每经记者｜叶峰 每经编辑｜彭水萍 昨日A股三大指数集体调整，深成指跌超1%，创业板指冲高回落跌近2%。从板块来看，AI应用概念逆势上涨，AI医疗概念反复活跃，电网设备概念午后走 强；下跌方面，商业航天、可控核聚变等板块跌幅居前。 Wind数据显示，在1月13日的非货币ETF市场中，广发中证传媒ETF基金份额增加31.20亿份，净流入额41.22亿元；永赢国证商用卫星通信产业ETF基金份额 增加8.94亿份，净流入额18.86亿元；嘉实中证软件服务ETF基金份额增加14.39亿份，净流入额15.07亿元。 | 证券代码 证券简称 | 涨跌幅 | 基金份额变化 净流入额 | | 基金制 | | --- | --- | --- | --- | --- | | | (%) | (亿份) | (亿元) | (亿元 | | 512980.SH 广发中证传媒ETF | 0.47 | 31.20 | 41.22 | 10 | | 159206.SZ 永赢国证商用卫星通信产业ETF | -4.50 | 8.94 | 18.86 | 15. | | 159852.SZ 嘉实中证软件服务ETF | -1.92 | 14 39 | ...

Market Overview - As of January 12, the total ETF margin balance in the two markets reached 122.95 billion, an increase of 3.51 billion from the previous trading day [1] - The financing balance was 114.98 billion, up by 3.24 billion, while the securities lending balance was 7.97 billion, increasing by 0.27 billion [1] - In the Shanghai market, the ETF margin balance was 86.99 billion, an increase of 2.50 billion, with a financing balance of 79.97 billion, up by 2.24 billion [1] - The Shenzhen market's ETF margin balance was 35.96 billion, increasing by 1.01 billion, with a financing balance of 35.01 billion, up by 0.99 billion [1] ETF Margin Balances - The top three ETFs by margin balance on January 12 were: - Huaan Yifu Gold ETF (7.32 billion) - E Fund Gold ETF (4.16 billion) - Huatai-PB CSI 300 ETF (4.06 billion) [2][3] ETF Financing Buy Amounts - The top three ETFs by financing buy amounts on January 12 were: - E Fund CSI Hong Kong Securities Investment Theme ETF (2.10 billion) - Hai Fu Tong CSI Short Bond ETF (1.97 billion) - Bosera CSI Convertible Bonds and Exchangeable Bonds ETF (1.31 billion) [4] ETF Financing Net Buy Amounts - The top three ETFs by financing net buy amounts on January 12 were: - Fuguo 7-10 Year Policy Financial Bonds ETF (681 million) - GF CSI Media ETF (437 million) - Hai Fu Tong CSI Short Bond ETF (208 million) [5] ETF Securities Lending Sell Amounts - The top three ETFs by securities lending sell amounts on January 12 were: - Huatai-PB CSI 300 ETF (51.19 million) - Huaxia CSI A500 ETF (39.12 million) - Southern CSI 500 ETF (24.49 million) [6]

具体来看，1月12日，有308只ETF获融资净买入，其中，富国中债7—10年政策性金融债ETF获融资净 买入额居首，净买入6.81亿元；融资净买入金额居前的还有广发中证传媒ETF、海富通中证短融ETF、 南方中证500ETF、永赢国证商用卫星通信产业ETF、南方中证1000ETF、嘉实中证软件服务ETF等。 Wind统计显示，截至1月12日，沪深两市ETF两融余额为1229.52亿元，较上一交易日增加35.1亿元。其 中，ETF融资余额为1149.79亿元，较上一交易日增加32.37亿元；ETF融券余额为79.73亿元，较上一交 易日增加2.73亿元。 （文章来源：证券时报网） ...

