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A neural evolutionary approach to financial modeling
2006
Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06
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This paper presents an approach to the joint optimization of neural network structure and weights which can take advantage of backpropagation as a specialized decoder. ...
The approach has been applied to a financial problem, whereby a factor model capturing the mutual relationships among several financial instruments is sought for. ...
INTRODUCTION The evolutionary approach that implements the conjunction of evolutionary algorithms (EAs) with neural networks (NNs) is a more integrated way of designing artificial neural networks (ANNs ...
doi:10.1145/1143997.1144263
dblp:conf/gecco/AzziniT06
fatcat:iku77g55cbhsxpwtt7sc4mnnju
On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices
2014
Journal of King Saud University: Computer and Information Sciences
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This paper develops and assesses the performance of a hybrid prediction model using a radial basis function neural network and non-dominated sorting multiobjective genetic algorithm-II (NSGA-II) for various ...
The performances of this model were evaluated in terms of four different measures using Standard and Poor 500 (S&P500) and Dow Jones Industrial Average (DJIA) stock data. ...
Multi-objective evolutionary algorithms have been suggested to determine the number of trade off solutions between the number of fuzzy rules and the prediction accuracy of financial time series (Hassan ...
doi:10.1016/j.jksuci.2013.12.005
fatcat:xx7bf7smmjdfznkvenf7oi2vtm
Multi-objective optimization with an evolutionary artificial neural network for financial forecasting
2009
Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09
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In this paper, we attempt to make accurate predictions of the movement of the stock market with the aid of an evolutionary artificial neural network (EANN). ...
Experiments were conducted with EANNs that updated connection weights through genetic operators (crossover and mutation) and/or with the aid of back-propagation. ...
RELATED WORK Some recent work with evolutionary neural networks includes a paper by Azzini and Tettamanzi [3] where the authors evolved a neural network for financial factor modeling. ...
doi:10.1145/1569901.1570096
dblp:conf/gecco/ButlerD09
fatcat:6rc4lc4jrfamlecxczubrdlc2y
A Survey on Impact of Bio-inspired Computation on Stock Market Prediction
2017
Journal of Engineering Science and Technology Review
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To predict the stock price most Artificial Neural Network (ANN) based model are used in the historical data along with statistical measures, technical indicators etc. ...
Most optimization techniques have been used for training the weights of forecasting models. Since no single optimization technique is invariably superior to others. ...
Where it used six neurons neural network and trained the normalized data with the bat algorithm. ...
doi:10.25103/jestr.103.15
fatcat:hash5nemh5fntcgwscnifoavay
Evolving Neural Networks for Static Single-Position Automated Trading
2008
Journal of Artificial Evolution and Applications
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The neural network uses open, high, low, and close quotes of the selected financial instrument from the previous day, as well as a selection of the most popular technical indicators, to decide whether ...
An artificial neural network is evolved to provide trading signals to a simple automated trading agent. ...
They make up so-called Evolutionary Artificial Neural Networks (EANNs) [36, 47, 48] , that is, biologically-inspired computational models that use evolutionary algorithms in conjunction with neural networks ...
doi:10.1155/2008/184286
fatcat:lnjfoxsyd5agxkqfzrsuvxf74m
A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction
2019
Financial Innovation
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A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model. ...
Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. ...
Acknowledgements The authors are grateful to the editor-in-chief and the anonymous reviewers for their valuable suggestions which helped in improving the quality of this paper. ...
doi:10.1186/s40854-019-0153-1
fatcat:vuzq7qm5rfdndljgukdven7xaa
Robust prediction of stock indices using PSO based adaptive linear combiner
2009
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC)
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The short and long term prediction performance of the new model is evaluated with test data and the results obtained are compared with those obtained from the conventional PSO based model. ...
The present paper employs a particle swarm optimization (PSO) based adaptive linear combiner for efficient prediction of various stock indices in presence of strong outliers in the training data. ...
A new neural network learning machine has been proposed using Wilcoxon norm [10] and has recently been successfully applied for function optimization task in presence of outliers in training samples. ...
doi:10.1109/nabic.2009.5393728
dblp:conf/nabic/MajhiPM09
fatcat:xsxvwgd42vdg3mldtq4tvalc3y
Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data-Snooping Bias
2015
Journal of Forecasting
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In this paper, we present two Neural Network based techniques, an adaptive evolutionary Multilayer Perceptron (aDEMLP) and an adaptive evolutionary Wavelet Neural Network (aDEWNN). ...
