Group 1 - Market sentiment remains stable with a slight increase in the sentiment indicator to 2.35 as of January 23, up from 2.25 the previous week, indicating a neutral model perspective [2][9] - The consistency of price and volume remains high, reflecting strong market sentiment and a correlation between capital attention and stock price movements [3][16] - The trading volume of the entire A-share market decreased significantly by 19.22% week-on-week, with an average daily trading volume of 27,989.42 billion yuan [19] Group 2 - The sector score rankings show that non-ferrous metals, communications, national defense, comprehensive, and electronics industries are leading, with non-ferrous metals achieving the highest short-term score of 100.00 [45][46] - The model indicates a preference for small-cap and growth styles, with the 5-day RSI showing a decline relative to the 20-day RSI, suggesting potential weakening of signals [45][54] - The industry crowding indicator shows no significant correlation with weekly price changes, indicating that sectors like oil and gas, utilities, and media are experiencing notable gains despite high crowding [48][50]

Quantitative Models and Construction Methods 1. **Model Name**: Timing System Model **Model Construction Idea**: The model uses the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the WIND All A Index to determine the market trend[2][7] **Model Construction Process**: - Define the short-term moving average (20-day) and long-term moving average (120-day) of the WIND All A Index - Calculate the difference between the two moving averages - If the short-term moving average is above the long-term moving average and the absolute difference exceeds 3%, the market is considered to be in an upward trend - Latest data: 20-day moving average = 6668, 120-day moving average = 6245, difference = 6.78%[2][7] **Model Evaluation**: The model effectively captures the market's upward trend and provides a clear signal for timing decisions[2][7] 2. **Model Name**: Industry Trend Allocation Model **Model Construction Idea**: This model identifies industry opportunities based on medium-term reversal expectations and performance trends[6][7] **Model Construction Process**: - Use medium-term reversal expectation signals to identify industries with potential recovery, such as innovative healthcare - Apply the TWO BETA model to recommend sectors like technology, commercial aerospace, space photovoltaics, and stablecoin concepts - Use performance trend signals to highlight opportunities in semiconductors, industrial metals, and chemicals[6][7] **Model Evaluation**: The model provides actionable insights into sector allocation, focusing on industries with strong growth potential or recovery signals[6][7] 3. **Model Name**: Position Management Model **Model Construction Idea**: This model determines the recommended equity allocation based on valuation levels and market trends[8] **Model Construction Process**: - Assess the valuation levels of the WIND All A Index using PE and PB ratios - Combine valuation levels with short-term trend signals to recommend an equity allocation - Current recommendation: 80% equity allocation for absolute return products based on the WIND All A Index[8] **Model Evaluation**: The model provides a systematic approach to managing equity exposure, balancing valuation and trend considerations[8] --- Model Backtesting Results 1. **Timing System Model**: - Moving average distance: 6.78% (absolute value > 3%, indicating an upward trend)[2][7] - Profitability effect: 2.7% (positive, supporting the upward trend)[2][7] 2. **Industry Trend Allocation Model**: - Recommended sectors: Innovative healthcare, technology, commercial aerospace, space photovoltaics, stablecoin concepts, semiconductors, industrial metals, and chemicals[6][7] 3. **Position Management Model**: - Recommended equity allocation: 80%[8] --- Quantitative Factors and Construction Methods No specific quantitative factors were explicitly mentioned in the report. The analysis primarily focuses on models rather than individual factors. --- Factor Backtesting Results No specific factor backtesting results were provided in the report. The focus remains on model-level performance and recommendations.

