Malthus living in a slum : urban concentration, infrastructures and economic grouwth

The link between urban concentration and economic growth at country level is not straightforward, as there are benefits as well as costs associated with urban concentration. Indeed, recent empirical evidence suggests different effects of urban concentration on growth depending on the level of development and the world region under analysis. This paper revisits the literature on urban concentration and economic growth to shed some light on these previous results. In particular, differences in the process of urbanisation, and in the quality of the urban environment itself, have been suggested as most likely defining the balance between benefits and costs from urban concentration, and are probably behind differences in the relationship between concentration and growth. However, empirical evidence in this regard remains very limited. The aim of the paper is to fill this gap by paying special and explicit attention to differences between world regions in terms of urban infrastructure, essentially access to basic urban services. The main contribution of the paper is to therefore provide empirical evidence on the role that the urban environment plays in the relationship between urban concentration and economic growth

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad

Evolutionary rule-based system for IPO underpricing prediction

Genetic And Evolutionary Computation Conference. Washington DC, USA, 25-29 June 2005Academic literature has documented for a long time the existence of important price gains in the first trading day of initial public offerings (IPOs).Most of the empirical analysis that has been carried out to date to explain underpricing through the offering structure is based on multiple linear regression. The alternative that we suggest is a rule-based system defined by a genetic algorithm using a Michigan approach. The system offers significant advantages in two areas, 1) a higher predictive performance, and 2) robustness to outlier patterns. The importance of the latter should be emphasized since the non-trivial task of selecting the patterns to be excluded from the training sample severely affects the results.We compare the predictions provided by the algorithm to those obtained from linear models frequently used in the IPO literature. The predictions are based on seven classic variables. The results suggest that there is a clear correlation between the selected variables and the initial return, therefore making possible to predict, to a certain extent, the closing price.This article has been financed by the Spanish founded research MCyT project TRACER, Ref: TIC2002-04498-C05-04M

Analysis of Ausubel auctions by means of evolutionary computation

IEEE Congress on Evolutionary Computation. Edimburgo, 2-5 September 2005The increasing use of auctions has led to a growing interest in the subject. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this paper is to develop a genetic algorithm to find bidders' optimal strategies for a specific dynamic multi-unit auction. The algorithm provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform canonical strategies

Applied Computational Intelligence for finance and economics

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.Publicad

Early bankruptcy prediction using ENPC

Bankruptcy prediction has long time been an active research field in finance. One of the main approaches to this issue is dealing with it as a classification problem. Among the range of instruments available, we focus our attention on the Evolutionary Nearest Neighbor Classifier (ENPC). In this work we assess the performance of the ENPC comparing it to six alternatives. The results suggest that this algorithm might be considered a good choice.Publicad

Effects of a rationing rule on the ausubel auction: a genetic algorithm implementation

The increasing use of auctions as a selling mechanism has led to a growing interest in the subject. Thus both auction theory and experimental examinations of these theories are being developed. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this article is to develop a genetic algorithm to find automatically a bidder optimal strategy while the other players are always bidding sincerely. To this end a specific dynamic multiunit auction has been selected: the Ausubel auction, with private values, dropout information, and with several rationing rules implemented. The method provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments that differ in the elasticity of their demand curves, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform sincere bidding when rationing is needed.Publicad

Malthus living in a slum: Urban concentration, infrastructures and economic growth

Today more than half of the 7 billion inhabitants of the planet live in urban areas, with this share expected to keep rising. Whereas in developed countries urbanisation has been a long and slow process, in developing countries this process is now characterised by a really fast pace and a high degree of urban concentration, with urban population tending to concentrate in one or few large metropolitan areas of disproportionate size. While urbanisation has been long recognised as a fundamental element of the process of economic development, sustainable urbanisation has become one of the main and more pressing challenges for developing countries, where millions live lacking adequate access to basic services like electricity, clean water and sanitation. Building on previous evidence on urban concentration and economic growth, in this paper we analyse differentiated effects of urban concentration on national economic performance. In order to do so, we rely on panel data from 1960 to 2010 and perform several estimation techniques including System GMM and IV estimations (using rainfall data in the instrumentalisation strategy). We contribute to the literature by providing empirical evidence on how different characteristics of the urban environment - in particular the quality of urban infrastructure - strongly determine the growth-enhancing benefits of urban concentration (something that previous studies on urban concentration and economic growth have not considered empirically). We analyse several measures of urban infrastructure and look at different world regions, taking a special focus on Sub-Saharan African countries, where find that urban concentration has been in most cases associated with lower growth due to significant deficiencies in terms of urban infrastructure

Resampled efficient frontier integration for MOEAs

This article belongs to the Section Multidisciplinary Applications.Mean-variance portfolio optimization is subject to estimation errors for asset returns and covariances. The search for robust solutions has been traditionally tackled using resampling strategies that offer alternatives to reference sets of returns or risk aversion parameters, which are subsequently combined. The issue with the standard method of averaging the composition of the portfolios for the same risk aversion is that, under real-world conditions, the approach might result in unfeasible solutions. In case the efficient frontiers for the different scenarios are identified using multiobjective evolutionary algorithms, it is often the case that the approach to averaging the portfolio composition cannot be used, due to differences in the number of portfolios or their spacing along the Pareto front. In this study, we introduce three alternatives to solving this problem, making resampling with standard multiobjective evolutionary algorithms under real-world constraints possible. The robustness of these approaches is experimentally tested on 15 years of market data.This research was funded by Spanish Ministry of Education under grant number CAS15/0025

Predicción del rendimiento inicial en mercados segmentados mediante Redes de Neuronas

En este trabajo se propone un sistema de predicción del rendimiento inicial de las acciones en mercados segmentados, a través del estudio del caso particular del sector tecnológico. El modelo incorpora, además de una serie de variables de corte transversal, un indicador de inercia en el mercado y una medida de fiabilidad de este último. Los resultados obtenidos sugieren que los perceptrones multicapa permiten ponderar la información relativa al estado del mercado de forma que las predicciones resulten más ajustadas.This paper presents and IPO underpricing prediction system for segmented markets using as an example tech IPOs. The model combines cross-sectional variables with both an index for the state of the market, and an indicator for the reliability of the mentioned index. The results show that multilayer perceptrons can be effective weighting the information regarding the market, which results in enhanced of predictive accuracy.Publicad