Investment Rating - The report initiates coverage with a "Buy" rating for the company, setting a target price of 27.9 RMB based on a 90x PE for 2026 [4]. Core Insights - The company is one of the few integrated manufacturers of semiconductor silicon epitaxial wafers in China, capable of the entire production process from crystal growth to substrate formation and epitaxial growth. In H1 2025, the company achieved revenue of 625 million RMB, a year-on-year increase of 15%, and a net profit attributable to shareholders of 59.71 million RMB, up 24% year-on-year, indicating a recovery in revenue and profit growth [2][20]. - The global semiconductor market is showing signs of recovery, with a market size of 346 billion USD in H1 2025, reflecting a year-on-year growth of 19%. The WSTS has revised its forecast for the entire year of 2025 to 728 billion USD, expecting a 15% year-on-year increase, which is 4 percentage points higher than previous expectations [2][39]. - The company focuses on power devices and analog chips, benefiting from the recovery in end-user demand in automotive electronics and industrial sectors. The global discrete device market is expected to reach 46.1 billion USD by 2028, with a CAGR of 8.5% from 2024 to 2028 [2][39]. Summary by Sections 1. Integrated Semiconductor Silicon Epitaxial Wafer Supplier - The company has established itself as a key supplier in the semiconductor industry, providing high-quality silicon epitaxial wafers used in various applications, including power devices and analog chips [14][18]. - The company's revenue and performance have been influenced by the cyclical nature of the semiconductor industry, with a notable recovery in H1 2025 [20][24]. 2. Demand Driven by Power Devices and Analog Chips - The semiconductor materials market is experiencing a mild recovery, with the global semiconductor materials market expected to reach 76 billion USD in 2025, growing by 8.4% year-on-year [30]. - The company has a strong overseas presence, with 86% of its revenue coming from international markets, indicating a robust global sales network [25][30]. 3. Strategic Focus on 8-inch and 12-inch Wafer Production - The company is expanding its production capacity, particularly in 12-inch wafers, to meet the growing demand driven by AI and HPC sectors. The IPO in 2024 raised 1.4 billion RMB to fund these initiatives [3][4]. - The company aims to enhance its product structure by focusing on advanced process logic chips and automotive-grade products, which are expected to improve its competitive position [2][3]. 4. Profit Forecast and Investment Recommendations - The company is projected to achieve revenues of 1.31 billion RMB, 1.62 billion RMB, and 1.97 billion RMB in 2025, 2026, and 2027, respectively, with corresponding net profits of 160 million RMB, 204 million RMB, and 260 million RMB, indicating strong growth potential [4][8]. - The report highlights the company's advantageous customer structure, which is expected to contribute to margin improvements as the 12-inch epitaxial wafer capacity comes online [4][8].

Quantitative Models and Construction Methods - **Model Name**: Dynamic Macro Event Factor-based CSI Hong Kong Stock Connect Consumer Index Timing Strategy **Model Construction Idea**: The model explores the impact of China's macroeconomic factors on the overall performance and trends of Hong Kong-listed consumer companies, using dynamic macro event factors to construct a timing strategy framework [2][3][20] **Model Construction Process**: 1. **Macro Data Selection**: Select 20+ macroeconomic indicators across four dimensions: economy, inflation, currency, and credit, such as PMI, PPI, M1, etc [21][23] 2. **Data Preprocessing**: - Align data frequency to monthly frequency by either taking the last trading day of the month or calculating the monthly average for daily data - Fill missing values using the median of the first-order difference of the past 12 months added to the previous value $ X_{t}=X_{t-1}+Median_{diff12} $ [27] - Apply filtering using one-sided HP filter to avoid future data leakage $ \hat{t}_{t|t,\lambda}=\sum\nolimits_{s=1}^{t}\omega_{t|t,s,\lambda}\cdot y_{s}=W_{t|t,\lambda}(L)\cdot y_{t} $ [28] - Derive factors using transformations such as year-on-year, month-on-month, and moving averages [29] 3. **Macro Event Factor Construction**: - Determine event breakthrough direction by calculating the correlation between data and next-period asset returns - Identify leading or lagging relationships by deriving