Modeling Financial Time Series with Artificial Neural Networks


Show simple item record Wong, Charles en_US Versace, Massimiliano en_US 2011-11-14T18:17:12Z 2011-11-14T18:17:12Z 2009-12-15 en_US
dc.description.abstract Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis. en_US
dc.description.sponsorship CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001) en_US
dc.language.iso en_US en_US
dc.publisher Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems en_US
dc.relation.ispartofseries BU CAS/CNS Technical Reports;CAS/CNS-TR-2009-012 en_US
dc.rights Copyright 2009 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission. en_US
dc.title Modeling Financial Time Series with Artificial Neural Networks en_US
dc.type Technical Report en_US
dc.rights.holder Boston University Trustees en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search OpenBU


Deposit Materials