Investors' attention and information losses under market stress

The paper proposes a novel point-wise entropy approach to measure the time-varying losses in the value of information that investors associate with market signals, financial and economic indicators, and news. We cast our approach in a Bayesian framework and assume that market agents update their beliefs to incoming signals based on a prior in-formation set. By exploiting the distribution rather than the time-series properties of in-formation signals, our method is able to construct univariate signal-specific, but also com-posite proxies of information loss, with the latter being more efficient in reducing mis-leading effects and interpretation errors. As an empirical illustration, we construct infor-mation loss proxies for the US equity market from several mainstream information signals and find that the majority of information loss indicators can influence investors' atten-tion, which then intermediates the impact of information signals on market outcomes. Fi-nally, we show that, by relying on composites rather than univariate proxies, market agents can diversify and thus reduce their information losses when interpreting signals associated with the same underlying event. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a novel point-wise entropy approach to measure time-varying losses in the value of information that investors associate with market signals, financial and economic indicators, and news. Utilizing composite proxies rather than univariate proxies can help reduce misleading effects and interpretation errors when interpreting signals. Most information loss indicators can influence investors' attention, which in turn impacts market outcomes.