This paper studies on “Early Warning Systems” (EWS) by investigating possible contagion risks, based on structured financial networks. Early warning indicators improve standard crisis prediction models performance. Using network analysis and machine learning algorithms we find evidence of contagion risk on the dates where we observe significant increase in correlations and centralities. The effectiveness of machine learning reached 98.8%, making the predictions extremely accurate. The model provides significant information to policymakers and investors about employing the financial network as a useful tool to improve portfolio selection by targeting assets based on centrality.

This study quantifies the effects of the Fed’s quantitative easing (QE) and tapering programs’ announcements on professionals’ consensus forecasts of U.S. macroeconomic and financial variables at different forecast horizons. The results of a vector autoregression (VAR) analysis show that the first QE (QE1) program is more effective in terms of significantly affecting the variability of near and medium term forecasts on GDP, inflation and short-term interest rates. This is not the case for these variables of long forecast horizons across all QE/tapering announcements, the forecasts of U.S. currency and long-term rates present significant short-lived responses, while the tapering displays a dominant effect on the volatility of long-term rates across long-term forecast horizons. A dynamic correlation analysis among different horizon forecasts also reveals that the Fed successfully anchor inflation and real economic growth expectations during the expansionary policy (QE) periods. Additional findings show the anchoring of the expectations across different horizons on short-term rates, as opposed to long-term rates, during the QE1 program. During the contractionary (tapering) period, the decrease in the correlations among different horizons for the short-term rates’ forecasts is a sign that the Fed increases the range of possible outcomes and highlights a signal of a monetary policy change.

This article investigates the capital structure dynamics of European SMEs by assessing the impact of firm-specific, institutional, and macroeconomic factors over the period 2005–2015, including the European Sovereign Debt Crisis (ESDC). In this setup, we perform a dynamic panel data analysis, along with several model specifications and robustness tests on listed SMEs of EU-28, dividing them into firm categories (micro, small, and medium) and country groups (core, periphery, high technology, and new EU member countries). We find that the effect of capital structure determinants do not differ significantly across size and country groups. The results suggest that profitability, asset structure, and size have been the driving forces of listed SME’s leverage, regardless of the size of the companies and the country group. At a macroeconomic and institutional level, taxation is the most significant variable for all the subgroups. Finally, the ESDC seems to increase the leverage of the listed SMEs in the periphery and the new member states countries, leaving the core countries practically unaffected.