Shopping Cart | Login | Register | Language: English

Using Genetic Programming Systems as Early Warning to Prevent Bank Failure

Copyright © 2012. 14 pages.
OnDemand Chapter PDF Download
Download link provided immediately after order completion
$37.50
Available. Instant access upon order completion.
DOI: 10.4018/978-1-61350-162-7.ch014
Sample PDFCite

MLA

Almanza, Alma Lilia Garcia, Serafín Martínez Jaramillo, Biliana Alexandrova-Kabadjova and Edward Tsang. "Using Genetic Programming Systems as Early Warning to Prevent Bank Failure." Information Systems for Global Financial Markets: Emerging Developments and Effects. IGI Global, 2012. 369-382. Web. 20 Apr. 2014. doi:10.4018/978-1-61350-162-7.ch014

APA

Almanza, A. L., Jaramillo, S. M., Alexandrova-Kabadjova, B., & Tsang, E. (2012). Using Genetic Programming Systems as Early Warning to Prevent Bank Failure. In A. Yap (Ed.), Information Systems for Global Financial Markets: Emerging Developments and Effects (pp. 369-382). Hershey, PA: Business Science Reference. doi:10.4018/978-1-61350-162-7.ch014

Chicago

Almanza, Alma Lilia Garcia, Serafín Martínez Jaramillo, Biliana Alexandrova-Kabadjova and Edward Tsang. "Using Genetic Programming Systems as Early Warning to Prevent Bank Failure." In Information Systems for Global Financial Markets: Emerging Developments and Effects, ed. Alexander Y. Yap, 369-382 (2012), accessed April 20, 2014. doi:10.4018/978-1-61350-162-7.ch014

Export Reference

Mendeley
Favorite
Using Genetic Programming Systems as Early Warning to Prevent Bank Failure
Access on Platform
Browse by Subject
Top

Abstract

The main advantage of creating understandable rules is that users are able to interpret and identify the events that may trigger bankruptcy. By using the method that we propose in this work, it is possible to identify when certain financial indicators are getting close to specific thresholds, something that can turn into an undesirable situation. This is particularly relevant if the companies we are referring to are banks. The contribution of this chapter is to improve the prediction by means of a multi-population approach. The experimental results were evaluated using the Receiver Operating Characteristic (ROC) described in Fawcett and Provost (1997). We show that our approach could improve the Area Under the ROC Curve in 5% with respect to the same method proposed in Garcia et al. (2010). Additionally, a series of experiments were performed in order to find out the reasons of success of the EDR.
Chapter Preview
Top

Introduction

Computers have changed several aspects of our lives in an irreversible way and this applies from the design of electronic devices, cars, airplanes to scientific research and social interaction. Today more than ever our society relies heavily on computers and the digital infrastructure which allows the transmission of information and knowledge in a worldwide scale. Economics and Finance are by no means the exception and the way in which humans interact in economic and financial terms has changed because of the computers.

The use of computers in economics is widespread and nowadays it would be very difficult to point out an area of economic research which has not been changed by the use of computers. Examples of the use of computers in economic related areas range from game theory to agent based simulations and econometrics. Additionally, some of the economic problems and their solutions posses algorithmic nature, an aspect which brings them into the arena of computational complexity. Moreover, some of the limitations of the classical economic models have favoured the introduction of alternative computational methods in economic research.

Computational finance1 is a wide and complex area of research in which computational applications range from Monte Carlo simulations to computer intensive statistical methods and the application of artificial intelligence techniques in financial problems. Strong competition in finance is common as there is always the need to innovate in order to gain competitive advantage or to obtain more profits than other rival firms.

There exists many applications of non-trivial2 computational techniques in finance. Among such techniques we can find Genetic Algorithms, Genetic Programming (GP) by Koza (1992), Neural Networks, support vector machines, constraint satisfaction among many others. One of the reasons behind the use of non conventional computational techniques in finance is that in machine learning, classification problems are very common and problems like bankruptcy prediction or credit scoring can be modelled as a classification problem. As a consequence, traditional statistical methods might benefit from machine learning approaches to solve similar problems as it has happened the other way around. For example, a local search algorithm known as Guided Local Search (GLS) developed by Voudouris and Tsang (2009), borrowed ideas from the traditional Operations Research field. Nowadays, traditional methods used in finance could benefit from the application of computational techniques either to gain better understanding of the problem or as an alternative way of solving financial problems.

