Abstract
In order to forecast the net unit value of funds in the new energy industry, this paper selects the net unit fund value as a sample, each15 fund in the front-end, middle-end, and back-end of the new energy industry, uses the H-P filter method for data denoising, then uses OLS and Tobit models for modeling regression analysis. Conclusion: front-end industry funds in the new energy industry perform uncommonly well; middle-end investment risk is low; back-end investment risk is high. The heterogeneity analysis partly indicates a positive reinforcing relationship between the size of the fund and its long-term trend.
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