IDEAS home Printed from https://ideas.repec.org/p/nan/wpaper/1502.html
   My bibliography  Save this paper

Strategy Change and Wealth Accumulation: An Analysis of S&P 500 Data

Author

Listed:
  • Weihong HUANG

    (Division of Economics, School of Humanities and Social Sciences, Nanyang Technological University, 14 Nanyang Drive, Singapore 637332)

  • Yu ZHANG

    (Division of Economics, School of Humanities and Social Sciences, Nanyang Technological University, 14 Nanyang Drive, Singapore 637332)

Abstract

This paper studies investors' strategy change frequency and their wealth accumulation by financial investments. Artificial investors are put into a real stock market. They trade S&P 500 following common strategies in practice. Fundamental analysis generally surpasses technical analysis in all market situa- tions except boom periods. Though investors' strategy change behavior, which is driven by the past performance of strategies, seems reasonable, a faster strat- egy change does not guarantee a higher final wealth. Active strategy change hurts investor' wealth in bear markets and in markets with major trend rever- sals. In bull markets, both fast and slow strategy change behaviors work better than a moderate speed of strategy change. A detailed decomposition of wealth accumulation via financial investment shows the dependence of wealth on in- vestors' past transactions. This may explain the relation between investors' strategy change frequency and their wealth.

Suggested Citation

  • Weihong HUANG & Yu ZHANG, 2015. "Strategy Change and Wealth Accumulation: An Analysis of S&P 500 Data," Economic Growth Centre Working Paper Series 1502, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
  • Handle: RePEc:nan:wpaper:1502
    as

    Download full text from publisher

    File URL: http://www3.ntu.edu.sg/hss2/egc/wp/2015/2015-02.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    2. Day, Richard H. & Huang, Weihong, 1990. "Bulls, bears and market sheep," Journal of Economic Behavior & Organization, Elsevier, vol. 14(3), pages 299-329, December.
    3. Chiarella, Carl & He, Xue-Zhong & Hommes, Cars, 2006. "A dynamic analysis of moving average rules," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1729-1753.
    4. William A. Brock & Cars H. Hommes, 1997. "A Rational Route to Randomness," Econometrica, Econometric Society, vol. 65(5), pages 1059-1096, September.
    5. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    6. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    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. He, Xue-Zhong & Li, Kai, 2012. "Heterogeneous beliefs and adaptive behaviour in a continuous-time asset price model," Journal of Economic Dynamics and Control, Elsevier, vol. 36(7), pages 973-987.
    2. Qi Nan Zhai, 2015. "Asset Pricing Under Ambiguity and Heterogeneity," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2015, August.
    3. Qi Nan Zhai, 2015. "Asset Pricing Under Ambiguity and Heterogeneity," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 16, July-Dece.
    4. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
    5. Zheng, Min & Liu, Ruipeng & Li, Youwei, 2018. "Long memory in financial markets: A heterogeneous agent model perspective," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 38-51.
    6. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    7. Anufriev, Mikhail & Panchenko, Valentyn, 2009. "Asset prices, traders' behavior and market design," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1073-1090, May.
    8. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    9. Heemeijer, Peter & Hommes, Cars & Sonnemans, Joep & Tuinstra, Jan, 2009. "Price stability and volatility in markets with positive and negative expectations feedback: An experimental investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1052-1072, May.
    10. Xue-Zhong He & Youwei Li, 2017. "The adaptiveness in stock markets: testing the stylized facts in the DAX 30," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1071-1094, November.
    11. He, Xue-Zhong & Li, Kai, 2015. "Profitability of time series momentum," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 140-157.
    12. Chiarella Carl & Di Guilmi Corrado, 2015. "The limit distribution of evolving strategies in financial markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 137-159, April.
    13. Min Zheng & Duo Wang & Xue-Zhong He, 2009. "Asymmetry of technical analysis and market price volatility," Published Paper Series 2009-6, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    14. Yu Zhang & Weihong Huang, 2018. "Impact of strategy switching on wealth accumulation," Journal of Evolutionary Economics, Springer, vol. 28(4), pages 961-983, September.
    15. He, Xue-Zhong & Li, Youwei & Zheng, Min, 2019. "Heterogeneous agent models in financial markets: A nonlinear dynamics approach," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 135-149.
    16. Bowden, Mark P., 2012. "Information contagion within small worlds and changes in kurtosis and volatility in financial prices," Journal of Macroeconomics, Elsevier, vol. 34(2), pages 553-566.
    17. Anufriev, Mikhail & Bao, Te & Tuinstra, Jan, 2016. "Microfoundations for switching behavior in heterogeneous agent models: An experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 129(C), pages 74-99.
    18. Chiarella, Carl & He, Xue-Zhong & Zheng, Min, 2011. "An analysis of the effect of noise in a heterogeneous agent financial market model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 148-162, January.
    19. Schmitt, Noemi & Westerhoff, Frank H., 2019. "Trend followers, contrarians and fundamentalists: Explaining the dynamics of financial markets," BERG Working Paper Series 151, Bamberg University, Bamberg Economic Research Group.
    20. Dieci, Roberto & Westerhoff, Frank, 2010. "Heterogeneous speculators, endogenous fluctuations and interacting markets: A model of stock prices and exchange rates," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 743-764, April.

    More about this item

    Keywords

    Financial Investment Strategy; Strategy Change Frequency; Wealth Accumulation; Standard & Poor's 500;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G19 - Financial Economics - - General Financial Markets - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nan:wpaper:1502. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/dentusg.html .

    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: Magdalene Lim (email available below). General contact details of provider: https://edirc.repec.org/data/dentusg.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.