"Let Me Get Back to You" — A Machine Learning Approach to Measuring NonAnswers
Andreas Barth, Sasan Mansouri, Fabian Wöbbeking
Management Science,
forthcoming
Abstract
Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.
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Gas Storages full – economic outlook less gloomy The severe slump in the German economy expected last fall has not...
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The Appropriateness of the Macroeconomic Imbalance Procedure for Central and Eastern European Countries
Geraldine Dany-Knedlik, Martina Kämpfe, Tobias Knedlik
Empirica,
No. 1,
2021
Abstract
The European Commission’s Scoreboard of Macroeconomic Imbalances is a rare case of a publicly released early warning system. It was published first time in 2012 by the European Commission as a reaction to public debt crises in Europe. So far, the Macroeconomic Imbalance Procedure takes a one-size-fits-all approach with regard to the identification of thresholds. The experience of Central and Eastern European Countries during the global financial crisis and in the resulting public debt crises has been largely different from that of other European countries. This paper looks at the appropriateness of scoreboard of the Macroeconomic Imbalances Procedure of the European Commission for this group of catching-up countries. It is shown that while some of the indicators of the scoreboard are helpful to predict crises in the region, thresholds are in most cases set too narrow since it largely disregarded the specifics of catching-up economies, in particular higher and more volatile growth rates of various macroeconomic variables.
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Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Macroeconomic Dynamics,
No. 1,
2021
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Information Feedback in Temporal Networks as a Predictor of Market Crashes
Stjepan Begušić, Zvonko Kostanjčar, Dejan Kovač, Boris Podobnik, H. Eugene Stanley
Complexity,
September
2018
Abstract
In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.
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19.04.2018 • 7/2018
Joint Economic Forecast Spring 2018: Germany’s Economic Experts Raise Forecast Slightly
Berlin, 19 April – Germany’s leading economic experts raised their forecasts for 2018 and 2019 slightly in their Spring Joint Economic Forecast released on Thursday in Berlin. They now expect economic growth of 2.2 percent for this year and 2.0 percent for 2019, versus 2.0 percent and 1.8 percent respectively in their autumn forecast. “The German economy is still booming, but the air is getting thinner as unused capacities are shrinking“, notes Timo Wollmershaeuser, ifo Head of Economic Forecasting. Commenting on the new German government’s economic policy, he adds: “It is precisely when the government’s coffers are full that fiscal policy should reflect the implications of its actions for overall economic stability and the sustainability of public finances. The extension of statutory pension benefits outlined in the coalition agreement runs counter to the idea of sustainability.”
Oliver Holtemöller
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The Appropriateness of the Macroeconomic Imbalance Procedure for Central and Eastern European Countries
Martina Kämpfe, Tobias Knedlik
IWH Discussion Papers,
No. 16,
2017
Abstract
The experience of Central and Eastern European countries (CEEC) during the global financial crisis and in the resulting European debt crises has been largely different from that of other European countries. This paper looks at the specifics of the CEEC in recent history and focuses in particular on the appropriateness of the Macroeconomic Imbalances Procedure for this group of countries. In doing so, the macroeconomic situation in the CEEC is highlighted and macroeconomic problems faced by these countries are extracted. The findings are compared to the results of the Macroeconomic Imbalances Procedure of the European Commission. It is shown that while the Macroeconomic Imbalances Procedure correctly identifies some of the problems, it understates or overstates other problems. This is due to the specific construction of the broadened surveillance procedure, which largely disregarded the specifics of catching-up economies.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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The Quantity Theory Revisited: A New Structural Approach
Makram El-Shagi, Sebastian Giesen
Macroeconomic Dynamics,
No. 1,
2015
Abstract
We propose a unified identification scheme to identify monetary shocks and track their propagation through the economy. We combine three approaches dealing with the consequences of monetary shocks. First, we adjust a state space version of the P-star type model employing money overhang as the driving force of inflation. Second, we identify the contemporaneous impact of monetary policy shocks by applying a sign restriction identification scheme to the reduced form given by the state space signal equations. Third, to ensure that our results are not distorted by the measurement error exhibited by the official monetary data, we employ the Divisia M4 monetary aggregate provided by the Center for Financial Stability. Our approach overcomes one of the major difficulties of previous models by using a data-driven identification of equilibrium velocity. Thus, we are able to show that a P-star model can fit U.S. data and money did indeed matter in the United States.
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