CONVERGENCE AND ECONOMIC PERFORMANCE IN GREECE: EVIDENCE AT REGIONAL AND PREFECTURE LEVEL

The purpose of this paper is to test regional convergence and to investigate interregional disparities in terms of per capita income in Greece. The novelty of our study lies in the use of a disaggregated dataset for an extended time period (1971–2003) at two regional levels (NUTS II & NUTS III). Our results indicate that there is β convergence between prefectures but not among regions, while no evidence of σ convergence is found at both regional levels. Also, the GDP geographic concentration and population density have a negative impact on growth, which outweighs the positive growth effect of population geographic concentration and GDP spatial inequality. Thus, policies aiming at the decentralization of economic activity in Greece might enhance growth and regional equality simultaneously. Finally, we do not find economic dualism across geographic areas; however, rich prefectures seem to converge faster than poor ones.     

A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices

The popular sentiment-based investor index S^BW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, Huang et al. (2015) developed a new investor sentiment index, S^PLS, which can predict monthly stock returns based on a linear framework. However, the linear model may lead to misspecification and lack of robustness. We provide statistical evidence that the relationship between stock returns, S^BW and S^PLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither S^BW nor S^PLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.     

Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design

Bankruptcy prediction has received a growing interest in corporate finance and risk management recently. Although numerous studies in the literature have dealt with various statistical and artificial intelligence classifiers, their performance in credit risk forecasting needs to be further scrutinized compared to other methods. In the spirit of Chen, Härdle and Moro (2011, Quantitative Finance), we design an empirical study to assess the effectiveness of various machine learning topologies trained with big data approaches and qualitative, rather than quantitative, information as input variables. The experimental results from a ten-fold cross-validation methodology demonstrate that a generalized regression neural topology yields an accuracy measurement of 99.96%, a sensitivity measure of 99.91% and specificity of 100%. Indeed, this specific model outperformed multi-layer back-propagation networks, probabilistic neural networks, radial basis functions and regression trees, as well as other advanced classifiers. The utilization of advanced nonlinear classifiers based on big data methodologies and machine learning training generates outperforming results compared to traditional methods for bankruptcy forecasting and risk measurement.     

On the predictability of time-varying VAR and DSGE models

Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic fluctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspecifications as they are able to solve the trade-off between theoretical coherence and empirical fit. However, these models are still linear and they do not consider time variation for parameters. The time-varying properties in VAR or DSGE models capture the inherent nonlinearities and the adaptive underlying structure of the economy in a robust manner. In this article, we present a state-space time-varying parameter VAR model. Moreover, we focus on the DSGE–VAR that combines a microfounded DSGE model with the flexibility of a VAR framework. All the aforementioned models as well simple DSGEs and Bayesian VARs are used in a comparative investigation of their out-of-sample predictive performance regarding the US economy. The results indicate that while in general the classical VAR and BVARs provide with good forecasting results, in many cases the TVP–VAR and the DSGE–VAR outperform the other models.     

The Escalation of Deception in Organizations

Drawing on a number of recent high-profile cases of corporate corruption, we develop a process model that explains the escalation of deception in corrupt firms. If undetected, an initial lie can begin a process whereby the ease, severity and pervasiveness of deception increases overtime so that it eventually becomes an organization level phenomenon. We propose that organizational complexity has an amplifying effect. A␣feedback loop between organization level deception and each of the escalation stages positively reinforces the process. In addition, moderators are proposed that will halt escalation at various stages. By conceptualizing corporate deception as a social process, the paper contributes to a growing body of research that looks beyond 'bad' individuals for the causes of corporate illegality. Peter Fleming is Professor of Work, Organization and Society Queen Mary College, University of London. He has held academic positions at Cambridge University and Melbourne University. His research interests center on the emerging politics of power, control and ethics in contemporary corporations. He has published extensively in academic journals including Organization Studies, Journal of Management Studies, Journal of Business Ethics, Sociology, Sociological Review, Work, Employment and Society and has just published a book (with Andre Spicer) entitled Contesting the Corporation: Power, Resistance and Struggle in Organizations with Cambridge University Press (2007). Stelios Zyglidopoulos is a University Lecturer in Strategy at the Judge Business School of the University of Cambridge. Prior to that, he taught at Erasmus University in the Netherlands, and the Rochester Institute for Technology, in Rochester, NY. He received his PhD in Strategy and Organization from McGill University in Montreal, Canada. Dr. Zyglidopoulos has lived and worked in Greece, Canada, The Netherlands, and the United States. He has a number of years of business experience, mostly in sales and marketing, and is a reservist for the Greek Navy. His research interests focus on the management of corporate reputation; the evolution of corporate social performance; imprinting and organizational evolution; and the internationalization process of high-tech clusters.     

Uncertainty,firm entry,and investment dynamics

Previous macro- and micro-level evidence indicate that fluctuations in idiosyncratic uncertainty have an important effect on investment, both directly and indirectly through financial market frictions. The objective of this paper was to explore, beyond the two traditional and complementary channels, a new one: firm entry. By utilizing a novel and large dataset on Greek firms covering the entire economy over the period 2000–2014 and employing a panel-VAR methodology, we examine and evaluate the impact of shocks to the number of startups, idiosyncratic uncertainty, and financial conditions on the investment growth at the industry level. Our findings can be summarized as follows. First, a shock to the number of new firms has significant effects on investment that persist for many years. Second, although all the three variables are important drivers of investment growth dynamics, uncertainty has the largest impact (explaining about the 15% of the variability of investment growth), firm creation follows (it explains about the 7%), while financial conditions have the smallest direct effect (explaining the 3.5%). Finally, we demonstrate that firm entry constitutes an important propagation mechanism for the transmission of uncertainty shocks in the investment growth trajectories.     

The Response of Bank Retail Rates to Money Market Rates in the BRIC Economies: An Application of the Disaggregated GETS Model

The purpose of this paper is to examine the interest rate transmission mechanism for the emerging BRIC economies (Brazil, Russia, India, and China). We analyze the way interbank rates are transmitted to the bank retail rates, and we test the symmetry hypothesis. A disaggregated general-to-specific model is applied for estimating interest rate pass-through and examining whether retail rates respond symmetrically or asymmetrically to upward/downward interbank rate changes. Overall, our empirics show evidence of sluggish and incomplete pass-through from market rates to bank loan and deposit rates. We show that banks' speed of upward and downward adjustment behavior is symmetric in both loan and deposit markets.     

CO2 Emissions and Income Dynamics: What Does the Global Evidence Tell Us?

This paper analyzes the co-evolution of two major determinants of social welfare, namely, income and carbon emissions. In particular, by using a distribution dynamics approach based on Markov chains, we investigate the shape and behavior of the joint distribution of per-capita income and carbon dioxide emissions. We arrive at several interesting conclusions, especially in the context of international negotiations on climate change. First, evidence does not support theoretical models predicting the existence of a poverty-environment trap. Specifically, in the long-run two main groups of countries will emerge: poor versus polluting countries. Second, the typical development path leads initially to high emission levels and, subsequently, to high income. Third, the convergence process towards the stationary distribution is very slow. Finally, for carbon emissions, whenever it is observed, the environmental Kuznets curve seems to be only a transitory phenomenon.