Investment Rating - The report maintains an "Outperform" rating for the communication industry [1] Core Insights - The communication industry is experiencing sustained high demand, driven by advancements in AI and the development of intelligent computing centers [2][3] - Major cloud service providers (CSPs) are significantly increasing their capital expenditures (Capex) to enhance AI capabilities, with projections indicating a combined Capex of $361 billion from major overseas players by 2025, and over 360 billion yuan from domestic firms [2] - The emergence of silicon photonic modules is set to revolutionize optical communication, offering lower costs, reduced power consumption, and higher integration [4][67] Summary by Sections AI Data Center Interconnection Development Trends - The AI arms race has entered a new phase, with rapid iterations in intelligent computing center interconnection technologies [2][16] - The demand for AI capabilities is driving CSPs to invest heavily in their infrastructure, leading to increased Capex and a focus on self-developed ASIC chips [32] Development of Optical Modules/Silicon Photonic Modules - Silicon photonic modules are expected to capture a larger market share, growing from 34% in 2023 to 52% by 2029, driven by their advantages in cost and performance [55][67] - The market for silicon photonic modules is projected to reach $10.3 billion by 2029, with a CAGR of 45% over the past five years [4] Cutting-edge Optical Communication Technologies - New technologies such as Co-Packaged Optics (CPO), Optical Circuit Switching (OCS), and Optical Input/Output (OIO) are anticipated to play significant roles in the future of optical communication [5][96] - The global market for OCS is expected to exceed $1.6 billion, while the DCI market could reach $28.4 billion by 2029 [5] Major CSPs and Their Developments - Google, AWS, and Meta are leading the development of self-researched ASIC chips, with significant advancements in their respective architectures [3][32] - CSPs are increasingly focusing on high-density optical connections and innovative interconnection solutions to meet the growing demands of AI applications [21][106]

Investment Rating - The report maintains an "Outperform the Market" rating for the securities industry [4][6]. Core Insights - The securities industry has shown high performance in the first three quarters of 2025, with total revenue reaching 421.42 billion yuan, a year-on-year increase of 42.57%, and net profit attributable to shareholders reaching 169.29 billion yuan, up 62.48% year-on-year [1][13]. - The growth in revenue and profit is attributed to a steady rise in the equity market, increased trading volumes, and a recovery in wealth management services [1][29]. - Financial investment assets have become the main expansion direction for securities companies, totaling 6,991.8 billion yuan by the end of Q3 2025, accounting for 47% of total assets [2][32]. Summary by Sections Revenue Structure - Investment income has increased its share, with brokerage income at 111.78 billion yuan, up 74.64%, and investment income at 187.04 billion yuan, up 43.86% [13][30]. - Total assets and net assets of listed securities firms reached 14.92 trillion yuan and 2.85 trillion yuan, respectively, reflecting a year-on-year increase of 21.74% and 13.16% [19][20]. High Growth in Capital-Intensive Business - Self-operated business revenue reached 186.86 billion yuan, a year-on-year increase of 43.8%, with major contributors being CITIC Securities, Guotai Junan, and China Galaxy [30][31]. - The report highlights a significant increase in equity investment scale, with self-operated equity securities and derivatives reaching 8.475 trillion yuan, up 32.6% from the end of 2024 [34][35]. Brokerage Business - The brokerage business has benefited from active market trading, with all listed securities firms reporting positive growth in brokerage income [13][14]. - The average trading commission rate remains low, indicating potential for further revenue growth [16][30]. Investment Banking Business - The domestic equity financing scale has shown recovery, with IPOs continuing to rebound and underwriting activities improving [29][30]. - The report notes a 61.49% year-on-year increase in the total amount raised through initial public offerings [29]. Asset Management Growth - The asset management scale continues to grow, with a steady transition towards public fund management [22][23]. - The report indicates that the new asset management regulations are promoting the public fund transformation of securities firms [24][25].

