- The report does not include any quantitative models or factors for analysis or construction[1][2][3] - The content primarily focuses on market performance, index comparisons, turnover rates, risk premiums, PE-TTM, dividend yields, and net-breaking rates of various broad-based indices[1][2][3] - No quantitative models or factors are explicitly mentioned or analyzed in the provided content[1][2][3]

证券研究报告·公司深度报告 2025 年 12 月 11 日 江海证券研究发展部 执业证书编号：S1410524050001 | 投资评级： | 增持（首次） | | --- | --- | | 当前价格: | 3.22元 | | 目标价格: | 3.64元 | | 目标期限: | 6 个月 | | 市场数据 | | | --- | --- | | 总股本(百万股) | 2232.03 | | A 股股本(百万股) | 2232.03 | | B/H 股股本(百万股) | -/- | | A 股流通比例(%) | 98.46 | | 12 个月最高/最低(元) | 4.08/2.12 | | 第一大股东 | 沈臻宇 | | 第一大股东持股比例(%) | 4.92 | | 上证综指/沪深 300 | 3878.00/4531.05 | % 1 个月 3 个月 12 个月 相对收益 -4.31 -6.61 8.72 绝对收益 -6.94 -5.01 23.37 数据来源：聚源 注：相对收益与沪深 300 相比 医药行业研究组 分析师：吴春红 誉衡药业 002437.SZ 医药生物行业 双核心业务筑牢基本盘，多矩阵 ...