- The report introduces a quantitative model based on Convolutional Neural Networks (CNNs) to analyze price-volume data and predict future prices. The model standardizes price-volume data into graphical representations and maps learned features to industry theme indices, such as the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Material Equipment Theme Index, and CSI Nonferrous Metals Index[78][80][81] - The construction process of the CNN model involves transforming individual stock price-volume data within a specific window into standardized graphical charts. These charts are then input into the CNN for feature extraction and prediction modeling. The learned features are subsequently applied to identify and allocate industry themes[78][80] - The evaluation of the CNN model highlights its ability to capture complex patterns in price-volume data and effectively map these patterns to industry themes. This approach provides a novel perspective for quantitative investment strategies[78][81] - Backtesting results indicate that the CNN model's latest configuration suggests a focus on themes such as petrochemicals, chemicals, machine tools, semiconductors, and nonferrous metals. Specific indices include the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Material Equipment Theme Index, and CSI Nonferrous Metals Index[80][81]

Market Performance - The Sci-Tech 50 Index increased by 2.62% over the last five trading days, while the ChiNext Index decreased by 0.34%. The large-cap value index fell by 1.64%, and the large-cap growth index dropped by 1.34%. The Shanghai 50 Index declined by 1.54%, whereas the small-cap index represented by the CSI 2000 rose by 3.33%. The building materials and oil & petrochemical sectors performed well, while banks and telecommunications lagged behind [1]. Risk Premium and Valuation Levels - As of January 23, 2026, the static PE of the CSI All Share Index minus the yield of 10-year government bonds indicates a risk premium of 2.46%, with a two-standard deviation boundary at 4.68%. The valuation levels show that the CSI All Share Index's PETTM is at the 84th percentile, with the Shanghai 50 and CSI 300 at 72% and 73%, respectively. The ChiNext Index is close to 63%, while the CSI 500 and CSI 1000 are at 70% and 68%, respectively. The ChiNext Index's valuation is relatively at the historical median level [1]. ETF Fund Flow - In the last five trading days, ETF funds experienced an outflow of 326.5 billion yuan, while the financing balance increased by approximately 6.5 billion yuan. The average daily trading volume across the two markets was 27.727 billion yuan [2]. Industry Themes and Indices - The latest thematic allocation includes sectors such as petrochemicals, chemicals, machine tools, semiconductors, and non-ferrous metals. Specific indices mentioned are the CSI Petrochemical Industry Index, CSI Sub-segment Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Materials and Equipment Theme Index, and the National Non-ferrous Metals Index [2][3]. Long-term Market Sentiment - The report includes observations on the proportion of stocks above the 200-day long-term moving average, indicating market sentiment trends [13]. Financing Balance - The report tracks the financing balance, which is a critical indicator of market liquidity and investor sentiment [16]. Individual Stock Performance - A statistical distribution of individual stocks based on their return ranges since the beginning of the year is provided, highlighting performance variations across different stocks [18]. Oversold Indices - The report notes instances of indices being oversold, which may present potential buying opportunities [20].

- The short-term timing model maintains an optimistic signal this week, supported by macro fundamentals, valuation caution, neutral sentiment, and optimistic liquidity[18][19][22] - The short-term timing strategy has achieved an annualized return of 16.78% since 2012, with an annualized excess return of 11.67%, and a maximum drawdown of 15.05%, significantly outperforming the benchmark strategy[20][21][24] - The growth-value rotation model suggests overweighting growth stocks this week, driven by favorable macro fundamentals, valuation metrics, and sentiment indicators. The model has delivered an annualized return of 13.34% since 2012, with an annualized excess return of 4.99%[30][31][33] - The small-cap and large-cap rotation model has shifted from favoring small caps to large caps this week. The model utilizes 11 effective rotation indicators, including financing buy balance changes and R007 rate trends. Since 2014, the strategy has consistently generated positive annual excess returns, with a 2026 year-to-date excess return of 1.43%[34][35][36] - The comprehensive signal smoothing for small-cap rotation achieved an annualized return of 20.85%, with an annualized excess return of 13.11%, and a maximum drawdown of 40.70%[36]

Quantitative Models and Construction - **Model Name**: SAR Indicator **Construction Idea**: The SAR indicator is used to identify market trends and reversals based on price movements[14][15] **Construction Process**: The SAR indicator is calculated using the following formula: $ SAR_{t+1} = SAR_t + AF \times (EP - SAR_t) $ - **SAR_t**: Current SAR value - **AF**: Acceleration factor, which increases as the trend continues - **EP**: Extreme point, the highest high or lowest low during the trend The SAR flips direction when the price crosses the current SAR value, signaling a potential trend reversal[14][15] **Evaluation**: The SAR indicator effectively captures market reversals and reflects strong market dynamics[14][15] - **Model Name**: Sentiment Model **Construction Idea**: The sentiment model evaluates market sentiment using factors such as limit-up and limit-down board data[14][17] **Construction Process**: - Factors include net limit-up ratio, next-day return after limit-down, limit-up ratio, limit-down ratio, and high-frequency board trading returns - Each factor is scored, and the sentiment model aggregates these scores to produce a final sentiment score ranging from 0 to 5[14][17] **Evaluation**: The sentiment model provides a stable measure of market sentiment, indicating a positive trend[14][17] - **Model Name**: High-Frequency Capital Flow Model **Construction Idea**: This model uses high-frequency capital flow data to generate buy/sell signals for major indices[14][17] **Construction Process**: - Signals are generated for indices such as CSI 300, CSI 500, and CSI 1000 based on capital flow trends - The model evaluates aggressive and conservative long/short positions for each index[14][17] **Evaluation**: The model demonstrates strong predictive capabilities for index movements, supporting buy signals across major indices[14][17] Model Backtesting Results - **SAR Indicator**: No specific numerical backtesting results provided[14][15] - **Sentiment Model**: Sentiment score = 2 (out of 5), indicating stable market sentiment[14][17] - **High-Frequency Capital Flow Model**: - CSI 300: Aggressive long = 1, Aggressive short = 1, Conservative long = 1, Conservative short = 1 - CSI 500: Aggressive long = 1, Aggressive short = 1, Conservative long = 1, Conservative short = 1 - CSI 1000: Aggressive long = 1, Aggressive short = 1, Conservative long = 1, Conservative short = 1[14][17] Quantitative Factors and Construction - **Factor Name**: Small Market Cap Factor **Construction Idea**: Measures the performance of small-cap stocks and their market dynamics[18][19] **Construction Process**: - Metrics include valuation spread, pairwise correlation, market volatility, and return reversal - Composite score = 0.28, calculated using these metrics[18][19] **Evaluation**: The factor shows moderate crowding, indicating stable performance[18][19] - **Factor Name**: Low Valuation Factor **Construction Idea**: Tracks stocks with low valuation metrics to identify undervalued opportunities[18][19] **Construction Process**: - Metrics include valuation spread (-1.39), pairwise correlation (0.24), market volatility (1.39), and return reversal (-1.90) - Composite score = -0.42, reflecting moderate crowding[18][19] **Evaluation**: The factor exhibits negative crowding, suggesting potential risks in its effectiveness[18][19] - **Factor Name**: High Profitability Factor **Construction Idea**: Focuses on stocks with strong profitability metrics[18][19] **Construction Process**: - Metrics include valuation spread (-0.61), pairwise correlation (0.15), market volatility (0.15), and return reversal (1.57) - Composite score = 0.31, indicating moderate crowding[18][19] **Evaluation**: The factor demonstrates stable performance with moderate crowding[18][19] - **Factor Name**: High Growth Factor **Construction Idea**: Identifies stocks with high growth potential based on financial metrics[18][19] **Construction Process**: - Metrics include valuation spread (1.12), pairwise correlation (-0.49), market volatility (-0.21), and return reversal (0.97) - Composite score = 0.35, reflecting moderate crowding[18][19] **Evaluation**: The factor shows positive crowding, indicating strong market interest[18][19] Factor Backtesting Results - **Small Market Cap Factor**: Composite score = 0.28[18][19] - **Low Valuation Factor**: Composite score = -0.42[18][19] - **High Profitability Factor**: Composite score = 0.31[18][19] - **High Growth Factor**: Composite score = 0.35[18][19]

- The liquidity shock indicator for the CSI 300 Index was 5.09 on Friday, indicating that the current market liquidity is 5.09 standard deviations higher than the average level over the past year [4][8] - The PUT-CALL ratio of the SSE 50ETF options trading volume increased to 0.98 on Friday, suggesting a rise in investor caution regarding the short-term trend of the SSE 50ETF [4][8] - The five-day average turnover rates for the SSE Composite Index and Wind All A Index were 1.50% and 2.21%, respectively, indicating a decrease in trading activity [4][8] - The SAR technical indicator showed a reversal within the week, indicating strong market contention between bulls and bears [4][7][14] - The sentiment model score was 2 out of 5, with both the trend model and weighted model signals being positive [4][14] - The high-frequency capital flow model indicated a buy signal for major broad-based indices, including the CSI 300, CSI 500, and CSI 1000 [4][14] - The congestion levels for small-cap, low-valuation, high-profitability, and high-growth factors were 0.28, -0.42, 0.31, and 0.35, respectively [4][18][19][21] - The congestion levels for the non-ferrous metals, comprehensive, communication, electronics, and defense industries were relatively high, with the defense and electronics industries showing significant increases [4][25][27][28]

Group 1: Report Industry Investment Rating - Not mentioned in the provided content Group 2: Core Views of the Report - The current hedging cost - performance in the derivatives market is prominent, and the timing is right. Both the futures and options have low hedging costs, meeting the risk - hedging needs of the current high - level volatile A - share market [3][4]. - The core of futures hedging lies in contract optimization, which can effectively reduce hedging costs. By selecting contracts with the highest annualized basis rate and dynamically shifting positions, the hedging effect can be optimized [4]. - The combined futures and options hedging strategy achieves the best balance between risk and return. After a sharp market decline, switching to put options can avoid the drawbacks of futures hedging and retain upside potential while hedging downside risks [5]. Group 3: Summaries According to the Table of Contents Preface - In January 2026, the A - share market entered a high - level volatile phase after the 17 - day consecutive bull market. The high valuation of major indices increased the market correction risk, while the hedging costs of options and futures were low, making it a good time for investors to engage in hedging [12]. What is Hedging Cost? - **Futures Hedging Cost**: The hedging cost of stock index futures is the premium. The annualized basis rate represents the hedging cost of each contract, and relevant data can be found on the Huatai Futures Tianji platform [14]. - **Options Hedging Cost**: The hedging cost of stock index options is the time value of the option contract. The implied volatility index reflects the size of the option time value, and relevant data can also be found on the Huatai Futures Tianji platform. The price of options is also affected by the premium of stock index futures [15][16]. - **Analysis of Current Market Hedging Costs**: The hedging costs of both stock index futures and options are at historical lows. The premium of futures contracts is at a low level, and some near - month contracts have even shown a premium. The implied volatility of the CSI 1000 index has dropped to 20.65% [17]. Stock Index Futures and Options Hedging Strategies - **Spot Selection**: The report selects the CSI 1000 index - enhanced fund "SMXXXX87" as the spot for hedging. Index - enhanced funds are suitable for hedging with derivatives, and most neutral hedge funds in the market are equivalent to the combination of index - enhanced funds and short - positions in stock index futures [18][19][20]. - **Back - testing Settings**: The back - test targets are CSI 1000 stock index futures and options, covering the period from July 22, 2022, to January 9, 2026. All positions are opened at the opening time, with specific trading costs for futures and options [21]. - **Stock Index Futures Hedging**: Using near - month futures contracts for long - term hedging can significantly reduce portfolio volatility but sacrifices some returns. The annualized return of the hedging portfolio decreased by 7.66%, while risk indicators such as volatility and drawdown were greatly improved [21][24]. - **Stock Index Futures Hedging (Contract Optimization)**: By optimizing the contract selection, the hedging cost can be reduced. The optimization method is to select the contract with the highest annualized basis rate for opening positions and shift positions based on the basis rate difference. The back - test shows that the annualized return of the optimized futures hedging strategy is about 2.5% higher than that of the near - month futures hedging strategy [23][25][26]. - **Stock Index Futures and Options Hedging (Using Options after a Sharp Decline)**: This strategy combines futures and options. When the market experiences a sharp decline, it switches from short - positions in futures to long - positions in put options. The back - test shows that the annualized return of this strategy reaches 22.68%, the maximum drawdown is reduced to 11.11%, and the Sharpe ratio is increased to 2.0, outperforming the pure futures hedging strategy and the CSI 1000 index - enhanced fund [34][35][37]. - **Summary**: The report summarizes the net value curves and risk - return indicators of each strategy, showing that the combined futures and options hedging strategy has the best performance [37][38][39]. Stock Index Futures and Options Hedging Case Analysis - **CSI 1000 Case Period Trend**: The report selects the historical period from October 10, 2024, to February 10, 2025, for case analysis. This period is similar to the current market situation and has a relatively complete market cycle [42]. - **Near - Month Futures Hedging Case Analysis**: The near - month futures hedging strategy continuously holds short - positions in near - month futures contracts and shifts positions when the contract expiration is less than 10 trading days. In this four - month period, the derivatives end of this strategy transferred positions 3 times, with a total loss of 315.8 points [45]. - **Optimized Futures Hedging Case Analysis**: The optimized futures hedging strategy selects contracts with the highest annualized basis rate for opening positions and shifts positions based on the basis rate difference and contract expiration. In this four - month period, the strategy transferred positions 4 times, with a total loss of 259 points, less than the near - month futures hedging strategy [48][49]. - **Optimized Futures and Options Hedging Case Analysis**: This strategy switches from short - positions in futures to long - positions in put options when the market experiences a sharp decline. In this four - month period, the strategy transferred positions 7 times, with a total profit of 282.8 points, outperforming the optimized futures hedging strategy [52][53]. Conclusion - The report comprehensively analyzes stock index futures and options hedging strategies, defines their hedging costs, designs and back - tests three types of hedging strategies. The optimized futures and options hedging strategy achieves the best balance between risk and return. The case analysis further verifies its effectiveness, and investors can use the quantitative timing futures - options hedging strategy in the current market environment for portfolio appreciation [57].

