Rank Prediction for Portfolio Management Using Artificial Neural Networks

Jiyoon Bae; Ghudae Sim; Hyungbin Yun; Junhee Seok

doi:10.1109/ICUFN.2018.8436983

Rank Prediction for Portfolio Management Using Artificial Neural Networks

Jiyoon Bae, Ghudae Sim, Hyungbin Yun, Junhee Seok

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	15-17
Number of pages	3
ISBN (Print)	9781538646465
DOIs	https://doi.org/10.1109/ICUFN.2018.8436983
Publication status	Published - 2018 Aug 14
Event	10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic Duration: 2018 Jul 3 → 2018 Jul 6

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2018-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Other

Other	10th International Conference on Ubiquitous and Future Networks, ICUFN 2018
Country/Territory	Czech Republic
City	Prague
Period	18/7/3 → 18/7/6

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea grant (NRF-2017R1C1B2002850) and a grant from Mirae Asset Global Investments Co. Ltd. Correspondence should be addressed [email protected] By using a modified version of a previously proposed approach using vocal tract length perturbation (VTLP) and a novel data augmentation approach based on stochastic feature mapping (SFM) in a speaker adaptive feature space, Cui showed that the proposed method can improve recognition performance by using both cross-entropy (CE) and state-level minimum Bayes risk (sMBR) as the cost function of ANN models. Hellstrom [6] proposed rank measure method taking into account a large number of securities and grades them according to the relative returns. Hellstrom showed that rank measure, besides being more related to a real trading situation, can be more predictable than the individual returns. As the rank prediction is easier than numeric prediction, in field of finance, to be specific, quantitative analysis, for overcoming existing algorithm, it is expected to be a useful method to apply results of rank prediction to predict profit.

Publisher Copyright:
© 2018 IEEE.

Keywords

arfificial neural network
portfolio management
stock market prediction

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

Access to Document

10.1109/ICUFN.2018.8436983

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 (pp. 15-17). Article 8436983 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2018-July). 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 et al.
ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2018. p. 15-17 8436983 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference 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., 8436983, International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2018-July, 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

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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.",

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author = "Jiyoon Bae and Ghudae Sim and Hyungbin Yun and Junhee Seok",

note = "Funding Information: This work was supported by the National Research Foundation of Korea grant (NRF-2017R1C1B2002850) and a grant from Mirae Asset Global Investments Co. Ltd. Correspondence should be addressed [email protected] By using a modified version of a previously proposed approach using vocal tract length perturbation (VTLP) and a novel data augmentation approach based on stochastic feature mapping (SFM) in a speaker adaptive feature space, Cui showed that the proposed method can improve recognition performance by using both cross-entropy (CE) and state-level minimum Bayes risk (sMBR) as the cost function of ANN models. Hellstrom [6] proposed rank measure method taking into account a large number of securities and grades them according to the relative returns. Hellstrom showed that rank measure, besides being more related to a real trading situation, can be more predictable than the individual returns. As the rank prediction is easier than numeric prediction, in field of finance, to be specific, quantitative analysis, for overcoming existing algorithm, it is expected to be a useful method to apply results of rank prediction to predict profit. Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 ; Conference date: 03-07-2018 Through 06-07-2018",

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N1 - Funding Information: This work was supported by the National Research Foundation of Korea grant (NRF-2017R1C1B2002850) and a grant from Mirae Asset Global Investments Co. Ltd. Correspondence should be addressed [email protected] By using a modified version of a previously proposed approach using vocal tract length perturbation (VTLP) and a novel data augmentation approach based on stochastic feature mapping (SFM) in a speaker adaptive feature space, Cui showed that the proposed method can improve recognition performance by using both cross-entropy (CE) and state-level minimum Bayes risk (sMBR) as the cost function of ANN models. Hellstrom [6] proposed rank measure method taking into account a large number of securities and grades them according to the relative returns. Hellstrom showed that rank measure, besides being more related to a real trading situation, can be more predictable than the individual returns. As the rank prediction is easier than numeric prediction, in field of finance, to be specific, quantitative analysis, for overcoming existing algorithm, it is expected to be a useful method to apply results of rank prediction to predict profit. Publisher Copyright: © 2018 IEEE.

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Rank Prediction for Portfolio Management Using Artificial Neural Networks

Abstract

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Keywords

ASJC Scopus subject areas

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