Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 3, 2022 erstellt
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Description:
This paper studies the dynamic information flows between stock and corporate bond markets. Using accurately measured returns on corporate bond exchange-traded funds (ETFs), we find that returns on a portfolio of stocks of firms issuing the bonds in the ETFs positively predict corporate bond ETF returns, but not vice versa. The return predictability is stronger for ETFs tracking the indices of corporate bonds with lower credit rating and higher yields. Inspired by these findings, we use machine learning algorithms to select individual stocks with superior power to lead the ETFs; the resulting stock portfolio has even stronger ability to predict the ETF returns. By contrast, a randomly formed stock portfolio does not predict the ETF returns. These results are consistent with the notion of gradual information diffusion across asset markets