lagged event factors (0-4 periods) and selecting the most suitable lag period - Generate event factors using three types: data breaking through moving average, data breaking through median, and data moving in the same direction, with different parameters (e.g., moving average length: 2-12, rolling window: 2-12, same direction period: 1-5) [30][32] 4. **Event Factor Evaluation and Screening**: - Use two metrics: win rate of returns and volatility-adjusted returns during opening positions - Initial screening criteria: t-test significance at 95% confidence level, win rate >55%, occurrence frequency > rolling window period/6 [31][32] 5. **Combining Event Factors**: Select the highest win rate event factor as the base factor, then combine it with the second-highest win rate factor with a correlation <0.85. If the combined factor improves the win rate, it is selected; otherwise, the base factor is used [33] 6. **Dynamic Exclusion**: If no event factor passes the screening, the macro indicator is marked as empty for the period and excluded from scoring [33] 7. **Optimal Rolling Window Determination**: Test rolling windows of 48, 60, 72, 84, and 96 months to find the most suitable parameter for each macro indicator based on volatility-adjusted returns during opening positions [33] 8. **Final Macro Indicators**: Five macro factors were selected based on their performance in the sample period: - PMI: Raw Material Prices (96-month rolling window) - US-China 10Y Bond Spread (72-month rolling window) - Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY (48-month rolling window) - M1: YoY (48-month rolling window) - New Social Financing: Rolling 12M Sum: YoY (96-month rolling window) [34][35] 9. **Timing Strategy Construction**: - If >2/3 of factors signal bullishness, the category factor signal is marked as 1 - If <1/3 of factors signal bullishness, the category factor signal is marked as 0 - If the proportion of bullish signals falls between these ranges, the category factor is marked with the specific proportion - The score of each category factor is used as the timing position signal for the period [3][35] **Model Evaluation**: The strategy effectively captures systematic opportunities and avoids systematic risks, demonstrating superior performance compared to the benchmark in terms of annualized returns, maximum drawdown, Sharpe ratio, and return-drawdown ratio [2][3][20] --- Model Backtesting Results - **Dynamic Macro Event Factor-based CSI Hong Kong Stock Connect Consumer Index Timing Strategy** - **Annualized Return**: 10.44% - **Annualized Volatility**: 18.47% - **Maximum Drawdown**: -29.72% - **Sharpe Ratio**: 0.59 - **Return-Drawdown Ratio**: 0.35 [2][11][22] --- Quantitative Factors and Construction Methods - **Factor Name**: PMI: Raw Material Prices **Factor Construction Idea**: Use raw data to capture macroeconomic trends affecting asset returns [35] **Factor Construction Process**: Utilize raw data with a 96-month rolling window [35] - **Factor Name**: US-China 10Y Bond Spread **Factor Construction Idea**: Reflect the impact of interest rate differentials on asset returns [35] **Factor Construction Process**: Utilize raw data with a 72-month rolling window [35] - **Factor Name**: Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY **Factor Construction Idea**: Measure credit expansion and its influence on asset returns [35] **Factor Construction Process**: Utilize raw data with a 48-month rolling window [35] - **Factor Name**: M1: YoY **Factor Construction Idea**: Capture monetary supply changes and their impact on asset returns [35] **Factor Construction Process**: Utilize raw data with a 48-month rolling window [35] - **Factor Name**: New Social Financing: Rolling 12M Sum: YoY **Factor Construction Idea**: Reflect credit growth and its effect on asset returns [35] **Factor Construction Process**: Utilize raw data with a 96-month rolling window [35] **Factor Evaluation**: The selected factors demonstrated strong performance in the sample period, with high win rates and volatility-adjusted returns during opening positions [34][35] --- Factor Backtesting Results - **PMI: Raw Material Prices** - **Rolling Window**: 96 months [35] - **US-China 10Y Bond Spread** - **Rolling Window**: 72 months [35] - **Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY** - **Rolling Window**: 48 months [35] - **M1: YoY** - **Rolling Window**: 48 months [35] - **New Social Financing: Rolling 12M Sum: YoY** - **Rolling Window**: 96 months [35]