The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange traded funds ...
The aim of this paper is threefold: First, we introduce two NN hybrid techniques; an adaptive evolutionary Multilayer Perceptron (aDEMLP) and an adaptive evolutionary Wavelet Neural Network (aDEWNN). ...
doi:10.1002/for.2338
fatcat:6cfv4akxzjebhampledvzqt5gq
A New Particle Swarm Optimization Based Stock Market Prediction Technique
2016
International Journal of Advanced Computer Science and Applications
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This prediction model is used with some common indicators to maximize the return and minimize the risk for the stock market. ...
In this paper, our earlier presented particle swarm optimization with center of mass technique (PSOCoM) is applied to the task of training an adaptive linear combiner to form a new stock market prediction ...
[4] used the standard particle swarm optimization (PSO) algorithm to develop an efficient forecasting model for prediction of S&P500 and DJIA stock indices. ...
doi:10.14569/ijacsa.2016.070442
fatcat:hy37nl2ozjca3hfueqs7cffhp4
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019
[article]
2019
arXiv
pre-print
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as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). ...
Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. ...
The authors of [130] used RNN models, Recurrent Computationally Efficient Functional Link Neural Network (RCEFLANN) and Functional Link Neural network (FLANN), with their weights optimized using various ...
arXiv:1911.13288v1
fatcat:npvyhewuvvcvri4e43jwj3c45y
An Optimized Machine Learning Model for Stock Trend Anticipation
2020
Ingénierie des Systèmes d'Information
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This work implies the base model, boosted model and deep learning model along with optimization techniques. ...
From the experimental result, the optimized deep learning model, ABC-LSTM was turned out superior to all other considered financial models LSSVM, Gradient Boost, LSTM, ABC-LSSVM, ABC-Gradient Boost by ...
The authors evaluated the constructed model with thirteen numbers of financial benchmark datasets and made comparison with artificial neural network with Levenberg -Marquardt (LM) algorithm. ...
doi:10.18280/isi.250608
fatcat:vsbnosuxpjalxlyckwl3iyqi6i
Modeling the behavior of the stock market with an Artificial Immune System
2010
IEEE Congress on Evolutionary Computation
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To aid in this research the AIS models are compared with a k-Nearest Neighbors (kNN) algorithm, an artificial neural network (ANN) and a benchmark market portfolio to compare simulated trading results. ...
In general the practice of using the natural immune system to inspire a learning algorithm has been established as a viable alternative to modeling the stock market when implementing a supervised learning ...
Although this area has been popular the majority of the research in forecasting financial assets has been with genetic algorithms [4] , genetic programming [5] and hybrids such as evolutionary neural ...
doi:10.1109/cec.2010.5585978
dblp:conf/cec/ButlerK10
fatcat:p6htpys7pfegthklepgupngpt4
Estimating stock closing indices using a GA-weighted condensed polynomial neural network
2018
Financial Innovation
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The outputs of these PDs along with the original features are fed to a single output neuron, and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm. ...
We developed a model that uses partial descriptions (PDs) and is limited to only two layers for the PNN architecture. ...
Acknowledgements The author would like to thank to the Editor and the reviewers for their valuable comments and constructive suggestions that helped to improve the content of the paper in a large extent ...
doi:10.1186/s40854-018-0104-2
fatcat:lfp4z3ryebg7tgcwqsitxrpcv4
A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19
2021
Scientific Programming
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To design the neural network to minimize processing time and search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. ...
The created network was able to predict the closing price with high accuracy, and the proposed model ANN-PSOCoG showed that it could predict closing price values with an infinitesimal error, outperforming ...
based evolutionary algorithm and PSO algorithms to optimize the structure and parameters of FNT. ...
doi:10.1155/2021/6656150
doaj:e6b12d352e7047d491ec3dd4978935f8
fatcat:q6wur677hbecta64s62x4ypp3y
An empirical study on the various stock market prediction methods
2022
Register: Jurnal Ilmiah Teknologi Sistem Informasi
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The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. ...
The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. ...
Jaliya: Conceptulization, investigation, resouces, software, supervision, validation, visualization and wrtingreview & editing. ...
doi:10.26594/register.v8i1.2533
fatcat:vtircrcxzzg3hinmpwszwrlkhu
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