Emotions in Macroeconomic News and their Impact on the European Bond Market

We show how emotions extracted from macroeconomic news can be used to explain and forecast future behaviour of sovereign bond yield spreads in Italy and Spain. We use a big, open-source, database known as Global Database of Events, Language and Tone to construct emotion indicators of bond market affective states. We find that negative emotions extracted from news improve the forecasting power of government yield spread models during distressed periods even after controlling for the number of negative words present in the text. In addition, stronger negative emotions, such as panic, reveal useful information for predicting changes in spread at the short-term horizon, while milder emotions, such as distress, are useful at longer time horizons. Emotions generated by the Italian political turmoil propagate to the Spanish news affecting this neighbourhood market.Comment: Journal of International Money and Finance (to appear); 39 pages; 14 figure

Emotions in macroeconomic news and their impact on the European bond market

We show how emotions extracted from macroeconomic news can be used to explain and forecast future behaviour of sovereign bond yield spreads in Italy and Spain. We use a big, open-source, database known as Global Database of Events, Language and Tone to construct emotion indicators of bond market affective states. We find that negative emotions extracted from news improve the forecasting power of government yield spread models during distressed periods even after controlling for the number of negative words present in the text. In addition, stronger negative emotions, such as panic, reveal useful information for predicting changes in spread at the short-term horizon, while milder emotions, such as distress, are useful at longer time horizons. Emotions generated by the Italian political turmoil propagate to the Spanish news affecting this neighbourhood market

Big data in Economics and Finance: Special Session at The 6th International Conference on Machine Learning, Optimization, and Data Science (LOD2020)

The financial and macroeconomic worlds are nowadays experiencing structural changes due to the availability of large amounts of unstructured data, also known as Big Data. The Big Data and Forecasting of Economic Developments project (bigNOMICS) of the Centre for Advanced Studies of the European Commission, Joint Research Centre organizes the Special Session on Big data in Economics and Finance within The 6th International Conference on Machine Learning, Optimization, and Data Science. This special session gathers together researchers and practitioners from diverse backgrounds, ranging from finance, economics, business to computer science, with the goal of discussing the latest applications of Big Data technologies to economics and finance. The special session consists of a total of twelve presentations covering four macro-areas: the usage of news and text data for business and financial applications (session 1), exploring the usefulness of big data and machine learning technologies in finance (session 3), financial and macroeconomic risk assessment (session 5), the relevance of such technologies from an industrial and institutional perspective (session 7).JRC.A.5-Scientific Developmen

Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model

Timely information about the state of regional economies can be essential for planning, implementing and evaluating locally targeted economic policies. However, European regional accounts for output are published at an annual frequency and with a two-year delay. To obtain robust and more timely measures in a computationally efficient manner, we propose a mixed-frequency dynamic factor model that accounts for national information to produce high-frequency estimates of the regional gross value added (GVA). We show that our model produces reliable nowcasts of GVA in 162 regions across 12 European countries.Comment: JEL: C22, C53, R11; keywords: factor models, mixed-frequency, nowcasting, regional dat

Data Science Technologies in Economics and Finance: A Gentle Walk-In

AbstractThis chapter is an introduction to the use of data science technologies in the fields of economics and finance. The recent explosion in computation and information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as Big Data. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can be used towards effective and personalized models. In this context, the recent use of data science technologies for economics and finance provides mutual benefits to both scientists and professionals, improving forecasting and nowcasting for several kinds of applications. This chapter introduces the subject through underlying technical challenges such as data handling and protection, modeling, integration, and interpretation. It also outlines some of the common issues in economic modeling with data science technologies and surveys the relevant big data management and analytics solutions, motivating the use of data science methods in economics and finance

CAS: Centre for advanced studies

An introduction to the Centre for Advanced Studies.JRC.A.5-Scientific Developmen

Une analyse de la spécification des facteurs des taux d'intérêts : Une perspective internationale

