Group 1 - The report emphasizes the importance of identifying trend strength through moving average strategies, which can help in recognizing market trends effectively [3][4][9] - It introduces three key moving average indicators: moving average arrangement score, moving average dispersion distance, and moving average time series change, which collectively help in assessing market trends [8][10][13] - The report suggests that a higher composite score indicates a stronger upward trend, while a lower score suggests a stronger downward trend [13][21] Group 2 - The report outlines a funding flow strategy that aggregates various funding flow indicators to identify market consensus, focusing on both institutional and retail investor behaviors [26][28] - It highlights the significance of funding flow volatility over mere funding direction, suggesting that stable funding behavior can indicate potential market reversals [31][41] - The report proposes a combined approach of trend strength and funding consensus to select indices with clear trends and stable funding, enhancing investment decision-making [42][39] Group 3 - The report presents historical performance data for industry rotation and ETF rotation strategies, showing annual returns and excess returns compared to equal-weighted benchmarks [50][52] - It indicates that the industry rotation strategy has shown significant excess returns in certain years, particularly in 2020 with a return of 76.84% [50] - The ETF rotation strategy also demonstrated strong performance in 2019 and 2020, with excess returns of 30.33% and 26.62% respectively [52]

Quantitative Models and Construction Methods 1. Model Name: Multi-Cycle Timing Model for Domestic Interest Rates - **Model Construction Idea**: The model uses kernel regression algorithms to identify support and resistance lines of interest rate trends. It evaluates the breakthrough patterns of interest rate movements across different investment cycles to form multi-cycle composite timing signals[10][24]. - **Model Construction Process**: - **Data Input**: Yield-to-Maturity (YTM) data for 5-year, 10-year, and 30-year government bonds[6][10]. - **Cycle Classification**: - Long cycle: Monthly frequency - Medium cycle: Bi-weekly frequency - Short cycle: Weekly frequency[10][21]. - **Signal Generation**: - A signal is generated when at least two cycles show consistent directional breakthroughs (upward or downward). - For example, for the 5-year YTM, the current signal is "bearish" as both the long and medium cycles show upward breakthroughs, while the short cycle shows no signal[10]. - **Scoring Mechanism**: - Each cycle contributes one "vote" for upward or downward breakthroughs. - A composite score is calculated based on the total votes, and the final signal is determined[10][13][17]. - **Model Evaluation**: The model effectively captures interest rate trends and provides actionable timing signals for different bond maturities[10][24]. 2. Model Name: Multi-Cycle Timing Model for US Interest Rates - **Model Construction Idea**: The domestic timing model is applied to the US Treasury market to generate timing signals for 10-year US Treasury YTM[21]. - **Model Construction Process**: - **Data Input**: 10-year US Treasury YTM data[21]. - **Cycle Classification**: Same as the domestic model (long, medium, and short cycles)[21]. - **Signal Generation**: - The current signal is "neutral" as only the short cycle shows an upward breakthrough, while the long and medium cycles show no signal[21]. - **Model Evaluation**: The model demonstrates adaptability to international markets, providing consistent timing signals for US Treasuries[21]. --- Model Backtesting Results 1. Multi-Cycle Timing Model for Domestic Interest Rates - **5-Year YTM**: - Long-term annualized return: 5.48% - Maximum drawdown: 2.88% - Return-to-drawdown ratio: 1.91 - Short-term annualized return (since end-2024): 2.11% - Maximum drawdown: 0.59% - Return-to-drawdown ratio: 3.57 - Long-term excess return: 1.07% - Short-term excess return: 0.87%[6][28][29]. - **10-Year YTM**: - Long-term annualized return: 6.06% - Maximum drawdown: 2.74% - Return-to-drawdown ratio: 2.21 - Short-term annualized return (since end-2024): 2.39% - Maximum drawdown: 0.58% - Return-to-drawdown ratio: 4.14 - Long-term excess return: 1.65% - Short-term excess return: 1.36%[28][29]. - **30-Year YTM**: - Long-term annualized return: 7.34% - Maximum drawdown: 4.27% - Return-to-drawdown ratio: 1.72 - Short-term annualized return (since end-2024): 3.03% - Maximum drawdown: 0.92% - Return-to-drawdown ratio: 3.31 - Long-term excess return: 2.43% - Short-term excess return: 2.97%[28][29][33]. 2. Multi-Cycle Timing Model for US Interest Rates - The report does not provide specific backtesting results for the US model, but the current signal is "neutral" based on the latest data[21]. --- Quantitative Factors and Construction Methods 1. Factor Name: Interest Rate Structure Indicators (Level, Term, Convexity) - **Factor Construction Idea**: Transform YTM data into structural indicators (level, term, and convexity) to analyze the interest rate market from a mean-reversion perspective[7]. - **Factor Construction Process**: - **Level Structure**: Represents the average interest rate level. - Current value: 1.63% - Historical percentiles: 25% (3 years), 15% (5 years), 7% (10 years)[7]. - **Term Structure**: Represents the slope of the yield curve. - Current value: 0.45% - Historical percentiles: 34% (3 years), 21% (5 years), 23% (10 years)[7]. - **Convexity Structure**: Represents the curvature of the yield curve. - Current value: 0.01% - Historical percentiles: 26% (3 years), 16% (5 years), 13% (10 years)[7]. - **Factor Evaluation**: These indicators provide a comprehensive view of the interest rate market's structural characteristics, aiding in timing and allocation decisions[7]. --- Factor Backtesting Results - **Interest Rate Structure Indicators**: - The report does not provide specific backtesting results for these factors, but their historical percentiles indicate their relative positioning in the market[7].

