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

Nonlinear Combination of Financial Forecast with Genetic Algorithm

Complexity in the financial markets requires intelligent forecasting models for return volatility. In this paper, historical simulation, GARCH, GARCH with skewed student-t distribution and asymmetric normal mixture GRJ-GARCH models are combined with Extreme Value Theory Hill by using artificial neural networks with genetic algorithm as the combination platform. By employing daily closing values of the Istanbul Stock Exchange from 01/10/1996 to 11/07/2006, Kupiec and Christoffersen tests as the back-testing mechanisms are performed for forecast comparison of the models. Empirical findings show that the fat-tails are more properly captured by the combination of GARCH with skewed student-t distribution and Extreme Value Theory Hill. Modeling return volatility in the emerging markets needs “intelligent” combinations of Value-at-Risk models to capture the extreme movements in the markets rather than individual model forecast.Forecast combination; Artificial neural networks; GARCH models; Extreme value theory; Christoffersen test

Mean-Variance-Skewness Portfolio Selection Model Based on RBF-GA

The classical Markowitz’s mean-variance model in modern investment science uses variance as risk measure while it ignores the asymmetry of the return distribution. This article introduces skewness, V-type transaction costs, cardinality constraint and initial investment proportion, and builds a new class of nonlinear multi-objective portfolio model (mean-variance-skewness portfolio selection model). To solve the model, we develop a genetic algorithm(GA) which contains radial basis function(RBF) neural network, called RBF-GA. The experimental results show that the proposed model is more effective and more realistic than others

Efficient Frontier for Robust Higher-order Moment Portfolio Selection

This article proposes a non-parametric portfolio selection criterion for the static asset allocation problem in a robust higher-moment framework. Adopting the Shortage Function approach, we generalize the multi-objective optimization technique in a four-dimensional space using L-moments, and focus on various illustrations of a four-dimensional set of the first four L-moment primal efficient portfolios. our empirical findings, using a large European stock database, mainly rediscover the earlier works by Jean (1973) and Ingersoll (1975), regarding the shape of the extended higher-order moment efficient frontier, and confirm the seminal prediction by Levy and Markowitz (1979) about the accuracy of the mean-variance criterion.Efficient frontier, portfolio selection, robust higher L-moments, shortage function, goal attainment application.

Geometric Representation of the Mean-Variance-Skewness Portfolio Frontier Based upon the Shortage Function

The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine mean-variance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimalportfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the geometric representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop geometric representations of the MVS portfolio frontier using the shortage function and related approaches.shortage function, efficient frontier, mean-variance-skewness efficiency

High-low Strategy of Portfolio Composition using Evolino RNN Ensembles

trategy of investment is important tool enabling better investor's decisions in uncertain finance market. Rules of portfolio selection help investors balance accepting some risk for the expectation of higher returns. The aim of the research is to propose strategy of constructing investment portfolios based on the composition of distributions obtained by using high–low data. The ensemble of 176 Evolino recurrent neural networks (RNN) trained in parallel investigated as an artificial intelligence solution, which applied in forecasting of financial markets. Predictions made by this tool twice a day with different historical data give two distributions of expected values, which reflect future dynamic exchange rates. Constructing the portfolio, according to the shape, parameters of distribution and the current value of the exchange rate allows the optimization of trading in daily exchange-rate fluctuations. Comparison of a high-low portfolio with a close-to-close portfolio shows the efficiency of the new forecasting tool and new proposed trading strategy

Colombian Energy Market: Optimal Portfolio

This paper focuses on the study of an optimal portfolio in the Colombian Energy Market using the Artificial Intelligence techniques specifically, Fuzzy Modeling and Neural Networks. The methodology at first, is implemented using the Matlab Fuzzy Logic Toolbox and with the help of a script the process is automatized. Secondly, a Neural Network is implemented in Matlab and its results are compared with the ones obtained in the Matlab Neural Network Toolbox. The results of the Fuzzy model and the Neural Network are presented and conclusions of both techniques are discussed. Finally possible future work are proposed