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In economic activity, recessions represent a period of failure in Gross Domestic Product (GDP) and usually are presented as episodic and non-linear. For this reason, they are difficult to predict and appear as one of the main problems in macroeconomics forecasts. A classic example turns out to be the great recession that occurred between 2008 and 2009 that was not predicted. In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term forecasting accuracy. As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP. The algorithm is trained on Italian macroeconomic variables over the period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear Regression Model. As a result, both statistical and ML approaches are able to predict economic downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables (NARX) model.

This paper proposes a model of the dynamics of credit contagion through non-performing loans on financial networks. Credit risk contagion is modeled in the context of the classical SIS (Susceptibles-Infected-Susceptibles) epidemic processes on networks but with a fundamental novelty. In fact, we assume the presence of two different classes of infected agents, and then we differentiate the dynamics of assets subject to idiosyncratic risk from those affected by systemic risk by adopting a SIIS (Susceptible-Infected1-Infected2-Susceptible) model. In the recent literature in this field, the effect of systemic credit risk on the performance of the financial network is a hot topic. We perform numerical simulations intended to explore the roles played by two different network structures on the long-term behavior of assets affected by systemic risk in order to analyze the effect of the topology of the underlying network structure on the spreading of systemic risk on the structure. Random graphs, i.e., the Erdös–Rényi model, are considered “benchmark” network structures while core-periphery structures are often indicated in the literature as idealized structures, although they are able to capture interesting, specific features of real-world financial networks. Moreover, as a matter of comparison, we also perform numerical experiments on small-world networks.

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Purpose Using a sample of US firms more likely to be affected by agency problems, the purpose of this paper is to investigate the relationship between corporate value and financial policies and to study whether credit market freedom (CMF) affects this relationship. Design/methodology/approach The authors identify a sub-sample of non-financial US firms potentially affected by agency problems using a joint criterion of over-investment and high cash-holdings. A generalized method of moment econometric framework is then used to estimate the impact of cash-holdings and leverage policies on firm value for this sub-sample. This exercise is also performed by taking into account the level of CMF of the state where the firm operates. Findings The results show that the relationship between cash-holdings – or leverage – and firm value is “U-shaped.” In addition, when the authors focus on the role played by the level of CMF, the authors find a number of interesting facts: CMF facilitates the firms’ access to external finance, thereby relaxing the need of internal funds for investing; the relationship between cash-holdings and firm value is “U-shaped” only in states enjoying high levels of CMF; the probability of observing firms more likely to be affected by agency problems is higher in states with high levels of CMF. Research limitations/implications The empirical findings provide important insights to policymakers, shareholders and practitioners. To policymakers, the results suggest that providing institutional environments with greater CMF can enhance the firm access to external finance, the level of corporate investment and the economic growth. To shareholders, the findings highlight that the conflicts of interest between managers and shareholders may be more severe in states with higher CMF; therefore, adequate financing policies and corporate governance mechanisms must be used to mitigate these conflicts and maximize the firm value. Finally, to practitioners, the evidence suggests that, in valuing a firm, they must take into consideration whether the economic environment provides managers with more freedom to stockpile cash and invest sub-optimally. Originality/value The paper contributes to the corporate finance and governance literature in two respects. First, it provides new evidence on the shape of the relationship between cash holdings and firm value for firms affected by empire-building managers. Second, at the best of the knowledge, it is the first corporate finance study, which analyzes the role played by the CMF at the state level on the capital structure and the level of investment of the firms.

Building on Layard and Jackman's framework, we propose a simple model to analyse the relation between labour productivity and unemployment differentials in Italy and present some panel data evidence to support the theoretical predictions of the model. The empirical analysis strongly suggests that the productivity differential is one of the main factors driving the dynamics of the unemployment differential in Italy.

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