An agent-based model for the assessment of LTV caps

We assess the effects of regulatory caps in the loan-to-value (LTV) ratio for housing mortgages using an agent-based model. Sellers, buyers and banks interact within a computational framework that enables the application of LTV caps to a one-step housing market. We first conduct a simulation exercise; later, we calibrate the probability distributions based on actual European data from the Household Finance and Consumption Survey. In both cases, the application of an LTV cap results in a modified distribution of buyers in terms of property values, bidding prices and properties sold, depending on the probability distributions of the LTV ratio, wealth and debt-to-income ratios considered. The results are of similar magnitude to other studies in the literature embodying other analytical approaches, and they suggest that our methodology can potentially be used to gauge the impact of common macroprudential measures.     

Integrated reporting and capital markets in an international setting: The role of financial analysts

This study investigates the interplay between integrated reporting (IR) and capital markets. In particular, building on voluntary disclosure and information processing theories, we hypothesize and empirically find that IR adoption improves analysts' ability to make accurate earnings forecasts. Whereas previous studies focus on the South African context, we rely on an international sample that also allows us to study the moderating effect of the corporate governance regime (shareholder or stakeholder oriented). The results suggest that IR improves analysts' ability to make accurate predictions to a larger extent in North America than in Europe, and we derive interesting insights on the much‐debated nature of IR. This study offers valuable insights to policy makers interested in improving disclosure practices in the financial market.     

Using Artificial Neural Networks to forecast Exchange Rate,including VAR‐VECM residual analysis and prediction linear combination

The Euro US Dollar rate is one of the most important exchange rates in the world, making the analysis of its behavior fundamental for the global economy and for different decision‐makers at both the public and private level. Furthermore, given the market efficiency of the EUR/USD exchange rate, being able to predict the rate's future short‐term variation represents a great challenge. This study proposes a new framework to improve the forecasting accuracy of EUR/USD exchange rate returns through the use of an Artificial Neural Network (ANN) together with a Vector Auto Regressive (VAR) model, Vector Error Corrective model (VECM), and post‐processing. The motivation lies in the integration of different approaches, which should improve the ability to forecast regarding each separate model. This is especially true given that Artificial Neural Networks are capable of capturing the short and long‐term non‐linear components of a time series, which VECM and VAR models are unable to do. Post‐processing seeks to combine the best forecasts to make one that is better than its components. Model predictive capacity is compared according to the Root Mean Square Error (RMSE) as a loss function and its significance is analyzed using the Model Confidence Set. The results obtained show that the proposed framework outperforms the benchmark models, decreasing the RMSE of the best econometric model by 32.5% and by 19.3% the best hybrid. Thus, it is determined that forecast post‐processing increases forecasting accuracy.     

Does Teacher Subject Knowledge Contribute to Student Academic Performance in Developing and Least Developed Countries?

Little is known about the causal impact of teacher knowledge on student performance. In this research paper we intend to approach the potential causal effect (i.e. going beyond correlation) of sixth grade teachers’ knowledge on their students’ academic achievement for three Sub-Saharan African countries. To achieve this, we have used the heterogeneity of teachers’ subject knowledge and students’ correspondent academic achievement within-student between-subjects using student fixed effects. Concretely, our work is based on previous research by Bietenbeck, Piopiunik and Wiederhold. Compared to them, we do not use countries that lack representative information after keeping only those students taught by the same teacher for the subjects under analysis. This enables us to obtain more reliable results on this issue. Our results indicate that teacher subject knowledge in reading and mathematics does not have a significant influence on student academic achievement in these subjects for the countries under scrutiny. Many robustness checks have corroborated this conclusion, which contrasts with the positive effect found by Bietenbeck, Piopiunik and Wiederhold.     

An Active Asset Management Investment Process for Drawdown-Averse Investors

The contribution of this article is to present an investment process that allows the asset manager to limit risk exposure to macro-factors – including expectations on correlation dynamics – whilst allowing for selective exposure to risk factors using factor-portfolios that emulate the risk and return profile of market micro-factors. The design of the process provides the ability to explicitely limit risk exposures to macro-factors based on forward-looking narratives allowing the investor to reflect – in the resulting active allocation – expectations of financial or systemic crises by, say, restricting the overall exposure to the credit macro-factor that includes the risk factor exposures (micro-level) arising, for example, from corporate and supranational spreads whilst simultaneously increasing the exposure to flight-to-safety macro-factors under a local or global crisis. This process is better suited to drawdown-averse investors that are willing to forgo some upside in order to effectively limit significant portfolio losses from crises, systemic or otherwise. In order to improve the optimization over the rugged solution space resulting from superimposing macro-factors' risk envelopes on the factor-portfolios' tracking error allocation, a genetic-algorithm-based optimization is proposed. Copyright © 2015 John Wiley & Sons, Ltd.     

Smiles over Frowns: When Curved Lines Influence Product Preference

The present research examined the possibility that subtle face‐like features (a smile‐ or a frown‐like line) on a product can influence evaluations of and preferences for that product. The same within‐participants experiment was conducted twice, once in the United Kingdom and once in Colombia: participants viewed four variants (concave [smile‐like] line, convex [frown‐like] line, straight line, line absent) of three different products (tea, shampoo, juice) and both evaluated the products on visual analogue scales and completed a forced choice decision task. The results revealed a general tendency across scales, products, and countries for the participants to rate products more positively and to choose products more frequently when they displayed a concave line relative to a convex line; comparisons with the straight line and absent line conditions suggested that the effects were present for both concave and convex lines, but were stronger in the concave instance. These findings provide both a deeper theoretical understanding of the influence of subtle cues on evaluation and decision making, and concrete, practical information for both product designers and marketers.     

Determinants of website traffic: the case of European fashion apparel retailers

The globalization process together with the extraordinary growth in the use of Internet offer firms new growth opportunities. Retail firms are using a multichannel approach, involving the Internet, in their internationalization strategies. Website traffic as measured by unique users and online awareness is among the measures for assessing the success of the online channel. There is little evidence, however, linking website traffic and the characteristics of the retail multichannel internationalization process. In order to fill this gap, this paper analyses the factors explaining the retailer's international website traffic. Website traffic data for a sample of European fashion apparel retailers are modelled as dependent on characteristics of the organization and of its physical and virtual internationalization strategy. Following the results of several linear regression models, we infer the complementarity of online and offline channels in the international operation, as well as the contribution of social networks as traffic generators.     

University spillovers and new business location in high-technology sectors: Spanish evidence

This paper examines the relationship between knowledge spillovers from universities and new business location in high-technology sectors. We focus on the contribution to new business formation through spillovers stemming from three main university outputs: knowledge-based graduates, research activities, and technological knowledge. We construct a new dataset with information on 604 companies and 63 universities in Spain and group the data across 36 geographical areas from 2001 to 2004 (144 observations). After controlling for several traditional cost factors and agglomeration characteristics, we find that university spillovers are relevant in explaining the location of new businesses in high-technology sectors in Spain. Further, our analysis draws attention to the relevance of graduates as the main source of spillovers, while research activities and university technology do not have significant effects.