Asuncion Mochon

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.

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.

Testing BOI and BOB Algorithms for Solving the Winner Determination Problem in Radio Spectrum Auctions

Combinatorial auctions are a promising auction format for allocating radio spectrum, as well as other goods. An important handicap of combinatorial auctions is determining the winner bids among many options, that is, solving the winner determination problem (WDP). This paper tackles this computational problem using two approaches in a combinatorial first-price sealed bid auction. The first one, is an A* based on items (BOI). The second one, is an A* based on bids (BOB). These two techniques are tested in several scenarios for allocating radio spectrum licenses. The results obtained reveal that the search algorithm A* with the BOB formulation outperforms the other and always finds the optimal solution very quickly.

Testing bidding strategies in the clock-proxy auction for selling radio spectrum: A Genetic Algorithm approach

The clock-proxy auction is a combinatorial auction which is specially designed for environments where bidders have complex preference structures (complements and substitute items), as also occurs in the spectrum licenses market. In such an intricate context, it is difficult to find an optimal strategy. Nevertheless, if a particular environment is selected, evolutionary computation techniques can be used to find some bidding patterns. This research focuses on the sale of a portion of the spectrum called “digital dividend”, implementing a realistic model that could fit in most European countries. To this end, a simulator of the auction mechanism is created and a set of candidate bidding strategies are implemented. Subsequently, the developed GA tests the proposed strategies, searching for the behavior that maximizes the average profits for one bidder. Finally, the results are supported by an exhaustive validation test bed.

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.

A System for Personality and Happiness Detection

This work proposes a platform for estimating personality and happiness. Starting from Eysencks theory about humans personality, authors seek to provide a platform for collecting text messages from social media (Whatsapp), and classifying them into different personality categories. Although there is not a clear link between personality features and happiness, some correlations between them could be found in the future. In this work, we describe the platform developed, and as a proof of concept, we have used different sources of messages to see if common machine learning algorithms can be used for classifying different personality features and happiness.

Simulating digital dividend auctions: Service neutrality versus dedicated licences

The award of the digital dividend can consolidate auctions as the preferred mechanism for spectrum allocation. Knowing in advance an estimate of what the results of an auction with these characteristics could be would be unquestionably useful for those in charge of designing the process, even if at the end another method such as a beauty contest is chosen. This article provides a simulation of a digital dividend auction in a major-type European country. In one of the scenarios, the spectrum is not pre-allocated to any service in particular (service neutrality) while in the remaining four, blocks of spectrum are pre-allocated to DTT, mobile multimedia and mobile broadband communications. The results of the simulations reveal that the service neutrality scenario maximizes revenues for the seller and that, in general, DTT operators would seem to have fewer opportunities as the spectrum packaging is less protective for them.

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.

An experimental comparative study for interactive evolutionary computation problems

This paper presents an objective experimental comparative study between four algorithms: the Genetic Algorithm, the Fitness Prediction Genetic Algorithm, the Population Based Incremental Learning algorithm and the purposed method based on the Chromosome Appearance Probability Matrix. The comparative is done with a non subjective evaluation function. The main objective is to validate the efficiency of several methods in Interactive Evolutionary Computation environments. The most important constraint of working within those environments is the user interaction, which affects the results adding time restrictions for the experimentation stage and subjectivity to the validation. The experiments done in this paper replace user interaction with several approaches avoiding user limitations. So far, the results show the efficiency of the purposed algorithm in terms of quality of solutions and convergence speed, two known keys to decrease the user fatigue.