调整中见韧性：VIX 理性上行叠加期指资金积极布局 [Table_ReportTime] 2025 年 11 月 22 日 请阅读最后一页免责声明及信息披露 http://www.cindasc.com 1 [Table_FirstAuthor] 于明明 金融工程与金融产品首席分析师 执业编号：S1500521070001 联系电话：+86 18616021459 邮 箱：yumingming@cindasc.com 证券研究报告 于明明 金融工程与金融产品 首席分析师 执业编号：S1500521070001 联系电话：+86 18616021459 邮 箱：yumingming@cindasc.com 崔诗笛 金融工程与金融产品 金融工程分析师 执业编号：S1500523080001 联系电话：+86 18516560686 邮 箱：cuishidi@cindasc.com 孙石 金融工程与金融产品 金融工程分析师 执业编号：S1500523080010 联系电话：+86 18817366228 邮 箱：sunshi@cindasc.com 信达证券股份有限公司 CINDA SECURITIES CO.,L ...

Group 1: Monetary Policy and Economic Indicators - The Federal Reserve has lowered interest rates by 25 basis points to a range of 3.75%–4.00%, marking the second rate cut of the year, and plans to end balance sheet reduction by December 1, 2025, with all maturing U.S. Treasury securities being reinvested [1] - The breakeven inflation rate increased by 0.04% to 2.40%, while the U.S. September CPI rose by 3.02% year-on-year, up from 2.94%, indicating a rebound for five consecutive months [2] - The dollar index increased by 2.1% in October, influenced by hawkish statements from Fed Chairman Powell regarding future rate cuts [3] Group 2: Market Risks and Global Trends - The VIX index peaked in mid-October but significantly declined due to the easing of U.S.-China tariff risks, while geopolitical uncertainties remain high following the cancellation of a summit between Trump and Putin [3] - In 2024, global central banks have cumulatively purchased 1,044.63 tons of gold, marking the 17th consecutive quarter of net purchases, with a notable increase in global gold ETF holdings as of 2025 [3] Group 3: Gold and Silver Market Outlook - A new tax policy regarding gold transactions will take effect on November 1, 2025, which may initially pressure physical demand but could enhance the financial attributes of gold in the long term [4] - The short-term outlook for gold is cautiously bullish, with expectations of upward movement due to anticipated declines in real interest rates [5][6] - Silver prices are also expected to trend cautiously upward, sharing macroeconomic logic with gold amid expectations of lower future interest rates [7]

- The report mentions multiple quantitative models for market timing, including short-term, mid-term, and long-term models. Short-term models include the "Volume Model" (neutral for all broad-based indices), "Feature Volume Model" (bearish), "Feature Institutional Model" (bearish), and "Smart Algorithm Model" (bearish for CSI 300, neutral for CSI 500)[1][13][66]. Mid-term models include the "Limit-Up-Limit-Down Model" and "Calendar Effect Model," both neutral[14][67]. The long-term model is the "Long-Term Momentum Model," which is bullish[15][68]. Comprehensive models like "A-Share Comprehensive Weapon V3 Model" and "A-Share Comprehensive CSI 2000 Model" are bearish[16][69]. - The "Volume Model" is constructed based on trading volume trends, while the "Feature Volume Model" and "Feature Institutional Model" focus on specific volume characteristics and institutional trading patterns, respectively. The "Smart Algorithm Model" utilizes machine learning techniques to predict market movements[1][13][66]. The "Limit-Up-Limit-Down Model" analyzes price limits, and the "Calendar Effect Model" incorporates seasonal patterns[14][67]. The "Long-Term Momentum Model" evaluates price trends over extended periods[15][68]. - The "Comprehensive Weapon V3 Model" and "Comprehensive CSI 2000 Model" combine signals from multiple models across different timeframes to provide a holistic market outlook[16][69]. - The report evaluates these models qualitatively, noting that short-term models are generally neutral to bearish, mid-term models are neutral, and long-term models are bullish. Comprehensive models are bearish for A-shares[1][13][66][16][69]. - Testing results for the models are summarized as follows: Short-term models show mixed signals, with bearish predictions for specific indices like CSI 300 and CSI 2000. Mid-term models remain neutral, while the long-term momentum model indicates a bullish outlook. Comprehensive models suggest a bearish trend for A-shares[1][13][66][16][69]. - For Hong Kong stocks, the "Turnover Inverse Volatility Model" is bearish, indicating potential downward movement for the Hang Seng Index[16][70]. - The report also highlights shape-based models like the "Double Bottom Pattern" and "Cup-and-Handle Pattern." The "Double Bottom Pattern" portfolio outperformed the Shanghai Composite Index by 2.57% this week, with cumulative returns of 34.32% since December 31, 2020[43][48]. The "Cup-and-Handle Pattern" portfolio outperformed the Shanghai Composite Index by 1.28% this week, with cumulative returns of 70.89% since December 31, 2020[43][44]. - The report evaluates these shape-based models positively, noting their consistent outperformance compared to the benchmark index over time[43][44][48]. - Testing results for shape-based models: "Double Bottom Pattern" portfolio weekly return of 3.0%, cumulative return of 34.32% since December 31, 2020[43][48]. "Cup-and-Handle Pattern" portfolio weekly return of 1.71%, cumulative return of 70.89% since December 31, 2020[43][44].

