Job Market Paper

"Exuberant and Uninformed: How Financial Markets (Mis-)Allocate Capital During Booms"

30 Minute Presentation (PhD EVS) Slides

Non-technical Summary Blog Post

I develop a macroeconomic model with a central emphasis on the informational role of financial markets. Economic agents save by purchasing financial claims on firms. Crucially, agents produce information about firm productivity to guide their trading decisions. In the aggregate, this information determines the financial market's ability to allocate more capital to productive firms and, thus, pins down total factor productivity (TFP). Using this framework, I study how information varies in response to fundamental (productivity) and non-fundamental (sentiment) macroeconomic shocks. Both lead to similar co-movements in output, asset prices, and investment but affect traders' information production differently. Productivity booms crowd in information and, thus, amplify the initial shock by further increasing TFP. In contrast, sentiment shocks, defined as waves of optimism or pessimism, crowd out information production, which dampens sentiment booms through a decrease in TFP. I show that information production in the competitive equilibrium is generally constrained inefficient for two reasons. First, each agent produces information to extract rents from others (rent-extracting behavior). Second, atomistic agents fail to internalize that their information production helps improve capital allocation and TFP, which is partially revealed through prices (information spillover). As an application, I show that asset purchase programs can be an effective way to address the financial market inefficiencies. Finally, looking through the lens of the model, the US dot-com boom of the late 1990s appears to have been driven by productivity, whereas the US housing boom of the mid 2000s was driven by sentiment.

Booms driven by sentiment increase misallocation and decrease aggregate productivity by discouraging information production.

Booms driven by productivity decrease misallocation and increase aggregate productivity by encouraging information production.

Working Paper

"Does Dispersed Sentiment drive Returns, Turnover, and Volatility for Bitcoin?", joint with Janko Heineken (submitted)

We test the theoretical predictions of the differences-of-opinion literature by analyzing the extensive online discussion on Bitcoin to build a time-varying sentiment distribution, defining disagreement as dispersion in sentiment. High disagreement is associated with negative returns, high turnover growth, and high volatility, confirming the theory's predictions. However, we do not find that an increase in disagreement increases the price, which is seemingly at odds with the theoretical prediction of disagreement leading to overpricing. As the theory predicts, the disagreement effect weakens significantly after shorting instruments were introduced at the end of 2017. Our results are economically significant: at the monthly frequency, a one standard deviation increase in disagreement leads to a 9.2 percentage points lower cumulative return over the following eight months, and the adjusted R2 of regressing contemporaneous returns on average sentiment and disagreement is 0.33.

"Privacy Laws and the Value of Personal Data", joint with Simona Abis (Columbia), Mehmet Canayaz (Penn State), Roxana Mihet (UNIL) and Huan Tang (LSE)

We analyze how the adoption of the California Consumer Protection Act (CCPA), which limits buying or selling consumer data, heterogeneously affects firms with and without previously gathered data on consumers. Exploiting a novel and hand-collected data set of 11,436 conversational-AI firms with rich personal data on identifiable U.S. consumers, we find that the CCPA gives a strong protection and advantage to firms with in-house data on consumers. First, products of these firms experience significant appreciations in customer ratings and are able to collect more customer data relative to their competitors after the adoption of the CCPA. Second, publicly traded firms with in-house data exhibit higher valuations, profitability, asset utilization, and they invest more after the adoption of the CCPA. Third, earnings of such firms can be more accurately predicted by analysts. To rationalize these empirical findings, we build a general equilibrium model where firms produce final goods using labor and data in the form of intangible capital, which can be traded with other firms subject to an iceberg transportation cost. When the introduction of the CCPA increases the transportation cost, firms without in-house data suffer the most because they cannot adequately substitute the previously externally purchased data, while firms with in-house data expand their market share.

"Overconfidence and Information Acquisition in Financial Markets"

I develop a model in which overconfidence in the form of correlation neglect incentivizes costly information acquisition in financial markets. Traders' information has two sources of noise, one idiosyncratic and the other correlated between traders. Traders are overconfident in that they overestimate the share of idiosyncratic noise in their private information, i.e., they partly neglect correlated noise. I find that an infinitesimal amount of overconfidence is sufficient to generate trade when the private signal is exogenous and free. However, substantial amounts of overconfidence are needed when traders acquire costly information. I show that the model can be integrated into macroeconomic models and can be used to study trader heterogeneity. Finally, I consider an extension in which traders have limited resources for trading. Such funding constraints dampen the effect of new information on the price. Moreover, disagreement can affect the price level differently depending on the relative scarcity or abundance of trading capital.

Work in Progress

"Is there a bubble in the SNB stock?"

"Endogenous Leverage and Fragility"

Leverage cycles can generate large swings in asset prices and economic output, with devastating effects if leverage collapses. I develop a comprehensive framework of the nexus between leverage, information production, asset prices, and volatility. I find that two equilibria arise in financial markets with dispersed information and risk-averse lenders. The first equilibrium features high leverage and high asset prices but low information production and low volatility, whereas the second has the opposite properties. This result is obtained through a complementarity between high leverage and low information production. High leverage leads to a concentration of asset ownership in the hands of optimists, discouraging information acquisition as asset prices become upward biased. The lack of new information entering the market leads to a decline in volatility, allowing traders to borrow more against the asset, which has become virtually "safe." The model has implications for security design, endogenous fragility through information production, and financial regulation on leverage and transparency.