Group 1: Economic Overview - "反内卷" is a key focus for the "十五五" period, expected to drive China's economic narrative in the coming year[1] - China's PPI has experienced negative growth for 36 consecutive months since October 2022, with the GDP deflator remaining below zero for nine consecutive quarters starting mid-2023[14] - The low inflation environment mirrors the supply-demand mismatch seen during the late 1990s, indicating structural economic challenges[2] Group 2: Short-term Policy Measures - Short-term "反内卷" efforts will rely on policy interventions across industrial and macroeconomic dimensions, with a focus on administrative measures to address severe mismatches in certain sectors[3] - The fiscal deficit is projected to remain high at 3.8%-4.0% to support economic stability, with a total deficit expected to exceed 12 trillion yuan[52] - PPI is anticipated to recover from negative growth, with a target of achieving positive growth by mid-2026, supported by favorable base effects[67] Group 3: Long-term Reform Strategies - Long-term "反内卷" success hinges on structural reforms, including market-oriented reforms to eliminate local protectionism and enhance resource allocation efficiency[72] - The urbanization of approximately 250 million agricultural migrants is seen as a potential driver for increased domestic demand and income growth[74] - The government aims to transition from infrastructure-focused fiscal policies to those prioritizing social welfare and human capital investment[53]

Group 1: Economic Transition Insights - China is in a critical phase of economic transition, where the shift from old to new growth drivers is essential for overcoming growth bottlenecks and achieving high-quality development[1] - By 2027, emerging industries are expected to surpass traditional industries in terms of value added, with a projected value of approximately CNY 17.5 trillion for old industries and CNY 14.3 trillion for new industries in 2024[2] - The financial restructuring accompanying industrial transformation has been effective, with the non-performing loan ratio of major banks remaining stable compared to the previous transition period (1998-2002) when it exceeded 25%[2] Group 2: Employment and Structural Changes - Emerging industries have limited direct employment absorption capacity, with the construction industry employing between 12 to 20 million people, significantly outpacing the 6 million in high-tech manufacturing sectors[2] - The service sector's employment share is expected to structurally increase, which may take a longer time to address structural employment issues[2] - The transition from old to new economic drivers is characterized by a gradual shift in employment dynamics, requiring the widespread adoption of new business models and scenarios[2] Group 3: Industry Development Trends - Key emerging sectors such as semiconductors, software development, and maritime equipment are identified as future pillars of the economy, transitioning from "technological breakthroughs" to "commercial realization" phases[3] - The analysis of export comparative advantage indicates significant potential in sectors like optical instruments and chip manufacturing, which are crucial for national strategic goals[3] - The industrial lifecycle analysis shows that industries like software development and gaming are in a growth phase, with capital expenditure focused on technological upgrades and revenue growth[3]

Economic Growth Framework - The core task for the "14th Five-Year Plan" period is to achieve a per capita GDP of approximately $29,000 by 2035, positioning China among "medium-developed countries" [2] - The bottom-line target for annual real GDP growth is set at 4.2%, while the consensus target suggests a compound GDP growth rate of around 4.4% over the next decade [2] - The expected economic growth rate for the "14th Five-Year Plan" period is projected to be between 4.5% and 4.9%, with a likely internal control target of 4.8% to 5.0% for the upcoming year [2] New Growth Paradigm - The new growth paradigm emphasizes "dynamic iteration + moderate inflation + currency appreciation" as the driving forces for economic growth [1] - This paradigm shift is expected to fundamentally alter asset return characteristics and risk premiums, leading to a systematic outperformance of equity assets over fixed-income assets [1] - The transition in asset allocation is anticipated to move from a real estate-dominated structure to one centered around equity assets [1] Market Implications - The anticipated recovery in corporate profits, particularly in upstream cyclical industries, is expected to create structural opportunities in the stock market [3] - The bond market is likely to experience a rebound in interest rates as inflation indicators improve, with the yield curve expected to steepen [3] - The shift in asset attractiveness is projected to favor equities over real estate and fixed income, driven by improved earnings and valuation dynamics [3] Risks - Potential risks include volatility in overseas markets and uncertainties in domestic policy execution [4]