Market Overview - The Shanghai Composite Index fell by 0.81% to close at 3939.81 points, with a high of 3966.89 points during the day [1] - The Shenzhen Component Index decreased by 0.92% to 13080.49 points, reaching a peak of 13223.12 points [1] - The ChiNext Index dropped by 1.16% to 3069.22 points, with a maximum of 3115.31 points [1] ETF Market Performance - The median return of stock ETFs was -0.67% [2] - The highest performing scale index ETF was the Penghua SSE STAR 50 Enhanced Strategy ETF, with a return of 0.76% [2] - The highest performing industry index ETF was the China Securities Software ETF, yielding 1.44% [2] - The highest return among thematic index ETFs was the Guangfa CSI Media ETF, which achieved 2.38% [2] ETF Gains and Losses - The top three ETFs by gain were: - Guangfa CSI Media ETF (2.38%) - Penghua CSI Media ETF (2.35%) - China Securities Semiconductor Industry ETF (2.32%) [4] - The top three ETFs by loss were: - Huatai-PB CSI Battery Theme ETF (-4.57%) - China Securities Battery Theme ETF (-4.57%) - Fortune CSI Battery Theme ETF (-4.46%) [4] ETF Fund Flows - The top three ETFs by fund inflow were: - E Fund ChiNext ETF (1.12 billion) - Fortune SSE Composite Index ETF (653 million) - Huatai-PB CSI 300 ETF (540 million) [6] - The top three ETFs by fund outflow were: - Huaxia SSE 50 ETF (372 million) - Guolian An CSI All-Index Semiconductor Products and Equipment ETF (359 million) - Huabao CSI Bank ETF (333 million) [6] ETF Margin Trading Overview - The top three ETFs by margin buying were: - Huaxia SSE STAR 50 Component ETF (469 million) - E Fund ChiNext ETF (417 million) - Guotai Junan CSI All-Index Securities Company ETF (359 million) [8] - The top three ETFs by margin selling were: - Southern CSI 500 ETF (37.58 million) - Huatai-PB CSI 300 ETF (34.35 million) - Huaxia CSI 1000 ETF (18.28 million) [8] Industry Insights - Huaxin Securities highlighted that the media industry is experiencing structural opportunities driven by AI, focusing on three dimensions: state-owned enterprises leveraging AI for cultural strength, major companies enhancing AI applications, and new media catalyzing the industry chain [9] - Galaxy Securities emphasized that the long-term value of quality content production companies remains unchanged, as they are positioned in the upper-middle of the media industry value chain, representing a scarce resource [10]

Group 1 - The core viewpoint from Goldman Sachs suggests that adjusting investment portfolios according to overall policy trends can yield excess returns [1] - The chief analyst from Shenwan Hongyuan presents a two-phase bullish outlook for A-shares, with the first phase in 2025 focusing on technology and a potential peak in spring 2026, followed by a comprehensive market rally in the second half of 2026 [2] - UBS forecasts a prosperous year for the Chinese stock market, driven by favorable factors continuing from 2025 [2] Group 2 - Recent reports indicate strong demand in the lithium carbonate market, with expectations for increased storage demand impacting consumption structure [2] - The main contract for lithium carbonate reached a ceiling price of 95,200 yuan/ton, driven by supply constraints and low inventory [2] - The MACD golden cross signal formation indicates positive momentum for certain stocks [3]

Market Overview - On November 18, the A-share market continued to adjust at high levels, with the lithium battery sector experiencing a significant pullback, while real estate and coal sectors also saw notable declines, dragging down the three major stock indices [2] - The Nikkei 225 index in Japan fell by 3.22% to close at 48,702.98 points, and the South Korean composite index dropped by 3.32% to 3,953.62 points [2] - Domestic commodity futures showed a mixed performance, with coking coal and coke contracts leading the declines [2] - Government bond futures closed higher, with the 30-year bond futures (TL2512) rising by 0.06% to 116.530 yuan [2] Sector Performance - The AI application sector showed resilience, performing well against the market trend, while media and semiconductor ETFs led the market gains, with several ETFs rising over 2% [2] - Conversely, the Huatai-PB ETF and other related ETFs fell by over 4% [2] Institutional Insights - Goldman Sachs' chief China equity strategist, Liu Jinjun, suggested that adjusting investment portfolios based on overall policy trends could yield excess returns [4] - Shenwan Hongyuan's chief analyst, Fu Jingtai, presented a two-phase bullish outlook for A-shares, predicting a peak in early 2026 followed by a comprehensive market rally in the second half of 2026 [4] - UBS's head of China equity strategy, Wang Zonghao, forecasted a prosperous year for the Chinese stock market, driven by favorable factors continuing into 2025 [4] Lithium Market Analysis - Recent reports indicated that the lithium carbonate main contract (LC2601) reached a limit-up price of 95,200 yuan/ton on November 17, driven by supply constraints and low inventory levels [5] - The market is expected to maintain a bullish outlook for lithium prices in November, although caution is advised regarding potential profit-taking after price surges [5] - The energy research team at New Lake Futures highlighted that the demand for energy storage will continue to increase within the lithium consumption structure, emphasizing the need to monitor the impact of rising lithium prices on storage demand growth [4]

