Naslov
The Effect of Feature Selection on the Performance of Long Short-Therm Memory Neural Network in Stock Market Predictions

Autori
Botunac, Ive ; Pabjkota, Ante ; Matetić, Maja

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), ostalo

ISBN
978-3-902734-29-7

Skup
31st International DAAAM Virtual Symposium "Intelligent Manufacturing & Automation"

Mjesto i datum
Mostar, Bosna i Hercegovina ; online, 21.10.2020. - 24.10.2020

Vrsta sudjelovanja
Demonstracija

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
stock market ; machine learning ; feature selection ; neural network ; LSTM

Sažetak
Stock market predictions are a difficult and challenging task affected by numerous interrelated economic, political and social factors caused by non-linear and often unstable movements. Precisely due to the stated nature of financial time series, there is a need to develop advanced systems for stock market prediction. This research seeks to solve one of the problems of such systems, which is reflected in the selection of features to improve the performance of models that are an integral part of the system. In the paper, the wrapper method - recursive feature elimination and the filter method - feature importance, are used for feature selection. A forecasting model based on the long short-term memory (LSTM) neural network was defined to predict the movement of the stock's closing price. With this research we can conclude that for each selected stock there are certain features that have an impact on the results and that it is therefore necessary to carry out the selection of features individually.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti, Interdisciplinarne društvene znanosti

POVEZANOST RADA