Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/ibn/jmrjnl/v10y2018i5p9.html
   My bibliography  Save this article

Multi-factor Stock Selection Model Based on Kernel Support Vector Machine

Author

Listed:
  • Ru Zhang
  • Zi-ang Lin
  • Shaozhen Chen
  • Zhixuan Lin
  • Xingwei Liang
Abstract
In recent years, the combination of machine learning method and traditional financial investment field has become a hotspot in academic and industry. This paper takes CSI 300 and CSI 500 stocks as the research objects. First, this paper carries out kernel function test and parameter optimization for the kernel support vector machine system, and then predict and optimize the combination of market-neutral stock selection strategy and stock right strategy. The results of the experiment show that the multi-factor model based on SVM has a strong predictive power for the selection of stock, and it has a difference in the predictive power of different nuclear functions.

Suggested Citation

  • Ru Zhang & Zi-ang Lin & Shaozhen Chen & Zhixuan Lin & Xingwei Liang, 2018. "Multi-factor Stock Selection Model Based on Kernel Support Vector Machine," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 10(5), pages 9-18, October.
    • Handle: RePEc:ibn:jmrjnl:v:10:y:2018:i:5:p:9
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/jmr/article/download/0/0/35948/37121
    Download Restriction: no
    File URL: http://www.ccsenet.org/journal/index.php/jmr/article/view/76203
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Nai-fu & Zhang, Feng, 1998. "Risk and Return of Value Stocks," The Journal of Business, University of Chicago Press, vol. 71(4), pages 501-535, October.
    2. 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.
    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. Ganggang Guo & Yulei Rao & Feida Zhu & Fang Xu, 2020. "Innovative deep matching algorithm for stock portfolio selection using deep stock profiles," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.
    2. Shuang Zhang & Xingdong Feng, 2022. "Distributed identification of heterogeneous treatment effects," Computational Statistics, Springer, vol. 37(1), pages 57-89, March.
    3. Zeynep Cipiloglu Yildiz & Selim Baha Yildiz, 2022. "A portfolio construction framework using LSTM‐based stock markets forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2356-2366, April.
    4. Jujie Wang & Zhenzhen Zhuang & Liu Feng, 2022. "Intelligent Optimization Based Multi-Factor Deep Learning Stock Selection Model and Quantitative Trading Strategy," Mathematics, MDPI, vol. 10(4), pages 1-19, February.

    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. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    2. Ru Zhang & Tong Cao, 2018. "Multi-factor Stock Selection Model Based on Adaboost," Business and Economic Research, Macrothink Institute, vol. 8(4), pages 119-129, December.
    3. Ru Zhang & Chenyu Huang & Weijian Zhang & Shaozhen Chen, 2018. "Multi Factor Stock Selection Model Based on LSTM," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(8), pages 1-36, August.
    4. Lilian de Castro Medeiros & Aureliano Angel Bressan, 2015. "Value Premium and Country Risk as Dimensions to Estimate Conditional Returns: a Study of the Brazilian Market," Brazilian Business Review, Fucape Business School, vol. 12(3), pages 67-90, May.
    5. Federico Gagliolo & Gabriele Cardullo, 2020. "Value Stocks and Growth Stocks: A Study of the Italian Market," International Journal of Economics and Financial Issues, Econjournals, vol. 10(3), pages 7-15.
    6. Merkle, Christoph & Sextroh, Christoph J., 2021. "Value and momentum from investors’ perspective: Evidence from professionals’ risk-ratings," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 159-178.
    7. Maria Elisabete Neves & Mário Abreu Pinto & Carla Manuela de Assunção Fernandes & Elisabete Fátima Simões Vieira, 2021. "Value and growth stock returns: international evidence (JES)," International Journal of Accounting & Information Management, Emerald Group Publishing Limited, vol. 29(5), pages 698-733, October.
    8. 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.
    9. Shazia Ghani, 2011. "A re-visit to Minsky after 2007 financial meltdown," Post-Print halshs-01027435, HAL.
    10. Steininger, Lea & Hesse, Casimir, 2024. "Buying into new ideas: The ECB’s evolving justification of unlimited liquidity," Department of Economics Working Paper Series 357, WU Vienna University of Economics and Business.
    11. 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.
    12. Cagli, Efe Caglar & Taskin, Dilvin & Evrim Mandaci, Pınar, 2019. "The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models," Energy Economics, Elsevier, vol. 84(C).
    13. Andrew Weinbach & Rodney J. Paul, 2009. "National television coverage and the behavioural bias of bettors: the American college football totals market," International Gambling Studies, Taylor & Francis Journals, vol. 9(1), pages 55-66, April.
    14. Plantinga, Andrew J. & Provencher, Bill, 2001. "Internal Consistency In Models Of Optimal Resource Use Under Uncertainty," 2001 Annual meeting, August 5-8, Chicago, IL 20712, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    15. Growitsch Christian & Nepal Rabindra & Stronzik Marcus, 2015. "Price Convergence and Information Efficiency in German Natural Gas Markets," German Economic Review, De Gruyter, vol. 16(1), pages 87-103, February.
    16. Oxelheim, Lars & Rafferty, Michael, 2005. "On the static efficiency of secondary bond markets," Journal of Multinational Financial Management, Elsevier, vol. 15(2), pages 117-135, April.
    17. Baoqiang Zhan & Shu Zhang & Helen S. Du & Xiaoguang Yang, 2022. "Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 861-882, October.
    18. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    19. Gaio, Luiz Eduardo & Stefanelli, Nelson Oliveira & Pimenta, Tabajara & Bonacim, Carlos Alberto Grespan & Gatsios, Rafael Confetti, 2022. "The impact of the Russia-Ukraine conflict on market efficiency: Evidence for the developed stock market," Finance Research Letters, Elsevier, vol. 50(C).
    20. Anastasios Evgenidis & Stephanos Papadamou, 2021. "The impact of unconventional monetary policy in the euro area. Structural and scenario analysis from a Bayesian VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5684-5703, October.

    More about this item

    Keywords

    multi-factor model; support vector machine; quantitative investment;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:ibn:jmrjnl:v:10:y:2018:i:5:p:9. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.