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Predictors of clean energy stock returns: An analysis with best subset regressions

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  • Ciner, Cetin
  • Kosedag, Arman
  • Lucey, Brian

Abstract

We investigate the determinants of clean energy stock returns by considering a large set of variables. We focus on the Covid-19 period and use a novel statistical technique, best subset regressions with non-Gaussian errors, for variable selection. Our examination shows that clean energy stocks are significantly exposed to small company and emerging market equities, a new finding to the literature. Moreover, we find no influence from the oil market, contrary to conclusions of a large part of the prior work.

Suggested Citation

  • Ciner, Cetin & Kosedag, Arman & Lucey, Brian, 2023. "Predictors of clean energy stock returns: An analysis with best subset regressions," Finance Research Letters, Elsevier, vol. 55(PA).
    • Handle: RePEc:eee:finlet:v:55:y:2023:i:pa:s1544612323002842
    DOI: 10.1016/j.frl.2023.103912
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    References listed on IDEAS

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