Quantitative Models and Construction Barra Risk Model - **Model Name**: Barra Risk Model (Barra CNE6) - **Model Construction Idea**: The model aims to reduce the dimensionality of asset returns, enabling the calculation of covariance matrices between assets, which are essential for portfolio optimization. It uses constrained weighted least squares (WLS) to address multicollinearity and heteroscedasticity issues, constructing pure factor portfolios that isolate exposure to individual factors [3][9][11] - **Model Construction Process**: 1. The cross-sectional asset returns are modeled using a multi-factor linear regression: $R_{t}=\alpha+\beta\lambda_{t}+\varepsilon_{t}$ Here, $\beta$ represents factor exposures, $\lambda_{t}$ denotes factor returns, and $\varepsilon_{t}$ is the residual [9][10] 2. The covariance matrix of asset returns is derived as: $\Sigma_{R}=\beta\Sigma_{A}\beta^{T}+\Sigma_{E}$ $\Sigma_{A}$ is the covariance matrix of factors, and $\Sigma_{E}$ is the covariance matrix of residuals [11][12] 3. Factor exposures are standardized using market capitalization-weighted normalization: $$\widehat{\boldsymbol{\beta}_{t-1}^{j}}=\frac{{\boldsymbol{\beta}_{t-1}^{j}}-\frac{\sum_{i}^{N}s_{i,t-1}\beta_{i,t-1}^{j}}{\sum_{i}^{N}s_{i,t-1}}}{s t d({\boldsymbol{\beta}_{t-1}^{j}})}$$ Here, $s_{i,t-1}$ represents the market capitalization of stock $i$ at time $t-1$ [18][32] 4. Industry factor returns are constrained to ensure neutrality: $\sum_{i=1}^{P}s_{I_{i}}\lambda_{i}^{I_{i}}=0$ [18][22] 5. Factor returns are estimated using constrained WLS: $$\lambda_{t}=C_{t}(C_{t}\beta_{t-1}W^{-1}\beta_{t-1}C_{t})^{-1}C_{t}\beta_{t-1}W^{-1}R_{t}$$ Here, $W$ is the weight matrix, and $C_{t}$ represents constraints [20][25] - **Model Evaluation**: The model effectively isolates factor exposures, enabling better evaluation of factor returns. However, pure factor portfolios have low investability due to constraints like short-selling limitations [19][21] --- Quantitative Factors and Construction Style Factors - **Factor Names**: Size, Volatility, Liquidity, Momentum, Quality, Value, Growth, Dividend Yield - **Factor Construction Idea**: These factors represent different market characteristics, such as size, volatility, and growth, and are used to explain asset returns and identify systematic risks [3][15][26] - **Factor Construction Process**: 1. **Size**: Logarithm of market capitalization (LNCAP) [114] 2. **Volatility**: Includes Beta, historical sigma, daily standard deviation, and cumulative range [114] 3. **Liquidity**: Calculated using turnover ratios (monthly, quarterly, annual) and annualized traded value ratio [114] 4. **Momentum**: Includes short-term reversal, seasonality, industry momentum, and relative strength [114][115] 5. **Quality**: Includes earnings variability, accruals, profitability metrics, and investment quality [114][116] 6. **Value**: Includes book-to-price ratio, earnings-to-price ratio, and enterprise multiple [114][116] 7. **Growth**: Historical growth rates for earnings per share and sales per share [114][116] 8. **Dividend Yield**: Dividend-to-price ratio [114][116] - **Factor Evaluation**: Single-factor tests show limited stock selection ability, with low significance and effectiveness. However, after constructing pure factor models, the significance of factors improves, especially for Volatility and Momentum [66][78] Residual Factor - **Factor Name**: Residual Factor - **Factor Construction Idea**: Residuals represent the unexplained portion of stock returns after accounting for industry, style, and country factors. They are tested for nonlinear relationships with stock returns [79][82] - **Factor Construction Process**: 1. Residuals are derived from the regression model: $R_{t}=\beta_{t-1}C_{t}\gamma_{t}+\delta_{t}$ Here, $\delta_{t}$ represents residuals [23][79] 2. Residuals are used as stock selection factors and tested using layered backtesting [79][82] - **Factor Evaluation**: Residual factors exhibit strong nonlinear relationships with stock returns, showing robust stock selection ability. Middle-layer groups outperform top and bottom groups significantly [79][82] --- Backtesting Results Pure Factor Model - **Size**: Annualized return -2.75%, annualized volatility 0.026, maximum drawdown 35.53%, Sharpe ratio -1.08 [76][77] - **Volatility**: Annualized return 1.93%, annualized volatility 0.049, maximum drawdown 12.43%, Sharpe ratio 0.39 [76][77] - **Liquidity**: Annualized return -5.90%, annualized volatility 0.033, maximum drawdown 60.88%, Sharpe ratio -1.81 [76][77] - **Momentum**: Annualized return -5.57%, annualized volatility 0.042, maximum drawdown 58.64%, Sharpe ratio -1.32 [76][77] - **Growth**: Annualized return -0.21%, annualized volatility 0.015, maximum drawdown 9.24%, Sharpe ratio -0.15 [76][77] - **Dividend Yield**: Annualized return -0.85%, annualized volatility 0.016, maximum drawdown 17.09%, Sharpe ratio -0.52 [76][77] - **Quality**: Annualized return 0.35%, annualized volatility 0.016, maximum drawdown 8.45%, Sharpe ratio 0.23 [76][77] - **Value**: Annualized return 1.38%, annualized volatility 0.028, maximum drawdown 13.83%, Sharpe ratio 0.49 [76][77] Residual Factor - **Middle Layer (Group 5)**: Annualized return 17.98%, annualized volatility 26.94%, Sharpe ratio 0.68, maximum drawdown 52.50% [82] - **Top vs Bottom Layer (Group 5 vs Group 10)**: Excess annualized return 13.58%, excess Sharpe ratio 1.50 [82] --- Index Attribution Results Positive Excess Return Indices - **Indices**: CSI 500 (3.41%), ChiNext Index (18.23%) - **Key Drivers**: Small-cap, high volatility, low liquidity, high growth, low dividend yield styles; leading sectors include non-ferrous metals, electronics, communication, and new energy [101][110] Negative Excess Return Indices - **Indices**: CSI 1000 (-0.22%), CSI A500 (-1.60%), CSI 300 (-4.30%), SSE 50 (-10.27%) - **Key Drivers**: Large-cap, low volatility, high liquidity, low growth, high dividend yield styles; underperforming sectors include banking, non-bank finance, and food & beverage [101][110]

金融工程定期报告 证券研究报告·金融工程报告 2025 年 12 月 10 日 江海证券研究发展部 分析师：梁俊炜 执业证书编号：S1410524090001 A 股市场快照：宽基指数每日投资动 部下跌，其中中证 500（-0.71%）和上证 50（-0.71%）跌幅最大。当年涨跌情况， 创业板指（49.87%）涨幅最大，其次是中证 2000（33.25%）和中证 500（24.37%）， 中证 1000（23.88%）和中证全指（22.15%）涨幅缩小，而上证 50（11.67%）涨幅 最小。另外，创业板指连续四日连阳。 ◆均线比较：所有跟踪指数仍在 5、10 及 20 日均线之上。中证 1000 重新跌回 60 ◆资金占比与换手：2025 年 12 月 9 日, 中证 2000（25.43%）交易金额占比最高， 相关研究报告 日均线。市场持续震荡。 态 2025.12.09 A 股市场快照：宽基指数每日投资动 态 2025.12.08 A 股市场快照：宽基指数每日投资动 态 2025.12.05 其次是沪深 300（24.33%）和中证 1000（21.11%）。各宽基指数当前换手率分别为 中证 2000 ...