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on multi-factor stock selection models targeting benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices[10][11][13] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization[11] - The report monitors the performance of single-factor Maximized Factor Exposure (MFE) portfolios across different stock selection spaces, including CSI 300, CSI 500, CSI 1000, CSI A500 indices, and public fund heavy positions index[10][14][39] - The MFE portfolio construction process involves optimizing the portfolio to maximize single-factor exposure while controlling for constraints such as style exposure, industry exposure, individual stock weight deviation, and turnover rate[39][40][41] - The optimization model for MFE portfolios is defined as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style factor deviation, industry deviation, individual stock weight deviation, and component stock weight limits[39][40] - The report highlights the weekly, monthly, and yearly performance of various factors in different stock selection spaces, such as CSI 300, CSI 500, CSI 1000, CSI A500 indices, and public fund heavy positions index[17][19][21][23][25] - Factors such as three-month volatility, one-month volatility, and three-month turnover performed well in the CSI 300 space recently, while factors like one-year momentum and single-quarter profit growth rate performed poorly[17][18] - In the CSI 500 space, factors like one-month turnover and BP showed strong performance recently, while one-year momentum and standardized unexpected earnings performed poorly[19][20] - In the CSI 1000 space, factors such as illiquidity shock and expected net profit growth performed well recently, while standardized unexpected revenue and one-year momentum showed weak performance[21][22] - In the CSI A500 space, factors like three-month volatility and one-month turnover performed well recently, while one-year momentum and standardized unexpected earnings performed poorly[23][24] - In the public fund heavy positions index space, factors such as one-month volatility and three-month turnover performed well recently, while standardized unexpected revenue and one-year momentum showed weak performance[25][26] - The report tracks the performance of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500 index enhancement funds, with detailed statistics on excess returns across different time periods[27][28][31][33][35][38]

Quantitative Models and Construction Methods 1. Model Name: Excellent Fund Performance Enhancement Portfolio - **Model Construction Idea**: Transition from benchmarking broad-based indices to benchmarking active equity funds, leveraging the holdings of excellent funds and applying quantitative methods to enhance selection, achieving "the best among the best"[4][18][51] - **Model Construction Process**: - Benchmark the portfolio against the median return of active equity funds, represented by the mixed equity fund index (885001.WI)[18][51] - Use a performance-layered perspective to select funds, neutralizing return factors to avoid style concentration issues[51] - Optimize the portfolio to control deviations in individual stocks, industries, and styles relative to the selected fund holdings[52] - Incorporate transaction costs and position effects into return calculations, with the portfolio position set at 90%[18][51] - **Model Evaluation**: Demonstrates strong stability and the ability to consistently outperform the median of active equity funds[52] 2. Model Name: Outperformance Stock Selection Portfolio - **Model Construction Idea**: Focus on stocks with significant earnings surprises, leveraging both fundamental and technical analysis to identify stocks with strong support and resonance[5][57] - **Model Construction Process**: - Filter stocks based on research reports with "earnings surprise" in the title and analysts' comprehensive upward revisions of net profit[5][57] - Conduct dual-layer selection on the earnings surprise stock pool using fundamental and technical dimensions[5][57] - Construct a portfolio of stocks with both fundamental support and technical resonance[5][57] - Benchmark the portfolio against the mixed equity fund index, with a 90% position[24] - **Model Evaluation**: The portfolio consistently ranks in the top 30% of active equity funds annually, demonstrating strong performance[58] 3. Model Name: Brokerage Golden Stock Performance Enhancement Portfolio - **Model Construction Idea**: Use the brokerage golden stock pool as the stock selection space and constraint benchmark, optimizing the portfolio to control deviations in individual stocks, styles, and industries[6][32] - **Model Construction Process**: - Use the brokerage golden stock pool, which reflects both top-down industry allocation and bottom-up stock selection capabilities of brokerage analysts[62] - Optimize the portfolio to further refine stock selection within the golden stock pool, aiming to outperform the mixed equity fund index[6][62] - Benchmark the portfolio against the mixed equity fund index, with a 90% position[32] - **Model Evaluation**: The portfolio consistently ranks in the top 30% of active equity funds annually, showcasing stable outperformance[63] 4. Model Name: Growth and Stability Portfolio - **Model Construction Idea**: Focus on the temporal release of excess returns in growth stocks, using a "time-series first, cross-sectional second" approach to construct a two-dimensional evaluation system for growth stocks[7][67] - **Model Construction Process**: - Introduce an "excess return release map" to identify the strongest phases of excess return release around positive events, such as earnings surprises and pre-announcements of earnings growth[67] - Segment the growth stock pool based on the number of days until the scheduled financial report disclosure date, prioritizing stocks with closer disclosure dates[7][67] - Use multi-factor scoring to select high-quality stocks when the sample size is large[7][67] - Implement mechanisms to reduce portfolio turnover, mitigate risks, and ensure stability, such as weak balancing, transition, buffering, and risk avoidance mechanisms[67] - Benchmark the portfolio against the mixed equity fund index, with a 90% position[39] - **Model Evaluation**: The portfolio consistently ranks in the top 30% of active equity funds annually, effectively capturing the strongest phases of excess return release for growth stocks[68] --- Model Backtesting Results 1. Excellent Fund Performance Enhancement Portfolio - **Annualized Return (2012.1.4-2025.6.30)**: 20.31% (considering position and transaction costs)[53] - **Annualized Excess Return vs. Mixed Equity Fund Index**: 11.83%[53] - **Performance Ranking**: Top 30% of active equity funds in most years since 2012[53] 2. Outperformance Stock Selection Portfolio - **Annualized Return (2010.1.4-2025.6.30)**: 30.55% (considering position and transaction costs)[58] - **Annualized Excess Return vs. Mixed Equity Fund Index**: 24.68%[58] - **Performance Ranking**: Top 30% of active equity funds in all years since 2010[58] 3. Brokerage Golden Stock Performance Enhancement Portfolio - **Annualized Return (2018.1.2-2025.6.30)**: 19.34% (considering position and transaction costs)[63] - **Annualized Excess Return vs. Mixed Equity Fund Index**: 14.38%[63] - **Performance Ranking**: Top 30% of active equity funds annually from 2018 to 2025[63] 4. Growth and Stability Portfolio - **Annualized Return (2012.1.4-2025.6.30)**: 35.51% (considering position and transaction costs)[68] - **Annualized Excess Return vs. Mixed Equity Fund Index**: 26.88%[68] - **Performance Ranking**: Top 30% of active equity funds in most years since 2012[68]

