We model economic policy uncertainty (EPU) in the four largest euro area countries by applying machine learning techniques to news articles. The unsupervised machine learning algorithm used makes it possible to retrieve the individual components of overall EPU endogenously for a wide range of...

We present evidence that referenda have a significant, detrimental outcome on investment. Employing an unsupervised machine learning algorithm over the period 2008-2017, we construct three important uncertainty indices underlying reports in the Scottish news media: Scottish independence...

We estimate a FAVAR with Bayesian techniques in order to investigate the impact of loan supply conditions on euro area corporate investment and its financing structure. We identify shocks to overall demand and loan supply with sign and impact restrictions. Although tightened financial conditions...

This paper investigates the link between corporate debt and investment for a group of five peripheral euro area countries. Using firm-level data from 2005-2014, we postulate a non-linear corporate leverage-investment relationship and derive thresholds beyond which leverage has a negative and...

We develop a measure of overall financial risk in China by applying machine learning techniques to textual data. A pre-defined set of relevant newspaper articles is first selected using a specific constellation of risk-related keywords. Then, we employ topical modelling based on an unsupervised...

Focussing on repo specialness premia, using ISIN-specific transaction-by-transaction data of one-day maturity repos, we document a gradual shift from cash- to securities-driven transactions in euro area repo markets over the period 2010-2018. Compared to earlier studies focussing only on...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...

This paper shows that newspaper articles contain timely economic signals that can materially improve nowcasts of real GDP growth for the euro area. Our text data is drawn from fifteen popular European newspapers, that collectively represent the four largest Euro area economies, and are machine...

In this paper, we apply textual analysis and machine learning algorithms to construct an index capturing trade tensions between US and China. Our indicator matches well-known events in the US-China trade dispute and is exogenous to the developments on global financial markets. By means of local...