Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach

Abstract

This paper aims to identify factors that drive US clean-energy stock price movements in the short and long term, using a wide range of variables representing the carbon emission market, non-green financial assets, non- renewable commodities, macroeconomic fundamentals, investor attention and sentiment, and global stress and uncertainty. The empirical investigation is carried out in the context of the elastic-net regularization (ENET) approach and dynamic simulations of the autoregressive distributed lag (DYNARDL) model, with proper consideration to the potential presence of structural changes. From among 26 candidate variables, the ENET selects the clean technology market, public attention to clean energy, oil, and gold as the primary factors contributing the most to the behavior of clean-energy stock prices. The estimation results of the DYNARDL model suggest that the clean technology market and oil are vital determinants in the short and long run, while public attention and gold tend to affect clean-energy stock prices only in the short run. Furthermore, the respective magnitudes of influence of the four variables are larger in the short term than in the long term. Our findings offer practical implications for socially responsible investors and policymakers.