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http://dx.doi.org/10.9723/jksiis.2019.24.1.023

Portfolio System Using Deep Learning  

Kim, SungSoo (숭실대학교 글로벌미디어학부)
Kim, Jong-In (숭실대학교 글로벌미디어학부, (주)Fait)
Jung, Keechul (숭실대학교 글로벌미디어학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.24, no.1, 2019 , pp. 23-30 More about this Journal
Abstract
As deep learning with the network-based algorithms evolve, artificial intelligence is rapidly growing around the world. Among them, finance is expected to be the field where artificial intelligence is most used, and many studies have been done recently. The existing financial strategy using deep-run is vulnerable to volatility because it focuses on stock price forecasts for a single stock. Therefore, this study proposes to construct ETF products constructed through portfolio methods by calculating the stocks constituting funds by using deep learning. We analyze the performance of the proposed model in the KOSPI 100 index. Experimental results showed that the proposed model showed improved results in terms of returns or volatility.
Keywords
Portfolio; Deep Learning; Autoencoder; ETF;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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1 Shim, H. and Kim, K., "A Business Model Using Artificial Intelligence (AI) of Korean Companies," KITA, TRADE FOCUS, Vol. 2, No. 97, 2018.
2 "Status of Machine Learning at Home and Abroad in the Field of Finance," FSI, 2017.
3 KEB Hana Bank, "2018 Korean Roboadvidser Report," KEB Hana Bank HAI Robo Center, 2018.
4 Hwang, R., Kim, S., Lee, D., and Nam D., "A Directional Distance Function Approach on the Efficiency of Chinese Commercial Banks," Journal of the Korea Industrial Information Systems Research, Vol. 17, No. 2, pp. 81-94, 2017.   DOI
5 Ban, J., Kim, M., and Jeon, Y., "Search Frequency in Internet Portal Site and the Expected Stock Returns," Journal of the Korea Industrial Information Systems Research, Vol. 21, No. 5, pp. 73-83, 2016.   DOI
6 Ryu, J., Shin, H., "Portfolio Selection Strategy Using Deep Learning," Journal of Information Technology and Architecture, Vol. 15, No. 1, pp. 43-50, 2018.   DOI
7 Kim H., Kim K., and Jeong D., "A Study on the Price Determination of KOSPI 200 Futures using Artificial Neural Network Model," Korea Insurance Research Institute, Insurance Financial Research, Vol. 13, No. 3, pp. 155-176, 2003.
8 Kim, S. and Ahn, H., "Development of an Intelligent Trading System Using Support Vector Machines and Genetic Algorithms," Journal of Intelligence and Information System, Vol. 16, No. 1, pp. 71-92, 2010.
9 Lee, W., "A Deep Learning Analysis of the KOSPI's Directions," Journal of the Korean Data and Information Science Society, Vol. 28, No. 2, pp. 287-295, 2017.   DOI
10 Kim, Y., Shin, E., and Hong, T., "Comparison of Stock Price Index Prediction Performance Using Neural Networks and Support Vector Machine," The Journal of Internet Electronic Commerce Resarch, Vol. 4, No. 3, p p. 221-243, 2004.
11 Joo, I., Choi, S., "Stock Prediction Model based on Bidirectional LSTM Recurrent Neural Network," Journal of Korea Institute of Information, Electronics, and Communication Technology, Vol. 11, No. 2, pp. 204-208, 2018.   DOI
12 Shin, D., Choi, K., and Kim, C., "Deep Learning Model for Prediction Rate Improvement of Stock Price Using RNN and LSTM," Journal of Advanced Information Technology and Convergence, Vol. 15, No. 10, pp. 9-16, 2017.
13 Kim, S., Hong, K., "Development and Performance Analysis of Predictive Model for KOSPI 200 Index using Recurrent Neural Networks," Journal of the Korea Industrial Information Systems Research, Vol. 22, No. 6, pp. 23-29, 2017.   DOI
14 Heaton, J. B., Polson, N. G., and Witte, J. H., "Deep Portfolio Theory," University of Chicago, 2016.