Group 1 - The core viewpoint of the article indicates that the growth-oriented selected portfolio is expected to achieve a cumulative return of 84.1% by 2025, outperforming the 885001 index by 50.9% [1] - The article suggests that the ICIR weighted return method is superior to the IC mean weighted method from the perspective of index enhancement [1] Group 2 - The market outlook for the upcoming week (January 19-23, 2026) anticipates a potential upward trend, supported by a liquidity shock indicator of 3.32, which is significantly higher than the previous week's 0.60, indicating current market liquidity is 3.32 times the average level over the past year [2] - The PUT-CALL ratio for the Shanghai Stock Exchange 50 ETF has increased to 0.80 from 0.64, reflecting a growing caution among investors regarding the short-term performance of the index [2] - The five-day average turnover rates for the Shanghai Composite Index and Wind All A Index are 1.71% and 2.71%, respectively, indicating increased trading activity, positioned at the 84.10% and 92.01% percentiles since 2005 [2] - The article notes that the RMB exchange rate fluctuated last week, with onshore and offshore rates increasing by 0.19% and 0.12%, respectively [2] - New RMB loans in December reached 910 billion, exceeding the Wind consensus estimate of 679.4 billion and the previous value of 390 billion [2] - The broad money supply (M2) grew by 8.5% year-on-year, surpassing the Wind consensus estimate of 7.93% and the previous value of 8% [2] - Technical analysis indicates that the Wind All A Index broke above the reversal indicator on December 1, and the market score based on the moving average strength index is currently at 213, positioned at the 76.93% percentile for 2023 [2] - The sentiment model score is 2 out of 5, with a positive trend model signal and a negative weighted model signal [2] - The quantitative team has issued buy signals for the CSI 300, CSI 500, and CSI 1000 indices based on high-frequency capital flow analysis [2] Group 3 - The market review for the previous week (January 12-16, 2026) shows that the Shanghai 50 Index fell by 1.74%, while the CSI 300 Index decreased by 0.57%. In contrast, the CSI 500 Index rose by 2.18%, and the ChiNext Index increased by 1% [3] - The current overall market PE (TTM) stands at 23.3 times, which is at the 82.0% percentile since 2005 [3] Group 4 - The factor crowding degree remains stable, with small-cap factor crowding at 0.20, low valuation factor crowding at -0.75, high profitability factor crowding at 0.35, and high profitability growth factor crowding at 0.55 [4] Group 5 - The industry crowding degree is relatively high in telecommunications, non-ferrous metals, comprehensive sectors, electronics, and national defense industries, with significant increases noted in the crowding degree of national defense and electronics [5]

Quantitative Models and Construction Methods 1. Model Name: Deposit Migration Signal - **Model Construction Idea**: The model is designed to identify the phenomenon of "deposit migration," where household deposits decrease while non-bank deposits increase, using monthly deposit data[5][13][14] - **Model Construction Process**: 1. Calculate the proportion of newly added household deposits and non-bank deposits to total deposits for a given month: - $ \text{Household Deposit Share} = \frac{\text{Cumulative New Household Deposits (12 months)}}{\text{Cumulative New Total Deposits (12 months)}} $ - $ \text{Non-Bank Deposit Share} = \frac{\text{Cumulative New Non-Bank Deposits (12 months)}}{\text{Cumulative New Total Deposits (12 months)}} $ 2. Compare the current month's values with the average of the previous three months: - If the current month's value is greater than the average, it is considered "rising"; otherwise, it is "falling"[13][14] 3. Define the "deposit migration" signal as a scenario where the household deposit share decreases while the non-bank deposit share increases[14] 4. Evaluate the