证券研究报告 | 2025年4月17日 DeepSeek辅助识别财务瑕疵 ——基于财报文本的情感语调的分析 策略研究 · 策略专题 证券分析师：陈凯畅 021-60375429 chengkaichang@guosen.com.cn S0980523090002 证券分析师：王 开 021-60933132 wangkai8@guosen.com.cn S0980521030001 请务必阅读正文之后的免责声明及其项下所有内容 核心观点 请务必阅读正文之后的免责声明及其项下所有内容 ➢ 财务造假样本分析：从CSMAR数据库"财务违规表"筛选2010-2021年样本，通讯服务行业造假占比最高，金融和公用事业最低。2010-2018年造假公 司数量及占比上升，2019年后下降，且约58.3%的造假行为在1-2年内暴露或终止。信息披露违规成为主流，虚构利润和虚列资产减少。 ➢ 特征池构建：基于上市公司定期财务报告，从8个维度构建378个比率型指标，经筛选处理后保留100个指标，形成特征池，包含5483个财务造假样本 和42046个控制样本。 ➢ 情感语调因子构建：利用DeepSeek R1模型分析财报文本情感语调， ...

Report Industry Investment Rating - No relevant content provided Core Viewpoints of the Report - The styrene industry chain starts from crude oil, goes through the production of benzene and ethylene, then to the production of styrene and its derivatives, and is finally applied in multiple fields such as packaging, automotive, electronics, and construction. It is an important part of the petrochemical industry, with characteristics of high dependence on crude oil, a long chain, and wide - ranging demand. The factors affecting styrene futures prices are complex. Fundamental quantitative factors cover 9 aspects, and volume - price factors include 7 aspects. By back - testing and screening, setting parameters such as back - testing time, handling fees, and leverage, and combining factors in a simple equal - weight addition way, a trend strength signal can be output [3]. - The fundamental multi - factor portfolio has an annualized return of 50.7% and a Sharpe ratio of 2.85 since 2019. The volume - price multi - factor portfolio has an annualized return of 35.3% and a Sharpe ratio of 2.14 since 2019. In the comprehensive model, all single factors are equally weighted, with an annualized return of 32.2% and a Sharpe ratio of 1.86 since 2019. Fundamental factors and volume - price factors have a low correlation. Investors can adjust the proportion of fundamental and volume - price factors in the comprehensive model according to their target returns and risk requirements [4]. Summary According to the Directory 1. Styrene Single - Commodity Timing Factor Framework - Styrene is an important organic chemical raw material with a clear upstream - downstream industrial chain. The model divides factors into two categories: fundamental quantitative factors and volume - price factors. Fundamental factors are constructed from dimensions such as inventory, basis, upstream inventory, profit, and overseas prices. Volume - price factors are constructed from dimensions such as momentum, moving averages, and technical indicators based on daily - frequency market data. As of the writing of the report, the model includes 9 fundamental quantitative factors and 7 volume - price factors [8][10]. - When back - testing and screening factors, the back - testing time for most fundamental factors and volume - price factors starts from October 2019, with the out - of - sample back - testing starting from January 2023 and ending in December 2024. The handling fee is set at a bilateral rate of 0.03%, and the leverage is 1x. Other settings such as cumulative return calculation, factor value mapping, and signal update rules are also specified [11][12][13] 2. Introduction and Back - Testing Results of Styrene Fundamental Quantitative Factors 2.1 Styrene Weekly Shipment Volume - A significant increase in styrene weekly shipment volume may lead to an oversupply situation if downstream demand does not increase synchronously, causing price decline. The data used is from the East China region, Jiangsu Province, China, and is published every Monday. Since 2019, its back - testing performance shows an annualized return of 30.3%, a Sharpe ratio of 1.68, a Calmar ratio of 1.23, a win rate of 51.0%, an average holding period of 13.7 days, and a maximum drawdown of 24.6% [19]. 2.2 Styrene Overseas Price - An increase in overseas styrene prices may push up domestic prices, while a decrease may suppress domestic prices. This factor mainly considers prices in the US Gulf, Rotterdam, and South Korea. The data is published with a one - day lag. Since 2016, its back - testing performance shows an annualized return of 19.6%, a Sharpe ratio of 0.99, a Calmar ratio of 0.73, a win rate of 52.5%, an average holding period of 19.1 days, and a maximum drawdown of 27% [21]. 