Macro Environment - The current macroeconomic situation is characterized as "weak recovery, low inflation," with policies focused on stabilizing growth and preventing risks [5][11] - The State Council issued a plan to optimize the market allocation of factors, which is expected to enhance economic efficiency and provide a more flexible policy environment for related industries [5][11] - Policies supporting traditional industries such as automotive, steel, and construction have been introduced, alongside new initiatives for emerging sectors like new energy storage and artificial intelligence [5][11] Market and Industry Performance - In September, the bond market showed significant differentiation, with the 10-year government bond futures index slightly rising by 0.02%, while the 30-year futures contract fell by 2.28% [48][51] - The equity market favored growth sectors, with the advanced manufacturing index rising by 8.99% and technology (TMT) by 5.6%, while sectors like healthcare and finance saw declines [53][58] - The top-performing industries in September included electric equipment (21.17%), non-ferrous metals (12.79%), and electronics (10.96%), while sectors like social services and non-bank financials faced declines [58][63] Monthly Allocation Recommendations - The report suggests a balanced allocation between growth and value styles, with a focus on sectors such as TMT, pharmaceuticals, and securities [6][69] - The anticipated easing of monetary policy by the Federal Reserve is expected to enhance market risk appetite, although the crowded midstream manufacturing sector may increase short-term volatility risks [6][69]

Core Insights - The report focuses on utilizing wavelet analysis for precise prediction of component stock closing prices and optimizing investment portfolios, demonstrating a comprehensive strategy that integrates multiple models for effective stock selection and investment [4][7]. - The strategy has been validated through backtesting, showing significant outperformance against benchmark indices across various styles, including large-cap blue chips (CSI 300), mid-cap growth stocks (CSI 500), and composite indices (CSI 800) [4][7]. Summary by Sections 1. Main Trends in Time Series Applications - The report discusses the differences and advantages of mainstream trend models in time series analysis, highlighting the effectiveness of wavelet analysis compared to traditional methods like HP filter and Fourier transform [13][19]. 2. Detailed Wavelet Analysis - Wavelet analysis is presented as a powerful tool for multi-scale analysis, capable of localizing both time and frequency characteristics of financial time series, making it suitable for analyzing non-stationary data without prior transformation [30][31]. - The methodology includes a three-level decomposition process that separates long-term trends and short-term fluctuations, providing a robust framework for subsequent modeling [72]. 3. Core Predictive Models and Wavelet Decomposition - The strategy employs ARIMA for predicting the low-frequency trend component (cA3) and GARCH for capturing the high-frequency volatility component (cD1), effectively addressing the unique characteristics of financial time series [74][79]. - The combination of these models allows for a comprehensive understanding of both long-term trends and short-term market dynamics, enhancing predictive accuracy [72][79]. 4. Strategy Backtesting Results and Analysis - The backtesting period spans from January 4, 2019, to July 25, 2025, with a weekly rebalancing strategy based on predicted data, demonstrating the strategy's effectiveness across different indices [96]. - The constructed portfolios consistently outperformed their respective benchmarks, with significant improvements in key risk-return metrics such as annualized returns and Sharpe ratios [7][96].