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Stock price dynamics: nonlinear trend, volume, volatility, resistance and money supply

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  • G. Caginalp
  • M. Desantis

Abstract

We present a methodology to study a data set of 119 260 daily closed-end fund prices using mixed-effects regressions with the objective of understanding price dynamics. There is strong statistical support that relative price change depends significantly on (i) the recent trend in a nonlinear manner, (ii) recent changes in valuation, (iii) recent changes in money supply (M2), (iv) longer-term trend, (v) recent volume changes and (vi) proximity to a recent high price. The dependence on the volatility is more subtle, as short-term volatility has a positive influence, while the longer term is negative. The cubic nonlinearity in the weighted price trend shows that a percentage daily gain of up to 2.78% tends to yield higher prices, but larger gains lead to lower prices. Thus, the nonlinearity of price trend establishes an empirical and quantitative basis for both underreaction and overreaction within one large data set, facilitating an understanding of these competing motivations in markets. Increasing money supply is found to have a significant positive effect on stock price, while proximity to recent high prices has a negative effect. The data set consists of daily prices during the period 26 October 1998 to 30 January 2008.

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  • G. Caginalp & M. Desantis, 2011. "Stock price dynamics: nonlinear trend, volume, volatility, resistance and money supply," Quantitative Finance, Taylor & Francis Journals, vol. 11(6), pages 849-861.
    • Handle: RePEc:taf:quantf:v:11:y:2011:i:6:p:849-861
    DOI: 10.1080/14697680903220356
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    1. repec:bla:jfinan:v:59:y:2004:i:5:p:2145-2176 is not listed on IDEAS
    2. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    3. David M. Grether, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 95(3), pages 537-557.
    4. Ahmet Duran & Gunduz Caginalp, 2007. "Overreaction diamonds: precursors and aftershocks for significant price changes," Quantitative Finance, Taylor & Francis Journals, vol. 7(3), pages 321-342.
    5. Paul Slovic & Melissa L. Finucane & Ellen Peters & Donald G. MacGregor, 2004. "Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality," Risk Analysis, John Wiley & Sons, vol. 24(2), pages 311-322, April.
    6. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    7. Smith, Vernon L & Suchanek, Gerry L & Williams, Arlington W, 1988. "Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets," Econometrica, Econometric Society, vol. 56(5), pages 1119-1151, September.
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    1. Caginalp, Gunduz & DeSantis, Mark, 2020. "Nonlinear price dynamics of S&P 100 stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    2. Zhu, Dingju, 2016. "Qualitative and quantitative combined nonlinear dynamics model and its application in analysis of price, supply–demand ratio and selling rate," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 54-72.
    3. Konstantin Hausler & Wolfgang Karl Hardle, 2021. "Cryptocurrency Dynamics: Rodeo or Ascot?," Papers 2103.12461, arXiv.org, revised Jan 2022.
    4. Daouda Lawa tan Toe & Salifou Ouedraogo, 2022. "Dynamic relationship between trading volume, returns and returns volatility: an empirical investigation on the main African’s stock markets," Journal of Asset Management, Palgrave Macmillan, vol. 23(5), pages 429-444, September.
    5. Andrey Kudryavtsev, 2019. "The Effect Of Trading Volumes On Stock Returns Following Large Price Moves," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 64(220), pages 85-116, January –.
    6. Gunduz Caginalp & Mark DeSantis, 2019. "Nonlinear price dynamics of S&P 100 stocks," Papers 1907.04422, arXiv.org.
    7. Halil Altintas & Kassouri Yacouba, 2018. "Asymmetric Responses of Stock Prices to Money Supply and Oil Prices Shocks in Turkey: New Evidence from a Nonlinear ARDL Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 8(4), pages 45-53.
    8. Häusler, Konstantin & Härdle, Wolfgang, 2021. "Rodeo or ascot: Which hat to wear at the crypto race?," IRTG 1792 Discussion Papers 2021-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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