investment Rating - No relevant content provided Core Viewpoints - As of September 22, 2025, the valuation yields and spreads of private enterprise industrial bonds and real estate bonds in the outstanding credit bonds are generally higher than those of other varieties. Compared with last week, the yields of most varieties in non - financial and non - real estate industrial bonds have increased, while the adjustment range of real estate bond yields is relatively small. In financial bonds, the yields of medium - term varieties from 1 - 3 years have mostly declined [2][3][8] - In public urban investment bonds, the weighted average valuation yields in Jiangsu and Zhejiang are below 2.7%, and the yields of urban investment bonds in prefecture - level and district - county levels in Guizhou exceed 4.5%. In private urban investment bonds, the weighted average valuation yields in coastal provinces such as Shanghai, Zhejiang, Guangdong, and Fujian are below 3%, and the yields of varieties in prefecture - level cities in Guizhou and Yunnan are higher than 4% [2][14][22] Summary by Directory Chart 1: Outstanding Credit Bond Weighted Average Valuation Yield - Displays the weighted average valuation yields of various types of outstanding credit bonds as of September 22, including urban investment bonds, non - financial non - real estate industrial bonds (state - owned and private enterprises), real estate bonds (state - owned and private enterprises), financial bonds, etc. [10] Chart 2: Outstanding Credit Bond Weighted Average Spread - Presents the weighted average spreads of various types of outstanding credit bonds as of September 22, with the calculation benchmark being the same - term China Development Bank bonds [11] Chart 3: Change in Outstanding Credit Bond Weighted Average Valuation Yield Compared to Last Week - Shows the changes in the weighted average valuation yields of various types of outstanding credit bonds as of September 22 compared to last week, calculated based on the yields of September 22 and September 15 [12] Chart 4: Change in Outstanding Credit Bond Weighted Average Spread Compared to Last Week - Illustrates the changes in the weighted average spreads of various types of outstanding credit bonds as of September 22 compared to last week, calculated based on the spreads of September 22 and September 15 [13] Chart 5: Public Urban Investment Bond Weighted Average Valuation Yield - Details the weighted average valuation yields of public urban investment bonds in different administrative levels and regions as of September 22, such as provincial, prefecture - level, and district - county levels in various provinces [15] Chart 6: Public Urban Investment Bond Weighted Average Spread - Displays the weighted average spreads of public urban investment bonds in different administrative levels and regions as of September 22 [17] Chart 7: Change in Public Urban Investment Bond Weighted Average Valuation Yield Compared to Last Week - Shows the changes in the weighted average valuation yields of public urban investment bonds in different administrative levels and regions as of September 22 compared to last week [19] Chart 8: Private Urban Investment Bond Weighted Average Valuation Yield - Presents the weighted average valuation yields of private urban investment bonds in different administrative levels and regions as of September 22 [23] Chart 9: Private Urban Investment Bond Weighted Average Spread - Displays the weighted average spreads of private urban investment bonds in different administrative levels and regions as of September 22 [25]

- The report discusses the construction of forward and reverse arbitrage strategies in stock index futures markets. Forward arbitrage involves selling futures contracts and buying spot when the spot is undervalued and futures are overvalued, while reverse arbitrage involves buying futures contracts and selling spot when the spot is overvalued and futures are undervalued[45][46] - The formulas for calculating the arbitrage returns are provided. For forward arbitrage, the formula is: $$ P = \frac{(F_t - S_t) - (S_t + F_t M_t)(1 + r_t)^{\frac{T-t}{360}} - S_t C_s - F_t C_f}{S_t + F_t M_t} $$ For reverse arbitrage, the formula is: $$ P = \frac{(S_t - F_t) - (S_t M_l + F_t M_f)(1 + r_f)^{\frac{T-t}{360}} - S_t C_s - F_t C_f - S_t r^{\frac{T-t}{360}}}{S_t M_l + F_t M_f} $$[46] - The report evaluates the risks associated with the arbitrage process, including margin call risk, basis non-convergence risk, dividend