Neural networks optimization through genetic algorithm searches: A review

Neural networks and genetic algorithms are the two sophisticated machine learning techniques presently attracting attention from scientists, engineers, and statisticians, among others. They have gained popularity in recent years. This paper presents a state of the art review of the research conducted on the optimization of neural networks through genetic algorithm searches. Optimization is aimed toward deviating from the limitations attributed to neural networks in order to solve complex and challenging problems. We provide an analysis and synthesis of the research published in this area according to the application domain, neural network design issues using genetic algorithms, types of neural networks and optimal values of genetic algorithm operators (population size, crossover rate and mutation rate). This study may provide a proper guide for novice as well as expert researchers in the design of evolutionary neural networks helping them choose suitable values of genetic algorithm operators for applications in a specific problem domain. Further research direction, which has not received much attention from scholars, is unveiled

A Comparison of Linear and Nonlinear Models in Forecasting Market Risk: The Evidence from Turkish Derivative Exchange

This paper aims to compare the volatility forecasting performance of linear and nonlinear models for ISE-30 future index which is traded in Turkish Derivatives Exchangefor the period between 04.02.2005-17.06.2011. As a result of analyses, we conclude that ANN model has better forecasting performance than traditional ARCH-GARCH models. This result is important in many fields of finance such as investment decisions, asset pricing, portfolio allocation and risk managemen

Using Genetic Algorithms for Real Estate Appraisal

The main aim of this paper is the interpretation of the existing relationship between real estate rental prices and geographical location of housing units in a central urban area of Naples (Santa Lucia and Riviera of Chiaia neighborhoods). Genetic algorithms (GA) are used for this purpose. Also, to verify the reliability of genetic algorithms for real estate appraisals and, at the same time, to show the forecasting potentialities of these techniques in the analysis of housing markets, a multiple regression analysis (MRA) was applied comparing results of GA and MRA

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Revisión del Estado del Arte en Métodos de Redes Neuronales, Máquinas de Kernel y Computación Evolutiva para Predicción de Precios Financieros

A review of the representative models of machine learning research applied to the foreign exchange rate and stock price prediction problem is conducted. The article is organized as follows: The first section provides a context on the definitions and importance of foreign exchange rate and stock markets. The second section reviews machine learning models for financial prediction focusing on neural networks, SVM and evolutionary methods. Lastly, the third section draws some conclusions.El siguiente artículo revisa algunos de los trabajos de investigación mas representativos relacionados con aprendizaje computacional aplicado al problema de predicción de tipos de cambio y precios de acciones. El artículo esta organizado de la siguiente forma: La primera sección se concentra en contextualizar definiciones relevantes y la importancia del problema de predicción en el mercado de acciones y de tasa de cambio. La segunda sección contiene la revisión de modelos de aprendizaje computacional para predicción de precios financieros enfocándose en tres subareas: Redes Neuronales, SVM y métodos evolutivos. La tercera sección presenta las conclusiones

Optimización de Estrategia de Trading en el Colcap, a partir de la optimización estocástica con redes neuronales.

En este trabajo se evalúa una estrategia de trading teniendo como benchmark al Colcap y sus criterios de riesgo y retorno para evaluar si la optimización de la misma, mediante el uso de un software que utiliza las redes neuronales y los algoritmos genéticos como herramienta, resulta en retornos significativamente más altos que los que ofrece el índice bursátil Colombiano, teniendo en cuenta información rezagada del mismo.Capítulo I : Justificación ; Capitulo II : Marco Teórico ; Propuesta metodológica ; Optimización: ; Costos de transacción ; Performance de la estrategia vs el Benchmark ; Conclusiones ; Referencias.Magíster en Mercados BursátilesMaestrí