David Quintana

Soft computing techniques applied to finance

Abstract 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.

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 bidders 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.

Robust technical trading strategies using GP for algorithmic portfolio selection

GP is applied to learn trading rules that are used to automatically manage a portfolio of stocks.A new Random Sampling method is used to increase the robustness of the strategies evolved.The new Random Sampling method produces strategies able to withstand extreme market environments.The new Random Sampling method produces solutions that perform during out-of-sample testing similarly as during training.The results are based on testing a portfolio of 21 Spanish equities. This paper presents a Robust Genetic Programming approach for discovering profitable trading rules which are used to manage a portfolio of stocks from the Spanish market. The investigated method is used to determine potential buy and sell conditions for stocks, aiming to yield robust solutions able to withstand extreme market conditions, while producing high returns at a minimal risk. One of the biggest challenges GP evolved solutions face is over-fitting. GP trading rules need to have similar performance when tested with new data in order to be deployed in a real situation. We explore a random sampling method (RSFGP) which instead of calculating the fitness over the whole dataset, calculates it on randomly selected segments. This method shows improved robustness and out-of-sample results compared to standard genetic programming (SGP) and a volatility adjusted fitness (VAFGP). Trading strategies (TS) are evolved using financial metrics like the volatility, CAPM alpha and beta, and the Sharpe ratio alongside other Technical Indicators (TI) to find the best investment strategy. These strategies are evaluated using 21 of the most liquid stocks of the Spanish market. The achieved results clearly outperform Buy&Hold, SGP and VAFGP. Additionally, the solutions obtained with the training data during the experiments clearly show during testing robustness to step market declines as seen during the European sovereign debt crisis experienced recently in Spain. In this paper the solutions learned were able to operate for prolonged periods, which demonstrated the validity and robustness of the rules learned, which are able to operate continuously and with minimal human intervention. To sum up, the developed method is able to evolve TSs suitable for all market conditions with promising results, which suggests great potential in the method generalization capabilities. The use of financial metrics alongside popular TI enables the system to increase the stock return while proving resilient through time. The RSFGP system is able to cope with different types of markets achieving a portfolio return of 31.81% for the testing period 2009-2013 in the Spanish market, having the IBEX35 index returned 2.67%.

Early bankruptcy prediction using ENPC

Abstract 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.

Time-stamped resampling for robust evolutionary portfolio optimization

Traditional mean-variance financial portfolio optimization is based on two sets of parameters, estimates for the asset returns and the variance-covariance matrix. The allocations resulting from both traditional methods and heuristics are very dependent on these values. Given the unreliability of these forecasts, the expected risk and return for the portfolios in the efficient frontier often differ from the expected ones. In this work we present a resampling method based on time-stamping to control the problem. The approach, which is compatible with different evolutionary multiobjective algorithms, is tested with four different alternatives. We also introduce new metrics to assess the reliability of forecast efficient frontiers.

Portfolio optimization using SPEA2 with resampling

The subject of financial portfolio optimization under real-world constraints is a difficult problem that can be tackled using multiobjective evolutionary algorithms. One of the most problematic issues is the dependence of the results on the estimates for a set of parameters, that is, the robustness of solutions. These estimates are often inaccurate and this may result on solutions that, in theory, offered an appropriate risk/return balance and, in practice, resulted being very poor. In this paper we suggest that using a resampling mechanism may filter out the most unstable. We test this idea on real data using SPEA2 as optimization algorithm and the results show that the use of resampling increases significantly the reliability of the resulting portfolios.

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.

Extended mean--variance model for reliable evolutionary portfolio optimization

Real world optimization of financial portfolios pose a challenging multiobjective problem that can be tackled using Evolutionary Algorithms. The fact that the optimization process is subject to the presence of uncertainty concerning asset returns is likely to lead to unreliable solutions. This work suggests extending the classic mean--variance optimization problem with a third explicit robustness objective. This results on sets of portfolios that can be subsequently grouped together according to their reliability. This additional information allows for a better informed decision making regarding asset allocation.

Two-layered evolutionary forecasting for IPO underpricing

In this paper we present a two-layered evolutionary system based on Voronoi regions to predict the initial return of a sample of initial pubic offerings. The proposed solution partitions the input space by evolving a set of prototypes using evolution strategies and subsequently fits specialized models to each of them. The exercise is repeated to produce a set of predictive models. The forecast for the return of new patterns is obtained averaging the solutions provided by different models into a single figure. The system is benchmarked against alternatives with the result of a strong relative performance.

Bidding with memory in the presence of synergies: a genetic algorithm implementation

A genetic algorithm has been developed to solve bidding strategies in a dynamic multi-unit auction: the Ausubel auction. The genetic algorithm aims to maximize each bidders payoff. To this end, a memory system about past experiences has been implemented. An extensive set of experiments have been carried out where different parameters of the genetic algorithm have been used in order to make a robust test bed. The present model has been studied for several environments that involve the presence or absence of synergies. For each environment, the bidding strategies, along with their effects on revenue and efficiency, are analyzed. No theoretical predictions have been developed yet for this auction format when values involve synergies; therefore, the aim of this work is to study the auction outcome where theoretical predictions are unknown. The results obtained with the genetic algorithm developed in this research reveal that without synergies, bidders tend to bid sincerely. Nevertheless, in the presence of synergies, when bidders have memory about their past results, they tend to shade their bids.