risk, tracking error risk, and liquidity risk[46] - The report also discusses the method for predicting dividend points, which can affect the basis rate. The prediction is based on historical dividend patterns and uses the formula: $$ \text{Dividend Points} = \sum \left( \frac{\text{Per Share Dividend} \times \text{Index Closing Price} \times \text{Component Stock Weight}}{\text{Component Stock Closing Price}} \right) $$[47][50] - The report provides specific values for the annualized basis rates of the main contracts for IF, IC, IM, and IH, which are -4.66%, -12.51%, -14.77%, and -0.06% respectively[11] - The cross-period spread rates for the main contracts of IF, IC, IM, and IH are at the 78.30%, 73.30%, 84.20%, and 63.90% percentiles respectively since 2019[11] - The report includes a summary of market and industry investment consensus and differences from sell-side strategy teams, highlighting that 8 brokerages believe in enhanced policy easing expectations, 7 believe in active market liquidity, and 6 believe in a significant increase in market risk appetite[38][40]

Investment Rating - The report initiates coverage with a "Buy" rating and sets a target price of 103.2 CNY based on a 65x PE for 2026 [5]. Core Views - The company is positioned as a comprehensive solution provider in environmental regulation, with a strong focus on data services driving performance growth. The data services segment is expected to be a major growth driver from 2025 to 2027, with projected revenues of 27 billion, 46 billion, and 64 billion CNY, reflecting year-on-year growth rates of 77%, 67%, and 39% respectively [3][5]. Summary by Sections Investment Logic - The company has 25 years of experience in environmental regulation equipment, focusing on four main scenarios: data services, industrial processes, specialized applications, and public/commercial use. The data services segment saw a 16.2% year-on-year revenue increase in the first half of 2025, with new orders up 200% from January to August, providing strong support for sustained performance [2]. Data Center Business Growth - The data center business is identified as the company's largest growth point, driven by rapid increases in global data center investments and a shift from air cooling to liquid cooling solutions. The company has established a comprehensive solution covering all aspects of temperature control for data centers, enhancing cooling performance and energy efficiency [3][4][5]. Industrial Demand - The industrial temperature control market is projected to reach 23.6 billion USD by 2025, with the public and power sectors accounting for 43% of this market. The company has extensive experience in thermal management systems across various power projects, positioning it to benefit from the growing demand in specialized industrial fields [4]. Financial Projections - The company is expected to achieve revenues of 43.9 billion, 63.8 billion, and 83.4 billion CNY from 2025 to 2027, with corresponding net profits of 2.9 billion, 4.2 billion, and 6.7 billion CNY, reflecting significant growth rates [5][7].

Report Industry Investment Rating No relevant content provided. Core Viewpoints - Last week (9/15 - 9/19), bond - type ETFs had a total net capital outflow of 5.1 billion yuan, with interest - rate bond ETFs, credit - bond ETFs, and convertible - bond ETFs having net outflows of 1.9 billion yuan, 0.7 billion yuan, and 2.5 billion yuan respectively. Convertible - bond ETFs and credit - bond ETFs had significant drawdowns, while the net value of interest - rate bond ETFs changed little [2][11]. - The second batch of sci - tech bond ETFs will be listed on September 24. With the establishment of these 14 new funds, the total scale of sci - tech bond ETFs has exceeded 170 billion yuan, and the overall scale of bond ETFs has exceeded 600 billion yuan for the first time [3][14]. Summary by Directory 1. Issuance Progress Tracking - The second batch of 14 sci - tech bond ETFs from 14 public funds such as ICBC Credit Suisse Fund and Morgan Fund started issuing on September 12. They were submitted on August 20, approved on September 8, and scheduled for issuance on September 12. The total issuance scale of these 14 sci - tech bond ETFs reached 40.786 billion yuan, and 13 of them had an issuance scale of over 2.9 billion yuan [3][14]. 