Forecasting stock indices with back propagation neural network

Wang, Jianzhou; Wang, Jujie; Zhang, Zhe George; Guo, Shu-Po

doi:10.1016/j.eswa.2011.04.222

Cited by 401 publications

(160 citation statements)

References 22 publications

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“…The month to month shutting value information with the Shanghai Composite Index from January 1993 to December 2009 are utilized to represent the utilization of the WDBP neural system based calculation in anticipating the stock record. To demonstrate the benefit of this new approach for stock file conjecture, the WDBP neural system is contrasted and the single Back Propagation neural system utilizing the genuine information set [4][5][6][7][8][9][10].…”

Section: Literature Reviewmentioning

confidence: 99%

Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

Sun¹,

Tang²,

Guo³

2016

Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

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Abstract. Financial time arrangement, for example, stock cost and trade rates are, regularly, non-straight and non-stationary. Beforehand, numerous scientists have endeavored to conjecture those utilizing factual models and machine learning models. Measurable models expect the time arrangement to be stationary and straight, accordingly bringing about expansive factual mistakes. Examination and expectation of securities exchange time arrangement information has pulled in impressive enthusiasm from the exploration group in the course of the most recent decade. Quick advancement and development of modern calculations for measurable examination of time arrangement information, and accessibility of elite equipment has made it conceivable to prepare and dissect high volume securities exchange time arrangement information successfully, continuously. In monetary field, exceptions speak to unpredictability of securities exchange, which assumes an imperative part in administration, portfolio choice and subordinate evaluating. Along these lines, anticipating anomalies of securities exchange is of the immense significance in principle and application. In this paper, the issue of anticipating anomalies in light of versatile gathering models of Intense Erudition Machines (IEMs) is considered. We discovered that the prescribed novel model is material for exception estimating and beats the strategies in light of auto relapse (AR) Intense Erudition Machines (IEMs) models.

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Section: Literature Reviewmentioning

confidence: 99%

Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

Sun¹,

Tang²,

Guo³

2016

Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

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“…Initially, the segmentation of the database is done randomly in a training group (80% of the data), a test group (10% of the data) and a validation group (10% of the data). The random segmentation of networks for training is based on work published by several researchers [4,[15][16][17].…”

Section: A Hybrid Modelmentioning

confidence: 99%

Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model

Beluco

Bandeira

Beluco

2017

JRFM

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Neural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange (NYSE). This hybrid model brings together a self organizing map (SOM) with a multilayer perceptron with back propagation algorithm (MLP-BP). The SOM aims to segment the database into different clusters, where the differences between them are highlighted. The MLP-BP is used to construct a descriptive mathematical model that describes the relationship between the indicators and the closing value of each cluster. The model was developed from a database consisting of the NYSE Composite US 100 Index over the period of 2 April 2004 to 31 December 2015. As input variables for neural networks, ten technical financial indicators were used. The model results were fairly accurate, with a mean absolute percentage error varying between 0.16% and 0.38%.

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“…In the last decades, several approaches have been proposed for time series analysis and forecasting. Two major classes of these approaches are the traditional statistical models and the soft computing approaches [2]. Statistical models generally assume that the time series under study is generated from a linear process [3].…”

Section: Introductionmentioning

confidence: 99%

FEDD: Feature Extraction for Explicit Concept Drift Detection in time series

Cavalcante

Minku

Oliveira

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Abstract-A time series is a sequence of observations collected over fixed sampling intervals. Several real-world dynamic processes can be modeled as a time series, such as stock price movements, exchange rates, temperatures, among others. As a special kind of data stream, a time series may present concept drift, which affects negatively time series analysis and forecasting. Explicit drift detection methods based on monitoring the time series features may provide a better understanding of how concepts evolve over time than methods based on monitoring the forecasting error of a base predictor. In this paper, we propose an online explicit drift detection method that identifies concept drifts in time series by monitoring time series features, called Feature Extraction for Explicit Concept Drift Detection (FEDD). Computational experiments showed that FEDD performed better than error-based approaches in several linear and nonlinear artificial time series with abrupt and gradual concept drifts.

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Forecasting stock indices with back propagation neural network

Cited by 401 publications

References 22 publications

Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model

FEDD: Feature Extraction for Explicit Concept Drift Detection in time series

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