Artificial neural networks to forecast london stock exchange

However, the volatile nature of the traders, to trade stocks and other securities, thus providing a stock market makes it a very high risk investment. Thus, a lot of marketplace virtual or real [2]. Researchers have used An index is a statistical composite measure of the various methods in computer science and economics in their movement in the overall market or industry. Basically, indexes quests to gain a piece of this volatile information and make great allow measuring the performance of a group of companies over fortune out of the stock market investment.

Artificial neural networks to forecast london stock exchange

Introduction

In the RBF network, the radial basis function of Gaussian type instead of a sigmoid function is used for activating neurons in hidden layer of a perceptron network. Methodology The neural network we used for this research was RBF which is one of the most frequently used networks for regression.

RBF has been widely used to capture a variety of nonlinear patterns see [ 26 ] thanks to their universal approximation properties see [ 27 ]. In order to optimize the outputs of the network and to maximize the accuracy of the forecasts we had to optimize parameters of ANN.

The most popular method for learning in multilayer networks is called backpropagation.

Artificial neural networks to forecast london stock exchange

It was first invented by Bryson and Ho [ 28 ]. But there are some drawbacks to backpropagation. Furthermore, the convergence of this algorithm is slow and it generally converges to any local minimum on the error surface, since stochastic gradient descent exists on a surface which is not flat.

So the gradient method does not guarantee to find optimal values of parameters and imprisonment in local minimum is quite possible.

As genetic algorithms have become a popular optimization tool in various areas, in our implementation of ANN, backpropagation will be substituted by the GA as an alternative learning technique in the process of weights adaptation.

Genetic algorithms GAwhich are EC algorithms for optimization and machine learning, are stochastic search techniques that guide a population of solutions towards an optimum using the principles of evolution and natural genetics [ 30 ].

Adopted from biological systems, genetic algorithms are based loosely on several features of biological evolution [ 31 ]. In order to work properly, they require five components [ 32 ], that is, a way of encoding solutions to the problem on chromosomes, an evaluation function which returns a rating for each chromosome given to it, a way of initializing the population of chromosomes, operators that may be applied to parents when they reproduce to alter their genetic composition, parameter settings for the algorithm, the operators, and so forth.

GA are also characterized by basic genetic operators which include reproduction, crossover, and mutation [ 33 ]. Given these genetic operators and five components stated above, a genetic algorithm operates according to the following steps stated in [ 29 ].

When the components of the GA are chosen appropriately, the reproduction process will continually generate better children from good parents; the algorithm can produce populations of better and better individuals, converging finally on results close to a global optimum.

Additionally, GA can efficiently search large and complex i. Also, GA should not have the same problem with scaling as backpropagation.

An Artificial Neural Network Model to Forecast Exchange Rates

One reason for this is that it generally improves the current best candidate monotonically. It does this by keeping the current best individual as part of their population while they search for better candidates. In addition, as Kohonen [ 34 ] demonstrated that nonhierarchical clustering algorithms used with artificial neural networks can cause better results of ANN, unsupervised learning technique will be used together with RBF in order to find out whether this combination can produce the effective improvement of this network in the domain of financial time series.

We will combine RBF with the standard unsupervised technique called K-means see [ 35 ]. K-means algorithm, which belongs to a group of unsupervised learning methods, is a nonhierarchical exclusive clustering method based on the relocation principle. The most common type of characteristic function is location clustering.

And the most common distance function is Euclidean. The K-means will be used in the phase of nonrandom initialization of weight vector w performed before the phase of network learning.

In many cases it is not necessary to interpolate the output value by radial functions, it is quite sufficient to use one function for a set of data clusterwhose center is considered to be a center of activation function of a neuron. The values of centroids will be used as initialization values of weight vector w.

Weights should be located near the global minimum of the error function 1 and the lower number of epochs is supposed to be used for network training. The reason why we decided to use K-means is that it is quite simple to implement and in addition to that, in the domain of nonextreme values, it is relatively efficient algorithm.This book focuses on forecasting foreign exchange rates via artificial neural networks (ANNs), creating and applying the highly useful computational techniques of Artificial Neural Networks (ANNs.

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An Artificial Neural Network Model to Forecast Exchange Rates - Open Access Library

World Academy of . TRADITIONAL STOCK MARKET PREDICTION Toronto Stock Exchange, Amsterdam Stock Exchange, London Stock Exchange, Paris Bourse, Philippine Stock Exchange, the Many traditional methods have been applied to predict Singapore Exchange, Kuala Lumpur Stock Exchange, the either stock market moving price or stock market closing price.

work aims at using of Artificial Neural Network techniques to predict the stock price of companies listed under LIX15 index of National Stock Exchange (NSE).

The . Application of Artificial Neural Network for stock market predictions: A review of literature Dase R.K.* 1 and Pawar D.D. 2 *1 timberdesignmag.come College, Nanded, MS, India, of-sample forecasting of the Cyprus Stock Exchange by using daily data. Lev Blynski and Alex Faseruk (21) in studied and forecast.

An Artificial Neural Network Model to Forecast Exchange Rates