2.3 Styrene Basis - When the market supply is tight, the basis widens; when the supply is excessive, the basis narrows. The data is from the Guojun Futures database and is published daily. Since 2019, its back - testing performance shows an annualized return of 27.7%, a Sharpe ratio of 1.12, a Calmar ratio of 0.71, a win rate of 51.9%, an average holding period of 2.6 days, and a maximum drawdown of 39.1% [23]. 2.4 Pure Benzene: Port Inventory - A low level of pure benzene port inventory may increase the production cost of styrene. The data is from the East China region and is published every Friday. Since 2019, its back - testing performance shows an annualized return of 15.8%, a Sharpe ratio of 0.67, a Calmar ratio of 0.42, a win rate of 50.8%, an average holding period of 38.2 days, and a maximum drawdown of 37.3% [25]. 2.5 Styrene: Non - Integrated Plant: Production Gross Margin - A high production gross margin of non - integrated styrene plants may encourage enterprises to increase production, leading to an increase in market supply. The data is from the Steel Union and is published after the market closes. Since 2019, its back - testing performance shows an annualized return of 12.5%, a Sharpe ratio of 0.46, a Calmar ratio of 0.3, a win rate of 50.4%, an average holding period of 11.3 days, and a maximum drawdown of 34.4% [27]. 2.6 Styrene Capacity Utilization Rate - An increase in styrene capacity utilization rate may lead to an oversupply situation and price decline. The data is from the Steel Union and is published weekly. Since 2019, its back - testing performance shows an annualized return of 16.5%, a Sharpe ratio of 0.91, a Calmar ratio of 0.85, a win rate of 50%, an average holding period of 28.6 days, and a maximum drawdown of 19.4% [27]. 2.7 Styrene Warehouse Receipts - An increase in warehouse receipts indicates sufficient market supply, while a decrease indicates tight supply. The data is from Flush and is published after the market closes. Since 2020, its back - testing performance shows an annualized return of 22.6%, a Sharpe ratio of 1.34, a Calmar ratio of 1.04, a win rate of 50.2%, an average holding period of 9.6 days, and a maximum drawdown of 21.7% [30]. 2.8 Styrene Arbitrage Spread - The internal - external spread has a mean - reversion characteristic. This factor considers styrene prices in Europe, Asia, and the Americas. The data is from the Steel Union and is updated with a one - day lag. Since 2019, its back - testing performance shows an annualized return of 33.8%, a Sharpe ratio of 1.68, a Calmar ratio of 0.93, a win rate of 53.2%, an average holding period of 12.5 days, and a maximum drawdown of 36.4% [32]. 2.9 Styrene: Spot Inventory - High inventory usually means sufficient or excessive market supply, while low inventory may indicate tight supply. The data is from the Steel Union and is updated every Monday. Since 2019, its back - testing performance shows an annualized return of 25.9%, a Sharpe ratio of 1.45, a Calmar ratio of 1.52, a win rate of 52.3%, an average holding period of 114.6 days, and a maximum drawdown of 17.1% [35]. 2.10 Fundamental Multi - Factor - By equally weighting the above 9 fundamental single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 50.7%, a Sharpe ratio of 2.85, a Calmar ratio of 2.08, a win rate of 52.6%, an average holding period of 6 days, and a maximum drawdown of 24.4% [37]. 3. Introduction and Back - Testing Results of Styrene Volume - Price Factors 3.1 Intraday Momentum - Intraday momentum is defined as the average of the daily high and low prices divided by the opening price. A larger value indicates a faster price increase. Since 2020, its back - testing performance shows an annualized return of 27.6%, a Sharpe ratio of 1.51, a Calmar ratio of 1.7, a win rate of 47.2%, an average holding period of 3.7 days, and a maximum drawdown of 16.2% [40]. 3.2 Median Double Moving Averages - Similar to double moving averages, but the price for calculating the moving average is the median of the daily high and low prices. Since 2019, its back - testing performance shows an annualized return of 18%, a Sharpe ratio of 0.81, a Calmar ratio of 0.56, a win rate of 51.6%, an average holding period of 8.5 days, and a maximum drawdown of 32.4% [42]. 3.3 Kaufman Adaptive Moving Average (KAMA) - Calculated through steps such as efficiency coefficient (ER) and smoothing constant (SC). Since 2019, its back - testing performance shows an annualized return of 21.1%, a Sharpe ratio of 1.23, a Calmar ratio of 1.19, a win rate of 48.8%, an average holding period of 30.6 days, and a maximum drawdown of 17.8% [45]. 