Conference Agenda
Please note that all times are shown in the time zone of the conference. The current conference time is: 1st Nov 2024, 12:49:08am CET
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Session Overview |
Session | |||
AP 14: Data, Attention, and Liquidity
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Presentations | |||
ID: 915
Valuing Financial Data 1MIT; 2Columbia Business School; 3NYU, Stern How should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information about others' characteristics. It can value data that is public or private, about one or many assets, relevant for dividends or for sentiment. While different data types, of course, have different valuations, heterogeneous investors also value the same data very differently, which suggests a low price elasticity for data demand. Heterogeneous investors' data valuations are also affected very differentially by market illiquidity.
ID: 1952
Wealth Dynamics and Financial Market Power University of Texas - Austin, United States of America We propose a dynamic theory of financial market concentration in settings where some investors trade strategically because of price impact. The distribution of risk and wealth determines market power, and wealth evolves over time given strategic portfolio choices. In equilibrium, the most well-capitalized investors remain under-diversified to capture rents, generating concentration and volatility in the wealth distribution. Conversely, wealth concentration leads to inflated asset prices, unequal returns to wealth, and poor liquidity that further exacerbates the distortions from market power. We discuss applications of our framework, and derive implications for evaluating welfare using asset pricing data.
ID: 741
Media Narratives and Price Informativeness 1Frankfurt School of Finance and Management gGmbH, Germany; 2George Washington University We show that an increase in stock return exposure to media attention to narratives, measured with standard methods for extracting topic attention from news text, leads to a lower stock price informativeness about future fundamentals. Empirically, narrative exposure explains over 86% of idiosyncratic variance in the cross-section, and both narrative exposure and non-systematic information channels—idiosyncratic variance and variance related to public information—decrease stock price informativeness. Moreover, stocks with high narrative exposure demonstrate elevated trading volume. To rationalize the empirical results, we suggest a mechanism based on disagreement among investors arising due to the differential processing of information in media narratives.
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