Andres Azqueta-Gavaldon
- 24 January 2023
- WORKING PAPER SERIES - No. 2767Details
- Abstract
- 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 machine learning algorithm to decompose financial risk into its thematic drivers. The resulting aggregated indicator can identify major episodes of overall heightened financial risks in China, which cannot be consistently captured using financial data. Finally, a structural VAR framework is employed to show that shocks to the financial risk measure have a significant impact on macroeconomic and financial variables in China and abroad.
- JEL Code
- C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C65 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Miscellaneous Mathematical Tools
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
F44 : International Economics→Macroeconomic Aspects of International Trade and Finance→International Business Cycles
G15 : Financial Economics→General Financial Markets→International Financial Markets
- 25 November 2020
- WORKING PAPER SERIES - No. 2494Details
- Abstract
- We propose a granular framework that makes use of advanced statistical methods to approximate developments in economy-wide expected corporate earnings. In particular, we evaluate the dynamic network structure of stock returns in the United States as a proxy for the transmission of shocks through the economy and identify node positions (firms) whose connectedness provides a signal for economic growth. The nowcasting exercise, with both the in-sample and the out-of-sample consistent feature selection, highlights which firms are contemporaneously exposed to aggregate downturns and provides a more complete narrative than is usually provided by more aggregate data. The two-state model for predicting periods of negative growth can remarkably well predict future states by using information derived from the node-positions of manufacturing, transportation and financial (particularly insurance) firms. The three-states model, which identifies high, low and negative growth, successfully predicts economic regimes by making use of information from the financial, insurance, and retail sectors.
- JEL Code
- C45 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Neural Networks and Related Topics
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
N1 : Economic History→Macroeconomics and Monetary Economics, Industrial Structure, Growth, Fluctuations
- 8 May 2020
- WORKING PAPER SERIES - No. 2403Details
- Abstract
- 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 (IndyRef)-related uncertainty; Brexit-related uncertainty; and Scottish policy-related uncertainty. Examining the relationship of these indices with investment on a longitudinal panel of 3,589 Scottish firms, the evidence suggests that Brexit-related uncertainty associates more strongly than IndyRef -related uncertainty to investment. Our preferred specification suggests that a one standard-deviation increase in Brexit uncertainty foreshadows a reduction in investment by 8% on average in the following year. Besides we find that the uncertainty associated with the Scottish referendum for independence while negligible at the aggregate level, relates more strongly with the investment of listed firms as well as those operating on the border with England. In addition, we present evidence of greater sensitivity to these indices among firms that are financially constrained or whose investment is to a greater degree irreversible.
- JEL Code
- C80 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→General
D80 : Microeconomics→Information, Knowledge, and Uncertainty→General
E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity
E66 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→General Outlook and Conditions
G18 : Financial Economics→General Financial Markets→Government Policy and Regulation
G31 : Financial Economics→Corporate Finance and Governance→Capital Budgeting, Fixed Investment and Inventory Studies, Capacity
- 9 January 2020
- WORKING PAPER SERIES - No. 2359Details
- Abstract
- 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 languages. The uncertainty indices computed from January 2000 to May 2019 capture episodes of regulatory change, trade tensions and financial stress. In an evaluation exercise, we use a structural vector autoregression model to study the relationship between different sources of uncertainty and investment in machinery and equipment as a proxy for business investment. We document strong heterogeneity and asymmetries in the relationship between investment and uncertainty across and within countries. For example, while investment in France, Italy and Spain reacts strongly to political uncertainty shocks, in Germany investment is more sensitive to trade uncertainty shocks.
- JEL Code
- C80 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→General
D80 : Microeconomics→Information, Knowledge, and Uncertainty→General
E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity
E66 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→General Outlook and Conditions
G18 : Financial Economics→General Financial Markets→Government Policy and Regulation
G31 : Financial Economics→Corporate Finance and Governance→Capital Budgeting, Fixed Investment and Inventory Studies, Capacity
- 6 August 2019
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 5, 2019Details
- Abstract
- This box presents a model-based economic policy uncertainty (EPU) index for the euro area by applying machine learning techniques to news articles from January 2000 to May 2019. The machine learning algorithm retrieves components of overall EPU, such as trade, fiscal, monetary or domestic regulations, for a wide range of languages. Recently, a steady and pronounced increase in the euro area EPU index has been observed, driven mainly by trade, domestic regulation and fiscal policy uncertainties.
- JEL Code
- C1 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General
C8 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs
E65 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Studies of Particular Policy Episodes