Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Portfolio management via two-stage deep learning with a joint cost

Authors
Yun, HyungbinLee, MinhyeokKang, Yeong SeonSeok, Junhee
Issue Date
1-4월-2020
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Deep learning; Long short-term memory; Portfolio management; Joint cost function
Citation
EXPERT SYSTEMS WITH APPLICATIONS, v.143
Indexed
SCIE
SCOPUS
Journal Title
EXPERT SYSTEMS WITH APPLICATIONS
Volume
143
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/56678
DOI
10.1016/j.eswa.2019.113041
ISSN
0957-4174
Abstract
Portfolio management is a series of processes that maximize returns and minimize risk by allocating assets efficiently. Along with the developments in machine learning technology, it has been studied to apply machine learning methods to prediction-based portfolio management. However, such methods have a few limitations. First, they do not consider the relations between assets for the prediction. In addition, the studies commonly focus on the prediction accuracy, neglecting the construction of portfolios. Furthermore, the methods have usually been evaluated with index data, which hardly represent actual prices to buy or sell an asset. To overcome these problems, Exchange Traded Funds (ETFs) are employed for base assets for the evaluation, and we propose a two-stage deep learning framework, called Grouped-ETFs Model (GEM), with a joint cost function. The GEM is designed to learn the features of inter-asset and groups in each stage. Also, the proposed joint cost can consider relative returns for the training while the relative returns are a crucial factor to construct a portfolio. The results of a rigorous evaluation with global ETF data indicate that the proposed GEM with the joint cost outperforms the equally weighted portfolio and the ordinary deep learning model by 33.7% and 30.1%, respectively. An additional experiment using sector ETFs verifies the generality of the proposed model where the results accord with those of the previous experiment. (C) 2019 Elsevier Ltd. All rights reserved.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > School of Electrical Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher SEOK, Jun hee photo

SEOK, Jun hee
공과대학 (전기전자공학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE