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
    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

    Publication series

    NameInternational Conference on Ubiquitous and Future Networks, ICUFN
    Volume2018-July
    ISSN (Print)2165-8528
    ISSN (Electronic)2165-8536

    Other

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

    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

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