Group 1 - The A-share market has ended its nine-day rally, with the Shanghai Composite Index stabilizing and major sectors like liquor, banking, real estate, and securities entering a consolidation phase [1] - Small-cap stocks have shown a rebound, but they are still over 20% below their April highs, indicating a potential for further recovery [3] - The market is experiencing a "volume washout," where the pullback is seen as a strategy for better upward movement, with hopes of reaching the 3400-point mark before the upcoming holiday [3][5] Group 2 - The overall market trend is expected to be oscillating upwards, with no significant negative news anticipated before the holiday, suggesting a stable environment for the index [5] - There is a rotation among sectors, with small-cap stocks rebounding today and expectations that large-cap stocks will lift the index in the near future [5] - The current market conditions are not favorable for speculative trading, and a focus on index and value investing is recommended instead [7]

证 券 研 究 报 告 【点评报告】 形态学研究之十五：形态学在 ETF 轮动上的研究 ❖ 摘要 ETF 轮动，是指在不同 ETF 之间切换，根据市场情况调整投资组合。可涉及 到不同资产类别、行业或地区。比如如何选择 ETF，轮动的依据是什么，比如 动量、估值、宏观经济指标等。 本篇，我们提出了使用形态作为原始数据，将 ETF 成分股合成 ETF 信号的方 式来做 ETF 轮动。在先前报告中，我们已经成功基于成分股的形态构建了指 数、ETF 的择时策略，现在就是设计指标将同一截面下的各个 ETF 指数数据 可比，就可以构建 ETF 轮动策略。 当我们把 ETF 指数每天的多空个股个数求和后，除以当时该 ETF 指数的成分 股个数，在进行 30 天的 HMA 均线求解后，该指标就表征了该 ETF 从结构上 形态学的看多或者看空力量的变化与对比，由于都除以了成分股个数，也消除 了不同 ETF 因为成分股个数不同不可比的情况。 基于历史数据回测，不管是固定时间点调仓、每日调仓、或者是买入持有信号 消失退出策略等，都可跑赢万得偏股混合型基金指数。固定时间点调仓策略优 势在于计算量少，交易次数低，手续费少，但收益率也一 ...

广发证券资深金工分析师 张钰东 SAC: S0260522070006 SAC: S0260517080003 chenyuanwen@gf.com.cn 广发金工安宁宁陈原文团队 摘要 ETF市场概况： 指数化投资理念愈发受到投资者认可，ETF产品凭借透明、低费率、交易便捷等优势，成为居民资产配置的重要工具，ETF 规模持续创新高，ETF资金流变动逐渐成为市场中的关注重点。 ETF交易机制特点： ETF具有独特的双层交易机制，即一级市场的申购赎回和二级市场的买卖交易，一级申赎指用一篮子股票换取ETF份 额，申赎会直接增减ETF的总份额。本报告旨在针对申赎导致的ETF资金流数据，探索用于ETF轮动的配置效果。 zhangyudong@gf.com.cn 广发证券首席金工分析师 安宁宁 SAC: S0260512020003 anningning@gf.com.cn 广发证券 资深金工分析师 陈原文 风险提示： 本专题报告所述模型用量化方法通过历史数据统计、建模和测算完成，所得结论与规律在市场政策、环境变化时可能存在失效风 险；策略在市场结构及交易行为的改变时有可能存在策略失效风险；因量化模型不同，本报告提出的 ...

广发证券资深金工分析师 张钰东 SAC: S0260522070006 zhangyudong@gf.com.cn 广发证券首席金工分析师 安宁宁 SAC: S0260512020003 anningning@gf.com.cn 广发证券 资深金工分析师 陈原文 SAC: S0260517080003 chenyuanwen@gf.com.cn 广发金工安宁宁陈原文团队 摘要 ETF市场概况： 指数化投资理念愈发受到投资者认可，ETF产品凭借透明、低费率、交易便捷等优势，成为居民资产配置的重要工具，ETF 规模持续创新高，ETF资金流变动逐渐成为市场中的关注重点。 ETF交易机制特点： ETF具有独特的双层交易机制，即一级市场的申购赎回和二级市场的买卖交易，一级申赎指用一篮子股票换取ETF份 额，申赎会直接增减ETF的总份额。本报告旨在针对申赎导致的ETF资金流数据，探索用于ETF轮动的配置效果。 ETF申赎资金流因子构建： 根据资金维度的不同，可以独立计算单只ETF的资金特征，也可以汇总跟踪相同指数的不同的ETF的资金流数 据，还可以基于PCF清单中的成分股名单及其权重，将ETF的资金流数据下沉到具体的股票 ...

