IDEAS home Printed from https://ideas.repec.org/a/eco/journ1/2020-01-8.html
   My bibliography  Save this article

Investment Performance of Machine Learning: Analysis of S&P 500 Index

Author

Listed:
  • Chia-Cheng Chen

    (Department of Finance, Ling Tung University of Science and Technology, Taichung, Taiwan)

  • Chun-Hung Chen

    (Department of Finance, National Yunlin University of Science and Technology, Doulium, Yunlin County, Taiwan,)

  • Ting-Yin Liu

    (Department of Business Affairs, Mingdao High School, Taichung, Taiwan.)

Abstract

This study aims to explore the prediction of S&P 500 stock price movement and conduct an analysis of its investment performance. Based on the S&P 500 index, the study compares three machine learning models: ANN, SVM, and Random Forest. With a performance evaluation of S&P 500 index historical data spanning from 2014 to 2018, we find: (1) By overall performance measures, machine learning models outperform benchmark market index. (2) By risk-adjusted measures, the empirical results suggest that Random Forest generates the best performance, followed by SVM and ANN.

Suggested Citation

  • Chia-Cheng Chen & Chun-Hung Chen & Ting-Yin Liu, 2020. "Investment Performance of Machine Learning: Analysis of S&P 500 Index," International Journal of Economics and Financial Issues, Econjournals, vol. 10(1), pages 59-66.
    • Handle: RePEc:eco:journ1:2020-01-8
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijefi/article/download/8925/pdf
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijefi/article/view/8925/pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, May.
    2. Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019. "The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests," Finance Research Letters, Elsevier, vol. 29(C), pages 315-322.
    3. Grinblatt, Mark & Titman, Sheridan, 1992. "The Persistence of Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 47(5), pages 1977-1984, December.
    4. Grinblatt, Mark & Titman, Sheridan, 1987. "The Relation between Mean-Variance Efficiency and Arbitrage Pricing," The Journal of Business, University of Chicago Press, vol. 60(1), pages 97-112, January.
    5. 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.
    6. Ciner, Cetin, 2019. "Do industry returns predict the stock market? A reprise using the random forest," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 152-158.
    7. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    8. Treynor, Jack L & Black, Fischer, 1973. "How to Use Security Analysis to Improve Portfolio Selection," The Journal of Business, University of Chicago Press, vol. 46(1), pages 66-86, January.
    9. 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.
    10. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    11. Chin-Sheng Huang & Yi-Sheng Liu, 2019. "Machine Learning on Stock Price Movement Forecast: The Sample of the Taiwan Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 189-201.
    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. Sharmin Islam & Md. Shakil Sikder & Md. Farhad Hossain & Partha Chakraborty, 2021. "Predicting the daily closing price of selected shares on the Dhaka Stock Exchange using machine learning techniques," SN Business & Economics, Springer, vol. 1(4), pages 1-16, April.

    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. Chia-Cheng Chen & Yisheng Liu & Ting-Hsin Hsu, 2019. "An Analysis on Investment Performance of Machine Learning: An Empirical Examination on Taiwan Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 1-10.
    2. Chin-Sheng Huang & Yi-Sheng Liu, 2019. "Machine Learning on Stock Price Movement Forecast: The Sample of the Taiwan Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 189-201.
    3. Butt, Prof. Khursheed A & Pandow, Bilal Ahmad, 2013. "An analysis into the Stock Selectivity skill of Indian Fund Managers," MPRA Paper 83500, University Library of Munich, Germany, revised 2013.
    4. Roberto Casarin & Andrea Piva & Loriana Pelizzon, 2008. "Italian Equity Funds: Efficiency and Performance Persistence," The IUP Journal of Financial Economics, IUP Publications, vol. 0(1), pages 7-28, March.
    5. Trabelsi, Mohamed Ali, 2010. "Choix de portefeuille: comparaison des différentes stratégies [Portfolio selection: comparison of different strategies]," MPRA Paper 82946, University Library of Munich, Germany, revised 01 Dec 2010.
    6. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    7. Feng, Xunan & Johansson, Anders C., 2015. "Can mutual funds pick stocks in China? Evidence from the IPO market," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 170-186.
    8. Elton, Edwin J. & Gruber, Martin J., 2013. "Mutual Funds," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1011-1061, Elsevier.
    9. Muhammad Asad Ali & Muhammad Aqil & Syed Hasnain Alam Kazmi & Syed Imran Zaman, 2023. "Evaluation of risk adjusted performance of mutual funds in an emerging market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1436-1449, April.
    10. Yundan Guo & Li Shen, 2023. "Commercial Retirement FOFs in China: Investment and Persistence Performance Analysis," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    11. Trabelsi, Mohamed Ali, 2008. "Peut-on encore parler des mesures de performance ? [One is able again to speak of performance measures?]," MPRA Paper 25443, University Library of Munich, Germany.
    12. Elton, Edwin J. & Gruber, Martin J., 1997. "Modern portfolio theory, 1950 to date," Journal of Banking & Finance, Elsevier, vol. 21(11-12), pages 1743-1759, December.
    13. Fernando Rubio, 2005. "Eficiencia De Mercado, Administracion De Carteras De Fondos Y Behavioural Finance," Finance 0503028, University Library of Munich, Germany, revised 23 Jul 2005.
    14. Dariusz Filip & Tomasz Rogala, 2021. "Analysis of Polish mutual funds performance: a Markovian approach," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 115-130, March.
    15. Filip Dariusz & Rogala Tomasz, 2021. "Analysis of Polish mutual funds performance: a Markovian approach," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 115-130, March.
    16. Hanna, J. Douglas & Ready, Mark J., 2005. "Profitable predictability in the cross section of stock returns," Journal of Financial Economics, Elsevier, vol. 78(3), pages 463-505, December.
    17. Annaert, Jan & van den Broeck, Julien & Vander Vennet, Rudi, 2003. "Determinants of mutual fund underperformance: A Bayesian stochastic frontier approach," European Journal of Operational Research, Elsevier, vol. 151(3), pages 617-632, December.
    18. Lehmann, Bruce & Timmermann, Allan, 2007. "Performance measurement and evaluation," LSE Research Online Documents on Economics 24505, London School of Economics and Political Science, LSE Library.
    19. David M. Ritzwoller & Joseph P. Romano, 2019. "Uncertainty in the Hot Hand Fallacy: Detecting Streaky Alternatives to Random Bernoulli Sequences," Papers 1908.01406, arXiv.org, revised Apr 2021.
    20. Christiane Goodfellow & Dirk Schiereck & Steffen Wippler, 2013. "Are behavioural finance equity funds a superior investment? A note on fund performance and market efficiency," Journal of Asset Management, Palgrave Macmillan, vol. 14(2), pages 111-119, April.

    More about this item

    Keywords

    ANN; SVM; Random Forest; Machine Learning; Investment Performance;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    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:eco:journ1:2020-01-8. 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: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    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.