行业配置报告（2023年12月）：行业配置策略与ETF组合构建-20231201-西南证券-16页_1mb

Summary of 2023 Q4 Industry Allocation Report

The report provides insights into industry allocation strategies using three different models: similarity expectation difference, analyst expectation marginal change, and trading concentration. Key metrics include monthly and annual performance comparisons with benchmark indices, along with risk factor rankings. The review emphasizes that conclusions are based on historical data and includes specific ETF recommendations and risk disclaimers for investment consideration.

Key Findings from Analysis

• Similarity Expectation Difference Model (Latest View): The model recommends industries including agriculture, forestry, fishing, textiles, transportation, light manufacturing, and computing. In November 2023, the portfolio returned 0.03%, with an outperformance vs. benchmark of -0.66%. Top contributors were agriculture (+195%) and light manufacturing (+0.04%). RFC component: Agriculture, power and utilities, non-bank finance, steel, light manufacturing, and transportation.

• Analyst Expectation Marginal Change Model (Latest View): This model focuses on petroleum and petrochemicals, food and beverage, automotive, home appliances, power and utilities, and textiles. In November 2023, the return was 15%, outperforming the benchmark by 0.69%. Top contributors include automotive (+241%) and food and beverage (+0.05%). RTC component: Automotive, home appliances, petroleum and petrochemicals, food and beverage, media, power and utilities.

• Trading Concentration Model (Latest View): The model highlights pharmaceuticals, consumer services, and textiles. In November 2023, the portfolio gained 1.37%, outperforming the benchmark by 0.68%. Key configurations include pharmaceuticals (293% weight) and consumer services (264%).

ETF Portfolio Recommendations

For the December 2023 ETF selection, the following sectors are suggested: agriculture, pharmaceuticals, textiles, home appliances, food and beverage, and computing. This includes specific ETF codes and names for implementation, available in relevant markets.

Risk Assessment and Disclaimers

• All model-based recommendations are derived from historical data and may not reflect future performance due to changing market conditions. Third-party data accuracy is noted as a potential risk.
• ETF combinations should not be construed as investment advice or guarantees. Fund performance is subject to market fluctuations, style shifts, and other factors, with inherent risks. Investors must assess their risk tolerance before acting.
• The report is updated monthly, incorporating ongoing strategy tracking and backtesting results from 2016-2023.

For detailed implementation, refer to the full report's Table_Author and appendices.