- The report tracks high-frequency stock selection factors, including price range factor, price-volume divergence factor, regret avoidance factor, and slope convexity factor, with their out-of-sample performance being generally strong[2][3][11] - **Price Range Factor**: Measures the activity of stock transactions within different intraday price ranges, reflecting investors' expectations of future stock trends. High price range transaction volume and transaction count factors are negatively correlated with future stock returns, while low price range average transaction volume factor is positively correlated with future stock returns. The factor is constructed by combining three sub-factors: high price 80% range transaction volume factor (VH80TAW), high price 80% range transaction count factor (MIH80TAW), and low price 10% range average transaction volume factor (VPML10TAW). These sub-factors are weighted at 25%, 25%, and 50%, respectively, and are industry market value neutralized[12][14][17] - **Price-Volume Divergence Factor**: Measures the correlation between stock price and trading volume. When price and volume diverge, the likelihood of future price increases is higher, while convergence indicates a higher likelihood of price decreases. The factor is constructed using high-frequency snapshot data to calculate the correlation between snapshot transaction price and snapshot trading volume, as well as snapshot transaction price and transaction count. Two sub-factors are used: price and transaction count correlation factor (CorrPM) and price and trading volume correlation factor (CorrPV). These sub-factors are equally weighted and industry market value neutralized[22][23][25] - **Regret Avoidance Factor**: Based on behavioral finance theory, this factor utilizes investors' regret avoidance emotions to construct effective stock selection factors. It examines the proportion and degree of stock price rebound after being sold by investors. The factor is constructed using transaction data to identify active buy/sell directions, with additional restrictions on small orders and closing trades to enhance performance. Two sub-factors are used: sell rebound proportion factor (LCVOLESW) and sell rebound deviation factor (LCPESW). These sub-factors are equally weighted and industry market value neutralized[26][32][35] - **Slope Convexity Factor**: Derived from the elasticity of supply and demand, this factor uses high-frequency snapshot data from limit order books to calculate the slope and convexity of buy and sell orders. The factor is constructed by aggregating order volume data by level and calculating the slope of buy and sell order books. Two sub-factors are used: low-level slope factor (Slope_abl) and high-level seller convexity factor (Slope_alh). These sub-factors are equally weighted and industry market value neutralized[36][41][43] - **High-frequency "Gold" Portfolio Strategy**: Combines the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with equal weights to construct an enhanced strategy for the CSI 1000 Index. The strategy includes mechanisms to reduce transaction costs, such as weekly rebalancing and turnover rate buffering. The strategy's annualized excess return is 10.20%, with an IR of 2.38 and maximum excess drawdown of 6.04%[44][46][47] - **High-frequency & Fundamental Resonance Portfolio Strategy**: Combines high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to construct an enhanced strategy for the CSI 1000 Index. The strategy's annualized excess return is 14.49%, with an IR of 3.46 and maximum excess drawdown of 4.52%[48][50][52]

Macro Liquidity - The US dollar index has declined, and the degree of "inversion" in the China-US interest rate spread has narrowed. The nominal and real yields of 10Y US Treasuries have decreased or remained unchanged, driven by a decline in inflation expectations [2][13][19]. Market Trading Activity - Overall market trading activity has decreased, with the volatility of major indices showing mixed trends. The trading activity in sectors such as non-ferrous metals, electric vehicles, steel, electronics, automotive, and real estate remains above the 80th percentile [3][25]. - The volatility of major indices, including the Shanghai Composite and CSI 300, has increased, while the volatility of the ChiNext and STAR Market indices has decreased. Sectors like electronics, automotive, and chemicals have seen a rapid increase in volatility [3][31]. Analyst Predictions - Analysts have continued to raise net profit forecasts