OMX Vilnius akcijų indekso prognozavimas naudojant dirbtinius neuronų tinklus

Akcijų rinkos prognozavimas - sunki užduotis kiekvienam investuotojui. Akcijų rinkoje egzistuojantys sąryšiai yra sudėtingi ir sunkiai numatomi. Kadangi investavimo pelningumas yra tiesiogiai susijęs su rinkos nuspėjamumu, kyla poreikis naudoti modernesnes ir tikslesnes prognozavimo priemones. Užsienio mokslo darbuose vis daugiau dėmesio skiriama netiesiniams laiko eilučių prognozavimo modeliams. Šiame straipsnyje aprašomas OMX VUnius indekso prognozavimo, naudojant dirbtinius neuronų tinklus, modelis. Daugiasluoksnis neuronų modelis taikomas kitos dienos ir kito mėnesio būsimoms indekso reikšmėms bei indekso krypčiai nuspėti. Neuronų tinklas apmokomas naudojant atgalinio sklidimo algoritmą. Analizuojamos kelios tinklo struktūros ir išrenkama pati tinkamiausia. Metodo tikslumas palyginamas su keliais tradiciniais statistikos metodais (slankiuoju vidurkiu ir tiesine regresija).Predicting a stock market is a challenging task for every investor. Stock market contains difficult relations and its behavior is heavily forecasted. As the investment's profitability is directly related to the market's predictability, the need for more accurate and sophisticated forecasting techniques arises. The academic literature is showing a growing interest in implementing non- linear techniques in a time series prediction. The paper goes through the process of creating a time series prediction model for OMX VUnius stock index using artificial neural network approach. A multi layer perceptron model is applied in order to make periodical daüy and monthly forecasts for both the actual index fiiture value and the direction of the index. The neural network is trained using back-propagation method, several topologies are analyzed and the most suitable is selected. The method accuracy is compared to several traditional statistical methods (moving averages and linear regression)

Forecasting OMX Vilnius stock index – a neural network approach

Predicting a stock market is a challenging task for every investor. Stock market contains difficult relations and its behavior is heavily forecasted. As the investment’s profitability is directly related to the market’s predictability, the need for more accurate and sophisticated forecasting techniques arises. The academic literature is showing a growing interest in implementing non- linear techniques in a time series prediction. The paper goes through the process of creating a time series prediction model for OMX Vilnius stock index using artificial neural network approach. A multi layer perceptron model is applied in order to make periodical daily and monthly forecasts for both the actual index future value and the direction of the index. The neural network is trained using back-propagation method, several topologies are analyzed and the most suitable is selected. The method accuracy is compared to several traditional statistical methods (moving averages and linear regression)