Johannes Beutel

Investors’ subjective capital gains expectations are a key element explaining stock price fluctuations.  Survey measures of these expectations display excessive optimism (pessimism) at market peaks (troughs). We formally reject the hypothesis that this is compatible with rational expectations. We incorporate subjective price beliefs with such properties into a standard asset-pricing model with rational agents (internal rationality). The model gives rise to boom-bust cycles that temporarily delink stock prices from fundamentals. It quantitatively replicates many asset-pricing moments, including the observed strong positive correlation between the price dividend ratio and survey return expectations, which cannot be matched by rational expectations. 

We present a stock market model that quantitatively replicates the joint behavior of stock prices, trading volume and investor expectations. Stock prices in the model occasionally display belief-driven boom and bust cycles that delink asset prices from fundamentals and redistribute considerable amounts of wealth from less to more experienced investors. Although gains from trade arise only from subjective belief differences, introducing financial transactions taxes (FTTs) remains undesirable. While FTTs reduce the size and length of boom-bust cycles, they increase the likelihood of such cycles, thereby overall return volatility and wealth redistribution. Contingent FTTs, which are levied only above a certain price threshold, give rise to problems of equilibrium multiplicity and non-existence.

We study the effects of central bank communication about financial stability on individuals’ expectations and risk-taking. Using a randomized information experiment, we show that communication causally affects individuals’ beliefs and investment behavior, consistent with an expectations channel of financial stability communication. Individuals receiving a warning from the central bank expect a higher probability of a financial crisis and reduce their demand for risky assets. This reduction is driven by downward revisions in individuals’ expected Sharpe ratios due to lower expected returns and higher perceived downside risks. In addition, these individuals deposit a smaller fraction of their savings at riskier banks.

This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.

This paper studies sustainable investing using data from a representative survey of German households and a quantitative asset pricing model with heterogeneous investors. About a third of households have green investments worth 11\% of household wealth. Green investments are currently relatively risky, with equity as the main pathway, while green bank accounts are rare. We find substantial heterogeneity in green taste for both safe and risky green assets throughout the wealth distribution, which can either increase or decrease demand for these assets. Model counterfactuals show that nonpecuniary benefits and hedging demands currently make green equity more expensive for firms. Nevertheless, the rise of sustainable investing has introduced a greenium of about 1\%, as investors who are now aware of green stocks bid up their prices. Many households desire green bank accounts which could substantially increase overall green finance. Feeding treatment effects from an RCT in the survey into our model suggests that greater awareness of climate finance could also lead to a further burst in green equity investment.

We causally test alternative theories of expectation formation and asset pricing. Using a randomized information experiment we show overreaction is a key feature of individuals' belief formation. Individuals excessively extrapolate past returns and earnings growth into future returns.  The average response to the price-earnings ratio is opposite to the academic consensus and individuals' reaction to stock market news depends on their information preference.  Conditional on their beliefs, individuals' choices are consistent with the standard Merton model of portfolio choice. Our evidence suggests belief overreaction and heterogeneous subjective mental models as key ingredients to asset pricing models. 

We provide the first large-scale survey-based evidence on tail risk beliefs. Inconsistent with a static form of tail risk neglect, individuals assign large probabilities to tail events such as a financial crisis, a new pandemic, a China-Taiwan conflict, a severe energy crisis, or a Cyber attack with systemic consequences. They expect large GDP declines and substantial stock market declines, conditional on these events. However, our evidence supports dynamic tail risk neglect, as predicted by diagnostic beliefs. Extrapolation in response to news is a causal mechanism, that is able to generate pro-cyclical tail beliefs, i.e. lower perceived tail risk during booms. Moreover, individuals who view the boom state as representative, underreact to bad news. Tail risk beliefs causally affect individuals' inclination to invest into housing, equity, or precious metals. The direction of the effect depends on the type of tail event, hence tail risk is not one-dimensional, and safe haven assets are event-specific. We construct novel measures of individuals’ memory database and show they are linked to individuals’ downside tail beliefs. Our results suggest memory as a micro-foundation for diagnostic beliefs as a fruitful avenue for modeling tail beliefs and their financial implications. 

Behavioral biases in investors' expectations can lead to a decoupling of asset prices from fundamentals, raising the possibility of financial instability. Central bank communication could be a tool to mitigate these issues, but there is no theoretical guidance on how to manage asset price expectations optimally. We fill this gap by developing a model of central bank communication that recognizes investors' behavioral biases. We find optimal communication depends on the way belief formation departs from rationality. It is optimal for central banks to tilt their communication negatively when investors are overreactive to their communication. Full-disclosure is optimal when they under-react or react rationally, even if investors have incorrect prior beliefs. Empirically, we find that the negative-tilt strategy is followed by most central banks. Our results are robust to dynamic considerations.

We study the link between the global financial cycle and macroeconomic tail risks using quantile vector autoregressions. Contractionary shocks to financial conditions and monetary policy in the United States cause elevated downside risks to growth around the world. By tightening financial conditions globally, these shocks affect the left tail of the conditional output growth distribution more strongly than the center of the distribution. This effect is particularly pronounced for countries with less flexible exchange rate arrangements, higher foreign currency exposures, and higher levels of private sector leverage, suggesting that exchange rate policies and macroprudential policies can mitigate downside risks to growth.

We use the Covid-19 pandemic as a natural experiment to estimate the effects of a global disaster shock on economic activity, international trade, and financial markets. To identify the shock, we exploit cross-country variation in the timing and intensity of pandemic-induced social distancing at daily frequency. An unexpected reduction in human mobility relative to pre-pandemic levels leads to significant declines in high-frequency measures of global economic activity, such as daily nitrogen dioxide concentrations and daily maritime trade carried by large cargo ships. Global stock markets decline, and sovereign credit spreads persistently widen after the pandemic disaster shock.