3.4 On - Balance Volume (OBV) - Calculated based on the relationship between daily closing prices and trading volumes, and a long - short double moving average strategy is constructed. Since 2020, its back - testing performance shows an annualized return of 21.2%, a Sharpe ratio of 1.17, a Calmar ratio of 1.28, a win rate of 50.4%, an average holding period of 72.4 days, and a maximum drawdown of 16.6% [49]. 3.5 Commodity Channel Index (CCI) - When CCI breaks through + 100, it is a potential selling signal; when it breaks through - 100, it is a potential buying signal. Since 2019, its back - testing performance shows an annualized return of 28.9%, a Sharpe ratio of 1.72, a Calmar ratio of 1.98, a win rate of 51.0%, an average holding period of 29.9 days, and a maximum drawdown of 12.0% [53]. 3.6 TRIX - Defined through exponential moving averages and a long - short double moving average strategy is constructed based on its daily change rate. Since 2019, its back - testing performance shows an annualized return of 28.9%, a Sharpe ratio of 1.72, a Calmar ratio of 1.98, a win rate of 51.0%, an average holding period of 29.9 days, and a maximum drawdown of 14.6% [55]. 3.7 MESA Adaptive Moving Average - Hilbert transform is used to process price data. MAMA and FAMA lines are calculated, and a double moving average strategy is constructed for timing. Since 2019, its back - testing performance shows an annualized return of 20.5%, a Sharpe ratio of 1.11, a Calmar ratio of 1.11, a win rate of 49.8%, an average holding period of 29.3 days, and a maximum drawdown of 18.5% [55]. 3.8 Volume - Price Multi - Factor - By equally weighting the above 7 volume - price single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 35.3%, a Sharpe ratio of 2.14, a Calmar ratio of 2.41, a win rate of 51.5%, an average holding period of 10.3 days, and a maximum drawdown of 14.7% [59]. 4. Fundamental Quantitative and Volume - Price Multi - Factor Comprehensive Model 4.1 All - Factor Portfolio Long - Short Model - By equally weighting all 16 single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 32.2%, a Sharpe ratio of 1.86, a Calmar ratio of 2.07, a win rate of 46.6%, an average holding period of 5.1 days, and a maximum drawdown of 15.6% [61]. 4.2 Only - Long Model - Fundamental only - long model: By equally weighting the first 9 single factors, when a short - selling signal is generated, it is regarded as closing the existing long position or staying in cash; when a long - buying signal is triggered, open a long position or hold the existing long contract. Since 2019, the back - testing shows an annualized return of 29.6%, a Sharpe ratio of 1.89, a Calmar ratio of 1.31, an average holding period of 6.7 days, and a maximum drawdown of 22.6%. - Volume - price only - long model: By equally weighting the latter 7 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 22.1%, a Sharpe ratio of 1.57, a Calmar ratio of 1.68, an average holding period of 10.6 days, and a maximum drawdown of 13.1%. - All - factor comprehensive only - long model: By equally weighting all 16 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 20.0%, a Sharpe ratio of 1.32, a Calmar ratio of 1.27, an average holding period of 7.6 days, and a maximum drawdown of 15.8% [64][67][69]. 4.3 Only - Short Model - Fundamental only - short model: By equally weighting the first 9 single factors, when a long - buying signal is generated, it is regarded as closing the existing short position or staying in cash; when a short - selling signal is triggered, open a short position or hold the existing short contract. Since 2019, the back - testing shows an annualized return of 20.0%, a Sharpe ratio of 1.48, a Calmar ratio of 1.28, an average holding period of 6.3 days, and a maximum drawdown of 15.7%. - Volume - price only - short model: By equally weighting the latter 7 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 12.5%, a Sharpe ratio of 0.87, a Calmar ratio of 0.9, an average holding period of 16.7 days, and a maximum drawdown of 13.9%. - All - factor comprehensive only - short model: By equally weighting all 16 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 11.8%, a Sharpe ratio of 0.87, a Calmar ratio of 0.82, an average holding period of 9 days, and a maximum drawdown of 14.4% [72][75][76].