Group 1: DeepSeek-V3.2 Series Release - The release of DeepSeek-V3.2 marks a significant advancement in open-source large models, achieving performance levels comparable to top closed-source models[3] - The Speciale version of DeepSeek-V3.2 has excelled in international competitions, ranking second in the ICPC and winning gold medals in the IMO, demonstrating its potential to reach human-level intelligence[4] - DeepSeek-V3.2 integrates thinking modes with tool invocation, enhancing the model's generalization and execution capabilities across complex scenarios[5] Group 2: Market Trends and Predictions - The 2025 Winter FORCE Conference is set to focus on Agentic AI, with significant updates expected for the Doubao model family and AI application capabilities[9] - Doubao model's daily token usage surged from 120 billion in May 2024 to over 30 trillion by September 2025, indicating a 253-fold increase in usage[10] - The report predicts that the 2026 monetary policy will emphasize "wide credit" rather than merely "wide loans," aligning with fiscal measures to support economic growth[35] Group 3: Company Coverage and Financial Projections - Faway Automobile Components (600742) is rated "Overweight" with a target price of RMB 14.10, based on stable automotive parts business and expansion into robotics and low-altitude economy[13] - Projected revenues for Faway are RMB 208.72 million, RMB 220.62 million, and RMB 231.65 million for 2025, 2026, and 2027 respectively, with net profits of RMB 6.30 million, RMB 6.99 million, and RMB 7.75 million[13] - The company is actively developing humanoid robots and EVTOL interior designs, leveraging its automotive parts manufacturing expertise[15]

