Ph.D. Thesis Defense Announcement for Xian Li

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Ph.D. Thesis Defense Announcement for Xian LiNovember 18, 2013
The Tetherless World Constellation is proud to announce the successful completion of Xian Li's Thesis Defense.

TITLE: Dynamics of Investor Attention on the Social Web

ADVISOR: Professor James Hendler

ABSTRACT: The World Wide Web has been revolutionizing how investors produce and consume information while participating financial markets. Both the amount of information and the speed it flows around have achieved unprecedented magnitudes. The most preeminent change is the existence of ever-growing investor communities on the social web, which give rise to multidimensional information channels in real time. One the other hand, as an information consumer, what is immediately impacted is investor attention. Like other valuable resources in the economy, investor attention is limited. Therefore, it is crucial to understand how investors allocate their attention resources and the corresponding impacts for the financial markets.

Leveraging statistical analysis of “big data” related to real investors, this dis- sertation investigates micro-structures, temporal dynamics, and market impacts of investors’ selective attention by analyzing their tweeting activities on the social web. A hierarchy of complex systems is studied ranging from individual investor’s cognitive processes at the micro level to economic outcomes at macro scales. The contribution of this thesis is composed of three parts, each of which is summarized as follows.

Contribution I investigates mechanisms of cognitive control in individual investor’s temporal selective attention. We develop formalisms of “cognitive niches”, i.e. interplays between heuristics from adaptive cognitive control, to account for the selectivity of investor attention. Utilization of these cognitive niches is validated by empirical observations of investors’ tweeting activities on assets. Such selective mechanisms are further shown to be contextual, depending on types of assets, investing experience as well as investing approach. Embedded in a highly connected social environment, investor attention is found to employ the “social proof” heuristic, and the drawing power of the crowd in directing investor attention is significant and exceeds that of salient exogenous stimuli, especially when uncertainty in the financial market is high.

Contribution II characterizes the dynamical system of collective investor attention on the social web. We identified stylized facts of collective cognition in terms of fluctuation and memory persistence. Temporal fingerprints left by collective investor attention share several common properties with other complex systems with strong heterogeneity and interactions, such as clustering and memory persistence. In spite of scale-invariant fluctuations and long-range correlations as we identified from empirical observations. However, these regularities are not uniform across assets but suggest multi-scaling. To explicitly model the feedback mechanisms in collective investor attention, we propose a stochastic branching process as a coarse-grained generative model, which is shown to be a good fit of empirical tweeting behaviors especially during busy trading hours. Such results not only highlight significant endogeneity, or self-reflexivity, within the system of collective investor attention, but also provide more quantitative and real-time measurements of investor attention on the social web.

Contribution III quantifies interactions between dynamics of investor attention on the social web and price movements in the financial market. First, we show that these two systems are significantly correlated at a variety of timescales especially at small and intermediate timescales. At the small timescale, we found feedback relationships between investors’ tweeting activity and ranges of price movements, suggesting behavioral causes of “volatility clustering”. Furthermore, we illustrate distinct magnitudes and relaxation patterns of volatilities conditioning on investor attention of different cognitive controls. At intermediate timescales, we identified bidirectional causal relationships between collective investor attention on the social web and trading activities on the market, including volatilities, returns and trading volumes. By disentangling investor attention by nature in terms of cognitive controls, we demonstrate that both the magnitudes and lifespan of such lead-lag relationships vary. A robustness check demonstrates that as a social tape, dynamics of investor attention on the social web has its own information content more than known behavioral biases.