Group 1 - The A-share market showed mixed performance with the Shanghai Composite Index relative to the growth index declining by 0.45%, closing at 2675.86 points and a trading volume of 171.84 billion [1] - The Shanghai Composite Index relative to the growth index has decreased by 5.59% over the past month, increased by 0.79% over the past three months, and has declined by 1.15% year-to-date [1] - The Shanghai Composite Index style index series is based on the Shanghai Composite Index, calculating style scores based on growth and value factors, selecting the top 150 listed companies for the growth and value indices [1] Group 2 - The top ten holdings of the Shanghai Composite Index relative to the growth index include Kweichow Moutai (12.65%), Zijin Mining (3.77%), and others, with the Shanghai Stock Exchange accounting for 100% of the holdings [2] - The industry composition of the holdings in the Shanghai Composite Index relative to the growth index includes Information Technology (19.80%), Industrials (19.31%), Consumer Staples (18.67%), and others [2] Group 3 - The index sample is adjusted every six months, with adjustments occurring on the next trading day after the second Friday of June and December, with a sample adjustment ratio generally not exceeding 20% [3] - In special circumstances, the index may undergo temporary adjustments, and companies that are delisted will be removed from the index sample [3]

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 ...

一、本周指数增强组合表现 沪深300指数增强组合本周超额收益-1.25%，本年超额收益1.61%。 中证500指数增强组合本周超额收益-1.53%，本年超额收益2.17%。 中证1000指数增强组合本周超额收益-0.88%，本年超额收益3.15%。 二、本周选股因子表现跟踪 沪深300成分股中非流动性冲击、三个月换手、一个月换手等因子表现较 好。 中证500成分股中预期净利润环比、非流动性冲击、3个月盈利上下调等因子 表现较好。 中证1000成分股中三个月机构覆盖、三个月换手、一个月换手等因子表现较 好。 公募基金重仓股中非流动性冲击、一个月换手、三个月换手等因子表现较 好。 三、本周公募基金指数增强产品表现跟踪 沪深300指数增强产品本周超额收益最高1.04%，最低-2.85%，中位 数-0.53%。 中证500指数增强产品本周超额收益最高0.86%，最低-1.80%，中位 数-0.62%。 中证1000指数增强产品本周超额收益最高1.23%，最低-1.30%，中位 数-0.29%。 主 要 结 论 一 国信金工指数增强组合表现跟踪 国信金工指数增强组合的构建流程主要包括收益预测、风险控制和组合优化三块，我 ...

