Vol. 13 No. 2 (2024), 40-54

Macroeconomic trends are closely related to the performance of asset prices. More industry rotation models focus on macro data to study investment strategies, but macroeconomic data is usually released with a lag. In recent years, China’s A-share market has been dominated by structural markets, with hotspots switching frequently. Most investors prefer to chase after popular industries, but the returns of popular industries are volatile, and the longterm returns are not satisfactory. In addition, more industry rotation models focus on macro data and industry boom to study the investment strategy, which can capture the medium and long-term market but cannot capture the short-term market opportunities. To solve the above problems, this paper researches the reasonable division of macrocycles and macro timing indicators. It focuses the research scope of the underlying assets on non-popular industries, combining macro timing signals, momentum factors, and resistance indicators to construct an industry rotation strategy, which reduces the return volatility of the portfolio and makes the portfolio more sensitive to the short-term investment opportunities, which is conducive to capturing the short-term market opportunities. This paper determines the allocation of long and short assets in the portfolio based on macro timing signals, while for long assets, the momentum factor is used to capture industries with strong short- and medium-term momentum effects, and then through the resistance level indicator, some industries with high short-term downside risk are eliminated. The strategy is applied to construct an industry rotation model in non-popular industries to analyze the model’s performance and the strategy’s generalization effect. The industry rotation strategy proposed in this paper has a better performance and generalization ability, and the performance of the industry rotation model is relatively stable in terms of in-sample and out-of-sample returns.