Quantitative Factors and Construction Factor Name: KeyPeriod_ret_zero - **Construction Idea**: This factor focuses on the return characteristics during horizontal trading periods within key intraday timeframes, leveraging Level 2 tick data to refine minute-frequency factors[7][25][41] - **Construction Process**: - Identify horizontal trading periods based on minimal price fluctuations - Calculate returns during these periods using tick-level data - Aggregate and smooth the data over different time horizons (e.g., 5-day, 20-day)[25][27] - **Evaluation**: Demonstrates strong predictive power for stock selection, with high IC stability and win rates[7][25] Factor Name: KeyPeriod_ret_low5pct - **Construction Idea**: This factor captures return characteristics during significant downward price movements within key intraday timeframes[7][25][64] - **Construction Process**: - Identify periods where returns fall within the bottom 5% of all intraday returns - Calculate and aggregate these returns over different time horizons - Apply smoothing techniques to enhance signal stability[25][27] - **Evaluation**: Exhibits robust performance in identifying underperforming stocks, with high IC values and win rates[7][25] Factor Name: KeyPeriod_price_low5pct - **Construction Idea**: This factor focuses on price levels during periods of low prices (bottom 5%) within key intraday timeframes[7][25][88] - **Construction Process**: - Identify periods where prices fall within the bottom 5% of all intraday prices - Aggregate and smooth the data over different time horizons - Incorporate buy/sell distinctions for further refinement[25][32] - **Evaluation**: Effective in capturing undervalued stocks, with strong IC performance and high win rates[7][25] Factor Name: KeyPeriod_amount_top30pct - **Construction Idea**: This factor targets periods of high transaction amounts (top 30%) within key intraday timeframes[7][25][110] - **Construction Process**: - Identify periods where transaction amounts are in the top 30% of all intraday amounts - Aggregate and smooth the data over different time horizons - Differentiate between buy and sell transactions for enhanced granularity[25][35] - **Evaluation**: Demonstrates strong predictive power for high-liquidity stocks, with high IC values and win rates[7][25] Factor Name: KeyPeriod_amount_low50pct - **Construction Idea**: This factor captures periods of low transaction amounts (bottom 50%) within key intraday timeframes[7][25][133] - **Construction Process**: - Identify periods where transaction amounts are in the bottom 50% of all intraday amounts - Aggregate and smooth the data over different time horizons - Incorporate buy/sell distinctions for further refinement[25][35] - **Evaluation**: Useful for identifying low-liquidity stocks, though performance is less consistent compared to other factors[7][25] Factor Name: KeyPeriod_sync_low50pct - **Construction Idea**: This factor measures volume-price divergence during periods of low synchronization (bottom 50%) within key intraday timeframes[7][25][155] - **Construction Process**: - Identify periods where volume and price movements are least synchronized - Aggregate and smooth the data over different time horizons - Differentiate between buy and sell transactions for enhanced granularity[25][38] - **Evaluation**: Effective in capturing unique market dynamics, with strong IC performance and high win rates[7][25] --- Backtesting Results KeyPeriod_ret_zero - **IC Mean**: -5.36% (20-day horizon)[27] - **Win Rate**: 85.1% (20-day horizon)[27] - **IR**: 1.34 (2020-2025)[55] KeyPeriod_ret_low5pct - **IC Mean**: 5.47% (20-day horizon)[27] - **Win Rate**: 84.1% (20-day horizon)[27] - **IR**: 1.41 (2020-2025)[77] KeyPeriod_price_low5pct - **IC Mean**: 5.59% (20-day horizon)[32] - **Win Rate**: 85.3% (20-day horizon)[32] - **IR**: 2.22 (2020-2025)[97] KeyPeriod_amount_top30pct - **IC Mean**: 11.23% (20-day horizon)[35] - **Win Rate**: 84.8% (20-day horizon)[35] - **IR**: 1.37 (2020-2025)[123] KeyPeriod_amount_low50pct - **IC Mean**: -10.50% (20-day horizon)[35] - **Win Rate**: 75.0% (20-day horizon)[35] - **IR**: 0.77 (2020-2025)[145] KeyPeriod_sync_low50pct - **IC Mean**: 6.00% (20-day horizon)[38] - **Win Rate**: 81.5% (20-day horizon)[38] - **IR**: 1.44 (2020-2025)[172]

