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

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

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

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

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

Detección de inercia sectorial en salidas a bolsa mediante modelos arima y redes neuronales

En este trabajo se explora la posibilidad de existencia de mercados segmentados en las salidas a bolsa que pudiesen reflejarse en inercia a corto plazo. Se propone que el rendimiento inicial de las acciones pertenecientes a los sectores tecnológico, de telecomunicaciones y medios de comunicación por un lado y el del resto, por otro, podría estar relacionado con la rentabilidad inicial de otras acciones pertenecientes al mismo sector. Para contrastar ésto, se analizan una serie de índices diarios que son objeto de predicción mediante modelos ARIMA y redes neuronales artificiales. Los resultados aportan indicios de presencia de inercia y de que esta afecta de forma distinta en función del área de actividad.In this work, we explore the possible existence of segmented short-term serial dependence in IPOs. We propose that average first-day underpricing of TMT companies might be affected by the average initial return of the companies taken public in the same sector over the previous days. In order to analyse this, we create a set of indexes to be predicted using artificial neural networks and ARIMA models. Their forecasting ability suggests that both the existence of inertia and a segmented market cannot be ruled out.Publicad

Revisión de precios y reputación de asesores financieros: dos propuestas de índices para explicar el rendimiento a corto plazo de las salida

En este trabajo se proponen dos nuevos constructos para explicar el rendimiento de las acciones el día en que son admitidas a cotización. El primero de ellos captura la influencia del precio final de oferta en relación con el rango no vinculante propuesto a los inversores durante la preventa. El segundo tiene por objeto medir la importancia de la reputación de los asesores financieros encargados de gestionar la salida a bolsa. La capacidad explicativa de estas alternativas se evaluará a través de modelos de regresión lineal centrados en la estructura de la colocación.Salida a bolsa y rendimiento inicial