Quantitative Models and Construction Diffusion Index Model - **Model Name**: Diffusion Index Model - **Model Construction Idea**: The model is used to monitor the critical points of future diffusion index changes, predicting potential turning points in the market[6][43] - **Model Construction Process**: - The horizontal axis represents the relative price change of stocks in the micro-cap index components over a future period, ranging from +10% to -10% - The vertical axis represents the length of the review or forecast window, ranging from 20 days to 10 days - For example, a value of 0.16 at the intersection of a -5% price change (horizontal axis) and a 15-day window (vertical axis) indicates the diffusion index value under these conditions - The model uses historical data to calculate the diffusion index for different scenarios and predicts the likelihood of market turning points based on these values[43][45] - **Model Evaluation**: The model provides a systematic way to identify potential market turning points, but its accuracy depends on the stability of the index components and market conditions[6][43] - **Model Testing Results**: - Current diffusion index value: 0.91 - Historical signals: - Left-side threshold method triggered a sell signal on May 8, 2025, with a value of 0.9850[47] - Right-side threshold method triggered a sell signal on May 15, 2025, with a value of 0.8975[51] - Dual moving average method triggered a buy signal on April 30, 2025[52] --- Quantitative Factors and Construction Leverage Factor - **Factor Name**: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of companies, indicating their risk and potential return[5][38] - **Factor Construction Process**: Calculated as the ratio of total debt to equity or assets, normalized for comparison across companies[5][38] - **Factor Evaluation**: Demonstrated strong performance in the current week, with a rank IC of 0.143, significantly above its historical average of -0.006[5][38] Turnover Factor - **Factor Name**: Turnover Factor - **Factor Construction Idea**: Reflects the liquidity of stocks, with higher turnover indicating more active trading[5][38] - **Factor Construction Process**: Calculated as the ratio of trading volume to free float market capitalization over a specific period[5][38] - **Factor Evaluation**: Rank IC of 0.051 this week, outperforming its historical average of -0.08[5][38] PB Inverse Factor - **Factor Name**: PB Inverse Factor - **Factor Construction Idea**: Represents the inverse of the price-to-book ratio, identifying undervalued stocks[5][38] - **Factor Construction Process**: Calculated as 1 divided by the price-to-book ratio, normalized for comparison[5][38] - **Factor Evaluation**: Rank IC of 0.042 this week, slightly above its historical average of 0.034[5][38] Free Float Ratio Factor - **Factor Name**: Free Float Ratio Factor - **Factor Construction Idea**: Measures the proportion of shares available for public trading, indicating potential liquidity[5][38] - **Factor Construction Process**: Calculated as the ratio of free float shares to total shares outstanding[5][38] - **Factor Evaluation**: Rank IC of 0.04 this week, outperforming its historical average of -0.012[5][38] 10-Day Return Factor - **Factor Name**: 10-Day Return Factor - **Factor Construction Idea**: Captures short-term momentum by analyzing recent stock performance[5][38] - **Factor Construction Process**: Calculated as the percentage change in stock price over the past 10 trading days[5][38] - **Factor Evaluation**: Rank IC of 0.025 this week, significantly above its historical average of -0.061[5][38] Non-Adjusted Stock Price Factor - **Factor Name**: Non-Adjusted Stock Price Factor - **Factor Construction Idea**: Reflects the raw stock price without adjustments for splits or dividends[5][38] - **Factor Construction Process**: Directly uses the stock's current market price[5][38] - **Factor Evaluation**: Rank IC of -0.19 this week, underperforming its historical average of -0.017[5][38] PE_TTM Inverse Factor - **Factor Name**: PE_TTM Inverse Factor - **Factor Construction Idea**: Represents the inverse of the price-to-earnings ratio based on trailing twelve months, identifying undervalued stocks[5][38] - **Factor Construction Process**: Calculated as 1 divided by the PE_TTM ratio, normalized for comparison[5][38] - **Factor Evaluation**: Rank IC of -0.143 this week, underperforming its historical average of 0.018[5][38] ROE (Single Quarter) Factor - **Factor Name**: ROE (Single Quarter) Factor - **Factor Construction Idea**: Measures the profitability of companies based on their return on equity for a single quarter[5][38] - **Factor Construction Process**: Calculated as net income divided by shareholders' equity for the most recent quarter[5][38] - **Factor Evaluation**: Rank IC of -0.124 this week, underperforming its historical average of 0.023[5][38] Nonlinear Market Cap Factor - **Factor Name**: Nonlinear Market Cap Factor - **Factor Construction Idea**: Captures the nonlinear relationship between market capitalization and stock performance[5][38] - **Factor Construction Process**: Applies a nonlinear transformation to market capitalization data, such as logarithmic or polynomial adjustments[5][38] - **Factor Evaluation**: Rank IC of -0.116 this week, underperforming its historical average of -0.033[5][38] Log Market Cap Factor - **Factor Name**: Log Market Cap Factor - **Factor Construction Idea**: Measures the logarithmic transformation of market capitalization to reduce skewness[5][38] - **Factor Construction Process**: Calculated as the natural logarithm of market capitalization[5][38] - **Factor Evaluation**: Rank IC of -0.116 this week, underperforming its historical average of -0.033[5][38] --- Factor Backtesting Results - **Leverage Factor**: Rank IC 0.143[5][38] - **Turnover Factor**: Rank IC 0.051[5][38] - **PB Inverse Factor**: Rank IC 0.042[5][38] - **Free Float Ratio Factor**: Rank IC 0.04[5][38] - **10-Day Return Factor**: Rank IC 0.025[5][38] - **Non-Adjusted Stock Price Factor**: Rank IC -0.19[5][38] - **PE_TTM Inverse Factor**: Rank IC -0.143[5][38] - **ROE (Single Quarter) Factor**: Rank IC -0.124[5][38] - **Nonlinear Market Cap Factor**: Rank IC -0.116[5][38] - **Log Market Cap Factor**: Rank IC -0.116[5][38]

Group 1 - The report emphasizes the importance of "crowding" as a key indicator reflecting market sentiment in popular sectors, constructed from four dimensions: volume, price, capital, and analyst forecasts [1][11][12] - The current trading crowding in the TMT sector has rebounded from the bottom to a high level, with many segments of the AI industry chain also showing high crowding, although some remain at moderate levels [2][12] - The report suggests that when crowding is low, it indicates a bottoming phase for stock prices, while high crowding suggests potential for significant price corrections [1][11] Group 2 - The transaction ratio has reached a historical high of 46%, raising concerns about whether the AI trading sentiment has peaked [3][17][20] - The report indicates that while a high transaction ratio may lead to increased volatility, it does not typically signal a systemic end to the market trend, as internal rotation and high-low switching can help digest short-term overheating [3][20] - Historical examples are provided, showing that significant changes in industry trends or fundamentals can lead to new trend formations despite high transaction ratios [3][20] Group 3 - The report introduces a "rotation intensity" indicator to measure the speed of internal rotation within the AI sector, noting that a convergence in rotation intensity often leads to a mainline market trend [4][28] - Following the Spring Festival, the main directions within AI have become clearer, with the computer and media sectors leading the gains, resulting in a decrease in rotation intensity [4][28][29] - The relationship between the AI index and rotation intensity suggests a pattern of "linked rises and rotating adjustments," indicating resilience in the sector rather than systemic declines [4][34] Group 4 - U.S. Treasury yields are highlighted as a significant factor affecting the pricing of high-valuation growth assets, with rising yields typically suppressing market risk appetite [5][37] - The report notes a strong correlation between TMT performance and U.S. Treasury yields, suggesting that changes in yields should be closely monitored from a trading perspective [5][37][39] Group 5 - The report discusses the importance of earnings performance in the AI sector, noting that the correlation between stock price movements and earnings growth is strongest during earnings disclosure periods [7][43] - It is observed that when the market focuses on fundamentals, the TMT sector may face adjustment pressures, while periods of trading on expectations can lead to better performance [7][43][45] - The example of optical modules is provided, illustrating how sustained earnings performance can lead to a strong positive correlation with stock price movements [7][51][52]