Differences Between Short‐ and Long‐Term Risk Aversion: An Optimal Asset Allocation Perspective

This paper studies the long‐term asset allocation problem of an investor with different risk aversion attitudes to the short and the long term. We characterize investor's preferences with a utility function exhibiting a regime shift in risk aversion at some point of the multiperiod investment horizon that is estimated using threshold nonlinearity methods. Our empirical results for a portfolio of cash, bonds and stocks suggest that long‐term risk aversion is higher than short‐term risk aversion and increases with the investment horizon. The exposure of the investment portfolio from stocks to bonds and cash increases with the degree of risk aversion.     

Firms’ performance under different bankruptcy systems: a Europe–USA empirical analysis

This study empirically analyses the effect that the bankruptcy law has on firms’ performance based on its financial situation. To do this, we considered the different types of efficiency and their influence on firms’ value. The study was carried out for Germany, Spain, the United States, France and the United Kingdom. We applied System‐GMM estimation to dynamic panel data. The main results show that under creditor‐oriented systems, there is a decrease in the value of both financially distressed firms and those filing for bankruptcy.     

Analysis of Bitcoin prices using market and sentiment variables

This paper proposes an empirical model for analysing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010–17 and 2010–19. Price discovery is achieved through the Gonzalo–Granger permanent‐transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period, a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalised by individuals' search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018–19 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.     

Urban density and pupil attainment

We explore the association between urban density and pupil attainment using three cohorts of pupils in schooling in England. Although—as widely recognised—attainment in dense urban places is low on average, this is not because urban environments disadvantage pupils, but because the most disadvantaged pupils with low average attainments attend the most urbanised schools. To control for this, we exploit changes in urban density faced by pupils during compulsory transition from Primary to Secondary school, and measure educational progress at the end of the Secondary phase, relative to attainment at the end of Primary schooling. Our results suggest that there are small but significant benefits from education in schools in more densely urbanised settings. We detect this density advantage even amongst pupils moving relatively short distances between Primary and Secondary schools within urban areas, so we cannot attribute it to broad urbanisation effects experienced by pupils making rural–urban school moves. A more likely explanation lies in greater school choice and competition between closely co-located educational providers.     

Granger causality detection in high-dimensional systems using feedforward neural networks

This paper proposes a novel methodology to detect Granger causality on average in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between elements of high-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure: first, we maximize the transfer of information between input and output variables in the network in order to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function in order to identify the variables that have the predictive ability and, hence, indicate that Granger causality is present in the others. The penalty function inducing sparsity is applied to the weights that characterize the nodes of the neural network. We show the correct identification of these weights so as to increase sample sizes. We apply this method to the recently created Tobalaba network of renewable energy companies and show the increase in connectivity between companies after the creation of the network using Granger causality measures to map the connections.     

Investigating the consequences of word of mouth from a WOM sender’s perspective in the services context

Abstract

Employing scenario-based experiments, this paper examines the effect of word of mouth (WOM) on the sender’s intention to give future WOM about the focal service provider and the self-enhancement derived from articulating WOM. Extant WOM literature considers self-enhancement as a key driver of positive WOM. This paper provides empirical evidence that a reverse effect exists and that self-enhancement is also an outcome of WOM behaviour. Results indicate that the impact of WOM on self-enhancement has substantive significance and holds for both positive and negative WOM. The effect of WOM on intentions is only partially supported. Finally, tie strength between the WOM participants partially moderates the relationship between the variables. Importantly, the impact of negative WOM is stronger than that of positive WOM.     

The forward discount puzzle and market efficiency

We argue that the forward discount puzzle is primarily a statistical phenomenon and that statistical rejections of Uncovered Interest Parity do not necessarily constitute valid rejections of market efficiency. We find by using a Taylor expansion a theoretical negative bias in existing regressions of UIP. We propose two alternative tests for market efficiency, one of which is designed to measure the degree of market inefficiency. Our results from these tests indicate that for all four of the bilateral dollar parities studied the foreign exchange market is efficient despite decisive clear rejections of UIP using the conventional regression approach.     

Statistical tests of distributional scaling properties for financial return series

Existing empirical evidence of distributional scaling in financial returns has helped motivate the use of multifractal processes for modelling return processes. However, this evidence has relied on informal tests that may be unable to reliably distinguish multifractal processes from other related classes. The current paper develops a formal statistical testing procedure for determining which class of fractal process is most consistent with the distributional scaling properties in a given sample of data. Our testing methodology consists of a set of test statistics, together with a model-based bootstrap resampling scheme to obtain sample p-values. We demonstrate in Monte Carlo exercises that the proposed testing methodology performs well in a wide range of testing environments relevant for financial applications. Finally, the methodology is applied to study the scaling properties of a data-set of intraday equity index and exchange rate returns. The empirical results suggest that the scaling properties of these return series may be inconsistent with purely multifractal processes.     

Investing in the size factor

This paper investigates the role of the size factor for constructing investment portfolios and proposes a dynamic extension that accommodates the risk-free asset and time-varying weights. These weights are determined by a set of state variables given by the term structure of sovereign interest rates, variables describing market risk aversion such as the VIX index and the CRB Industrial return, and indexes reflecting investor sentiment towards the economic outlook. The empirical section explores the suitability of these state variables and analyses the out-of-sample performance of size factors idiosyncratic to the US, the UK and European financial markets that are compared against the dynamic version that optimizes the weights in each period. The results provide support to the different size factors except for periods of economic distress in which the optimal dynamic strategies are clearly superior.