Investment Rating - The report maintains an "Outperform" rating for JD Group [3][21][5] Core Views - The company achieved a revenue of 299.1 billion yuan in Q3 2025, representing a year-on-year growth of 15%. The retail segment contributed 250.6 billion yuan, growing 11% year-on-year, driven by strong performance in daily necessities and marketing revenue [1][9] - The logistics revenue reached 52.1 billion yuan, also up 15% year-on-year, while new business revenue surged by 214%, primarily due to the rapid growth of JD's food delivery service [1][9] - Non-GAAP net profit for the quarter was 5.8 billion yuan, with a non-GAAP net profit margin of 1.9%, down 3.2 percentage points from the previous year [2][10] - The company is expected to see revenue growth driven by the daily necessities and platform model post-subsidy phase, with continuous optimization of supply chain efficiency and improving gross margins [3][21] Financial Summary - Revenue projections for 2025-2027 are adjusted to 1,334.9 billion, 1,433.4 billion, and 1,558.7 billion yuan, with growth rates of +15.2%, +7.4%, and +8.7% respectively [4][21] - Adjusted net profit estimates for the same period are 30 billion, 41.5 billion, and 57.4 billion yuan, with growth rates of -37.2%, +38.1%, and +38.3% respectively [4][21] - The company’s PE ratio for 2026 is approximately 9x, indicating a favorable valuation [3][21]

