Adaptive Prediction of Stock Exchange Indices by State Space Wavelet Networks

Brdyś, Mietek A.; Borowa, A.; Idźkowiak, Piotr; Brdyś, Marcin T.

doi:10.2478/v10006-009-0029-z

Cited by 8 publications

(10 citation statements)

References 24 publications

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“…The more information investor has, the more accurate is his prediction. Analysis of stock exchange trends is not easy, but economic studies provide many mathematical models for stock exchange data processing and prediction [2,3,16,17,25]. Also efficient software tools allow for stock exchange data presentation and forecasting trends with a certain probability [27].…”

Section: Introductionmentioning

confidence: 99%

Decision System for Stock Data Forecasting Based on Hopfield Artificial Neural Network

Paluch

Jackowska-Strumiłło

2016

IAPGOS

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The paper describes a new method using Hopfield artificial neural network combined with technical analysis fractal analysis and feed-forward artificial neural networks for predicting share prices for a next day on a Stock Exchange. The developed method and networks are implemented in an Expert System, which is proposed as a valuable comprehensive, analytical tool. A new algorithm for artificial neural networks training and testing is also presented. It automatically chooses the best network structure, and the most important input parameters.Streszczenie. Artykuł opisuje nową metodę zastosowania sztucznej sieci neuronowej Hopfielda połączonej z analizą techniczną, fraktalną oraz jednokierunkowymi sztucznymi sieciami neuronowymi do przewidywania przyszłych cen akcji na Giełdzie Papierów Wartościowych. Opisane nowe metody zostały zaimplementowane w systemie ekspertowym, który jest polecany jako kompleksowe narzędzie do badania aktualnych i przyszłych zachowań rynku. Zaprezentowany został również algorytm nauki testowania sztucznych sieci neuronowych, który na końcu wybiera najlepszą z nich.Słowa kluczowe: Hybrydowy inteligentny system, sztuczna sieć neuronowa Hopfielda

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Section: Introductionmentioning

confidence: 99%

Decision System for Stock Data Forecasting Based on Hopfield Artificial Neural Network

Paluch

Jackowska-Strumiłło

2016

IAPGOS

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“…The more information investor has, the more accurate is his prediction. Analysis of stock exchange trends is not easy, but economic studies provide many mathematical models for stock exchange data processing and prediction [2,3,12,13,21]. Also efficient software tools allow for stock exchange data presentation and forecasting trends with a certain probability [22].…”

Section: Introductionmentioning

confidence: 99%

“… Artificial Neural Networks -the most common use of ANN on Stock Exchange is: prediction of future stock market indices [3,17,19], exchange rates [12], share prices [14], etc.…”

Section: Introductionmentioning

confidence: 99%

Intelligent information system for stock exchange data processing and presentation

Paluch¹,

Jackowska-Strumiłło²

2015

2015 8th International Conference on Human System Interaction (HSI)

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The paper describes the architecture of the information expert system designed and dedicated to stock exchange data processing, analyzing and presentation. The system uses Artificial Neural Networks (ANN) combined with technical analysis and fractal analysis to predict future price of stock exchange assets. It also enables selection of companies, which assets will be increased. The selection of companies is based on comparing algorithms implementing Hopfield network. The system displays a list of the companies with the highest expected profit sorted in descending order.

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“…Artificial neural network methodologies and their combinations like fuzzy neural networks are some of the well-known methods of forecasting, which are successfully utilized and evaluated in various fields including supply chain (see the works of Efendigil et al (2009) (in fuzzy), Brdyś et al (2009) (in stock exchange), Sumi et al (2012) (in rainfall forecasting), Ozkr and Balgil (2013) (in the fuzzy approach), Georgiadis (2013) (in system dynamics), and Soleimani et al (2014) (in risk management)). Indeed, the ability of the self-learning of the neural network-based methodologies makes them powerful techniques of forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

Kumar¹,

Soleimani

Govindan

2014

International Journal of Applied Mathematics and Computer Science

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Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies' capabilities in collecting End-of-Life (EOL) products, customers' interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.Keywords: artificial neural network, adaptive network based fuzzy inference system, closed-loop supply chain, forecasting methods, fuzzy neural network.

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Adaptive Prediction of Stock Exchange Indices by State Space Wavelet Networks

Cited by 8 publications

References 24 publications

Decision System for Stock Data Forecasting Based on Hopfield Artificial Neural Network

Decision System for Stock Data Forecasting Based on Hopfield Artificial Neural Network

Intelligent information system for stock exchange data processing and presentation

Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

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