点击下载 专业财经新闻源头 第一财经APP 2025.04. 23 (2) 生产队的驴 第二财 谨慎加仓? 4月23日A股市场投资情绪 扫码参与投票 资本市场是投资者信心的晴雨表。投资者情绪的波动会影响其对未来收益 十二十四出山北 土井三星分哈十八十 亚少十八十二寸十十七七十日节目/m 于1/17 的主观判断,边川影响仅页付小,形成百刀后对印切切厂土亚有影响。找11J 想通过几个问题,了解投资者对每日市场的看法。4月23日共有18169位用 户参与了调研,具体情况如下: | 上证指数 | 深证成指 | 创业板指 | | --- | --- | --- | | ▼ 0.10% | ▲0.67% | ▲ 1.07% | | A股市场呈现"指数分化、板块轮动" 特征。沪指止步八连阳，3300点年线阻力显著。新能 源车、机器人等成长板块推动中小创反弹。 | | | 3185家上涨 涨跌停比 2:35 个股涨跌呈现"深强沪弱" 格局, 由结构性行情主导, 中小盘成长股表现优于权重股。涨停跌停分化,涨 停股集中在机器人、新能源车、消费电子等政策受 益与技术突破板块。 两市成交额 两市成交额创4月以来新高, 验证市场活跃度 ...

Group 1 - The core viewpoint of the article is that Xi'an's real estate market is experiencing a significant downturn, with new home prices declining for six consecutive months and second-hand home prices dropping for 18 months [2][10][12] - In contrast to Xi'an, Chengdu's real estate market is thriving, with new home transactions increasing by 19.7% year-on-year in Q1 2024 [2][4] - Xi'an's new home transaction volume has plummeted since its peak in 2018, with 2024's new home transaction area at 13.28 million square meters, only half of 2018's volume [15][22] Group 2 - Xi'an's economic growth has been sluggish, with a GDP growth rate of 4.6% in 2023, lagging behind the national average of 5.2% [18][20] - The city's industrial investment growth was negative at -5.8% in the first half of 2024, with industrial investment down by 13.3% [24][28] - Despite a population increase of 89,400 in 2024, the city's housing affordability remains a concern, with a price-to-income ratio of 28.1 times, significantly above the international warning line of 6-8 times [31][32] Group 3 - Xi'an's industrial strength is underwhelming despite its military and aerospace capabilities, with total industrial output value in 2023 estimated at 239.9 billion yuan, far below leading industrial cities [44][46] - The city has only three industries with over 100 billion yuan in output, with the automotive industry being the most prominent [46][48] - The lack of integration between military research and market applications is a critical issue for Xi'an's industrial development [66][68] Group 4 - The article raises the question of whether Xi'an's housing prices will continue to adjust downward, noting that while the city has a competitive population advantage, the overall real estate market sentiment remains negative [69][73] - Global economic uncertainties and trade tensions could further impact Xi'an's economy, although its reliance on exports is relatively low [75][76]

