Core Insights - The report highlights the current industry allocation positions of the Bank of China’s multi-strategy system, with significant weights in non-bank financials (8.9%), comprehensive (8.5%), and telecommunications (7.7%) sectors [1] - The average weekly return for the CITIC primary industries is reported at 0.8%, with the telecommunications sector leading at 6.5% and banking lagging at -2.1% [3][10] - The report indicates that the composite strategy has achieved a cumulative return of 17.5% year-to-date, outperforming the CITIC primary industry equal-weight benchmark by 2.1% [3] Industry Performance Review - The top three performing industries for the week are telecommunications (6.5%), comprehensive financials (6.0%), and electric equipment & new energy (3.3%), while the worst performers are banking (-2.1%), national defense & military (-1.7%), and textiles & apparel (-1.7%) [3][10] - The report provides a detailed breakdown of weekly and monthly returns across various industries, indicating a strong performance in sectors like telecommunications and comprehensive financials [11] Valuation Risk Warning - The report employs a valuation warning system based on the PB ratio over the past six years, identifying industries with high valuation risks. Currently, the retail trade, national defense & military, media, and computer industries are flagged for high valuations, exceeding the 95% percentile [12][13] - The methodology for the valuation warning system involves excluding the top 10% of PB ratios to ensure robust estimates [12] Strategy Performance - The report outlines various strategies and their performance, with the highest excess return from the long-term reversal strategy (6.3%) and the lowest from the funds flow strategy (-2.0%) [3] - The current top three industries based on the high prosperity industry rotation strategy are non-bank financials, telecommunications, and non-ferrous metals [15][16] Macro Style Rotation - The macro style rotation strategy identifies the top six industries based on macroeconomic indicators, which include comprehensive financials, computers, media, national defense & military, comprehensive, and non-bank financials [21][23] - The report emphasizes the importance of macroeconomic indicators in predicting industry performance and the methodology used to rank industries based on their exposure to various styles [22] Emotional Momentum Tracking - The emotional momentum tracking strategy identifies the top three industries based on implied market sentiment, which are machinery, telecommunications, and light industry manufacturing [18][20] - This strategy focuses on capturing market sentiment before earnings expectations are published, utilizing daily return and turnover rate data [19]

Core Insights - The report highlights the current positioning of the Bank of China’s multi-strategy industry allocation system, with a comprehensive allocation of 8.6% across various sectors, including Electronics (7.5%), Non-ferrous Metals (7.4%), and Banking (7.3) [1] - The report tracks the performance of various strategies, noting that the S2 sentiment tracking strategy achieved a weekly excess return of 3.3%, while the S1 industry profitability tracking strategy underperformed with an excess return of -0.1% [2][3] - The report identifies the top-performing sectors for the week as Machinery (5.4%), Non-ferrous Metals (4.4%), and National Defense Industry (4.2%), while the worst performers were Oil & Petrochemicals (-0.9%), Pharmaceuticals (-0.9%), and Comprehensive Finance (-0.6%) [3][10] Industry Performance Review - The average weekly return for the 30 CITIC first-level industries was 1.9%, with a one-month average return of 4.2% [10] - The report provides a detailed breakdown of weekly and monthly performance for each industry, indicating significant variations in returns across sectors [11] Valuation Risk Warning - The report employs a valuation warning system based on the PB ratio over the past six years, identifying industries with a PB ratio above the 95th percentile as overvalued [12][13] - Currently, the industries triggering high valuation warnings include Retail Trade, National Defense Industry, and Media, all exceeding the 95% threshold [13][14] Strategy Performance - The report outlines the performance of various strategies, with the S4 long-term reversal strategy showing a significant excess return of 6.4% year-to-date [3][15] - The S3 macro style rotation strategy has a current excess return of 4.3%, indicating strong performance in the context of macroeconomic indicators [3][24] Sector Rankings - The report ranks the current high-prospect sectors based on profitability expectations, with Non-ferrous Metals, Communication, and Agriculture leading the rankings [16][19] - The sentiment tracking strategy (S2) identifies Machinery, Computer, and Textile as the top sectors based on implied sentiment indicators [19][20] Macro Indicators - The report highlights the top six industries favored by current macroeconomic indicators, which include Comprehensive Finance, Computer, Media, National Defense Industry, and Non-bank Financials [24][25]

Core Insights - The current industry allocation of the Bank of China multi-strategy system includes Computer (9.6%), Steel (9.2%), Non-ferrous Metals (7.8%), Consumer Services (7.2%), and Banking (6.8) among others, indicating a diversified investment approach across various sectors [1] - The average weekly return of the CITIC primary industries is 3.5%, with the best-performing sectors being Coal (10.5%), Steel (10.2%), and Non-ferrous Metals (9.6%), while the worst performers are Banking (-2.1%), Communication (-0.6%), and Comprehensive Finance (-0.1%) [3][11] - The composite strategy achieved a cumulative return of 3.4% this week, with an annual cumulative return of 16.4%, outperforming the CITIC primary industry equal-weight benchmark by 1.9% [3] - The highest weight strategy currently is the medium to long-term reversal strategy (S4) at 21.4%, while the lowest is the macro style rotation strategy (S3) at 8.0% [3] - Recent adjustments in positions indicate an increase in upstream cyclical sectors and a decrease in TMT sectors [3] Industry Performance Review - The top three industries in terms of weekly performance are Coal (10.5%), Steel (10.2%), and Non-ferrous Metals (9.6%), while the bottom three are Banking (-2.1%), Communication (-0.6%), and Comprehensive Finance (-0.1%) [11] - The average monthly return over the past month is 7.3%, indicating a positive trend across the industries [11] Valuation Risk Warning - The current PB valuation for the Retail, Automotive, Defense, and Media industries exceeds the 95th percentile of their historical valuations, triggering a high valuation warning [14][15] Strategy Performance - The S1 strategy focusing on high profitability industries shows a weekly excess return of -0.4%, while the S2 strategy tracking unverified sentiment has an excess return of 3.0% [3] - The S4 medium to long-term reversal strategy has the highest weight and has shown significant performance, indicating its effectiveness in the current market environment [3][16] Sector Rankings - The current top three sectors based on profitability expectations are Computer, Non-ferrous Metals, and Steel [17] - The S2 strategy ranks Mechanical, Computer, and Comprehensive as the top sectors based on implied sentiment [20] Macro Style Rotation - The macro style rotation strategy indicates a bullish outlook for Comprehensive Finance, Computer, Media, Defense, Electronics, and Comprehensive sectors based on current macro indicators [24][25]

