Published on All About Finance

Exuberant and uninformed: How financial markets (mis-)allocate capital during booms

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Financial markets play a crucial role in allocating scarce resources in modern economies. However, recent booms and busts suggest that financial markets might not always fulfill this role well. A recent example is the US housing boom of 2002–08, which preceded the Great Financial Crisis. Although the economy was in a boom, and housing and stock prices were increasing, overall productivity growth slowed, as shown in figure 1.

A line chart showing Figure 1: Total factor productivity growth in the United States declined during the housing boom from 2002 to 2008.

Figure 1: Total factor productivity growth in the United States declined during the housing boom from 2002 to 2008.

A potential explanation is that investors expect any investment to be profitable during booms and, therefore, become less diligent in choosing where to invest. Eventually, this less informed investing worsens the allocation of resources in the economy, ultimately decreasing overall productivity. Although suggestive, this narrative is loose and cannot be fully evaluated without a theory of information acquisition and its macroeconomic effects. My paper's contribution is to provide such a model in which information acquisition in financial markets varies with the state of the economy.

Model and Results

I model a dynamic economy populated by firms that differ in how productive they are. Households work and save by buying firms' stock, but they do not know how productive each firm is. To overcome this problem, households can acquire information about firm productivity, where higher quality information is more expensive. If households have better information, they can better identify more productive firms and invest more in them. As a result, better informed households lead to more efficient markets, in the sense that more productive firms receive more capital. The central component of the model is the household's decision on how much information to acquire, which depends on the state of the economy.

The main result is that whether booms strengthen or weaken the incentive to acquire high-quality information depends on which factor drives the boom. Booms driven by fundamental factors—for example, increases in overall productivity—encourage information acquisition, improve the allocation of capital, and increase overall productivity above and beyond the initial improvement . During such a boom, households acquire better information because their stakes are higher as firms are more productive. Therefore, if large increases in asset prices are justified by technological innovations, policy makers need not be concerned as financial markets become even more efficient.

This picture is reversed if booms are driven by non-fundamental factors—for example, excessive optimism. Such non-fundamental booms discourage information acquisition, worsen the allocation of capital, and decrease overall productivity. Households acquire worse information during non-fundamental booms because expectations about prices being too high or too low on average decrease the usefulness of high-quality information.  In the extreme case, when households expect all shares to be overpriced, firm-specific information becomes entirely worthless, as the household knows not to buy in any case. This result follows the evidence from the US housing boom, during which productivity declined as asset prices grew.


Policy makers should be concerned if non-fundamental factors drive booms. However, how can they distinguish between fundamental and non-fundamental booms?  The model provides an answer to this crucial question by predicting a positive correlation between dispersion in asset returns and information acquisition.

If households do not acquire information, no new information reaches the market and asset prices generally move in the same direction. In contrast, if households acquire high-quality information, a large quantity of information reaches the market and asset prices move according to this firm-specific information. Therefore, if asset prices increase across the board, but firms are undistinguishable, the boom will likely be driven by optimism. In contrast, if asset prices increase and there are still winners and losers, information acquisition still occurs, and fundamentals likely fuel the boom.

Policy makers can use this knowledge to "lean against the wind" in the face of excessive optimism or pessimism. An example of such policies is large-scale asset purchases, which central banks have used intensively in all developed countries. Since the beginning of their use, asset purchases have been suspected of distorting asset prices and worsening capital allocation (e.g., DNB 2017). The model provides a laboratory to assess this criticism.

The model confirms that asset purchases can inflate asset prices, discouraging information acquisition and worsening capital allocation. However, asset purchases can also have the opposite effect if they are used to decrease mispricing in financial markets, such as during depressions. In this case, asset purchases make asset prices less distorted, encourage information acquisition, and improve capital allocation. Therefore, to use asset purchases correctly, central banks must know which force is driving the boom or bust.

Empirical Evidence

A line chart showing Figure 2: Viewed through the lens of my model, the US dot-com boom leading up to 2001 was likely driven by productivity, whereas the housing boom between 2002 and 2008 was driven by optimism.

Figure 2: Viewed through the lens of my model, the US dot-com boom leading up to 2001 was likely driven by productivity, whereas the housing boom between 2002 and 2008 was driven by optimism.

There is ample empirical evidence that booms can worsen the allocation of capital (Gopinath et al. 2017; Doerr 2018) and labor (Borio et al. 2015) and decrease overall productivity (Gorton and Ordonez 2020). Moreover, recent empirical evidence by Davilá and Parlatore (2020) suggests that price informativeness indeed decreased during the US housing boom (figure 2). In contrast, price informativeness and productivity increased during the dot-com boom leading up to 2001. Viewed through the lens of my model, this pattern suggests that the dot-com boom of the 1990s was driven by productivity, whereas the US housing boom between 2002 and 2008 was driven by optimism.

Ilja Kantorovitch (@IKantorovitch) is a PhD candidate at Universitat Pompeu Fabra. You can find more of Ilja’s research at


Borio, Claudio, Karroubi, Enisse, Upper, Christian, and Zampoli, Fabrizio, "Financial Cycles, Labor Misallocation, and Economic Stagnation" (2015).

Dávila, Eduardo and Parlatore, Cecilia, "Identifying Price Informativeness," National Bureau of Economic Research (2020).

DNB, "2016 Annual report," De Nederlandsche Bank (2017).

Doerr, Sebastian, "Collateral, Reallocation, and Aggregate Productivity: Evidence from the U.S. Housing Boom," SSRN Electronic Journal (2018), pp. 1-66.

Gopinath, Gita, Kalemli-Özcan, Şebnem, Karabarbounis, Loukas, and Villegas-Sanchez, Carolina, "Capital Allocation and Productivity in South Europe," Quarterly Journal of Economics 132, 4 (2017), pp. 1915-1967.

Gorton, Gary and Ordoñez, Guillermo, "Good Booms, Bad Booms," Journal of the European Economic Association 18.2 (2020), pp. 618-665.


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