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A neural evolutionary approach to financial modeling

Antonia Azzini, Andrea G.B. Tettamanzi
2006 Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06  
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

Babita Majhi, Minakhi Rout, Vikas Baghel
2014 Journal of King Saud University: Computer and Information Sciences  
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

Matthew Butler, Ali Daniyal
2009 Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09  
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

Smruti Rekha Das, Debahuti Mishra, Minakhi Rout
2017 Journal of Engineering Science and Technology Review  
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

Antonia Azzini, Andrea G. B. Tettamanzi
2008 Journal of Artificial Evolution and Applications  
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

Sarat Chandra Nayak, Bijan Bihari Misra
2019 Financial Innovation  
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

Ritanjali Majhi, G. Panda, Babita Majhi
2009 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC)  
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

Georgios Sermpinis, Thanos Verousis, Konstantinos Theofilatos
2015 Journal of Forecasting  
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

Essam El.
2016 International Journal of Advanced Computer Science and Applications  
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]

Omer Berat Sezer, Mehmet Ugur Gudelek, Ahmet Murat Ozbayoglu
2019 arXiv   pre-print
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

Nalabala Deepika, Mundukur Nirupamabhat
2020 Ingénierie des Systèmes d'Information  
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

Matthew Butler, Dimitar Kazakov
2010 IEEE Congress on Evolutionary Computation  
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

Sarat Chandra Nayak, Bijan Bihari Misra
2018 Financial Innovation  
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

Razan Jamous, Hosam ALRahhal, Mohamed El-Darieby
2021 Scientific Programming  
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

Jaymit Bharatbhai Pandya, Udesang K. Jaliya
2022 Register: Jurnal Ilmiah Teknologi Sistem Informasi  
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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