Quantitative Models and Construction Methods 1. Model Name: Hong Kong Stock Selection Portfolio - **Model Construction Idea**: The model aims to select stocks from the analyst-recommended stock pool based on both fundamental and technical aspects, focusing on stocks with fundamental support and technical resonance[13][15]. - **Model Construction Process**: - **Step 1**: Construct an analyst-recommended stock pool based on events such as analyst earnings forecast upgrades, initial analyst coverage, and analyst report titles exceeding expectations[15]. - **Step 2**: Perform dual-layer selection on the stocks in the analyst-recommended stock pool from both fundamental and technical dimensions[15]. - **Step 3**: Select stocks that exhibit both fundamental support and technical resonance to construct the Hong Kong Stock Selection Portfolio[15]. - **Backtesting Period**: 2010-01-01 to 2025-06-30, with an annualized return of 19.11% and an excess return of 18.48% relative to the Hang Seng Index after considering transaction costs in a fully invested state[15]. - **Model Evaluation**: The model effectively combines fundamental and technical analysis to select outperforming stocks, demonstrating significant excess returns over the benchmark index[15]. Model Backtesting Results - **Hong Kong Stock Selection Portfolio**: - **Absolute Return**: 69.65% (2025 YTD)[17] - **Excess Return Relative to Hang Seng Index**: 37.18% (2025 YTD)[17] - **Annualized Return**: 19.11%[15] - **Excess Return Relative to Hang Seng Index**: 18.48%[15] - **Information Ratio (IR)**: 1.22[19] - **Tracking Error**: 14.55%[19] - **Maximum Drawdown**: 23.73%[19] - **Return-to-Drawdown Ratio**: 0.78[19] Quantitative Factors and Construction Methods 1. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: The factor measures the distance of the latest closing price from the highest closing price in the past 250 trading days, indicating the stock's momentum and trend-following potential[22]. - **Factor Construction Process**: - **Formula**: $$ \text{250-Day New High Distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max(Close, 250)}} $$ where $\text{Close}_{t}$ is the latest closing price, and $\text{ts\_max(Close, 250)}$ is the maximum closing price in the past 250 trading days[22]. - **Interpretation**: If the latest closing price sets a new high, the 250-Day New High Distance is 0; if the latest closing price falls from the new high, the distance is positive, indicating the extent of the decline[22]. - **Screening Criteria**: Stocks that have set a 250-day new high in the past 20 trading days are filtered based on analyst attention, relative stock strength, price path stability, and new high continuity[22][23]. - **Factor Evaluation**: The factor effectively captures momentum and trend-following characteristics, which are significant in the Hong Kong stock market[20]. Factor Backtesting Results - **250-Day New High Distance Factor**: - **Absolute Return**: 69.65% (2025 YTD)[17] - **Excess Return Relative to Hang Seng Index**: 37.18% (2025 YTD)[17] - **Annualized Return**: 19.11%[15] - **Excess Return Relative to Hang Seng Index**: 18.48%[15] - **Information Ratio (IR)**: 1.22[19] - **Tracking Error**: 14.55%[19] - **Maximum Drawdown**: 23.73%[19] - **Return-to-Drawdown Ratio**: 0.78[19]

- The market experienced a broad decline today, with the CSI 2000 index performing relatively well among scale indices, and the SSE Composite Index performing better among sector indices[2][6] - The banking, real estate, textile and apparel, pharmaceutical, and comprehensive industries performed relatively well, while the electronics, communications, computer, media, and new energy industries performed poorly[2][7] - Market sentiment was relatively high today, with 90 stocks hitting the daily limit up and 9 stocks hitting the daily limit down at the close[2][12] - The financing balance as of November 13, 2025, was 24,882 billion yuan, and the securities lending balance was 184 billion yuan, with the total margin balance accounting for 2.5% of the market's circulating market value[2][17][20] - The ETF with the highest premium on November 13, 2025, was the Internet ETF Shanghai-Hong Kong-Shenzhen, with a premium of 0.59%, while the ETF with the highest discount was the Zhejiang Merchants Zhijiang Phoenix ETF, with a discount of 0.73%[3][21] - The median annualized discount rates for the main contracts of the SSE 50, CSI 300, CSI 500, and CSI 1000 index futures over the past year were 0.39%, 3.23%, 10.86%, and 13.33%, respectively[3][26] - The stock with the most institutional research over the past week was Boying Special Welding, which was researched by 79 institutions[4][28] - The top ten stocks with net inflows from institutional seats on the Dragon and Tiger List on November 14, 2025, included Time-Space Technology, Hailu Heavy Industry, Zhongyi Technology, Lianhua Technology, Kangzhi Pharmaceutical, Kangpeng Technology, Worth Buying, Chengda Pharmaceutical, Zhaoyi Innovation, and Minsheng Health[4][33]