Quantitative Models and Construction Methods 1. Model Name: "百元转股溢价率" (Premium Rate per 100 Yuan Conversion) - **Model Construction Idea**: This model aims to compare the valuation of convertible bonds and their underlying stocks by calculating a time-series comparable valuation indicator, "百元转股溢价率" (Premium Rate per 100 Yuan Conversion), and using rolling historical percentiles to measure the relative allocation value[4][15]. - **Model Construction Process**: - Fit the relationship curve between the conversion premium rate and conversion value in the cross-sectional space at each time point - Substitute a conversion value of 100 into the fitted formula to obtain the "百元转股溢价率" - Formula: $$y_{i}=\alpha_{0}+\,\alpha_{1}\cdot\,{\frac{1}{x_{i}}}+\epsilon_{i}$$ where \(y_{i}\) represents the conversion premium rate of the \(i\)-th bond, and \(x_{i}\) represents the conversion value of the \(i\)-th bond[44] - **Model Evaluation**: Provides a relative valuation perspective for comparing convertible bonds and their underlying stocks[15] 2. Model Name: "修正 YTM – 信用债 YTM" (Adjusted YTM - Credit Bond YTM) - **Model Construction Idea**: This model isolates the impact of conversion terms on the yield-to-maturity (YTM) of convertible bonds to assess the relative allocation value between debt-heavy convertible bonds and credit bonds[5][15]. - **Model Construction Process**: - Adjust the YTM of debt-heavy convertible bonds by considering the probability of conversion and maturity - Formula: $$\text{Adjusted YTM} = \text{Convertible Bond YTM} \times (1 - \text{Conversion Probability}) + \text{Expected Annualized Return of Conversion} \times \text{Conversion Probability}$$ - Conversion probability is calculated using the Black-Scholes (BS) model, incorporating stock price, strike price, stock volatility, remaining maturity, and discount rate - Calculate the median difference between the adjusted YTM of convertible bonds and the YTM of credit bonds of the same rating and maturity: $$\text{"Adjusted YTM - Credit Bond YTM Median"} = \text{median}\{X_1, X_2, ..., X_n\}$$ where \(X_i\) represents the difference for the \(i\)-th convertible bond[45][46] - **Model Evaluation**: Highlights the cost-effectiveness of debt-heavy convertible bonds compared to credit bonds[5] 3. Model Name: Convertible Bond Style Rotation Model - **Model Construction Idea**: This model captures market sentiment using momentum and volatility deviation indicators to construct a convertible bond style rotation portfolio, with bi-weekly rebalancing[6][27]. - **Model Construction Process**: - Calculate the following sentiment indicators for each convertible bond: - 20-day momentum - Volatility deviation - Rank the indicators in reverse order and sum the rankings to determine the market sentiment capture indicator for each style index: $$\text{Market Sentiment Capture Indicator} = \text{Rank (20-day Momentum)} + \text{Rank (Volatility Deviation)}$$ - Allocate portfolio weights based on the rankings, with a preference for the style index with the lowest indicator value. If rankings are equal, allocate weights equally. If all three styles are selected, allocate 100% to the balanced low-valuation style[28] - **Model Evaluation**: Demonstrates superior performance compared to the equal-weighted convertible bond index, with a focus on balanced low-valuation styles[27][33] --- Quantitative Factors and Construction Methods 1. Factor Name: 转股溢价率偏离度 (Conversion Premium Deviation) - **Factor Construction Idea**: Measures the deviation of the conversion premium rate from its fitted value, enabling comparability across different parities[19][20]. - **Factor Construction Process**: $$\text{Conversion Premium Deviation} = \text{Conversion Premium Rate} - \text{Fitted Conversion Premium Rate}$$ - The number of convertible bonds determines the fitting quality[20] - **Factor Evaluation**: Provides a robust measure for identifying valuation discrepancies in convertible bonds[20] 2. Factor Name: 理论价值偏离度 (Theoretical Value Deviation, Monte Carlo Model) - **Factor Construction Idea**: Quantifies the price expectation gap by considering various convertible bond terms (e.g., conversion, redemption, downward revision, put options) through Monte Carlo simulation[19][20]. - **Factor Construction Process**: $$\text{Theoretical Value Deviation} = \frac{\text{Convertible Bond Closing Price}}{\text{Theoretical Value}} - 1$$ - Simulate 10,000 paths at each time point using the Monte Carlo model, with the same credit term interest rate as the discount rate[20] - **Factor Evaluation**: Effectively captures valuation discrepancies, particularly for equity-heavy convertible bonds[19][20] 3. Factor Name: 转债综合估值因子 (Comprehensive Convertible Bond Valuation Factor) - **Factor Construction Idea**: Combines the rankings of the above two factors to enhance valuation analysis across all domains (equity-heavy, balanced, debt-heavy)[19][20]. - **Factor Construction Process**: $$\text{Comprehensive Convertible Bond Valuation Factor} = \text{Rank (Conversion Premium Deviation)} + \text{Rank (Theoretical Value Deviation)}$$ - **Factor Evaluation**: Demonstrates superior performance in identifying undervalued convertible bonds across different styles[19][20] --- Backtesting Results of Models 1. Convertible Bond Style Rotation Model - **Annualized Return**: 23.38% - **Annualized Volatility**: 16.48% - **Maximum Drawdown**: -15.54% - **IR**: 1.42 - **Calmar Ratio**: 1.50 - **Monthly Win Rate**: 65.12%[33] --- Backtesting Results of Factors 1. 转股溢价率偏离度 Factor - **Equity-Heavy Convertible Bonds**: Enhanced excess return of 0.9% over the past 4 weeks[22] - **Balanced Convertible Bonds**: Enhanced excess return of 1.2% over the past 4 weeks[22] - **Debt-Heavy Convertible Bonds**: Enhanced excess return of -0.3% over the past 4 weeks[22] 2. 理论价值偏离度 Factor - **Equity-Heavy Convertible Bonds**: Enhanced excess return of 0.9% over the past 4 weeks[22] - **Balanced Convertible Bonds**: Enhanced excess return of 1.2% over the past 4 weeks[22] - **Debt-Heavy Convertible Bonds**: Enhanced excess return of -0.3% over the past 4 weeks[22] 3. 转债综合估值因子 Factor - **Equity-Heavy Convertible Bonds**: Enhanced excess return of 0.9% over the past 4 weeks[22] - **Balanced Convertible Bonds**: Enhanced excess return of 1.2% over the past 4 weeks[22] - **Debt-Heavy Convertible Bonds**: Enhanced excess return of -0.3% over the past 4 weeks[22]