Core Viewpoint - Intel's third-quarter financial report shows a return to profitability, ending a six-quarter losing streak, with net profit reaching $4.1 billion compared to a net loss of $16.6 billion in the same period last year, leading to a significant stock price increase of approximately 7.7% in after-hours trading [1][3]. Financial Performance - Intel's revenue exceeded expectations, marking the first quarterly report since the U.S. government's investment, which included $9 billion in federal funding for a 10% equity stake in the company [3]. - The company has experienced a stock price increase of over 90% year-to-date, primarily driven by developments since August, including significant investments from the U.S. government and Nvidia [3]. Market Environment - The improvement in Intel's performance is attributed to the overall market environment rather than a significant enhancement in the company's competitive position, with the AI boom driving demand for traditional servers and Intel's Xeon server products [5][7]. - The demand for CPUs is being supported by the expansion of data centers, which is indirectly benefiting Intel despite its CPUs not being central to AI training [5]. Investor Sentiment - Investors are advised to monitor the increased volatility in U.S. stocks, particularly in the tech sector, as indicated by the historical high difference between the VIX and VIX EQ indices, reflecting heightened anxiety over certain tech stocks [9][11]. - Upcoming earnings reports from other major U.S. tech companies are expected to face scrutiny amid rising concerns about market concentration and the AI boom [13].

- The report includes multiple quantitative models for market timing, such as the "Volume Model," "Low Volatility Model," "Feature Institutional Model," "Feature Volume Model," "Smart Algorithm Model," "Limit-Up-Limit-Down Model," "Calendar Effect Model," "Long-Term Momentum Model," and composite models like "A-Share Comprehensive Weapon V3 Model" and "A-Share Comprehensive Guozheng 2000 Model" [2][11][12][13][14] - The "Volume Model" is neutral in the short term, while the "Feature Volume Model" indicates bearish signals. The "Smart Algorithm Model" for CSI 500 also shows bearish signals, whereas the "Long-Term Momentum Model" is bullish for long-term market trends [11][13][14] - The "Composite Weapon V3 Model" and "Comprehensive Guozheng 2000 Model" both indicate bearish signals for A-shares, suggesting a negative outlook for the market [14][68] - For Hong Kong stocks, the "Turnover Inverse Volatility Model" continues to show bearish signals, indicating a negative outlook for the Hang Seng Index [15][63] - Backtesting results for the "Double Bottom Pattern" show a weekly decline of -2.06%, outperforming the Shanghai Composite Index by 1.37%. Since December 31, 2020, the cumulative return of the double bottom portfolio is 28.91%, compared to the Shanghai Composite Index's cumulative return of 10.04%, achieving an excess return of 18.88% [41][46] - Backtesting results for the "Cup-and-Handle Pattern" show a weekly decline of -5.45%, underperforming the Shanghai Composite Index by -2.02%. Since December 31, 2020, the cumulative return of the cup-and-handle portfolio is 62.41%, compared to the Shanghai Composite Index's cumulative return of 10.04%, achieving an excess return of 52.38% [41][42]

