Using a sample covering 50 advanced and emerging economies over 1990-2017, it is found that large current account deficits are followed by systematic negative surprises in economic growth. This regularity is verified both in the case of advanced economies and emerging economies. In addition, large current account deficits are reversed significantly faster than what forecasters anticipate and are followed by low asset returns and drops in sentiment. The findings are robust to changes in the specification and do not seem to be explained by efficient learning dynamics. This evidence indicates that analysts are unable to incorporate the negative information transmitted by large current account deficits and has implications for the understanding of past economic events and for the design of macro-prudential policies.

This paper is the first to consider the link between information conveyed by the facial expressions of a range of economic actors and economic activity. A collection of photographs is used to construct indicators of emotions communicated by the facial expressions. The indicators correspond to the US economy for the period 1996-2018. Significant links between the level of economic activity and indices of emotional states are observed. Beyond contemporaneous associations, the indicators are shown to anticipate business cycle dynamics. Indices summarizing emotions linked to policy making and the stock market contain more information than indicators linked to corporations. 

This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.

Crude oil is one of the most important commodities in the real economy and as such the relationship between oil prices and broader equity markets has attracted a lot of research attention. Recent work has considered directional spillovers or links between oil and equity markets. In recent times there has been a growing body of research into the impacts of news and media attention on asset returns, both in the context of and in particular with both the oil and equity markets but also within each of these. This paper considers how news or information flows about crude oil influence the spillover links between these assets. Using realized volatility estimates based on high frequency data, the empirical analysis reveals a number of novel results in terms of the behavior of these linkages. Increased news flow about oil reduces the impact of the broader equity market on the oil sector, implying that it is driven more by oil specific shocks and less by more general financial market conditions. It also increases the impact of the oil sector on the broader equity market. These results have potential implications for hedging and portfolio allocation.

We compare the medium-term GDP growth forecasts generated by experts to those generated by simple models. This study analyzes a large set of forecasts that covers 48 countries from 1997 to 2016. Out-of-sample exercises indicate that no noticeable difference in performance is observed for advanced economies. In contrast, in the case of emerging economies, model forecasts perform better than expert forecasts. In addition, similar patterns are found for a collection of forecasts from a different set of experts, which suggests that the reported regularity is prevalent. Further analyses suggest that the documented difference in performance can be explained by an optimism bias, excessive reactions to innovations in growth trajectories, and insufficient responses to the information contained in the current account balance.

The association between GDP growth forecasts and past information flows is evaluated for a sample of 49 countries during the period 1990–2014. The analysis exploits an extensive collection of forecasts available through IMF's historical database. The empirical results indicate a robust association between information arrival and subsequent mean forecast errors (the average difference between forecast and realization). Consistent with the overreaction hypothesis, more positive information is followed by higher mean forecast errors. The association is documented for multiple metrics of past information flows: growth performance, a novel metric of press sentiment, and lagged forecast errors. When advanced and emerging economies are differentiated, the regularity is detected for both groups but is stronger in the case of emerging economies.

We evaluate whether professional forecasters incorporate valuable information from public discussions on social media. The study covers the case of inflation in Argentina for the period 2016-2022.  We find solid evidence consistent with inattention. A simple indicator of attention to inflation on social media is shown to anticipate professional forecast errors. A one standard deviation increment in the indicator is followed by a rise of 0.4$\%$ in mean forecast errors in the subsequent month and by a cumulative increment of 0.7$\%$ over the next six months. Furthermore, social media content anticipates significant revisions in forecasts that target multiple months ahead inflation and calendar year inflation. These findings are different from previously documented forms of inattention. Consistent results are verified implementing out of sample forecasts and using content from an alternative social network. The study has implications for the use of professional forecasts in the context of policy-making and sheds new evidence on the nature of imperfect information in macroeconomics. 

Este trabajo propone un índice que describe las opiniones económicas transmitidas por usuarios argentinos en la red social Twitter. Luego de identificar mensajes económicos, éstos son clasificadas según la frecuencia con la que se utilizan palabras asociadas a incertidumbre. La evaluación cualitativa del índice sugiere un fuerte vínculo con eventos económicos y políticos de relevancia. Estimaciones de modelos estadísticos indican que el índice contiene información sobre el ciclo económico, la confianza del consumidor y la evolución del mercado cambiario. Análisis complementarios demuestran que el foco en el concepto de incertidumbre y el uso de técnicas de procesamiento de lenguaje natural constituyen elementos clave para el desempeño satisfactorio de este indicador de opiniones. 

We use Twitter content to generate an indicator of attention allocated to inflation. The analysis corresponds to Argentina for the period 2012-2019. The attention index provides valuable information regarding future levels of inflation. A one standard deviation increment in the index is followed by an increment of approximately 0.4% in expected inflation in the consecutive month. Out-of sample exercises confirm that social media content allows for gains in forecast accuracy. Beyond point forecasts, the index provides valuable information regarding inflation uncertainty. The proposed indicator compares favorably with other indicators such as media content, media tweets, google search intensity and consumer surveys. 

This study evaluates the performance of stock market indices after times of extreme opinions. The underlying conjecture is that extreme opinions are associated to overreactions in the perception of wealth. The analysis covers 34 countries from 1988 through 2013. In a novel approach, views regarding economic performance are approximated using content in the global economic press. Consistent with the overreaction conjecture, stock market indices are shown to under-perform following extreme optimistic views and over-perform after pessimistic views. A long-short contrarian portfolio earns 11% annually over the next five years. This persistent and predictable difference in returns cannot be explained by risk considerations and cannot be replicated using alternative strategies based on past returns or past economic growth.

We develop a comprehensive quantitative account of changing practices in economics in the last 122 years. The analysis uses word detection algorithms to partially characterize prevailing practices. We document a shift toward isolation from other disciplines during most of the twentieth century. In sharp contrast, the most recent decades show a strong move towards a more connected discipline. Periods of more connectedness are associated with openness to a broader set of features of economic agents and the economic environment. In parallel, the 1960s and 1970s show a notable acceleration in the move towards a more mathematical approach. This development did not reverse. As a result, the current state of the discipline is characterized by an embrace of mathematical tools together with openness to a wider set of aspects and findings developed in other disciplines. Most of the reported variables show surprisingly high correlations across disciplines and across journals.