Measuring Information in Financial Markets

[摘要] The measurement of information in financial markets is fundamental to our understanding of investor heterogeneity and the efficiency of stock prices. In the first chapter, I introduce Weighted Trading Correlation Network (WTCN) estimation to uncover hidden information linkages between investors based on observed pairwise trading correlations. I use daily-level institutional manager trades to compute network snapshots. I show that WTCNs have two topological features characteristic of social networks---an approximate power-law degree distribution and positive assortative mixing. These results are consistent with information transfers occurring through social interactions.In the second chapter, I develop Information Diffusion Centrality---a measure of network centrality based on the idea that investors exchange information bilaterally through one-on-one interactions in proportion to the strength of their connections to one another. I compare Information Diffusion Centrality to two measures of centrality based on different diffusion mechanisms in their ability to predict institutional trading performance. The first, Degree Centrality, supposes that investors are informationally linked, but there are no network flows. The second, Eigenvector Centrality, corresponds to a world where information spreads epidemically through simple contagions. I show that only Information Diffusion Centrality predicts higher performance, whereas Degree and Eigenvector Centrality predict zero or lower performance. These results may explain why valuable information tends to remain localized---i.e. why there is persistent information heterogeneity among investors---despite the fact that social networks facilitate the rapid transmission of ideas. In the third chapter, my co-authors and I examine whether the Easley and O'Hara (1987) PIN model's recently documented failure to identify private information arises from the model's inability to describe the data or from the model's reliance on order flows alone. We find that the PIN model mistakenly identifies private information from turnover because it is unable to describe the order flow data. We propose a model that addresses this shortcoming but also depends on order flow alone. We find that the extended model does not perform as well as the Odders-White and Ready (2008) model, which relies on both returns and order flow.

[发布机构] Rice University

[效力级别] Asymmetry