- The report introduces a timing system that uses the distance between the 120-day long-term moving average and the 20-day short-term moving average of the WIND All A Index to determine market trends. The short-term moving average is above the long-term moving average, with a distance of 3.38%, which is significantly greater than 3%, indicating the market has returned to an upward trend[2][6][11] - The "profitability effect" is used as a core indicator to assess market conditions. The current market trend line is at 6237 points, and the profitability effect is 3.12%, which is significantly positive, suggesting the upward trend is likely to continue[5][7][11] - The "Mid-term Distress Reversal Expectation Model" signals a focus on retail, tourism, and other service-oriented consumption sectors[5][7][11] - The "TWO BETA Model" continues to recommend the technology sector, with a focus on domestic computing power and commercial aerospace[5][7][11] - The "Industry Trend Model" indicates that sectors such as communication, industrial metals, and energy storage are maintaining an upward trend[5][7][11] - The valuation metrics for the WIND All A Index show that the PE ratio is at the 85th percentile, indicating a relatively high level, while the PB ratio is at the 50th percentile, indicating a medium level[5][7][11] - Based on the "Position Management Model," the report suggests an 80% equity allocation for absolute return products using the WIND All A Index as the primary stock allocation benchmark[5][7][11]

Group 1 - The article discusses the classification of market states into "trend" and "range" based on historical data, emphasizing the importance of recognizing these states for investment strategies [1][4] - In a trending market, momentum strategies like "buy high, sell higher" yield greater returns, while in a ranging market, mean-reversion strategies perform better [1][4] - A two-phase algorithm is developed to label historical trends and ranges in the Shanghai Composite Index, enhancing the accuracy of market state identification [2][3] Group 2 - The backtesting period is set from January 2020 to August 2025, reflecting a shift in market behavior post-2020, with increased frequency of state changes [7] - A feature variable system is constructed to identify market states, focusing on price, volume, and volatility, rather than traditional indicators [8][15] - The model training shows that all six feature indicators have an accuracy above 50%, with the volume feature achieving the highest accuracy of 63.48% [22][23] Group 3 - The decision tree model outperforms other models in predicting market states, achieving an accuracy of 80.10% in the test set [36][39] - The strategy based on the decision tree model yields a total return of 77.20%, significantly outperforming the benchmark [63] - The research highlights the potential of combining strategic signals for long-term market state identification with tactical signals for short-term changes to enhance strategy performance [64]