2012
DOI: 10.35808/ersj/351
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Time Series Prediction with Neural Networks for the Athens Stock Exchange Indicator

Abstract: The main aim of this study is to predict the daily stock exchange price index of the Athens Stock Exchange (ASE) using back propagation neural networks. We construct the neural network based on the minimum embedding dimension of the corresponding strange attractor. Multistep prediction for nine days ahead is achieved with this particular network indicating the increased possibility of this technique for immediate forecasts for very timeshort data sets, mostly daily and weekly.

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Cited by 40 publications
(28 citation statements)
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“…In the paper, authors used Vector Measure Construction Method (VMCM) as a methodical apparatus, to assess the problem related to quality of institutions (Hanias et al, 2007;Ugurlu et al, 2014). The time series analysis of economic variables reflecting quality of institutions in the selected 24 countries of CEE and Central Asia was performed.…”
Section: Methodsmentioning
confidence: 99%
“…In the paper, authors used Vector Measure Construction Method (VMCM) as a methodical apparatus, to assess the problem related to quality of institutions (Hanias et al, 2007;Ugurlu et al, 2014). The time series analysis of economic variables reflecting quality of institutions in the selected 24 countries of CEE and Central Asia was performed.…”
Section: Methodsmentioning
confidence: 99%
“…It can be implemented by identifying key indicators that directly or indirectly reflect the perspectives of all stakeholders in the effective operation of the company: owners, management personnel, employees, investors, creditors, society, etc. (Hanias et al, 2012). When evaluating the company's activities, it is more expedient to be guided more by production, innovation and financial indicators.…”
Section: Implementationsmentioning
confidence: 99%
“…We have applied the method of Procaccia (Grassberge andProcaccia, 1983a and1983b) to evaluate the minimum embedding dimension of each the system. In a second stage using the neural network (Hanias, Curtis, Thalassinos, 2007;Thalassinos et al, 2008 and we achieved an out of sample multi step time series prediction.…”
Section: Introductionmentioning
confidence: 97%