for the entire A-share market for 2025 and 2026. The proportion of stocks with upward revisions in net profit forecasts has increased across various sectors, including retail, finance, light industry, and public utilities [4][50]. - The net profit forecasts for major indices such as the CSI 300, CSI 500, and SSE 50 have been adjusted upwards for 2025 and 2026, while the ChiNext index has seen mixed adjustments [4][23][24]. Northbound Trading Activity - Northbound trading activity has decreased, with an overall net sell-off in A-shares. The trading volume ratio in sectors like non-ferrous metals, electronics, and banking has increased, while it has decreased in pharmaceuticals, machinery, and communications [5][29]. - Northbound trading has shown a net buying trend in sectors such as electronics, automotive, and electric vehicles, while net selling has occurred in computing, pharmaceuticals, and communications [5][33]. Margin Financing Activity - The activity of margin financing has dropped to its lowest point since mid-September 2025, with a net sell-off of 12.812 billion yuan. The main net buying has been in sectors like non-ferrous metals, military, and pharmaceuticals, while net selling has occurred in TMT, finance, and automotive sectors [6][35]. Fund Activity - The positions of actively managed equity funds have continued to increase, with significant net subscriptions in ETFs, primarily driven by individual investors. Active equity funds have mainly increased their positions in electronics, automotive, and media sectors, while reducing exposure in communications, finance, and real estate [6][8][52]. - The newly established equity fund scale has rebounded, with both active and passive funds seeing an increase in size. ETFs related to financials, non-ferrous metals, and electronics have been the main net buyers, while those related to communications, chemicals, and transportation have seen net selling [6][53].

Macro Liquidity - The US dollar index has declined, and the degree of "inversion" in the China-US interest rate spread has narrowed [2][13] - The nominal and real yields of 10-year US Treasuries have decreased or remained unchanged, with inflation expectations also falling [2][19] - Offshore dollar liquidity has tightened, while domestic interbank liquidity remains balanced and slightly loose [2][19] Market Trading Activity - Overall market trading activity has decreased, with the volatility of major indices showing mixed trends [3][25] - Trading heat in sectors such as non-ferrous metals, electric vehicles, steel, electronics, automotive, and real estate remains above the 80th percentile [3][25] - The volatility of the communication and electronics sectors remains above the 80th historical percentile [3][31] Analyst Predictions - Analysts have continued to raise net profit forecasts for the entire A-share market for 2025 and 2026 [4][43] - The proportion of stocks with upward revisions in net profit forecasts for 2025 and 2026 has increased [4][43] - Sectors such as retail, finance, light industry, and public utilities have seen upward revisions in net profit forecasts for 2025 and 2026 [4][43][44] Northbound Trading Activity - Northbound trading activity has decreased, with overall net selling of A-shares [5][29] - In the top 10 active stocks, the trading volume ratio for sectors like non-ferrous metals, electronics, and banking has increased [5][32] - Northbound trading has shown net buying in sectors such as electronics, automotive, and electric vehicles, while net selling occurred in computing, pharmaceuticals, and communications [5][33] Margin Financing Activity - Margin financing activity has dropped to its lowest point since mid-September 2025 [6][35] - The main net buying in margin financing has been in sectors like non-ferrous metals, military, and pharmaceuticals [6][38] - The proportion of financing purchases in sectors such as oil and petrochemicals, steel, and public utilities has increased [6][38] Fund Activity - The positions of actively managed equity funds have continued to rise, with net subscriptions in ETFs persisting [8][45] - Actively managed equity funds have mainly increased positions in sectors like electronics, automotive, and media [8][46] - New fund establishment has seen a rebound, with both actively and passively managed funds experiencing growth [8][50]