Quantitative Factors and Construction Methods - **Factor Name**: bp **Construction Idea**: Measures valuation by comparing net assets to market value **Construction Process**: Calculated as: $ bp = \frac{\text{Current Net Assets}}{\text{Current Total Market Value}} $ [13] **Evaluation**: Commonly used valuation factor, straightforward and widely applicable [13] - **Factor Name**: bp three-year percentile **Construction Idea**: Tracks the relative valuation of a stock over the past three years **Construction Process**: Represents the percentile rank of the current bp value within the last three years [13] **Evaluation**: Useful for identifying stocks with consistent valuation trends [13] - **Factor Name**: Quarterly ep **Construction Idea**: Measures profitability relative to net assets **Construction Process**: Calculated as: $ \text{Quarterly ep} = \frac{\text{Quarterly Net Profit}}{\text{Net Assets}} $ [13] **Evaluation**: Reflects short-term profitability, sensitive to quarterly fluctuations [13] - **Factor Name**: Quarterly ep one-year percentile **Construction Idea**: Tracks the relative profitability of a stock over the past year **Construction Process**: Represents the percentile rank of the current quarterly ep value within the last year [13] **Evaluation**: Helps identify stocks with improving or declining profitability trends [13] - **Factor Name**: Quarterly sp **Construction Idea**: Measures revenue generation relative to net assets **Construction Process**: Calculated as: $ \text{Quarterly sp} = \frac{\text{Quarterly Revenue}}{\text{Net Assets}} $ [13] **Evaluation**: Indicates operational efficiency, useful for growth-oriented analysis [13] - **Factor Name**: Quarterly sp one-year percentile **Construction Idea**: Tracks the relative operational efficiency of a stock over the past year **Construction Process**: Represents the percentile rank of the current quarterly sp value within the last year [13] **Evaluation**: Highlights trends in revenue generation efficiency [13] - **Factor Name**: Quarterly asset turnover **Construction Idea**: Measures revenue generation relative to total assets **Construction Process**: Calculated as: $ \text{Quarterly Asset Turnover} = \frac{\text{Quarterly Revenue}}{\text{Total Assets}} $ [13] **Evaluation**: Reflects operational efficiency, sensitive to asset-heavy industries [13] - **Factor Name**: Quarterly gross margin **Construction Idea**: Measures profitability relative to sales revenue **Construction Process**: Calculated as: $ \text{Quarterly Gross Margin} = \frac{\text{Quarterly Gross Profit}}{\text{Quarterly Sales Revenue}} $ [13] **Evaluation**: Indicates pricing power and cost control [13] - **Factor Name**: Quarterly roa **Construction Idea**: Measures profitability relative to total assets **Construction Process**: Calculated as: $ \text{Quarterly ROA} = \frac{\text{Quarterly Net Profit}}{\text{Total Assets}} $ [13] **Evaluation**: Reflects overall asset efficiency [13] - **Factor Name**: Quarterly roe **Construction Idea**: Measures profitability relative to net assets **Construction Process**: Calculated as: $ \text{Quarterly ROE} = \frac{\text{Quarterly Net Profit}}{\text{Net Assets}} $ [13] **Evaluation**: Commonly used profitability metric, sensitive to leverage [13] - **Factor Name**: Standardized unexpected earnings **Construction Idea**: Measures deviation of current earnings from historical growth trends **Construction Process**: Calculated as: $ \text{Standardized Unexpected Earnings} = \frac{\text{Current Quarterly Net Profit} - (\text{Last Year Same Quarter Net Profit} + \text{Average Growth of Last 8 Quarters})}{\text{Standard Deviation of Growth in Last 8 