- The A-share industry rotation model is constructed using six factors: capital, valuation, sentiment, momentum, overbought/oversold, and profitability[20] - The capital factor is based on the net inflow rate of industry funds, the valuation factor uses the industry's valuation percentile over the past year, the sentiment factor is derived from the proportion of rising constituent stocks, the momentum factor uses the MACD indicator, the overbought/oversold factor uses the RSI indicator, and the profitability factor uses the industry's consensus forecast EPS percentile over the past year[20] - The industry rotation model results show that the defense industry, home appliances, and computers have high comprehensive scores, while construction decoration, textiles and apparel, and automobiles have low scores[21] - The consensus stock selection model is based on momentum and price factors, combined with the similarity between high-frequency capital flow trends and stock price trends[4] - The model selects stocks from the top three secondary industries with the highest monthly gains, using factors such as momentum, valuation, and frequency of price increases, along with high-frequency capital flow data[23] - The consensus stock selection model's output includes stocks like Lead Intelligent, Changying Precision, and Kedali, among others[24]

- The A-share industry rotation model is constructed using six factors: capital, valuation, sentiment, momentum, overbought/oversold, and profitability[4][19] - The capital factor is based on the net inflow rate of industry funds, valuation is based on the industry's valuation percentile over the past year, sentiment is based on the proportion of rising constituent stocks, momentum uses the MACD indicator, overbought/oversold uses the RSI indicator, and profitability uses the consensus forecast EPS percentile over the past year[19] - The industry rotation model results show that steel, building materials, and computers have high comprehensive scores, while petroleum and petrochemicals, power equipment, and banks have low scores[4][20] - The consensus stock selection model is based on momentum and price factors, combined with high-frequency capital flow trends and stock price similarity[5][22] - The consensus stock selection model results for this period include stocks such as Shenghong Technology, Xiechuang Data, and Industrial Fulian[5][23] Model Backtest Results - A-share industry rotation model, steel industry score: 10, building materials industry score: 6, computer industry score: 5[21] - Consensus stock selection model, selected stocks: Shenghong Technology, Xiechuang Data, Industrial Fulian, etc.[23]

Quantitative Models and Construction Methods 1. Model Name: A-Share Industry Rotation Model - **Model Construction Idea**: The model is built using six factors: capital, valuation, sentiment, momentum, overbought/oversold, and profitability, to create a scoring system for evaluating industries comprehensively [13] - **Model Construction Process**: - **Capital Factor**: Uses the main net inflow rate of industry funds as the primary data source - **Valuation Factor**: Based on the valuation percentile of the industry over the past year - **Sentiment Factor**: Proportion of rising constituent stocks serves as the main data source - **Momentum Factor**: Uses the MACD indicator as the primary data source - **Overbought/Oversold Factor**: Relies on the RSI indicator as the key data source - **Profitability Factor**: Based on the consensus forecast EPS percentile of the industry over the past year [13] - **Model Evaluation**: The model provides a comprehensive scoring mechanism to assess industry performance, effectively identifying high-performing and low-performing industries [13] 2. Model Name: Consensus Stock Selection Model - **Model Construction Idea**: The model combines momentum, valuation, and price increase frequency factors with high-frequency capital flow data to select stocks with the highest similarity between capital flow trends and stock price trends [16] - **Model Construction Process**: - Identify high-performing secondary industries over the past 30 days - Calculate momentum factors, valuation factors, and price increase frequency for stocks within these industries - Use high-frequency minute-level data to calculate capital inflow/outflow changes for each stock - Select the top three secondary industries and pick five stocks from each based on the highest similarity between capital flow trends and stock price trends [16] --- Model Backtesting Results 1. A-Share Industry Rotation Model - **Top Scoring Industries**: Non-bank financials, communication, and automotive scored the highest, with total scores of 8, 8, and 7, respectively - **Low Scoring Industries**: Building materials, social services, and steel scored the lowest, with total scores of -10, -5, and -5, respectively [14][15] 2. Consensus Stock Selection Model - **Selected Industries**: Feed, precious metals, and animal health II were identified as the top-performing secondary industries - **Selected Stocks**: - Feed: Baiyang Co., Zhongchong Co., Zhenghong Technology, Bangji Technology, Guibao Pet - Precious Metals: Hunan Silver, Xiaocheng Technology, Western Gold, Hunan Gold, Shandong Gold - Animal Health II: Jinhe Bio, Haili Bio, Kexian Bio, Shengwu Co., Ruipu Bio [17] --- Quantitative Factors and Construction Methods 1. Factor Name: Capital Factor - **Construction Idea**: Measures the main net inflow rate of industry funds to assess capital movement trends [13] - **Construction Process**: Calculate the net inflow of main funds for each industry and normalize the data to create a scoring metric [13] 2. Factor Name: Valuation Factor - **Construction Idea**: Evaluates the relative valuation of an industry based on its historical percentile over the past year [13] - **Construction Process**: Calculate the percentile of the industry's valuation over the past year and assign scores accordingly [13] 3. Factor Name: Sentiment Factor - **Construction Idea**: Uses the proportion of rising constituent stocks to gauge market sentiment [13] - **Construction Process**: Calculate the percentage of rising stocks within an industry and normalize the data for scoring [13] 4. Factor Name: Momentum Factor - **Construction Idea**: Leverages the MACD indicator to measure momentum trends [13] - **Construction Process**: Compute the MACD values for each industry and assign scores based on the strength of momentum [13] 5. Factor Name: Overbought/Oversold Factor - **Construction Idea**: Uses the RSI indicator to identify overbought or oversold conditions [13] - **Construction Process**: Calculate RSI values for each industry and assign scores based on thresholds for overbought/oversold conditions [13] 6. Factor Name: Profitability Factor - **Construction Idea**: Assesses profitability using the consensus forecast EPS percentile over the past year [13] - **Construction Process**: Calculate the percentile of the consensus forecast EPS for each industry and assign scores accordingly [13] --- Factor Backtesting Results 1. Capital Factor - **Top Scoring Industries**: Automotive (+3), Non-bank financials (+2), Communication (+2) [15] 2. Valuation Factor - **Top Scoring Industries**: Automotive (+3), Non-bank financials (+3), Communication (+2) [15] 3. Sentiment Factor - **Top Scoring Industries**: Communication (+3), Automotive (+1), Non-bank financials (-2) [15] 4. Momentum Factor - **Top Scoring Industries**: Non-bank financials (+3), Communication (+1), Automotive (+1) [15] 5. Overbought/Oversold Factor - **Top Scoring Industries**: Non-bank financials (+3), Communication (+3), Automotive (+3) [15] 6. Profitability Factor - **Top Scoring Industries**: Automotive (+3), Non-bank financials (+3), Communication (+3) [15]