- The macro timing model recommends a 50% equity allocation for October based on signal strengths of 40% for economic growth and 60% for monetary liquidity[6][47][48] - The macro timing strategy has achieved a return of 13.57% from the beginning of 2025 to date, compared to a 25.65% return for Wind All A Index during the same period[6][47][49] - The rotation model suggests switching to the "Mao Index" style due to the positive slope of the Mao Index's 20-day closing price (0.08%) compared to the negative slope of the micro-cap index (-0.001%)[18][25][27] - The micro-cap timing risk control indicators, including volatility crowding degree (-17.37%) and 10-year government bond yield (-13.46%), indicate that systemic risk is within a controllable range[18][20][25] - Among stock selection factors, reversal factors performed the best last week with an IC mean of 22.65% for all A-shares, followed by technical factors (14.88%) and value factors (25.89%)[51][52][53] - For convertible bonds, quantitative bond selection factors such as stock consensus expectations, stock value, and bond valuation factors achieved positive IC means last week[57][58][59]

Investment Rating - The report suggests a cautious outlook for the agricultural sector, particularly in livestock and feed industries, with a focus on identifying quality companies for investment opportunities [3][4][5][6]. Core Insights - The agricultural sector, particularly the livestock segment, is experiencing significant price fluctuations and profitability challenges, with a recommendation to focus on low-cost, high-quality enterprises [3][4][5][6]. - The report highlights the potential for recovery in the beef and dairy markets as seasonal demand increases, while also noting the ongoing pressures in the pig farming sector due to price declines [3][4][5][6]. - The planting industry is facing short-term supply and demand pressures, but there is potential for improvement if crop yields decrease significantly [6][49]. Summary by Sections Swine Farming - Current pig prices are in a downward trend, with the average weight of pigs at 128.25 kg, indicating high inventory levels despite price drops [3][22]. - The report anticipates continued increases in pig output in the coming months, with limited seasonal accumulation space, suggesting further price declines [3][22]. - Long-term prospects remain positive for leading companies in the sector, with recommendations to focus on low-cost producers like Muyuan Foods and Wens Foodstuffs [3][23]. Poultry Farming - The poultry sector is stabilizing, with yellow feathered chicken prices showing resilience due to improved downstream demand and supply contraction [4][36]. - The report notes that while white feathered chicken prices are under pressure, overall profitability in poultry farming is expected to improve with a recovery in consumer demand [4][38]. Livestock - Beef prices are expected to rise as the consumption season approaches, while dairy cow inventory trends are decreasing [5][42]. - The report indicates that the beef and dairy sectors are currently facing losses, but a recovery is anticipated as demand increases and supply contracts [5][43]. Planting Industry - The planting sector is experiencing price volatility due to new corn harvests and ongoing uncertainties regarding soybean imports [6][48]. - The report emphasizes the importance of improving grain yields and suggests that a significant reduction in crop production could enhance the sector's outlook [6][49]. Feed and Aquaculture - Feed prices have stabilized, with no significant changes reported in the prices of various feed types [6][62]. - The aquaculture sector is showing positive trends, with certain fish prices increasing, indicating a potential recovery in this segment [6][62].

Investment Rating - The report suggests focusing on domestic AI development-driven sectors such as servers and IDC, as well as overseas AI development-driven sectors like servers and optical modules [5] Core Insights - OpenAI is expanding its collaboration and accelerating computing power investments, including a partnership with Broadcom for a 10GW custom AI accelerator, aiming for deployment by the second half of 2026 and completion by the end of 2029 [1] - The demand for optical modules is expected to increase significantly, with projections of 50 million, 75 million, and 100 million units needed in 2025, 2026, and 2027 respectively [1] - TSMC reported a higher-than-expected profit margin of 59.5% for Q3 2025, driven by strong AI demand, and provided a positive revenue guidance for Q4 2025 [1] - Domestic AI applications are entering a large-scale commercialization phase, as