A concrete example of the direct impact of computers in financial markets is the important amount of transactions which are automatically made by computer programs. Such form of transactions are known as automatic or algorithmic trading and there is a view shared by some specialists that such trading might be the responsible for some of the important drops in stock markets. For example in Martinez and Tsang (2009), the authors claim:

Financial markets are probably the most competitive, dynamic and complex of all markets. Automatic trading has increased dramatically in recent years and we still need to know the implications of such intensive trading on the prices and more importantly on the likelihood of financial crashes.

On a very different arena, as a direct consequence of the recent events, there has been a need to revise the models for credit risk, credit scoring and credit ratings. Moreover, under the uncertainties of this new financial era, to detect early signs of distress is very important and this is particularly relevant for banks. As a consequence, financial regulation is being revised and has been changing dramatically as it has not changed for a few decades; in fact, there is even more regulation being discussed now by different governments and international agencies. This brings us to the situation in which there is room for trying techniques (computational) which are beyond the traditional statistical ones.

Top

Complete Chapter List

Search this Book: Reset
Table of Contents
Preface
Alexander Y. Yap
Chapter 1
Donald Crooks, John Slayton, John Burbridge
Much has been written about information technology and its role in reinventing financial markets. Today’s markets are truly global, and the... Sample PDF
Information Technology and Financial Markets: Risk, Volatility and the Quants
$37.50
Chapter 2
Alexander Y. Yap
Trading anytime anywhere ubiquitously is rapidly becoming a popular trading practice in the financial marketspace. When highly volatile financial... Sample PDF
Trading Anytime Anywhere with Ubiquitous Financial Information Systems
$37.50
Chapter 3
Michael Kampouridis, Shu-Heng Chen, Edward Tsang
In a previous work, inspired by observations made in many agent-based financial models, we formulated and presented the Market Fraction Hypothesis... Sample PDF
The Market Fraction Hypothesis under Different Genetic Programming Algorithms
$37.50
Chapter 4
Xiaotie Deng, Feng Wang, Keren Dong
Algorithmic trading strategy making is a very important research issue which attracts more and more people’s interests. This chapter will introduce... Sample PDF
Algorithmic Trading Strategy Making: Algorithms and Applications
$37.50
Chapter 5
Alexander Y. Yap, Wonhi Synn
This chapter focuses on the theme of service innovation in the electronic brokerage sector. The discussion will cover the theories of “technology... Sample PDF
Technology Bundling: Innovation for Online Brokerage Services
$37.50
Chapter 6
Robert P. Schumaker, Hsinchun Chen
However, using computational approaches to predict stock prices using financial data is not unique. In recent years, interest has increased in... Sample PDF
Predicting Stock Price Movement from Financial News Articles
$37.50
Chapter 7
Joe Kelley
Virtual reality offers the promise that finally, most of the capabilities of the human mind and senses can be harnessed to improve global financial... Sample PDF
Virtual Reality Support for Trading
$37.50
Chapter 8
M. Kersch, G. Schmidt
Trading decisions in financial markets can be supported by the use of trading algorithms. To evaluate trading algorithms and to generate orders to... Sample PDF
Survey of Trading Systems for Individual Investors
$37.50
Chapter 9
Joe Kelley
We sketch a large-scale computable general equilibrium model of the macroeconomy that includes modern features such as financial derivatives. This... Sample PDF
Grid Super-Computable General Equilibrium Models
$37.50
Chapter 10
Seán O’Riain, Andreas Harth, Edward Curry
With increased dependence on efficient use and inclusion of diverse corporate and Web based data sources for business information analysis... Sample PDF
Linked Data Driven Information Systems as an Enabler for Integrating Financial Data
$37.50
Chapter 11
Roger F.A. van Daalen
The move towards electronic trading was believed by some to narrow the scope of information available to traders, due to the difference between the... Sample PDF
The Persisting Human Element of the Electronic Trading Habit
$37.50
Chapter 12
Joe Kelley
We present an extensive dynamic financial model that encompasses most models used today in finance and economics. We show that this model is a good... Sample PDF
DSP Acceleration for Dynamic Financial Models
$37.50
Chapter 13
Joe Kelley
We propose to use FPGA (Field Programmable Gate Arrays) to solve the nearly insurmountable computational challenges of Financial Network Models.... Sample PDF
FPGA Speedup for Financial Network Models
$37.50
Chapter 14
Alma Lilia Garcia Almanza, Serafín Martínez Jaramillo, Biliana Alexandrova-Kabadjova, Edward Tsang
The main advantage of creating understandable rules is that users are able to interpret and identify the events that may trigger bankruptcy. By... Sample PDF
Using Genetic Programming Systems as Early Warning to Prevent Bank Failure
$37.50