Macro and Strategy - The upstream resource sector is stabilizing, with coal prices slightly rising, while the oil and petrochemical sectors remain weak, with significant year-on-year declines in refined oil and natural gas prices [7][8] - The manufacturing sector shows overall recovery, with strong performance in machinery and equipment, while the automotive industry is gradually improving [7] - Consumer sectors are experiencing mixed recovery, with real estate showing marginal improvement and entertainment sectors rebounding significantly [8] Industry and Company - The Hong Kong stock market's December investment strategy suggests that the November pullback has created a favorable environment for 2026 [9] - The electronics sector is optimistic, with ASICs expected to open new markets and Quark's smart glasses enhancing AI edge trends [11][12] - The mechanical industry is focusing on humanoid robots and AI infrastructure, with significant developments in robot operating systems and standardization efforts [17][18] Investment Recommendations - Focus on AI-related sectors, including hardware localization and AI applications, as they are expected to be crucial in 2026 [10] - The materials and industrial sectors are anticipated to benefit from the "anti-involution" trend, with upstream metals and certain industrial companies likely to gain [10] - The innovative pharmaceutical sector is stable and worth holding, with potential for growth upon new project releases [10] Market Performance - The global smartphone market is projected to grow by 3.3% in 2025, with Apple expected to become the leading smartphone brand for the first time since 2011 [15][16] - The semiconductor industry is seeing broad growth, with companies like ADI reporting significant revenue increases and positive outlooks for 2026 [16] Key Events and Developments - The launch of Quark's smart glasses and Google's potential sale of TPU chips are notable developments in the electronics sector [12][13] - The introduction of new DDR5 and LPDDR5X products by Changxin Storage indicates growth opportunities in the storage market [14] Focused Investment Areas - Emphasis on humanoid robots and AI infrastructure, with specific attention to companies involved in energy supply and cooling solutions for AI data centers [19][21] - The low-altitude economy and smart welding robots are emerging sectors with significant growth potential [21][22]

【金工】新股募资规模环比回落，网下询价账户持续扩容——打新市场跟踪月报20251201 点击注册小程序 特别申明： 本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客 户，用作新媒体形势下研究信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿 订阅、接收或使用本订阅号中的任何信息。本订阅号难以设置访问权限，若给您造成不便， 敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相关人员为光大 证券的客户。 今 日 聚 焦 2025年11月，共11只新股上市，募集金额共计101.88亿元，新股发行规模环比走弱，但单月募资仍维持百 亿元水平。网下询价账户数量继续增长，新股认购需求保持高位。主板和双创板块新股首日平均涨幅分别 为177.8%和267.7%，初步询价配售对象分别为主板9614家、双创板块8371家。5亿规模账户当月打新收益 率约为A类：0.14%，B类0.14%。 （祁嫣然/陈颖） 2025-12-01 您可点击今日推送内容的第1条查看 【金工】市场情绪降温，基金抱团程度加强——金融工程量化月报20251201 截至2025年11月28日，沪深300上涨家 ...

程 20251128） 高频因子普遍反弹，多粒度因子多头表现明显改善。AI 增 强组合表现平稳，多数组合获得正收益。 本报告导读： 上周（特指 20251124-20251128，下同）高频因子普遍反弹，多粒度因子多头表现明 显改善。AI 增强组合表现平稳，多数组合获得正收益。 投资要点： [Table_Authors] 郑雅斌(分析师) 021-23219395 | | zhengyabin@gtht.com | | --- | --- | | 登记编号 | S0880525040105 | | | 余浩淼(分析师) | | | 021-23185650 | | | yuhaomiao@gtht.com | | 登记编号 | S0880525040013 | [Table_Report] 相关报告 量化择时和拥挤度预警周报（20251128） 2025.11.30 低频选股因子周报（2025.11.21-2025.11.28） 2025.11.29 权益黄金尽墨，全球资产 BL 模型 2 本周微录正 收益 2025.11.28 绝对收益产品及策略周报（251117-251121） 2025.11.27 上周 ...