Quarters}} $ [13] **Evaluation**: Useful for identifying earnings surprises [13] - **Factor Name**: Standardized unexpected revenue **Construction Idea**: Measures deviation of current revenue from historical growth trends **Construction Process**: Calculated as: $ \text{Standardized Unexpected Revenue} = \frac{\text{Current Quarterly Revenue} - (\text{Last Year Same Quarter Revenue} + \text{Average Growth of Last 8 Quarters})}{\text{Standard Deviation of Growth in Last 8 Quarters}} $ [13] **Evaluation**: Highlights revenue surprises [13] - **Factor Name**: Dividend yield **Construction Idea**: Measures dividend payout relative to market value **Construction Process**: Calculated as: $ \text{Dividend Yield} = \frac{\text{Annual Dividend}}{\text{Current Market Value}} $ [13] **Evaluation**: Commonly used for income-focused strategies [13] - **Factor Name**: 1-month turnover rate volatility **Construction Idea**: Measures the variability of turnover rates over the past month **Construction Process**: Calculated as the standard deviation of daily turnover rates over the past 20 trading days [13] **Evaluation**: Reflects liquidity and trading activity [13] - **Factor Name**: Fama-French three-factor residual volatility **Construction Idea**: Measures the volatility of residuals from a Fama-French three-factor model regression **Construction Process**: Calculated as the standard deviation of residuals from daily returns regressed on the Fama-French three factors over the past 20 trading days [13] **Evaluation**: Indicates idiosyncratic risk [13] Factor Backtesting Results - **Factor Name**: bp **IC Values**: Weekly: -8.41%, Monthly: 3.48%, Yearly: 1.72% [8] **Excess Return**: Weekly: -0.18%, Monthly: 1.03%, Yearly: 3.10% [11] - **Factor Name**: bp three-year percentile **IC Values**: Weekly: 2.04%, Monthly: 7.90%, Yearly: 2.82% [8] **Excess Return**: Weekly: 0.67%, Monthly: 0.82%, Yearly: 2.55% [11] - **Factor Name**: Quarterly ep **IC Values**: Weekly: -5.19%, Monthly: 3.65%, Yearly: 0.24% [8] **Excess Return**: Weekly: -1.30%, Monthly: -0.08%, Yearly: 1.51% [11] - **Factor Name**: Quarterly ep one-year percentile **IC Values**: Weekly: 1.73%, Monthly: 4.68%, Yearly: 1.02% [8] **Excess Return**: Weekly: -0.35%, Monthly: 0.93%, Yearly: 4.35% [11] - **Factor Name**: Quarterly sp **IC Values**: Weekly: -5.87%, Monthly: -1.49%, Yearly: 0.18% [8] **Excess Return**: Weekly: -0.28%, Monthly: -1.23%, Yearly: 0.24% [11] - **Factor Name**: Quarterly sp one-year percentile **IC Values**: Weekly: 1.93%, Monthly: 7.04%, Yearly: 2.70% [8] **Excess Return**: Weekly: -0.66%, Monthly: 0.55%, Yearly: 3.80% [11] - **Factor Name**: Standardized unexpected earnings **IC Values**: Weekly: 0.24%, Monthly: 2.19%, Yearly: 0.64% [8] **Excess Return**: Weekly: -0.60%, Monthly: -0.75%, Yearly: 3.99% [11] - **Factor Name**: Standardized unexpected revenue **IC Values**: Weekly: -1.03%, Monthly: 0.72%, Yearly: 0.61% [8] **Excess Return**: Weekly: -0.41%, Monthly: -0.72%, Yearly: 1.55% [11] - **Factor Name**: Dividend yield **IC Values**: Weekly: -2.91%, Monthly: 1.85%, Yearly: -0.07% [8] **Excess Return**: Weekly: -0.37%, Monthly: 1.27%, Yearly: -4.85% [11]

证券研究报告 | 2025年04月12日 低换手因子表现出色，中证 1000 指增组合年内超额 3.15% 核心观点 金融工程周报 国信金工指数增强组合表现跟踪 因子表现监控 以沪深 300 指数为选股空间。最近一周，非流动性冲击、三个月换手、一个 月换手等因子表现较好，而单季 EP、单季 ROE、预期 EPTTM 等因子表现 较差。最近一月，非流动性冲击、三个月换手、一个月反转等因子表现较好， 而单季 ROA、单季 ROE、单季 EP 等因子表现较差。 以中证 500 指数为选股空间。最近一周，预期净利润环比、非流动性冲击、 3 个月盈利上下调等因子表现较好，而 BP、预期 BP、单季 SP 等因子表现 较差。最近一月，一个月换手、股息率、预期净利润环比等因子表现较好， 而 BP、特异度、预期 BP 等因子表现较差。 以中证 1000 指数为选股空间。最近一周，三个月机构覆盖、三个月换手、 一个月换手等因子表现较好，而特异度、BP、预期 BP 等因子表现较差。最 近一月，一个月波动、一个月换手、三个月换手等因子表现较好，而特异度、 预期 PEG、预期净利润环比等因子表现较差。 以公募重仓指数为选股空间。最近 ...