Group 1 - The government shutdown has become a routine occurrence, with citizens expressing a sense of resignation as their salaries remain unaffected [1][3] - The financial markets are largely unfazed by the shutdown, with the VIX index showing minimal fluctuations and traders maintaining a calm demeanor [4][10] - Companies reliant on government contracts, particularly in the defense sector, are experiencing slight stock price increases, while other sectors like banking are seeing net redemptions in ETFs [6][8] Group 2 - The impact of the shutdown on GDP is projected to be minimal, with estimates suggesting a 0.18% decrease if the shutdown lasts over two weeks [3] - Tech companies report little disruption, attributing their resilience to diversified revenue streams, while government-dependent firms are adjusting payment schedules [6][8] - Market sentiment remains stable, with investors confident that the Federal Reserve will intervene if the situation escalates [10]

Quantitative Models and Construction Methods - **Model Name**: Volume Model **Construction Idea**: This model evaluates market trends based on trading volume dynamics[1][11][62] **Construction Process**: The model uses trading volume data across broad-based indices to generate neutral signals for short-term market timing[11][62] - **Model Name**: Low Volatility Model **Construction Idea**: This model assesses market trends by analyzing low-volatility characteristics[1][11][62] **Construction Process**: The model evaluates the volatility of broad-based indices and generates neutral signals for short-term market timing[11][62] - **Model Name**: Institutional Feature Model (LHB) **Construction Idea**: This model leverages institutional trading data from the "Dragon and Tiger List" to predict market trends[1][11][62] **Construction Process**: The model analyzes institutional trading patterns and generates bullish signals for short-term market timing[11][62] - **Model Name**: Feature Volume Model **Construction Idea**: This model uses specific volume characteristics to predict market trends[1][11][62] **Construction Process**: The model evaluates unique volume features and generates bearish signals for short-term market timing[11][62] - **Model Name**: Intelligent Algorithm Model (CSI 300 and CSI 500) **Construction Idea**: This model applies machine learning algorithms to predict market trends for specific indices[1][11][62] **Construction Process**: The model generates bearish signals for both CSI 300 and CSI 500 indices based on algorithmic predictions[11][62] - **Model Name**: Limit-Up/Down Model **Construction Idea**: This model evaluates market trends by analyzing the frequency of limit-up and limit-down events[1][12][63] **Construction Process**: The model generates neutral signals for mid-term market timing based on historical limit-up/down data[12][63] - **Model Name**: Calendar Effect Model **Construction Idea**: This model incorporates seasonal and calendar-based effects to predict market trends[1][12][63] **Construction Process**: The model generates neutral signals for mid-term market timing based on calendar patterns[12][63] - **Model Name**: Long-Term Momentum Model **Construction Idea**: This model evaluates long-term market trends using momentum indicators[1][13][64] **Construction Process**: The model generates bullish signals for long-term market timing based on momentum analysis[13][64] - **Model Name**: A-Share Comprehensive Weapon V3 Model **Construction Idea**: This composite model integrates multiple signals to provide a comprehensive market outlook[1][14][65] **Construction Process**: The model generates bearish signals for A-shares by combining various short, mid, and long-term indicators[14][65] - **Model Name**: A-Share Comprehensive Guozheng 2000 Model **Construction Idea**: This composite model focuses on the Guozheng 2000 index using integrated signals[1][14][65] **Construction Process**: The model generates bearish signals for the Guozheng 2000 index by combining multiple indicators[14][65] - **Model Name**: Turnover-to-Amplitude Model (Hong Kong Market) **Construction Idea**: This model evaluates the Hong Kong market by analyzing turnover relative to price amplitude[1][15][66] **Construction Process**: The model generates bullish signals for mid-term market timing in the Hong Kong market[15][66] Model Backtesting Results - **Volume Model**: Neutral signals for all broad-based indices[11][62] - **Low Volatility Model**: Neutral signals for all broad-based indices[11][62] - **Institutional Feature Model (LHB)**: Bullish signals for short-term market timing[11][62] - **Feature Volume Model**: Bearish signals for short-term market timing[11][62] - **Intelligent Algorithm Model (CSI 300 and CSI 500)**: Bearish signals for both indices[11][62] - **Limit-Up/Down Model**: Neutral signals for mid-term market timing[12][63] - **Calendar Effect Model**: Neutral signals for mid-term market timing[12][63] - **Long-Term Momentum Model**: Bullish signals for long-term market timing[13][64] - **A-Share Comprehensive Weapon V3 Model**: Bearish signals for A-shares[14][65] - **A-Share Comprehensive Guozheng 2000 Model**: Bearish signals for the Guozheng 2000 index[14][65] - **Turnover-to-Amplitude Model (Hong Kong Market)**: Bullish signals for mid-term market timing[15][66]