Core Insights - The report suggests increasing allocation to large-cap value stocks while focusing on TMT (Technology, Media, and Telecommunications) and cyclical sectors [3][30] - The industry rotation model has been optimized to adapt to market conditions, incorporating high-frequency factors and enhancing the strategy's effectiveness [4][26] - The latest industry rotation model identifies non-bank financials, steel, media, non-ferrous metals, environmental protection, and telecommunications as preferred sectors [30][33] Market Review and Fund Flow Tracking - As of October 31, 2025, the total monthly trading volume of A-shares reached 36.78 trillion yuan, with a slight decrease in daily average trading volume by 10.49% compared to the previous month [12][18] - The average stock return dispersion for the past month was 2.41%, indicating a slight decline but remaining above the median level for the past six months [12][18] - The industry rotation speed has continued to expand, significantly exceeding the average level since 2015 [12][18] Industry Rotation Model and ETF Fund Configuration - The report emphasizes the importance of focusing on large-cap value and cyclical sectors, particularly in the context of the current unclear market leadership [3][30] - The recommended ETF portfolio includes six funds: E Fund CSI 300 Non-Bank ETF, Guotai Junan CSI Steel ETF, GF CSI Media ETF, Southern CSI Non-Ferrous Metals ETF, Southern Yangtze River Protection Theme ETF, and Guotai Junan CSI All-Share Communication Equipment ETF [3][34] - The model's historical performance has shown consistent positive excess returns, outperforming major benchmark indices [5][42] Historical Performance and Model Effectiveness - The industry rotation model has maintained a strong performance over the years, achieving excess returns compared to industry averages, with a notable performance in 2025 [5][42] - The model's win rates over the past 1, 3, and 5 years are 83.33%, 69.44%, and 71.67% respectively, indicating its robustness [43][44] - The report highlights the significance of emotional and price-volume factors in capturing market dynamics, especially in weak market conditions [42][43]

Investment Rating - The report indicates a marginal increase in investment in the TMT (Technology, Media, and Telecommunications) sector, with specific recommendations for non-bank financials, communications, real estate, building materials, media, and banks [1]. Core Insights - The industry rotation model is driven by three main dimensions: fundamentals, price-volume, and sentiment, aiming to capture market microstructure and industry opportunities. The model has been backtested bi-weekly and expanded to include factors such as momentum, trends, capital flow, sentiment, market structure, and volatility [1]. - The sentiment score for the real estate sector has significantly improved, increasing by 0.98, while the media sector's price-volume factors have seen a notable increase of 3.24 [1]. Summary by Sections Industry Recommendations - The recommended ETFs include: - E Fund CSI 300 Non-Bank ETF - Guotai CSI All-Index Communication Equipment ETF - Southern CSI All-Index Real Estate ETF - Guotai CSI All-Index Building Materials ETF - GF CSI Media ETF - Huabao CSI Bank ETF [3]. Performance Metrics - The industry rotation strategy has increased by 0.25% over the past two weeks, with an excess return of 0.64% compared to an equal-weighted industry benchmark. Year-to-date, the strategy has risen by 34.89%, with a Sharpe ratio of 1.77 and a Calmar ratio of 2.88 [4][6].