Investment Rating - The industry investment rating is maintained at "Overweight" [1] Core Insights - The gaming sector shows strong performance with leading companies demonstrating significant revenue generation, particularly Tencent and Century Huatong, which ranked first and second in global mobile game publisher revenue [3][4] - The number of domestic game licenses issued in 2025 has increased by 29.39% year-on-year, indicating a supportive regulatory environment for the gaming industry [4] - The gaming sector is characterized by strong cash flow, ongoing policy support, and deepening AI game development, making it an attractive investment opportunity [6] Summary by Sections Industry Performance - Over the past 12 months, the industry has shown a relative return of -3.47% compared to the CSI 300 index, with an absolute return of 12.26% [2] Market Developments - In November 2025, 33 Chinese companies made it to the global top 100 mobile game publishers, collectively generating $1.95 billion, which accounts for 35.8% of the total revenue of the top 100 publishers [3] - The flagship game "Whiteout Survival" from Century Huatong contributed 54% of its revenue in November, with total earnings exceeding $3.8 billion [3] Regulatory Environment - The National Press and Publication Administration issued 1,532 domestic game licenses from January to November 2025, reflecting a 29.39% increase compared to the same period in 2024 [4]

- The report primarily focuses on tracking and analyzing the performance of broad-based indices in the A-share market, including their daily returns, moving averages, turnover rates, and valuation metrics such as PE-TTM and risk premiums[1][2][3] - The analysis highlights that all broad-based indices experienced gains on December 8, 2025, with the ChiNext Index (2.6%) and CSI 2000 (1.47%) showing the largest daily increases. For the year-to-date performance, the ChiNext Index (48.97%) recorded the highest growth, followed by CSI 2000 (33.97%) and CSI 500 (25.27%)[10][11] - All indices have surpassed their 5-day, 10-day, and 20-day moving averages, with CSI 1000 and CSI All Share also breaking above their 60-day moving averages. However, CSI 500 remains below its 60-day moving average, indicating a continued market recovery[14][15] - The turnover rates for December 8, 2025, were highest for CSI 2000 (4.34), followed by ChiNext Index (2.78) and CSI 1000 (2.47). The lowest turnover rates were observed for SSE 50 (0.26) and CSI 300 (0.61)[17] - The distribution of daily returns shows that the ChiNext Index has the largest negative kurtosis deviation, while CSI 1000 has the smallest. Similarly, the ChiNext Index exhibits the largest negative skewness, indicating a higher likelihood of extreme negative returns compared to other indices[23][25] - Risk premiums, calculated relative to the 10-year government bond yield, are highest for the ChiNext Index (2.60%) and CSI 2000 (1.46%), with their 5-year percentile ranks at 93.41% and 85.79%, respectively. In contrast, SSE 50 (0.57%) and CSI 300 (0.80%) have lower risk premiums and percentile ranks[27][31] - The PE-TTM ratios for broad-based indices show that CSI 1000 (97.52%) and CSI 500 (95.54%) have the highest 5-year percentile ranks, while CSI 2000 (84.3%) and the ChiNext Index (57.69%) are relatively lower. The ChiNext Index's 5-year percentile rank is below its danger threshold of 80%[39][43][44] - Dividend yields are highest for SSE 50 (3.30%) and CSI 300 (2.71%), while CSI 500 (1.37%) and CSI 2000 (0.75%) are the lowest. The ChiNext Index's 5-year historical percentile rank for dividend yield is relatively high at 66.69%[48][53][55] - The percentage of stocks trading below their book value (PB ratio < 1) is highest for SSE 50 (22.0%) and lowest for the ChiNext Index (1.0%), reflecting varying market valuation attitudes across indices[57]