短期扰动频现 华泰期货研究院 2025年04月11日 蔡劭立 F3063489 Z0014617 联系人： 朱思谋 F03142856 — 量价和政策信号— 2 【量价观察】美元兑人民币期权隐含波动率上行 (1.5) (1.0) (0.5) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 一月 三月 六月 一年 (1.5) (1.0) (0.5) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 一月 三月 六月 一年 上周美中利差 (1.5) (1.0) (0.5) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 一月 三月 六月 一年 上月新交所美元兑人民币期货升贴水(-) 上月美中利差 数据来源： Wind 华泰期货研究院 4 ◆3个月的美元兑人民币期权隐含波动率曲线显示出美元的升值趋势，Call端波动率显著高于Put端，波动率显著上升。 ◆ 美元兑人民币期权波动率显著上升，市场对美元兑人民币未来波动性的预期增强。 美元兑人民币期权隐含波动率 Delta为5的美元兑人民币3个月期权隐含波动率 数据来源： 3 Bloomberg Wind 华泰期货研究院 3 ...

当ETF赛道深陷费率战与规模焦虑时，中证A500指数却以另类姿态撕开市场——这只诞生即被贴上"新锐"标 签的宽基指数，凭借对科创属性与中小市值的倾斜性覆盖，成为近两年机构博弈"贝塔收益"的主战场。 除了密集成立的指数基金以外，截至今年4月，全市场已有26只指数增强产品参与竞逐，不同产品之间分化 剧烈：两只成立时间间隔不到一个月的A500指数增强基金，目前的超额收益差值已经接近10%。 归根结底，A500指数"市值+行业双轮筛选"的编制原则，使得成份股市值和流动性分层显著，为量化模型留 足了"翻石头"的空间。因此，在选择A500指数增强基金时，基金经理的投资能力与增强策略变得至关重 要。 华安基金量化投资部助理总监、基金经理张序的突围密码，藏在八年磨一剑的"行业轮动+多因子"双擎模型 里。通过对行业轮动的深度理解和持续迭代，其管理的华安事件驱动量化基金自2020年执掌以来，连续五 年跑赢偏股混基指数，年化超额收益达9.3%，无论在公募量化还是主动股基均排名前1%。 而当市场还在争论主动量化与被动投资的边界时，华安基金已悄然完成中证A500产品线的战术合围。继 2024年精准卡位A500ETF之后，再次推出了由张 ...

Group 1 - The core viewpoint emphasizes the continued significance of low volatility factors as resonance factors in investment strategies, integrating macroeconomic quantitative insights with factor momentum [1][2] - The analysis indicates that the economic recovery is ongoing, liquidity is returning to a neutral-tight state, and credit indicators are improving, with no need for adjustments based on micro mappings [1][2] - The stock pool configurations for various indices such as CSI 300 and CSI 1000 show a consistent preference for low volatility and growth factors, with value factors also being selected in the CSI 500 index [2] Group 2 - Economic leading indicators are positioned in the late stage of an upward trend, with expectations of reaching a peak by June 2025 and entering a downward cycle by December 2025 [3][8] - Specific indicators such as PMI and fixed asset investment are showing positive trends, suggesting continued economic growth in the near term [3][9] - The liquidity environment is tightening, with short-term interest rates rising above their moving averages, indicating a shift towards a tighter monetary policy [11][15] Group 3 - Credit indicators have shown improvement, with social financing stock increasing for two consecutive months, reflecting a more favorable credit environment [16][18] - The asset allocation strategy suggests reducing bond and US stock positions while increasing allocations in A-shares and commodities, reflecting a bullish outlook on domestic markets [18][22] - The focus on liquidity as a key variable driving market performance indicates that fluctuations in liquidity will significantly impact stock volatility and overall market dynamics [19][22]