Quantitative Models and Construction Methods 1. Model Name: Volume Model - **Construction Idea**: The model uses trading volume data to predict market trends. - **Construction Process**: The model analyzes the trading volume of various broad-based indices to determine market sentiment. It categorizes the indices as neutral based on the volume data. - **Evaluation**: The model is considered neutral for all broad-based indices in the short term.[2][11] 2. Model Name: Low Volatility Model - **Construction Idea**: This model uses the volatility of stock prices to predict market trends. - **Construction Process**: The model evaluates the volatility of stock prices and categorizes the indices as neutral. - **Evaluation**: The model is considered neutral in the short term.[2][11] 3. Model Name: Institutional Feature Model - **Construction Idea**: This model uses institutional trading data from the "Dragon and Tiger List" to predict market trends. - **Construction Process**: The model analyzes the trading behavior of institutions listed on the "Dragon and Tiger List" and categorizes the indices as bullish. - **Evaluation**: The model is considered bullish in the short term.[2][11] 4. Model Name: Feature Volume Model - **Construction Idea**: This model uses specific volume features to predict market trends. - **Construction Process**: The model analyzes specific volume features and categorizes the indices as bearish. - **Evaluation**: The model is considered bearish in the short term.[2][11] 5. Model Name: Smart Algorithm Model (CSI 300) - **Construction Idea**: This model uses smart algorithms to predict market trends for the CSI 300 index. - **Construction Process**: The model applies smart algorithms to the CSI 300 index and categorizes it as neutral. - **Evaluation**: The model is considered neutral in the short term.[2][11] 6. Model Name: Smart Algorithm Model (CSI 500) - **Construction Idea**: This model uses smart algorithms to predict market trends for the CSI 500 index. - **Construction Process**: The model applies smart algorithms to the CSI 500 index and categorizes it as bearish. - **Evaluation**: The model is considered bearish in the short term.[2][11] 7. Model Name: Limit Up/Down Model - **Construction Idea**: This model uses the occurrence of limit up and limit down events to predict market trends. - **Construction Process**: The model analyzes the frequency of limit up and limit down events and categorizes the indices as neutral. - **Evaluation**: The model is considered neutral in the medium term.[2][12] 8. Model Name: Calendar Effect Model - **Construction Idea**: This model uses calendar effects to predict market trends. - **Construction Process**: The model analyzes historical calendar effects and categorizes the indices as neutral. - **Evaluation**: The model is considered neutral in the medium term.[2][12] 9. Model Name: Long-term Momentum Model - **Construction Idea**: This model uses long-term momentum to predict market trends. - **Construction Process**: The model analyzes long-term momentum indicators and categorizes the indices as bullish. - **Evaluation**: The model is considered bullish in the long term.[2][13] 10. Model Name: Comprehensive Weapon V3 Model - **Construction Idea**: This model combines multiple factors to predict market trends. - **Construction Process**: The model integrates various factors and categorizes the indices as bearish. - **Evaluation**: The model is considered bearish in the long term.[2][14] 11. Model Name: Comprehensive National Certificate 2000 Model - **Construction Idea**: This model combines multiple factors to predict market trends for the National Certificate 2000 index. - **Construction Process**: The model integrates various factors and categorizes the indices as bearish. - **Evaluation**: The model is considered bearish in the long term.[2][14] 12. Model Name: Turnover Inverse Amplitude Model - **Construction Idea**: This model uses the inverse amplitude of turnover to predict market trends. - **Construction Process**: The model analyzes the inverse amplitude of turnover and categorizes the indices as bullish. - **Evaluation**: The model is considered bullish in the medium term.[2][15] Model Backtest Results - **Volume Model**: Neutral for all broad-based indices in the short term.[2][11] - **Low Volatility Model**: Neutral in the short term.[2][11] - **Institutional Feature Model**: Bullish in the short term.[2][11] - **Feature Volume Model**: Bearish in the short term.[2][11] - **Smart Algorithm Model (CSI 300)**: Neutral in the short term.[2][11] - **Smart Algorithm Model (CSI 500)**: Bearish in the short term.[2][11] - **Limit Up/Down Model**: Neutral in the medium term.[2][12] - **Calendar Effect Model**: Neutral in the medium term.[2][12] - **Long-term Momentum Model**: Bullish in the long term.[2][13] - **Comprehensive Weapon V3 Model**: Bearish in the long term.[2][14] - **Comprehensive National Certificate 2000 Model**: Bearish in the long term.[2][14] - **Turnover Inverse Amplitude Model**: Bullish in the medium term.[2][15]

