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.     

Causes and consequences of the lack of strategic foresight in the decisions of multinational enterprises to enter China

Strategic foresight, in the sense of ‘understanding the future’ [R.A. Slaughter. Futures studies as an intellectual and applied discipline. American Behavioral Scientist 42(3) (1998) 372-385; A.N. Whitehead. Modes of Thought. Free Press, New York, 1966], can play a significant role in the long term success, or failure, of business corporations. However, in understanding the development and management of strategic foresight within business enterprises, instances where lack of foresight was exhibited, can be equally instructive, especially when these business organizations are some of the world’s largest multinational corporations and they are faced with a situation they had met before: new market entry.By drawing on 42 in depth interviews, conducted by one of the authors with executives from Multinational Enterprises (MNEs) currently operating in China, this paper identifies the causes and consequences in the lack of foresight exhibited by many MNEs in their China-market entry strategies. In this way the foresight failure is distilled into two factors: Failure of understanding, and Failure of anticipation.     

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.     

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.     

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.     

Sovereign bond market dependencies and crisis transmission around the eurozone debt crisis: a dynamic copula approach

We examine the dependency between the European government bond markets around the recent sovereign debt crisis. A dynamic copula approach is used to model the time-varying dependence structure of those government bond markets, evaluate the nature and strength of their dependencies over time, and gauge the transmission of the crisis shocks. Our results can be summarized as follows: i) the eurozone sovereign bond markets under consideration have a significant and positive dependence with the Greek and the EMU benchmark sovereign bond markets; ii) the dynamic-BB7 copula function best describes the dependence structure between these sovereign bond markets and provides evidence of asymmetric tail dependence; iii) the conditional probability of crisis transmission from Greece to other eurozone countries is higher than the other way around; and iv) Greece is the most vulnerable country when the eurozone entered into the sovereign debt crisis.     

Spillover across Eurozone credit market sectors and determinants

We examine spillover and its determinants among Eurozone sector level credit markets using time and frequency domain spillover approaches. Based on network theory and connectedness analysis, we identify the sectors that are major transmitters and receivers of spillover during normal and crisis periods. The rolling window analysis shows that short-run spillover among credit market sectors intensifies during global and Eurozone crisis periods. Further, using Bayesian model averaging, we find that overall financial conditions and stock market volatility are the main drivers of total and sector-level spillover. Our findings have important implications for policymakers and investors interested in Euro-area credit risk at the sector level.     

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.