2. Existing Product Tracking - As of September 19, 2025, the circulating market values of interest - rate bond ETFs, credit - bond ETFs, and convertible - bond ETFs were 140 billion yuan, 355.8 billion yuan, and 70.1 billion yuan respectively, with credit - bond ETFs accounting for 63% of the scale. Compared with last week, their circulating market values decreased by 2.2 billion yuan, 0.02 billion yuan, and 3.6 billion yuan respectively [4][16]. - Among credit - bond ETFs, the circulating market values of benchmark - making credit - bond ETFs and sci - tech bond ETFs were 123.7 billion yuan and 125.9 billion yuan respectively, with a decrease of 0.6 billion yuan and an increase of 2.3 billion yuan compared to last week [19]. 3. ETF Performance Tracking - Recently, the market has shown range - bound fluctuations. In the past two weeks, the cumulative unit net values of interest - rate bond ETFs and credit - bond ETFs closed at 1.18 and 1.02 respectively [23]. - As of September 19, with February 7 as the base date, the average cumulative yield of benchmark - making credit - bond ETFs dropped to 0.30%; with July 17 as the base date, the cumulative yield of sci - tech bond ETFs dropped to - 0.46% and remained in the negative range [24]. 4. Premium/Discount Rate Tracking - Last week, the average premium/discount rates of credit - bond ETFs, interest - rate bond ETFs, and convertible - bond ETFs were - 0.17%, - 0.03%, and - 0.15% respectively, indicating that the average trading price was lower than the fund's unit net value and the allocation sentiment was low. Specifically, the weekly average premium/discount rates of benchmark - making credit - bond ETFs and sci - tech bond ETFs were - 0.23% and - 0.06% respectively [6][30]. 5. Turnover Rate Tracking - Last week, the turnover rate was in the order of interest - rate bond ETFs > convertible - bond ETFs > credit - bond ETFs. The weekly turnover rate of interest - rate bond ETFs rose to 179%, that of credit - bond ETFs remained around 89%, and that of convertible - bond ETFs dropped to 100%. Specifically, products like Huaxia Shanghai Stock Exchange Benchmark - Making Treasury Bond ETF and Haitong Shanghai Stock Exchange 5 - Year Local Government Bond ETF had relatively high turnover rates [6][36].

Macro Liquidity - The US dollar index has rebounded, and the degree of the China-US interest rate "inversion" has deepened, with inflation expectations also rising [1][14] - Offshore US dollar liquidity has generally loosened, while the domestic interbank funding situation remains balanced [1][19] Market Trading Activity - Overall market trading activity has increased, with most industry trading activities remaining above the 80th percentile [2][25] - Major indices' volatility has also risen, with the communication sector's volatility exceeding the 80th historical percentile [2][31] - Market liquidity indicators have declined, with all sectors' liquidity indicators below the 40th historical percentile [2][36] Institutional Research - The electronic, pharmaceutical, communication, non-ferrous metals, and automotive sectors have seen high research activity, while sectors like steel, electricity, utilities, machinery, light industry, building materials, and real estate have shown a rising trend in research activity [3][43] Analyst Forecasts - Analysts have continued to lower the net profit forecasts for the entire A-share market for 2025/2026, with the proportion of stocks with upward revisions increasing [4][50] - The net profit forecasts for sectors such as non-bank financials, chemicals, coal, and retail have been raised for 2025/2026 [4][21] - The net profit forecast for the Shanghai Stock Exchange 50 index for 2025/2026 has been adjusted upward [4][23] Northbound Trading Activity - Northbound trading activity has increased, but there continues to be a net sell-off overall [5][31] - Based on the top 10 active stocks, the buy-sell ratio in sectors like electronics, electric new energy, and communication has risen, while it has decreased in non-bank financials, pharmaceuticals, and non-ferrous metals [5][32] Margin Financing Activity - Margin financing has reached a high point not seen since September 2024, with a net purchase of 466.70 billion yuan last week [6][35] - The main net purchases in margin financing were in the electronic, non-bank financial, and machinery sectors, while net sales were seen in military, non-ferrous metals, and coal sectors [6][39] Active Equity Funds and ETFs - Active equity funds have increased their positions, particularly in coal, communication, and home appliance sectors, while reducing positions in computers, non-bank financials, and electronics [7][45] - ETFs have continued to see net subscriptions, primarily in personal ETFs, with significant net purchases in non-bank financials, non-ferrous metals, and machinery sectors [7][52]

