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

PASS: a simple classifier system for data analysis

Let x be a vector of predictors and y a scalar response associated with it. Consider the regression problem of inferring the relantionship between predictors and response on the basis of a sample of observed pairs (x,y). This is a familiar problem for which a variety of methods are available. This paper describes a new method based on the classifier system approach to problem solving. Classifier systems provide a rich framework for learning and induction, and they have been suc:cessfully applied in the artificial intelligence literature for some time. The present method emiches the simplest classifier system architecture with some new heuristic and explores its potential in a purely inferential context. A prototype called PASS (Predictive Adaptative Sequential System) has been built to test these ideas empirically. Preliminary Monte Carlo experiments indicate that PASS is able to discover the structure imposed on the data in a wide array of cases

A DATA DRIVEN MACHINE LEARNING APPROACH TO DISCOVERING RULES OF PRICE BEHAVIOR IN A FINANCIAL MARKET SIMULATION

The field of agent-based simulation of financial markets has grown considerably in the last decade. However, the interpretation of simulation results has received far less attention. Typically, the results of a large number of simulations are reduced to one or two summary statistics, such as sample moments. While such summarization is useful, it overlooks a vast amount of additional information that might be gleaned by examining patterns of behavior that emerge at lower levels. In this paper we propose an approach to interpreting simulation results that involves the use of so-called data mining techniques to identify the rules of behavior that govern an underlying system. We demonstrate the approach by using data from a single run of an order market simulation to derive rules about the behavior of prices in that simulation.Information Systems Working Papers Serie

Predicting IPO underpricing with genetic algorithms

This paper introduces a rule system to predict first-day returns of initial public offerings based on the structure of the offerings. The solution is based on a genetic algorithm using a Michigan approach. The performance of the system is assessed comparing it to a set of widely used machine learning algorithms. The results suggest that this approach offers significant advantages on two fronts: predictive performance and robustness to outlier patterns. The importance of the latter should be emphasized as the results in this domain are very sensitive to their presence.We acknowledge financial support granted by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR) and Comunidad de Madrid (CCG10-UC3M/TIC-5029)

Assessing the Volume of Changes to Banking Assets and Liabilities Using Genetic Algorithms in Additional Funds Needed Model

This paper investigates Small-Medium Banks’ (SMBs) business plans in accordance with the structure of Additional Funds Needed (AFN) model. The Key Profitability Variables (KPVs) are the size and structure of deposits, loans, and their interest rates. This study employs a Genetic Algorithm (GA) problem with hard constraints, to point out the limits to changes in the structure of deposits and loans and the effects of those changes on the P&L of a banking institution. After examining 10,000 iterations with Evolver, an innovative optimization software that uses GA, OptQuest, and linear programming, the alternations, have been narrowed down to 3700 which satisfy both, a) constraints and b) maximization of profits. Having also the distributions, this paper concludes that it is a useful methodology that must be further exploited by applying risk weights, mainly for credit risk, to the structural components of the Balance Sheet, and to other competitive institutions. Keywords: banking institutions, genetic algorithms, additional funds needed, operational researc

Genetic Algorithm Optimisation for Finance and Investments

This paper provides an introduction to the use of genetic algorithms for financial optimisation. The aim is to give the reader a basic understanding of the computational aspects of these algorithms and how they can be applied to decision making in finance and investment. Genetic algorithms are especially suitable for complex problems characterised by large solution spaces, multiple optima, nondifferentiability of the objective function, and other irregular features. The mechanics of constructing and using a genetic algorithm for optimisation are illustrated through a simple example