Resumen de Three essays on information in financial markets and the macroeconomy
The idea that markets aggregate dispersed information is long-standing in the literature, going back to Hayek (1945) and constituting an active research field nowadays (Vives 2008). However, this feature is mostly absent in macroeconomic models. The main reason for this gap is that such models usually require a specific structure and include non-optimizing agents called noise traders, who keep prices from being fully revealing and add and remove resources from the economy. Both features are difficult to reconcile with conventional macroeconomic general equilibrium models.
This dissertation suggests an alternative approach to information aggregation in financial markets that is most closely related to Albagli, Hellwig, and Tsyvinski (2021) but uses overconfidence in the form of correlation neglect to incentivize costly information acquisition. This approach has multiple attractive features. First, overconfidence and correlation neglect are behavioral biases that have been documented for traders, financial managers, and experiment participants (see Biais et al. (2005), Allen and Evans (2005), and Ben-David, Graham, and Harvey (2013) for evidence of overconfidence and Brandts, Giritligil, and Weber (2015), Eyster and Weizsäcker (2016), Eyster et al. (2018), Enke and Zimmermann (2019), Grimm and Mengel (2020), and Chandrasekhar, Larreguy, and Xandri (2020) for evidence of correlation neglect). In contrast, noise trading is an abstract catch-all for a variety of phenomena. Second, it facilitates the application to macroeconomic general equilibrium models as boundedly-rational traders observe resource constraints, which is demonstrated in Chapter 1. Third, the approach yields a tractable model and allows studying questions that are difficult to embed in other frameworks, for example, trader heterogeneity and funding constraints. The latter point is shown in Chapter 2.
Empirical research on the acquisition and aggregation of information faces natural challenges because traders’ information sets are rarely directly observable. The proliferation of online discussions and textual analysis techniques has opened the door to constructing sentiment measures, reflecting heterogeneous information or beliefs. Janko Heineken and I use this approach to test the predictions of the differences-in-opinion theory in the presence of short-sale constraints on Bitcoin in Chapter 3. We find that meaningful measures of sentiment and disagreement can be extracted using this approach, which are highly correlated with the returns, turnover, and volatility of Bitcoin.
In Chapter 1, I take the idea of financial markets as aggregators of dispersed information to a macroeconomic model to study the effects of booms on capital misallocation. I find that booms driven by different forces also have different effects on misallocation. Fundamental booms, e.g., driven by productivity growth, lower misallocation by encouraging information production. In contrast, non-fundamental booms, e.g., driven by sentiments, increase misallocation by discouraging information production. I also show that the distinction between both types of booms and busts is also crucial for economic policy. For instance, asset purchases can increase economic activity and lower capital misallocation during non-fundamental busts but increase capital misallocation during fundamental busts. 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 seems to have been driven by sentiment.
In Chapter 2, 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 as in Chapter 1 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.
In Chapter 3, Janko Heineken and I test the theoretical predictions of the differences-of-opinion literature in the case of Bitcoin, for which beliefs and disagreement are central. We analyze the extensive online discussion on Bitcoin to build a time-varying sentiment distribution, defining disagreement as the dispersion in the sentiment distribution. We confirm the theory’s predictions as disagreement is associated with negative returns, high turnover growth, and volatility. Moreover, we find that disagreement predicts lower returns far into the future. However, this predictive effect vanishes towards the end of our sample when shorting instruments were introduced.
Albagli, Elias, Christian Hellwig, and Aleh Tsyvinski (2021). “Dispersed Information and Asset Prices”. TSE Working Paper n. 21-1172.
Allen, W. David and Dorla A. Evans (2005). “Bidding and Overconfidence in experimental Financial Markets”. Journal of Behavioral Finance 6.3, pp. 108–120.
Ben-David, Itzhak, John R. Graham, and Campbell R. Harvey (2013). “Managerial Miscalibration”. The Quarterly Journal of Economics 128.4, pp. 1547–1584.
Biais, Bruno, Denis Hilton, Karine Mazurier, and Sébastien Pouget (2005). “Judgemental Overconfidence, Self-Monitoring, and Trading Performance in an Experimental Financial Market”. The Review of Economic Studies 72.2, pp. 287–312.
Brandts, Jordi, Ayça Ebru Giritligil, and Roberto A. Weber (2015). “An experimental study of persuasion bias and social influence in networks”. European Economic Review 80, pp. 214–229.
Chandrasekhar, Arun G, Horacio Larreguy, and Juan Pablo Xandri (2020). “Testing models of social learning on networks: Evidence from two experiments”. Econometrica 88.1, pp. 1–32.
Enke, Benjamin and Florian Zimmermann (2019). “Correlation Neglect in Belief Formation”. The Review of Economic Studies 86.1, pp. 313–332.
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Grimm, Veronika and Friederike Mengel (2020). “Experiments on Belief Formation in Networks”. Journal of the European Economic Association 18.1, pp. 49–82.
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