Quantitative Models and Construction - **Model Name**: Macro Timing Strategy **Model Construction Idea**: The model evaluates macroeconomic growth and monetary liquidity signals to determine equity allocation levels[41][42] **Model Construction Process**: 1. The model assigns signal strengths to economic growth and monetary liquidity dimensions - Economic growth signal: 100% - Monetary liquidity signal: 50% 2. Equity allocation recommendation is derived based on these signals, with September's recommended equity position at 75% 3. Historical performance: From early 2025 to date, the strategy achieved a return of 11.75%, compared to Wind All A's return of 22.98%[41][42] **Model Evaluation**: The model provides a balanced view of macroeconomic and liquidity conditions, offering actionable insights for equity allocation[41][42] - **Model Name**: Rotation Model for Small-Cap Stocks **Model Construction Idea**: The model identifies style rotation opportunities between small-cap stocks and large-cap stocks (represented by the "茅指数")[19][20][22] **Model Construction Process**: 1. Relative net value comparison: Small-cap stocks/茅指数 relative net value is compared to its 243-day moving average - If above the moving average, small-cap stocks are preferred; otherwise, 茅指数 is recommended 2. 20-day closing price slope analysis: - Positive slope indicates preference for the respective index - Current slopes: Small-cap stocks (-0.08%) vs 茅指数 (0.24%) 3. Risk control indicators: - Volatility crowding degree (-35.58%) - 10-year government bond yield (-8.12%) - Both indicators are below risk thresholds (55% and 30%, respectively)[19][20][22] **Model Evaluation**: The model effectively captures style rotation signals and provides risk control measures for small-cap stock investments[19][20][22] --- Quantitative Factors and Construction - **Factor Name**: Stock Selection Factors **Factor Construction Idea**: Eight major stock selection factors are tracked across different stock pools (All A-shares, CSI 300, CSI 500, CSI 1000)[45][53][55] **Factor Construction Process**: 1. Factors include: - **Value**: Metrics like SP_TTM (past 12-month revenue/latest market value) - **Growth**: Metrics like OperatingIncome_SQ_Chg1Y (quarterly operating income YoY growth) - **Quality**: Metrics like ROE_FTTM (future 12-month expected net profit/shareholder equity average) - **Technical**: Metrics like Skewness_240D (240-day return skewness) - **Volatility**: Metrics like IV_CAPM (CAPM residual volatility)[53][55] 2. Weekly tracking of IC mean values and multi-long-short portfolio returns - Quality factors performed well last week, while others showed mixed results across stock pools[45][53][55] **Factor Evaluation**: Provides comprehensive insights into factor performance across different market segments, aiding in stock selection[45][53][55] - **Factor Name**: Convertible Bond Selection Factors **Factor Construction Idea**: Convertible bond factors are derived from the relationship between convertible bonds and their underlying stocks[50][53] **Factor Construction Process**: 1. Key factors include: - **Stock Consensus Expectation**: Predictive metrics for underlying stocks - **Stock Financial Quality**: Metrics like ROE_FTTM - **Convertible Bond Valuation**: Metrics like parity and bottom price premium rate[50][53] 2. Weekly tracking of IC mean values and multi-long-short portfolio returns - Positive IC mean values observed for stock consensus expectation, financial quality, stock value, and convertible bond valuation factors[50][53] **Factor Evaluation**: Offers robust predictive insights for convertible bond selection based on stock-related metrics[50][53] --- Backtesting Results Models - **Macro Timing Strategy**: - Return: 11.75% (2025 YTD) - Benchmark (Wind All A): 22.98%[41][42] - **Rotation Model for Small-Cap Stocks**: - Small-cap stocks/茅指数 relative net value: 1.88 (above 243-day moving average of 1.62) - 20-day closing price slopes: Small-cap stocks (-0.08%), 茅指数 (0.24%)[19][20][22] Factors - **Stock Selection Factors**: - IC mean values: Quality factors performed best last week[45][53][55] - **Convertible Bond Selection Factors**: - IC mean values: Positive for stock consensus expectation, financial quality, stock value, and convertible bond valuation factors[50][53]