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Stock index and price dynamics in the UK and the US: new evidence from a trading rule and statistical analysis

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  • Stephen Taylor

Abstract

The predictability of long time series of stock index levels and stock prices is investigated using both statistical and trading rule methodologies. The trading rule analysis uses a double moving-average rule and the methods of Brock, Lakonishok and LeBaron. Results are obtained for the FTA, FTSE-100, DJIA and S&P-500 indices, prices for twelve UK stocks and indices derived from these stock prices. Statistical analysis shows that the index and price series are not random walks. The trading rule analysis generally confirms this conclusion. However, small transaction costs would eliminate the profitability of the moving-average rule. