IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v132y2020ics0960077919305168.html
   My bibliography  Save this article

A new LSTM based reversal point prediction method using upward/downward reversal point feature sets

Author

Listed:
  • U, JuHyok
  • Lu, PengYu
  • Kim, ChungSong
  • Ryu, UnSok
  • Pak, KyongSok

Abstract

A novel Long-Short Term Memory (LSTM)-based prediction model of stock price reversal point was proposed by using upward/downward reversal point feature sets. (1) Based on the combinations of candlestick indicators and technical indicators, 27 sets of feature candidates were constructed, and then the feature sets suitable to each stock in terms of URP/DRP prediction were respectively extracted. (2) LSTM-based URP/DRP predictors were constructed, the results of which are combined to improve the prediction accuracy. Using this model, reversal point prediction has been conducted for 10 Chinese stocks and 10 American stocks. In results, the mean prediction accuracy (F1) was 68.6% and 55.2% for the Chinese and the American stock markets, respectively. Results show that the average prediction accuracy has been evaluated to be higher for Chinese market by 13.4% compared to American one. Comparing with Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN) model, F1 of proposed model has been increased by 5.9%, 11.7% and 5.3%, respectively.

Suggested Citation

  • U, JuHyok & Lu, PengYu & Kim, ChungSong & Ryu, UnSok & Pak, KyongSok, 2020. "A new LSTM based reversal point prediction method using upward/downward reversal point feature sets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    • Handle: RePEc:eee:chsofr:v:132:y:2020:i:c:s0960077919305168
    DOI: 10.1016/j.chaos.2019.109559
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077919305168
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2019.109559?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
    2. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    3. Rajagopal, 2015. "Market Trend Analysis," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 4, pages 95-118, Palgrave Macmillan.
    4. Song, Yu & Akagi, Fumio, 2016. "Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock marketAuthor-Name: Qiu, Mingyue," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 1-7.
    5. Jensen, Michael C., 1978. "Some anomalous evidence regarding market efficiency," Journal of Financial Economics, Elsevier, vol. 6(2-3), pages 95-101.
    6. G. Caginalp & H. Laurent, 1998. "The predictive power of price patterns," Applied Mathematical Finance, Taylor & Francis Journals, vol. 5(3-4), pages 181-205.
    7. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    8. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
    9. Chen, Shi & Bao, Si & Zhou, Yu, 2016. "The predictive power of Japanese candlestick charting in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 148-165.
    10. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    11. Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.
    12. Lu, Tsung-Hsun, 2014. "The profitability of candlestick charting in the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 65-78.
    13. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    14. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    15. Zhu, Min & Atri, Said & Yegen, Eyub, 2016. "Are candlestick trading strategies effective in certain stocks with distinct features?," Pacific-Basin Finance Journal, Elsevier, vol. 37(C), pages 116-127.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Deng, Ziwei & Li, Yuxuan & Zhu, Hongqiu & Huang, Keke & Tang, Zhaohui & Wang, Zhen, 2020. "Sparse stacked autoencoder network for complex system monitoring with industrial applications," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    2. Yan, Wan-Lin, 2023. "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    3. Kırbaş, İsmail & Sözen, Adnan & Tuncer, Azim Doğuş & Kazancıoğlu, Fikret Şinasi, 2020. "Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Chen, Wei & Zhang, Haoyu & Jia, Lifen, 2022. "A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    5. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Arup Kumar Mohanty & Mazin Abed Mohammed & Alaa S. Al-Waisy & Seifedine Kadry & Jungeun Kim, 2022. "A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market," Mathematics, MDPI, vol. 10(19), pages 1-23, October.
    6. Fonseca, Carla L.G. & de Resende, Charlene C. & Fernandes, Danilo H.C. & Cardoso, Rodrigo T.N. & de Magalhães, A.R. Bosco, 2021. "Is the choice of the candlestick dimension relevant in econophysics?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    7. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Ho, Andrew Fu Wah & Liu, Nan & Ong, Marcus Eng Hock & Cheong, Kang Hao, 2022. "A deep learning architecture for forecasting daily emergency department visits with acuity levels," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    2. Ehsan Hoseinzade & Saman Haratizadeh & Arash Khoeini, 2019. "U-CNNpred: A Universal CNN-based Predictor for Stock Markets," Papers 1911.12540, arXiv.org.
    3. Alhashel, Bader S. & Almudhaf, Fahad W. & Hansz, J. Andrew, 2018. "Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 92-108.
    4. Pereira, Pedro L. Valls, 2009. "Ombro-cabeça-ombro: testando a lucratividade do padrão gráfico de análise técnica no mercado de ações brasileiro," Textos para discussão 181, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    5. Chen, Shi & Bao, Si & Zhou, Yu, 2016. "The predictive power of Japanese candlestick charting in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 148-165.
    6. Lubnau, Thorben, 2014. "Spread trading strategies in the crude oil futures market," Discussion Papers 353, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    7. Piyapas Tharavanij & Vasan Siraprapasiri & Kittichai Rajchamaha, 2017. "Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand," SAGE Open, , vol. 7(4), pages 21582440177, October.
    8. Zhu, Min & Atri, Said & Yegen, Eyub, 2016. "Are candlestick trading strategies effective in certain stocks with distinct features?," Pacific-Basin Finance Journal, Elsevier, vol. 37(C), pages 116-127.
    9. Ni, Yensen & Cheng, Yirung & Huang, Paoyu & Day, Min-Yuh, 2018. "Trading strategies in terms of continuous rising (falling) prices or continuous bullish (bearish) candlesticks emitted," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 188-204.
    10. Michael McAleer & John Suen & Wing Keung Wong, 2016. "Profiteering from the Dot-Com Bubble, Subprime Crisis and Asian Financial Crisis," The Japanese Economic Review, Japanese Economic Association, vol. 67(3), pages 257-279, September.
    11. Matheus José Silva de Souza & Danilo Guimarães Franco Ramos & Marina Garcia Pena & Vinicius Amorim Sobreiro & Herbert Kimura, 2018. "Examination of the profitability of technical analysis based on moving average strategies in BRICS," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-18, December.
    12. Taylor, Nick, 2014. "The rise and fall of technical trading rule success," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 286-302.
    13. Shangkun Deng & Zhihao Su & Yanmei Ren & Haoran Yu & Yingke Zhu & Chenyang Wei, 2022. "Can Japanese Candlestick Patterns be Profitable on the Component Stocks of the SSE50 Index?," SAGE Open, , vol. 12(3), pages 21582440221, August.
    14. Horton, Marshall J., 2009. "Stars, crows, and doji: The use of candlesticks in stock selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 283-294, May.
    15. Chiang, Mi-Hsiu & Chiu, Hsin-Yu & Kuo, Wei-Yu, 2021. "Predictive ability of similarity-based futures trading strategies," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    16. Neely, Christopher J., 2003. "Risk-adjusted, ex ante, optimal technical trading rules in equity markets," International Review of Economics & Finance, Elsevier, vol. 12(1), pages 69-87.
    17. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    18. Cajueiro, Daniel O. & Tabak, Benjamin M., 2006. "Testing for predictability in equity returns for European transition markets," Economic Systems, Elsevier, vol. 30(1), pages 56-78, March.
    19. Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    20. Yaohu Lin & Shancun Liu & Haijun Yang & Harris Wu & Bingbing Jiang, 2021. "Improving stock trading decisions based on pattern recognition using machine learning technology," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-25, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:132:y:2020:i:c:s0960077919305168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.