Characterize Investor Attention on the Social Web

We study the allocation problem of investor attention, the scarce and important economic resource in the information era, through practitioners' tweeting behaviors. We develop formalisms of "cognitive niches", interplays between heuristics from adaptive cognitive control, to account for the selectivity of investor attention. Using asset-specific tweets as direct measures of investor attention allocation, we find evidence supporting contextual utilizations of these cognitive control strategies, depending on types of asset, investors' experience as well as investing philosophy. We demonstrate that as a result of adaptive cognitive control, investor attention can be different by nature. Such distinction is not captured by traditional indirect measures of investor attention, but reveals different implications for assets' short-term volatilities.

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The goal of this project is to explore key algorithms, technologies and protocols that will lead to the next level of development of the original Semantic Web vision of the "Web of Data" - a Web in which the unstructured texts of the current Web are integrated in a seamless way with information currently locked in structured databases.

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