Quantitative Models and Construction Methods Dividend Enhancement Portfolio - **Model Name**: Dividend Enhancement Portfolio - **Model Construction Idea**: The model focuses on selecting high-quality dividend stocks and constructing portfolios that outperform the benchmark dividend index by leveraging dividend quality and growth factors[7][15][21] - **Model Construction Process**: - The portfolio is constructed by selecting stocks with high dividend yields and strong dividend growth potential - The selection process incorporates a combination of fundamental analysis and quantitative screening to identify stocks with stable and growing dividends - The portfolio is rebalanced periodically to ensure alignment with the dividend quality and growth criteria[13][14][15] - **Model Evaluation**: The model demonstrates strong performance in capturing excess returns over the benchmark dividend index, particularly in volatile market conditions[7][15][21] Electronic Balanced Allocation Enhancement Portfolio - **Model Name**: Electronic Balanced Allocation Enhancement Portfolio - **Model Construction Idea**: This model aims to achieve excess returns by balancing exposure across various sub-sectors within the electronics industry, focusing on growth and stability[7][24][29] - **Model Construction Process**: - The portfolio is constructed by allocating weights to sub-sectors such as LED chips and semiconductor distribution, which exhibit strong growth potential - Quantitative screening is applied to identify leading companies within these sub-sectors - The portfolio is periodically rebalanced to maintain a balanced exposure across the electronics industry[13][14][24] - **Model Evaluation**: The model effectively captures excess returns and ranks in the top 15% of active technology-themed products in terms of weekly performance[7][24][29] --- Model Backtesting Results Dividend Enhancement Portfolio - **Weekly Excess Return**: Approximately 0.23% for the "Central SOE High Dividend 30 Portfolio" and 1.45% for the "Balanced Dividend 50 Portfolio"[7][15][21] - **Year-to-Date Excess Return**: The "Balanced Dividend 50 Portfolio" achieved an excess return of approximately 7.91% relative to the benchmark dividend index, ranking in the top 35% of all dividend-themed funds[21] Electronic Balanced Allocation Enhancement Portfolio - **Weekly Excess Return**: Approximately 0.89%, ranking in the top 15% of active technology-themed products[7][24][29]

查看完整报告 特别申明： 本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客户，用作新媒体形势下研究 信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿订阅、接收或使用本订阅号中的任何信息。本订阅号 难以设置访问权限，若给您造成不便，敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相 关人员为光大证券的客户。 今 日 聚 焦 点击注册小程序 【策略】多重利好因素叠加，市场探底回升——策略周专题（2025年11月第4期） 市场大方向或仍处在牛市中，不过短期或进入宽幅震荡阶段。与往年牛市相比，当前指数仍然有相当大的上涨 空间，但是在国家对于"慢牛"的政策指引之下，牛市持续的时间或许要比涨幅更加重要。不过短期来看，市场 可能缺乏强力催化，叠加年末部分投资者在行为上可能趋于稳健，股市短期或以震荡蓄势为主。 （张宇生/郭磊）2025-11-29 您可点击今日推送内容的第1条查看 【金工】量能决定短期反弹高度——金融工程市场跟踪周报20251130 本周A股市场震荡反弹，创业板指领涨主要宽基指数。量能表现方面，本周主要宽基指数量能逆势收缩，当前 量能状态与市场反弹表现不 ...

Group 1: Investment Opportunities in A-Share Index Adjustment - The upcoming December index adjustment is expected to create significant investment opportunities, particularly for stocks with a positive impact coefficient above 2, such as Tapa Group, Jiangzhong Pharmaceutical, and Zhengbang Technology [3][4] - The report highlights the importance of focusing on stocks that are newly added to major indices, with particular attention to Guangqi Technology and Zhongtian Technology, which are expected to experience substantial liquidity changes [4] - The passive fund outflows from stocks like Zhongji Xuchuang and Xinyi Sheng are projected to be limited due to their strong liquidity, despite their weights being reduced in various indices [4][5] Group 2: Animation and Film Industry Insights - The film industry is experiencing a recovery, with total box office revenue expected to exceed 50 billion yuan, driven by high-quality imported films and a resurgence in audience engagement [6][7] - The market is shifting towards high-quality content, with a notable increase in the contribution of narrative films to box office performance, indicating a growing demand for deep content [7] - Regulatory policies are expected to support the film industry, with initiatives aimed at expanding the understanding of mainstream themes and enhancing the supply of animated films and imported content [7][8] Group 3: Public REITs Market Development - The introduction of commercial real estate REITs marks a significant shift in China's public REITs market, moving from a focus solely on infrastructure to a dual focus on infrastructure and commercial real estate [8][9] - The potential market size for commercial real estate REITs is estimated to be between 800 billion and 1.5 trillion yuan, indicating a substantial opportunity for asset securitization in the commercial property sector [9][10] - The development of commercial real estate REITs is expected to enhance the liquidity and operational efficiency of the real estate market, addressing long-standing challenges in asset management [10][11]