Investment Rating - The industry investment rating is maintained at "Overweight" [1] Core Insights - The report highlights the emergence of space computing as a significant opportunity, driven by the urgent need to overcome terrestrial computing limitations and the unique advantages of space data centers [5][6] - The report outlines a three-step strategy for the development of space data centers in Beijing, emphasizing the importance of technological breakthroughs and the establishment of a new industrial chain [5] - The global tech giants are increasingly investing in space computing, indicating a competitive landscape that presents potential investment opportunities [6] Performance Overview - Over the past 12 months, the industry has shown strong performance with a relative return of 67.07% compared to the CSI 300 index, and an absolute return of 83.4% [2] - The industry has also demonstrated positive returns over shorter time frames, with a 10.88% relative return over the past month and a 9.42% return over the past three months [2] Strategic Developments - The report discusses the strategic importance of space data centers, which are expected to leverage the natural cooling of space and abundant solar energy to address the growing demand for computing power driven by artificial intelligence [5] - The first phase of the three-step strategy involves the development of a test satellite, "Chenguang-1," which is set to be launched by the end of 2025 or early 2026 [5] - The report emphasizes that the successful implementation of this strategy could lead to a significant transformation in the computing industry, with space data centers becoming a key component of the commercial space and AI sectors [5][6]

执业证书编号：S1410524040001 1. 江海证券-黑龙江省资本市场跟踪-黑龙 江省资本市场跟踪双周报— （2025.11.9-2025.11.22）– 2025.11.24 2. 江海证券-黑龙江省资本市场跟踪-黑龙 江省资本市场跟踪双周报— （2025.10.26-2025.11.8） – 2025.11.10 3. 江海证券-黑龙江省资本市场跟踪-黑龙 江省资本市场跟踪双周报— （2025.10.12-2025.10.25） – 2025.10.29 4. 江海证券-黑龙江省资本市场跟踪-黑龙 江省资本市场跟踪双周报— （2025.9.21-2025.10.11） – 2025.10.14 5. 江海证券-黑龙江省资本市场跟踪-黑龙 江省资本市场跟踪双周报— 机械军工行业研究组 黑龙江省资本市场跟踪双周报 — — 证券研究报告·黑龙江省资本市场跟踪报告 2025 年 12 月 8 日 江海证券研究发展部 风险提示：地方经济及政策变动风险、政策不及预期风险、资本市场波动风险、 宏观经济不确定性风险等。 敬请参阅最后一页之免责条款 2 分析师：张诗瑶 （2025.11.23-2025.12.6） 投 ...

aa 证券研究报告·金融工程报告 2025 年 12 月 8 日 江海证券研究发展部 分析师：梁俊炜 执业证书编号： S1410524090001 联系人：朱威 相关研究报告 金融工程定期报告 执业证书编号： S1410124010022 1.可转债跟踪周报：转债较权益超额 回撤，但股性攀升—2025.12.01 2.可转债跟踪周报：转债债性激增防 御 性 突 显 ， 回 撤 小 于 权 益 — 2025.11.24 3.可转债跟踪周报：转债债性支撑上 涨，表现优于权益—2025.11.17 4.可转债跟踪周报：转债继续随权益 上涨，可维持均衡配置—2025.11.10 5.可转债跟踪周报：转债放量延续上 扬，涨幅超越权益—2025.11.03 转债随权益小幅回暖，但有所缩量 核心内容： | 1 | 可转债市场表现 | 2 | | --- | --- | --- | | | 1.1 市场行情 | 2 | | 2 | 可转债个券表现 | 5 | | | 2.1 个券行情 | 5 | | | 2.2 估值分析 | 7 | | 3 | 可转债条款跟踪 | 8 | | | 4 风险提示 | 9 | ◆可转债市场表现： ...

证券研究报告·金融工程报告 2025 年 12 月 8 日 江海证券研究发展部 金融工程定期报告 金融工程研究组 A 股市场快照：宽基指数每日投资动态 2025.12.08 ◆市场表现：2025 年 12 月 5 日, 各宽基指数（表 1）全部上涨，其中中证 2000（1.87%） 投资要点： 分析师：梁俊炜 执业证书编号：S1410524090001 A 股市场快照：宽基指数每日投资动 和创业板指（1.36%）涨幅最大。当年涨跌情况，创业板指（45.19%）涨幅最大， 其次是中证 2000（32.03%）和中证 500（23.96%），中证 1000（23.24%）和中证全 指（21.6%）涨幅扩大，而上证 50（11.82%）涨幅最小。 ◆均线比较：所有跟踪指数已站上 5 日及 10 日均线。中证 1000 仍在 20 日均线之 下，其余跟踪指数已突破 20 日均线。上证 50、沪深 300、中证 2000 和创业板指相 继突破 60 日均线，市场逐渐修复。 相关研究报告 ◆资金占比与换手：2025 年 12 月 5 日, 中证 2000（25.69%）交易金额占比最高， 态 2025.12.05 A 股 ...