indicated by the increase in daily token usage from 120 billion in May 2024 to over 30 trillion by September 2025 [1][3] - The optical communication industry is expected to see growth, as evidenced by Shijia Photon's Q3 2025 revenue of 570 million yuan, a year-on-year increase of 103% [1] Summary by Sections Communication Sector - The telecommunications business revenue for the first eight months reached 1,182.1 billion yuan, a year-on-year increase of 0.8% [4][15] - The optical module exports saw a decline of 28.66% year-on-year in August, attributed to domestic companies building factories overseas [4][34] Server Sector - The server index decreased by 5.85% this week and 8.28% for the month, but OpenAI's initiatives are expected to drive demand for server chips [2][7] - TSMC's high profit margins and capacity expansion are expected to support the production of AI chips [2][7] Optical Module Sector - The optical module index fell by 7.55% this week and 12.35% for the month, but long-term demand is projected to rise due to significant investments in AI data centers [2][7] IDC Sector - The IDC index decreased by 6.24% this week and 8.91% for the month, but the domestic AI ecosystem is forming a rapidly iterating internal cycle [3][10]

近期债券市场开启了温和的反弹行情。近期的贸易摩擦和权益市场降温为市场情绪修复添砖加瓦。不过如果把时间线 拉长来看，其实从国庆节前一两个交易日开始，市场就已经出现一定程度的自发企稳迹象。这种内生反弹的动能来源 于三个方面：情绪面出清 + 资金面"兜底" + 宽松预期回归。 情绪"出清"下的自发修复。 债市暂现回暖。 市场得以回暖，一个重要的原因是市场此前对利空定价比较充分。我们持续跟踪和测算的"债市微观交易温度计"在 10 月 10 日的读数跌至 30%分位以下，这是近两年来的低位水平。历史经验显示，当情绪指标跌入低分位区间，往往 意味着市场对利空的反应已经比较充分，进一步下跌的动能显著减弱。换言之，市场的悲观预期在过去 2-3 个月已被 比较充分的消化和定价。在这种情绪结构下，市场更容易出现技术性修复行情。 资金利率给出"定心锚"。 情绪得以回暖的重要基础是资金面持续平稳。过去两三个月，债券利率虽然经历了一轮明显上行调整，但资金利率始 终维持在低位小幅区间震荡，并未出现明显收紧迹象。历史统计结果显示，当资金利率保持平稳时，债券利率上行幅 度通常有"边界"，而过去 7-9 月 10 年期国债收益率的累计上行幅度 ...

Group 1 - The simulated portfolio returns have rebounded this week, with most credit style portfolios outperforming interest rate style portfolios. The weekly returns for secondary ultra-long and city investment ultra-long strategies were 0.34% and 0.28% respectively, while credit style portfolios saw returns of 0.65% and 0.41% for the same strategies [2][14][15] - The recovery in returns has shifted from interest rate and medium-long duration strategies to ultra-long bond strategies. The average weekly return for credit style time deposit heavy portfolios increased by 3.6 basis points to 0.12%, the highest since August, while city investment heavy portfolios rose to 0.22%, an increase of approximately 12.1 basis points [2][16] - The average return for secondary capital bond heavy portfolios increased by nearly 20 basis points, with the secondary bond duration and mixed duration strategies showing weekly returns nearly equal to the ultra-long strategy. The secondary bond bullet strategy has shown a faster recovery, with cumulative negative returns since the third quarter narrowing to -0.36% [2][16] Group 2 - In terms of return sources, the coupon income from various strategy portfolios has declined, while the contribution from capital gains has increased. Among mainstream strategies, the coupon income for secondary bond bullet and duration strategies fell by more than 0.04 basis points, while city investment bonds and bank perpetual bonds maintained annualized coupon rates around 2.24% and 2.26% respectively [3][25] - The capital gains contribution for credit style portfolios accounted for most of the returns this week, with coupon contributions falling within the range of 5% to 30%, further compressing and increasing concentration compared to the previous week [3][25] Group 3 - Over the past four weeks, medium-long duration secondary perpetual strategies have shown cumulative returns at the forefront. The cumulative excess returns for perpetual bond duration, secondary bond bullet, and secondary bond duration strategies were 13 basis points, 11.2 basis points, and 11.1 basis points respectively [4][29] - The medium-long duration secondary perpetual bond strategy has rebounded significantly, but its volatility exceeds that of the downshift strategies. The cumulative return for the secondary bond downshift strategy reached 9.2 basis points, demonstrating both low volatility and strong recovery advantages [4][29] - From a strategy duration perspective, medium-long duration secondary perpetual bonds and ultra-long strategies exhibit stronger offensive attributes. The short-end time deposit strategy's excess returns have dropped to the lowest in three months, lacking aggressiveness in a bond bull market [4][32]