Report Summary 1. Report Industry Investment Rating - Not mentioned in the provided content 2. Core View of the Report - As of October 26, 2025, the model recommends non-bank finance, communication, real estate, building materials, media, and banking, with marginal increases in media and real estate investments [1] - The non-bank finance, communication, and real estate sectors are mainly driven by fundamentals, building materials and media are mainly influenced by sentiment, and banking is driven by both quantitative and fundamental factors [1] - The industry rotation model analyzes the market from three dimensions: fundamentals, volume-price, and sentiment, aiming to capture industry opportunities [1] 3. Summary by Relevant Catalogs Industry Rotation Model - The model backtests original factors on a bi-weekly basis and expands volume-price factors from dimensions such as momentum and trend, capital flow and sentiment, and market structure and volatility [1] - Six relatively effective factors are selected to construct the industry rotation strategy [1] Industry ETF Portfolio - The current industry ETF portfolio includes six ETFs: E Fund CSI 300 Non-Bank Finance ETF, Guotai CSI All-Index Communication Equipment ETF, Southern CSI All-Index Real Estate ETF, Guotai CSI All-Index Building Materials ETF, GF CSI Media ETF, and Huabao CSI Bank ETF [3] Performance of the Industry Rotation Strategy - In the past two weeks, the strategy rose 0.25%, with an excess return of 0.64% compared to the industry equal-weighted index [4][6] - Since the beginning of the year, the strategy has risen 34.89%, with a Sharpe ratio of 1.77 and a Calmar ratio of 2.88 in the past year [4] Strategy/Composite Factor Backtesting Results - Different factors have different IC means, IC standard deviations, ICIRs, frequencies of IC>0, and p-Values. For example, the成交均价因子 has an IC mean of 6.19%, an IC standard deviation of 27.11%, and an ICIR of 22.83% [10]