performance of the signal by analyzing the average return and win rate of the All-A Index one month after the signal is triggered[14][15] - **Model Evaluation**: The model effectively identifies periods of increased equity market activity driven by deposit migration, but its effectiveness may diminish when funds migrate to non-equity assets, such as bonds[14] 2. Model Name: Short-Term Timing Strategy - **Model Construction Idea**: This model integrates macroeconomic, valuation, sentiment, and liquidity indicators to generate weekly timing signals for the equity market[19][20] - **Model Construction Process**: 1. **Macroeconomic Indicators**: - Manufacturing PMI > 50 indicates economic expansion, providing a positive signal - Credit impulse and M1 growth rates are compared to historical percentiles to assess economic strength[19][22] 2. **Valuation Indicators**: - PE and PB ratios are compared to their historical percentiles; high percentiles indicate overvaluation, providing cautious signals[19][22] 3. **Sentiment Indicators**: - Beta dispersion, volume sentiment scores, and market volatility are analyzed to gauge market sentiment[20][22] 4. **Liquidity Indicators**: - Money market rates, exchange rate expectations, and leverage financing trends are used to assess liquidity conditions[20][22] 5. Combine the signals from the above indicators to generate an overall timing signal (optimistic, neutral, or cautious)[19][20] - **Model Evaluation**: The model demonstrates strong performance, with an annualized return of 16.65% and a maximum drawdown of 15.05%, significantly outperforming the benchmark strategy[21][24] 3. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: This model identifies periods to overweight growth or value styles based on macroeconomic, valuation, and sentiment factors[28][29] - **Model Construction Process**: 1. **Macroeconomic Factors**: - Growth is favored when the earnings cycle slope is steep and the credit cycle is strengthening - Value is favored when the interest rate cycle is high[28][30] 2. **Valuation Factors**: - Growth is favored when the PE valuation gap is narrowing - Value is favored when the PB valuation gap is widening[28][30] 3. **Sentiment Factors**: - Growth is favored when turnover and volatility differences between growth and value are high[29][30] 4. Combine the signals from the above factors to determine the allocation between growth and value styles[28][30] - **Model Evaluation**: The model achieves an annualized return of 13.30% with a maximum drawdown of 43.07%, outperforming the benchmark strategy[29][31] 4. Model Name: Small-Cap vs. Large-Cap Rotation Model - **Model Construction Idea**: This model uses 11 effective rotation indicators to determine the relative attractiveness of small-cap and large-cap stocks[32] - **Model Construction Process**: 1. Key indicators include financing purchase balance changes, thematic investment sentiment, PB dispersion, and trading volume of small-cap indices[32][34] 2. Signals are aggregated to generate a composite rotation signal, which determines the allocation between small-cap and large-cap stocks[32][34] - **Model Evaluation**: The model consistently generates positive annual excess returns, with a 2026 year-to-date excess return of 2.88%[33][34] --- Model Backtesting Results 1. Deposit Migration Signal - Average return of All-A Index one month after signal: 1.72% - Win rate: 64.9% - Median return: 1.88%[15] 2. Short-Term Timing Strategy - Annualized return: 16.65% - Annualized volatility: 14.80% - Maximum drawdown: 15.05% - Sharpe ratio: 0.9802 - Monthly win rate: 66.46%[21][24] 3. Growth-Value Style Rotation Model - Annualized return: 13.30% - Annualized volatility: 20.76% - Maximum drawdown: 43.07% - Sharpe ratio: 0.6098 - Monthly win rate: 58.60%[29][31] 4. Small-Cap vs. Large-Cap Rotation Model - Annualized return: 20.60% (composite signal) - Annualized excess return: 12.95% - Maximum drawdown: 40.70% - Monthly win rate: 50.11%[34][36]