Networks, Linking Complexity, and Cross Predictability

74 Pages Posted: 17 Jan 2020 Last revised: 23 Jan 2020

See all articles by Wu Zhu

Wu Zhu

University of Pennsylvania

Date Written: January 4, 2020

Abstract

This paper provides evidence that network complexity limits investors' ability to process non-local information, through the lens of return cross predictability. Using firm-to-firm citation networks, we find that the non-local indirectly-linked firms can well predict the return of the focal firm, while the predictability of the local directly-linked firms is weak. A long-short strategy using the indirect links yields a risk-adjusted monthly alpha of 198 (164) basis points with equal (value) weights. We further find that (i) the indirect citation links are much more complex than direct ones, (ii) the magnitude of cross predictability increases with the degree of link complexity, (iii) institutional investors don't adjust their positions in a stock with complex links, but in one with simple links immediately, (iv) firms with more complex links receive more public attention, are much larger in size, and exhibit less idiosyncratic volatility than those with simple links, (v) there is little role of the usual proxies for limited investor attention and arbitrage cost in explaining our anomalies, once controlling for the linking complexity, and (vi) there are no differences in expected returns of stocks with various link complexity.

Keywords: Return Cross Predictability, Citation Networks, Information Complexity, Link Complexity, Limited Investor Attention, Innovation, Asset Pricing

JEL Classification: G11,G14,G40

Suggested Citation

Zhu, Wu, Networks, Linking Complexity, and Cross Predictability (January 4, 2020). Available at SSRN: https://ssrn.com/abstract=3512490 or http://dx.doi.org/10.2139/ssrn.3512490

Wu Zhu (Contact Author)

University of Pennsylvania

Philadelphia, PA 19104
United States

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