Quantitative Models and Construction Methods Model 1: High Prosperity Industry Rotation Strategy (S1) - **Model Construction Idea**: The model aims to select industries with upward profit expectations by tracking industry profitability using a multi-factor model based on analysts' consensus expectations[16] - **Model Construction Process**: - Construct three major types of factors based on the original value, slope, and curvature of profit expectations - Screen candidate factors with annualized excess return >3% - Use hierarchical clustering to classify candidate factors into 8 categories and select the highest excess return factor from each category for rank equal-weighted composite - Exclude overvalued industries and select the top 3 industries with the highest factor values weekly[16] - **Model Evaluation**: The model effectively captures industries with high profitability expectations[16] Model 2: Implicit Sentiment Momentum Tracking Strategy (S2) - **Model Construction Idea**: The strategy constructs a sentiment momentum model that runs ahead of earnings expectation data by capturing "unproven sentiment" in the market[19] - **Model Construction Process**: - Perform cross-sectional regression of industry daily returns on daily turnover rate changes to strip out "expected sentiment" - Calculate the residual as "unproven sentiment" - Construct half-month and 12-month momentum factors based on cumulative unproven sentiment factor net value - Rank and equal-weight composite the two momentum factors - Exclude overvalued industries and select the top 3 industries with the highest factor values weekly[20] - **Model Evaluation**: The model captures market sentiment ahead of earnings expectation data[19] Model 3: Macro Style Rotation Strategy (S3) - **Model Construction Idea**: The strategy predicts the long-short situation of four industry styles (high beta, high valuation, 12-month momentum, high volatility) based on current macro indicators and their correlation with the returns of these styles[22] - **Model Construction Process**: - Construct a fundamental indicator system from "economic growth," "inflation," "currency," "credit," and "market sentiment" - Calculate the exposure of each industry to the four styles and estimate the expected long-short returns of the style factors - Use a weak voting classifier to predict the long-short of the styles - Map the style predictions to industries and select the top 6 industries with the highest total scores monthly[23] - **Model Evaluation**: The model effectively integrates macro indicators with industry style predictions[22] Model 4: Long-term Reversal Strategy (S4) - **Model Construction Idea**: The strategy leverages the momentum effect within 2 years and the reversal effect beyond 3 years in industries[27] - **Model Construction Process**: - Construct a "1-year momentum" factor excluding the most recent month's returns - Construct a "3-year reversal" factor using the period from 3 years ago to 2 years ago - Construct a turnover factor using the turnover rate of free float market value - Rank and equal-weight composite the three factors - Select the top 5 industries with the highest factor values monthly[27] - **Model Evaluation**: The model captures long-term reversal and medium-term momentum effects in industries[27] Model 5: Fund Flow Industry Rotation Strategy (S5) - **Model Construction Idea**: The strategy constructs an industry rotation model based on "market main fund flow and strength" and "late trading fund flow and strength"[29] - **Model Construction Process**: - Construct an "institutional order trend strength factor" using the net buy amount of institutional orders - Construct a "late trading fund flow and strength factor" using the average daily inflow of late trading funds - Rank and equal-weight composite the two factors - Select the top 5 industries with the highest fund inflow strength monthly[30] - **Model Evaluation**: The model effectively captures the flow and strength of market funds[29] Model 6: Financial Report Factor Failure Reversal Strategy (S6) - **Model Construction Idea**: The strategy leverages the phenomenon of financial report factors performing poorly in recent years to construct an industry rotation model based on the mean reversion theory of factor effectiveness[34] - **Model Construction Process**: - Classify financial report factors into categories and screen for "long-term effective factors" with annualized excess return >5.5% - Identify "short-term failure factors" that underperform the industry equal-weight benchmark for 4 consecutive months - Composite the highest annualized excess return factors from each category - Select the top 5 industries with the highest factor values monthly[35] - **Model Evaluation**: The model captures the mean reversion of financial report factors[34] Model 7: Multi-factor Scoring Composite Strategy (S7) - **Model Construction Idea**: The strategy is a quarterly rebalancing strategy that composites factors from "momentum," "liquidity," "valuation," and "quality" dimensions[39] - **Model Construction Process**: - Exclude industries with a weight below 2% in the CSI 800 - Select 2 factors from each dimension and rank equal-weight composite - Select the top 5 industries with the highest factor values quarterly[40] - **Model Evaluation**: The model effectively integrates multiple factor dimensions[39] Model Backtest Results - **S1**: Annualized excess return -1.8% YTD[66] - **S2**: Annualized excess return 5.6% YTD[66] - **S3**: Annualized excess return 2.7% YTD[66] - **S4**: Annualized excess return 4.8% YTD[66] - **S5**: Annualized excess return -0.2% YTD[66] - **S6**: Annualized excess return 0.6% YTD[66] - **S7**: Annualized excess return 3.9% YTD[66] - **Composite Strategy**: Annualized excess return 2.0% YTD[66]