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.

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.

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.

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.

This paper studies green investing using data from a representative survey of German households that inform a quantitative asset pricing model. We document substantial heterogeneity in nonpecuniary benefits and hedging across the wealth distribution, as well as sizeable unmet demand for green deposit accounts.  Model counterfactuals show that optimism about green equity returns, rather than tastes, is currently responsible for the greenium. Introducing green deposits at a spread would not crowd out green equity but increase overall green investment. Feeding results from an RCT into the model, we show green equity could increase with better information about green finance.

We causally test alternative theories of expectation formation.  Using a randomized information experiment we show overreaction is a key feature of individuals' return expectations, and individuals' response to the price-earnings ratio is opposite to the academic consensus.  Our evidence is inconsistent with standard models of expectation formation but subjective mental models that deviate from objective benchmarks can jointly explain the updating behavior in the experiment,  the link between individuals' prior perceptions and expectations, and the heterogeneity of their updating behavior. Conditional on their beliefs, individuals' sensitivity of risky portfolio shares is consistent with the standard Merton model of portfolio choice.

We provide the first large-scale survey-based evidence on tail risk beliefs. Individuals assign large probabilities to tail events such as a financial crisis, new pandemic, China-Taiwan conflict, severe energy crisis, or a large-scale Cyber attack, inconsistent with a static form of tail risk neglect. They expect large GDP declines and substantial stock market declines, conditional on these events. However, our evidence supports dynamic tail risk neglect in probabilities, as predicted by diagnostic beliefs. In particular, already pessimistic respondents react more to bad news than optimistic respondents. Similarly, bad news activates respondents to adjust their portfolios, while good news does not. 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. Memory provides a micro-foundation for diagnostic beliefs about tail risks.

Behavioral biases in investors' expectations can lead to a decoupling of asset prices from fundamentals, and risks to financial stability. Central bank communication could be a tool to mitigate these issues, but there is no theoretical guidance on how and when  to communicate  when some investors do not form expectations rationally. We develop a communication model that flexibly accounts for such deviations from rationality and allows for heterogeneity in investors' belief formation. We find that in the presence of these heterogeneities, communication may hinder risk-sharing  and lead to over- or under-production of risky assets.  Full disclosure is optimal only when investors update their beliefs in a way that is sufficiently homogeneous and close to the rational benchmark. Otherwise, no disclosure is optimal. Our results further suggest that it is more effective to communicate during normal periods, before investors' beliefs become too entrenched. We also discuss the effectiveness of communication relative to other policy tools, such as interest rate and macroprudential policies.

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.