The aim of this thesis is to model the dynamics of international term structure of interest rates taking into consideration several dependence channels.Thanks to a new international Treasury yield curve database, we observe that the explained variability decision criterion, suggested by the literature, is not able to select the best combination of factors characterizing the joint dynamics of yield curves. We propose a new methodology based on the maximisation of the likelihood function of a Gaussian state-space model with common and local factors. The associated identification problem is solved in an innovative way. By estimating several sets of countries, we select two global (and three local) factors which are also useful to forecast macroeconomic variables in each considered economy.In addition, our method allows us to detect hidden factors in the international bond returns. They are not visible through a classical principal component analysis of expected bond returns but they are helpful to forecast inflation and industrial production. Keywords: International treasury yield curves, common and local factors, state-space models, EM algorithm, International bond risk premia, principal components.Cette thèse concerne la modélisation de la dynamique des courbes des taux internationales avec prise en compte de plusieurs canaux de dépendance. A l’aide d’une nouvelle base de données des taux souverains internationaux, nous observons que le critère de la variabilité expliquée, proposé par la littérature, n’est pas capable de sélectionner une meilleure combinaison des facteurs décrivant la dynamique jointe des courbes des taux. Nous proposons une méthode nouvelle de section des facteurs fondée sur la maximisation de vraisemblance d’un modèle espace-état linéaire gaussien avec facteurs communs et locaux. Le problème d’identification associée est résolu d’une façon novatrice. En estimant différents combinaisons de pays, nous sélectionnons des deux facteurs globaux et trois locaux ayant un pouvoir prédictif des variables macro-économiques (activité économique et taux d’inflation) dans chaque économie considérée. Notre méthode nous permet aussi de détecter des facteurs cachés dans les rendements obligataires. Ils ne sont pas visibles à travers une analyse classique en composant principales des rendements obligataires et ils contribuent à la prévision du taux d’inflation et du taux de croissance de la production industrielle

Neural forecasting of the Italian sovereign bond market with economic news

In this paper we employ economic news within a neural network framework to forecast the Italian 10-year interest rate spread. We use a big, open-source, database known as Global Database of Events, Language and Tone to extract topical and emotional news content linked to bond markets dynamics. We deploy such information within a probabilistic forecasting framework with autoregressive recurrent networks (DeepAR). Our findings suggest that a deep learning network based on Long-Short Term Memory cells outperforms classical machine learning techniques and provides a forecasting performance that is over and above that obtained by using conventional determinants of interest rates alone.Comment: 24 pages, 8 figures, in pres

International Yield Curves and Principal Components Selection Techniques: An Empirical Assessment

Using a common database, we provide a controlled empirical comparison of recentlyproposed principal component (PC) methods for selecting a combination of common and local factors that characterize the joint dynamics of multi-country term structures. We build a database of daily Treasury yield curves for U.S., Germany, U.K. and Japan, using common criteria to \ufb01lter coupon bond data, to ensure liquidity, and to nterpolate the discount function. We then estimate each proposed PC method for all subgroups of these countries, using both yield levels and yield di\ufb00erences at weekly frequency. We \ufb01nd, in general, that the proposed methods do not agree with one another on the preferred combination of common and/or local factors. We identify the explained variability decision criterion as an important source of this lack of agreement and recommend consideration of alternative statistical model selection techniques for the purpose of identifying common and local yield curve factors in international data

Specification Analysis of International Treasury Yield Curve Factors

We show how to compute patterns of variation over time, both among and within countries, that determine the international term structure of interest rates, using maximum likelihood within a linear Gaussian state-space framework. The simultaneous estimation of common factors (shared by all countries) and local factors (speci\ufb01c to one country) requires development of a normalization procedure beyond that of ordinary factor analysis. By jointly estimating common and local factors we avoid sequential estimation e\ufb00ects that may explain the lack of agreement in the multi-country term structure literature regarding not only the total number of latent factors required to explain the joint dynamics of yield curves, but also the number of common and of local factors. Using data on international yield curves of U.S., Germany, U.K. and Japan from January 1986 to December 2009, we generally \ufb01nd (analyzing yields in level and in di\ufb00erence) that a model with two common factors and three correlated local factors is preferred to a model (of similar complexity) that includes one common factor only or a model with only correlated local factors. In addition, each common factor closely mimics (or is similar to) a local factor extracted from a pure local factor model. We also reach the conclusion that dependence across international yield curves are driven, \ufb01rst, by the instantaneous correlation between local factors of di\ufb00erent countries and, then, by the (full) autoregressive matrix of latent factors and by the matrix of common loadings