Group 1 - The report introduces a quantitative model solution for addressing the issue of value and growth style switching based on odds and win rates [1][8] - The recent performance of the growth style portfolio was 0.88%, while the value style portfolio achieved a return of 1.77% [1][8] Group 2 - The estimated odds for the growth style is 1.03, and for the value style, it is 1.07, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rates indicate that 3 out of 7 indicators favor growth, while 4 favor value, resulting in a win rate of 46.13% for growth and 53.87% for value [3][16] Group 3 - The investment expectation formula yields a value style investment expectation of 0.11 and a growth style expectation of -0.06, leading to a recommendation for the value style [4][18] - Since 2013, the annualized return of the style rotation model based on investment expectations is 26.20%, with a Sharpe ratio of 0.96 [4][18]

证券研究报告：金融工程报告 发布时间：2025-04-14 研究所 分析师:肖承志 SAC 登记编号:S1340524090001 Email:xiaochengzhi@cnpsec.com 研究助理:李子凯 SAC 登记编号:S1340124100014 Email:lizikai@cnpsec.com 近期研究报告 《陆股通 Q1 增持汽车电子机械，减持 电力通信化工——陆股通 2025Q1 持仓 点评》 - 2025.04.13 《上证受政策影响但未跌破重要点位， ETF 大幅流入科创芯片等 TMT 方向—— 行 业 轮 动 周 报 20250406 》 – 2025.04.06 《"924"以来融资资金防守后均见到行 情低点，仍关注科技配置机会——行业 轮动周报 20250330》 - 2025.03.31 《 Gemini 2.5 Pro 发 布 即 屠 榜 ， DeepSeek V3 完成模型更新——AI 动态 汇总 20250331》 – 2025.03.31 《低波风格持续，反转占优——中邮因 子周报 20250330》 – 2025.03.31 《为什么说本轮调整空间不会太大？ ——微盘股指 ...

敬请阅读末页的重要说明 整体维持震荡，结构转为看好小盘 ——技术择时信号 20250411 DTW 择时模型是基于相似性原理和 DTW 算法的量价择时模型，外资择时模型 是基于外资和内资关联资产背离情况的择时模型，样本外表现均较为出色。模 型最新信号及历史表现上线万得 PMS 平台——"DTW 择时模型_指数 6 位代 码"。"外资择时模型_多空/仅多_000300"也在万得 PMS 平台上定期跟踪。 表 择时模型最新信号 | 指数 | 最新信号 | 上周信号 | | --- | --- | --- | | 沪深 300 | 无信号 | 乐观 | | 上证 50 | 乐观 | 乐观 | | 中证 500 | 无相似片段 | 无信号 | | 中证 1000 | 乐观 | 中性 | | 中证 2000 | 无相似片段 | 无信号 | | 上证指数 | 中性 | 中性 | | 万得全 A | 中性 | 中性 | | 中证全指 | 中性 | 中性 | | 中证 A500 | 中性 | 乐观 | | 创业板指 | 中性 | 无信号 | | 科创 50 | 中性 | 中性 | | 红利低波 100 | 乐观 | 乐观 | ...