Open AccessFINANCIAL MARKET PREDICTION SYSTEM WITH EVOLINO NEURAL NETWORK AND DELPHI METHOD
Author(s) -
Nijolė Maknickienė,
Algirdas Maknickas
Publication year - 2013
Publication title -
journal of business economics and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.485
H-Index - 37
eISSN - 1611-1699
pISSN - 2029-4433
DOI - 10.3846/16111699.2012.729532
Subject(s) - artificial neural network , computer science , reliability (semiconductor) , portfolio , delphi , process (computing) , financial market , delphi method , investment (military) , simple (philosophy) , finance , artificial intelligence , econometrics , economics , power (physics) , physics , quantum mechanics , politics , political science , law , operating system , philosophy , epistemology
Use of artificial intelligence systems in forecasting financial markets requires a reliable and simple model that would ensure profitable growth. The model presented in the paper combines Evolino recurrent neural networks with orthogonal data inputs and the Delphi expert evaluation method for its investment portfolio decision making process. A statistical study demonstrates the reliability of the model and describes its accuracy. Capabilities of the model are demonstrated using a trading simulation.