Quantitative Models and Construction Methods 1. Model Name: Counter-Cyclical Allocation Model - **Model Construction Idea**: Predict the macroeconomic environment using proxy variables and allocate assets that perform best under the predicted environment[26][31] - **Model Construction Process**: - Use proxy variables to forecast the macroeconomic environment (e.g., Inflation, Growth, etc.) - Allocate assets based on historical performance under the predicted environment - For Q3 2025, the model predicted an "Inflation" environment, leading to allocations in CSI 300, CSI 2000, Nanhua Commodity Index, and ChinaBond Total Wealth Index[26] - **Model Evaluation**: Provides a systematic approach to asset allocation based on macroeconomic conditions[26] 2. Model Name: Macro Momentum Model - **Model Construction Idea**: Constructed using multiple dimensions such as economic growth, inflation, interest rates, exchange rates, and risk sentiment to time asset classes like stocks and bonds[26] - **Model Construction Process**: - Incorporate macroeconomic indicators, positioning data, volume-price factors, and sentiment factors - Apply the model to time assets such as CSI 300, ChinaBond Total Wealth Index, and gold contracts (AU9999)[26] - **Model Evaluation**: Offers a multi-dimensional perspective for timing asset allocation[26] 3. Model Name: Multi-Factor Industry Rotation Model - **Model Construction Idea**: Combines historical fundamentals, expected fundamentals, sentiment, volume-price technicals, and macroeconomic factors to rotate among industries[27] - **Model Construction Process**: - Match ETFs with their corresponding CSI Level-1 industries - Use a pool of 23 industries to construct the benchmark - Allocate weights to ETFs based on the model's output[27][29] - **Model Evaluation**: Provides a structured approach to industry rotation, leveraging multiple factor dimensions[27] 4. Model Name: Absolute Return Strategies (Blended Models) - **Model Construction Idea**: Combine macro timing and industry rotation strategies with asset rebalancing to achieve absolute returns[31][37] - **Model Construction Process**: - Implement 20/80 stock-bond rebalancing and risk parity strategies - Enhance these strategies with macro timing and industry ETF rotation[31][37] - **Model Evaluation**: Enhances traditional rebalancing strategies with timing and rotation components for better returns[31][37] --- Model Backtesting Results 1. Counter-Cyclical Allocation Model - CSI 300 Q3 2025 Return: 14.38%[26] - CSI 2000 Q3 2025 Return: 16.58%[26] - Nanhua Commodity Index Q3 2025 Return: 4.17%[26] - ChinaBond Total Wealth Index Q3 2025 Return: -1.08%[26] 2. Macro Momentum Model - CSI 300 September 2025 Return: 0.11%[26] - ChinaBond Total Wealth Index September 2025 Return: -0.31%[26] - AU9999 Gold Contract September 2025 Return: 5.72%[26] 3. Multi-Factor Industry Rotation Model - Weekly Return: 0.61% (Excess Return: 0.79% over Wind All A Index)[27][28] - Monthly Return (September 2025): 0.82% (Excess Return: 0.28% over Wind All A Index)[27][28] 4. Absolute Return Strategies (Blended Models) - **Macro Timing + 20/80 Rebalancing**: - Weekly Return: -0.10% - Monthly Return: -0.09% - YTD Return: 3.85% - Annualized Volatility: 3.38% - Max Drawdown: 1.78% - Sharpe Ratio: 1.61[32] - **Macro Timing + Risk Parity**: - Weekly Return: -0.01% - Monthly Return: -0.15% - YTD Return: 1.58% - Annualized Volatility: 1.75% - Max Drawdown: 1.50% - Sharpe Ratio: 1.27[32] - **Macro Timing + Industry ETF Rotation + 20/80 Rebalancing**: - Weekly Return: 0.22% - Monthly Return: 0.21% - YTD Return: 7.83% - Annualized Volatility: 5.28% - Max Drawdown: 2.54% - Sharpe Ratio: 2.12[32] - **Macro Timing + Industry ETF Rotation + Risk Parity**: - Weekly Return: 0.11% - Monthly Return: -0.03% - YTD Return: 2.94% - Annualized Volatility: 2.18% - Max Drawdown: 1.45% - Sharpe Ratio: 1.90[32] --- Quantitative Factors and Construction Methods 1. Factor Name: PB Earnings - **Factor Construction Idea**: Focuses on price-to-book ratios and earnings growth to identify undervalued stocks with growth potential[39][41] - **Factor Construction Process**: - Calculate PB ratios for stocks - Combine with earnings growth metrics to rank stocks[39][41] - **Factor Evaluation**: Targets value-oriented opportunities with growth potential[39][41] 2. Factor Name: High Dividend Yield - **Factor Construction Idea**: Selects stocks with high dividend yields for stable income generation[39][41] - **Factor Construction Process**: - Rank stocks based on dividend yield - Adjust for payout sustainability metrics[39][41] - **Factor Evaluation**: Suitable for income-focused strategies[39][41] 3. Factor Name: Small-Cap Value - **Factor Construction Idea**: Targets small-cap stocks with low valuations for higher growth potential[39][41] - **Factor Construction Process**: - Identify small-cap stocks - Rank based on valuation metrics like P/E and P/B ratios[39][41] - **Factor Evaluation**: Captures the small-cap premium with a value tilt[39][41] 4. Factor Name: Small-Cap Growth - **Factor Construction Idea**: Focuses on small-cap stocks with high growth potential[39][41] - **Factor Construction Process**: - Identify small-cap stocks - Rank based on growth metrics like revenue and earnings growth rates[39][41] - **Factor Evaluation**: Targets high-growth opportunities in the small-cap space[39][41] --- Factor Backtesting Results 1. PB Earnings - **10/90 Rebalancing**: - Weekly Return: -0.18% - Monthly Return: -0.04% - YTD Return: 2.49% - Annualized Volatility: 2.34% - Max Drawdown: 1.82% - Sharpe Ratio: -0.01[41] - **20/80 Rebalancing**: - Weekly Return: -0.39% - Monthly Return: -0.11% - YTD Return: 4.06% - Annualized Volatility: 4.71% - Max Drawdown: 3.79% - Sharpe Ratio: 0.19[41] 2. High Dividend Yield - **10/90 Rebalancing**: - Weekly Return: -0.12% - Monthly Return: -0.09% - YTD Return: 1.91% - Annualized Volatility: 2.09% - Max Drawdown: 1.39% - Sharpe Ratio: -0.18[41] - **20/80 Rebalancing**: - Weekly Return: -0.28% - Monthly Return: -0.22% - YTD Return: 2.88% - Annualized Volatility: 4.19% - Max Drawdown: 3.47% - Sharpe Ratio: 0.05[41] 3. Small-Cap Value - **10/90 Rebalancing**: - Weekly Return: -0.27% - Monthly Return: -0.07% - YTD Return: 5.35% - Annualized Volatility: 3.55% - Max Drawdown: 3.69% - Sharpe Ratio: 0.47[41] - **20/80 Rebalancing**: - Weekly Return: -0.57% - Monthly Return: -0.16% - YTD Return: 9.91% - Annualized Volatility: 7.14% - Max Drawdown: 7.74% - Sharpe Ratio: 0.60[41]