IDEAS home Printed from https://ideas.repec.org/a/jfr/bmr111/v5y2016i3p26-29.html
   My bibliography  Save this article

Study on Stock Selection Strategy Based on SPSS Regression Mean Quantization

Author

Listed:
  • Wenzhong Fan

Abstract

In the stock market, the main three kinds of state, rise, decline and the range of volatility, in the technical analysis indicators are MACD, KDJ, and other combinations. In the specific investment, technical analysis often has limitations such as delay or delay. In this paper, through the analysis of the various indicators of the combination, a new focus on the filter method, selection of different industries, a comprehensive comparison of the effectiveness of indicators. Based on the 30 day moving average, the extreme values of different technical indicators are selected, and the corresponding effective indexes are selected for clustering analysis.

Suggested Citation

  • Wenzhong Fan, 2016. "Study on Stock Selection Strategy Based on SPSS Regression Mean Quantization," Business and Management Research, Business and Management Research, Sciedu Press, vol. 5(3), pages 26-29, September.
    • Handle: RePEc:jfr:bmr111:v:5:y:2016:i:3:p:26-29
    as

    Download full text from publisher

    File URL: http://www.sciedupress.com/journal/index.php/bmr/article/download/10225/6223
    Download Restriction: no
    File URL: http://www.sciedupress.com/journal/index.php/bmr/article/view/10225
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arash Riasi & Deshen Wang, 2016. "Comparing the Performance of Different Data Mining Techniques in Evaluating Loan Applications," International Business Research, Canadian Center of Science and Education, vol. 9(7), pages 164-187, July.
    2. Chou, Ray Yeutien, 2005. "Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 561-582, June.
    Full references (including those not matched with items on IDEAS)

    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. Hallin, Marc & La Vecchia, Davide, 2020. "A Simple R-estimation method for semiparametric duration models," Journal of Econometrics, Elsevier, vol. 218(2), pages 736-749.
    2. Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
    3. Arısoy, Yakup Eser & Altay-Salih, Aslıhan & Akdeniz, Levent, 2015. "Aggregate volatility expectations and threshold CAPM," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 231-253.
    4. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Papers 1604.01338, arXiv.org.
    5. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    6. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    7. Vladimir Tsenkov, 2009. "Financial Markets Modelling," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 87-96.
    8. Harris, Richard D.F. & Yilmaz, Fatih, 2010. "Estimation of the conditional variance-covariance matrix of returns using the intraday range," International Journal of Forecasting, Elsevier, vol. 26(1), pages 180-194, January.
    9. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
    10. S. Bhaumik & M. Karanasos & A. Kartsaklas, 2008. "Derivatives Trading and the Volume-Volatility Link in the Indian Stock Market," William Davidson Institute Working Papers Series wp935, William Davidson Institute at the University of Michigan.
    11. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.
    12. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2006. "Vector Multiplicative Error Models: Representation and Inference," NBER Technical Working Papers 0331, National Bureau of Economic Research, Inc.
    13. repec:wyi:journl:002120 is not listed on IDEAS
    14. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti & Aris Kartsaklas, 2021. "Investors' trading behaviour and stock market volatility during crisis periods: A dual long‐memory model for the Korean Stock Exchange," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4441-4461, July.
    15. Ahmed, Walid M.A., 2017. "The impact of foreign equity flows on market volatility during politically tranquil and turbulent times: The Egyptian experience," Research in International Business and Finance, Elsevier, vol. 40(C), pages 61-77.
    16. Chan Jennifer So Kuen & Ng Kok-Haur & Nitithumbundit Thanakorn & Peiris Shelton, 2019. "Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-22, April.
    17. Ai Han & Yanan He & Yongmiao Hong & Shouyang Wang, 2013. "Forecasting Interval-valued Crude Oil Prices via Autoregressive Conditional Interval Models," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    18. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
    19. Min-Hsien Chiang & Ray Yeutien Chou & Li-Min Wang, 2016. "Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 126-144, February.
    20. Yin-Wong Cheung, 2007. "An empirical model of daily highs and lows," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 1-20.
    21. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.

    More about this item

    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:jfr:bmr111:v:5:y:2016:i:3:p:26-29. 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: Simon Lee (email available below). General contact details of provider: http://bmr.sciedupress.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.