Quantitative Models and Construction Methods - **Model Name**: Dividend Adjustment for Futures Basis **Construction Idea**: Adjust the futures basis by incorporating the expected dividend impact during the contract's lifespan[9][21] **Construction Process**: Futures basis is calculated as the difference between the futures contract closing price and the underlying index closing price. The adjustment accounts for dividends expected during the contract's lifespan, which are reflected in the futures price. The formula is: $ Annualized Basis = (Actual Basis + Expected Dividend Points) / Index Price × 360 / Remaining Days of Contract $[21] **Evaluation**: Provides a more accurate representation of the futures basis by accounting for dividend effects[21] - **Model Name**: Continuous Hedging Strategy **Construction Idea**: Optimize hedging by continuously rolling futures contracts based on expiration dates[44][45] **Construction Process**: - Hold the corresponding total return index for the spot side - Use 70% of funds for the spot side and 30% for shorting futures contracts - Roll futures contracts when the remaining days to expiration are less than 2 days, using the closing price for both closing and opening positions[45] **Evaluation**: Effective for maintaining consistent exposure but sensitive to transaction costs and market conditions[45] - **Model Name**: Minimum Basis Hedging Strategy **Construction Idea**: Select futures contracts with the smallest annualized basis for hedging[46] **Construction Process**: - Calculate the annualized basis for all available futures contracts - Open positions in the contract with the smallest basis - Hold the contract for 8 trading days or until the remaining days to expiration are less than 2 days[46] **Evaluation**: Reduces basis risk but requires frequent monitoring and adjustments[46] Quantitative Factors and Construction Methods - **Factor Name**: Cinda-VIX **Construction Idea**: Reflect market expectations of future volatility using a modified VIX calculation tailored to China's market[62] **Construction Process**: - Based on overseas VIX methodologies, adjusted for China's market conditions - Incorporates the term structure of volatility to capture expectations across different time horizons[62] **Evaluation**: Provides valuable insights into market sentiment and volatility expectations[62] - **Factor Name**: Cinda-SKEW **Construction Idea**: Measure the skewness in implied volatility across different strike prices to assess tail risk[67] **Construction Process**: - Analyze the implied volatility of options with varying strike prices - Higher SKEW values indicate increased tail risk expectations, while lower values suggest reduced concerns[67] **Evaluation**: Useful for understanding market sentiment regarding extreme events and tail risks[67] Model Backtesting Results - **Dividend Adjustment for Futures Basis**: - IC contract: Current basis -5.89%[22] - IF contract: Current basis -0.05%[27] - IH contract: Current basis +1.70%[32] - IM contract: Current basis -6.92%[37] - **Continuous Hedging Strategy**: - IC: Annualized return -3.07%, volatility 3.82%, max drawdown -9.27%, net value 0.9086[48] - IF: Annualized return 0.38%, volatility 2.97%, max drawdown -3.95%, net value 1.0116[53] - IH: Annualized return 0.97%, volatility 3.08%, max drawdown -4.22%, net value 1.0302[57] - IM: Annualized return -6.21%, volatility 4.72%, max drawdown -14.01%, net value 0.8345[59] - **Minimum Basis Hedging Strategy**: - IC: Annualized return -1.40%, volatility 4.60%, max drawdown -7.97%, net value 0.9577[48] - IF: Annualized return 1.18%, volatility 3.10%, max drawdown -4.06%, net value 1.0366[53] - IH: Annualized return 1.63%, volatility 3.09%, max drawdown -3.91%, net value 1.0511[57] - IM: Annualized return -4.04%, volatility 5.55%, max drawdown -11.11%, net value 0.8702[59] Factor Backtesting Results - **Cinda-VIX**: - 30-day volatility: - CSI 500: 32.58[62] - CSI 1000: 29.50[62] - HS 300: 22.97[62] - SSE 50: 24.31[62] - **Cinda-SKEW**: - 30-day skewness: - CSI 500: 98.22[68] - CSI 1000: 106.46[68] - HS 300: 104.77[68] - SSE 50: 99.82[68]

Group 1 - The VIX index incorporates three major risk events: the Federal Reserve FOMC meeting, non-farm payroll data, and tariff deadlines [1]