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On long memory behaviour and predictability of financial markets

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  • Vo, Long H.
  • Roberts, Leigh

Abstract

An immediate consequence of the Efficient Market Hypothesis (EMH) is the absence of auto-correlation of the return series of the financial prices and the exclusion of excess profitability made by any (active) trading strategy. However, the precondition for the validity of EMH, which assumes that all market participants can promptly receive and rationally react to the relevant information affecting the prices, might be (approximately) true for a long time horizon, but not for a short time horizon. By examining local long-range dependence (measured by the rolling Rescaled Range estimates of the Hurst index) of an empirical example, the local market inefficiency is inferred, and excess profitability of a simple trend-following trading strategies implies the potential for constructing a more profitable trading system by incorporating the former into the latter.

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File URL: http://researcharchive.vuw.ac.nz/handle/10063/3361
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Bibliographic Info

Paper provided by Victoria University of Wellington, School of Economics and Finance in its series Working Paper Series with number 3361.

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Date of creation: 2014
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Handle: RePEc:vuw:vuwecf:3361

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Postal: Alice Fong, Administrator, School of Economics and Finance, Victoria Business School, Victoria University of Wellington, PO Box 600 Wellington, New Zealand
Phone: +64 (4) 463-5353
Fax: +64 (4) 463-5014
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Web page: http://www.victoria.ac.nz/sef
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Related research

Keywords: Hurst index; Long memory; Market efficiency; Rescaled range analysis; Trading system; High-frequency trading;

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  1. Lo, Andrew W. (Andrew Wen-Chuan), 1989. "Long-term memory in stock market prices," Working papers 3014-89., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  2. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
  3. MacKinnon, James G, 1994. "Approximate Asymptotic Distribution Functions for Unit-Root and Cointegration Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 167-76, April.
  4. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-64, December.
  5. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
  6. Bollerslev, Tim & Wright, Jonathan H., 2000. "Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data," Journal of Econometrics, Elsevier, vol. 98(1), pages 81-106, September.
  7. Malliaropulos, Dimitrios & Priestley, Richard, 1999. "Mean reversion in Southeast Asian stock markets," Journal of Empirical Finance, Elsevier, vol. 6(4), pages 355-384, October.
  8. Torben G. Andersen & Tim Bollerslev, 1996. "Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns," NBER Working Papers 5752, National Bureau of Economic Research, Inc.
  9. R. F. Engle & A. J. Patton, 2001. "What good is a volatility model?," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 237-245.
  10. Fujii, Eiji, 2002. "Exchange Rate and Price Adjustments in the Aftermath of the Asian Crisis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 7(1), pages 1-14, January.
  11. B. Mandelbrot, 1972. "Statistical Methodology For Nonperiodic Cycles: From The Covariance To Rs Analysis," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 259-290 National Bureau of Economic Research, Inc.
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