Rank Prediction for Portfolio Management Using Artificial Neural Networks

Jiyoon Bae, Ghudae Sim, Hyungbin Yun, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The rank of equities is often used to determine the investment portfolio instead of prices because ranking is in general believed to be robust. In this paper, we propose a rank prediction method for portfolio management using ANN. While an ANN requires a large dataset to train the model, the sample size is usually insufficient in stock market data. Therefore, the proposed method uses data augmentation and an ensemble ANN model. In the simulation study, the proposed method shows 13 percentage of performance improvement from the other methods to predict the profit rank of equities in South-East Asian market.

Original languageEnglish
Title of host publicationICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages15-17
Number of pages3
Volume2018-July
ISBN (Print)9781538646465
DOIs
Publication statusPublished - 2018 Aug 14
Event10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic
Duration: 2018 Jul 32018 Jul 6

Other

Other10th International Conference on Ubiquitous and Future Networks, ICUFN 2018
CountryCzech Republic
CityPrague
Period18/7/318/7/6

Fingerprint

Neural networks
Profitability
Financial markets

Keywords

  • arfificial neural network
  • portfolio management
  • stock market prediction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

Cite this

Bae, J., Sim, G., Yun, H., & Seok, J. (2018). Rank Prediction for Portfolio Management Using Artificial Neural Networks. In ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks (Vol. 2018-July, pp. 15-17). [8436983] IEEE Computer Society. https://doi.org/10.1109/ICUFN.2018.8436983

Rank Prediction for Portfolio Management Using Artificial Neural Networks. / Bae, Jiyoon; Sim, Ghudae; Yun, Hyungbin; Seok, Junhee.

ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. Vol. 2018-July IEEE Computer Society, 2018. p. 15-17 8436983.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bae, J, Sim, G, Yun, H & Seok, J 2018, Rank Prediction for Portfolio Management Using Artificial Neural Networks. in ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. vol. 2018-July, 8436983, IEEE Computer Society, pp. 15-17, 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018, Prague, Czech Republic, 18/7/3. https://doi.org/10.1109/ICUFN.2018.8436983
Bae J, Sim G, Yun H, Seok J. Rank Prediction for Portfolio Management Using Artificial Neural Networks. In ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. Vol. 2018-July. IEEE Computer Society. 2018. p. 15-17. 8436983 https://doi.org/10.1109/ICUFN.2018.8436983
Bae, Jiyoon ; Sim, Ghudae ; Yun, Hyungbin ; Seok, Junhee. / Rank Prediction for Portfolio Management Using Artificial Neural Networks. ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. Vol. 2018-July IEEE Computer Society, 2018. pp. 15-17
@inproceedings{89cb89802bdc42f8bfe5bfdcdc7f1f28,
title = "Rank Prediction for Portfolio Management Using Artificial Neural Networks",
abstract = "The rank of equities is often used to determine the investment portfolio instead of prices because ranking is in general believed to be robust. In this paper, we propose a rank prediction method for portfolio management using ANN. While an ANN requires a large dataset to train the model, the sample size is usually insufficient in stock market data. Therefore, the proposed method uses data augmentation and an ensemble ANN model. In the simulation study, the proposed method shows 13 percentage of performance improvement from the other methods to predict the profit rank of equities in South-East Asian market.",
keywords = "arfificial neural network, portfolio management, stock market prediction",
author = "Jiyoon Bae and Ghudae Sim and Hyungbin Yun and Junhee Seok",
year = "2018",
month = "8",
day = "14",
doi = "10.1109/ICUFN.2018.8436983",
language = "English",
isbn = "9781538646465",
volume = "2018-July",
pages = "15--17",
booktitle = "ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Rank Prediction for Portfolio Management Using Artificial Neural Networks

AU - Bae, Jiyoon

AU - Sim, Ghudae

AU - Yun, Hyungbin

AU - Seok, Junhee

PY - 2018/8/14

Y1 - 2018/8/14

N2 - The rank of equities is often used to determine the investment portfolio instead of prices because ranking is in general believed to be robust. In this paper, we propose a rank prediction method for portfolio management using ANN. While an ANN requires a large dataset to train the model, the sample size is usually insufficient in stock market data. Therefore, the proposed method uses data augmentation and an ensemble ANN model. In the simulation study, the proposed method shows 13 percentage of performance improvement from the other methods to predict the profit rank of equities in South-East Asian market.

AB - The rank of equities is often used to determine the investment portfolio instead of prices because ranking is in general believed to be robust. In this paper, we propose a rank prediction method for portfolio management using ANN. While an ANN requires a large dataset to train the model, the sample size is usually insufficient in stock market data. Therefore, the proposed method uses data augmentation and an ensemble ANN model. In the simulation study, the proposed method shows 13 percentage of performance improvement from the other methods to predict the profit rank of equities in South-East Asian market.

KW - arfificial neural network

KW - portfolio management

KW - stock market prediction

UR - http://www.scopus.com/inward/record.url?scp=85052537937&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052537937&partnerID=8YFLogxK

U2 - 10.1109/ICUFN.2018.8436983

DO - 10.1109/ICUFN.2018.8436983

M3 - Conference contribution

AN - SCOPUS:85052537937

SN - 9781538646465

VL - 2018-July

SP - 15

EP - 17

BT - ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks

PB - IEEE Computer Society

ER -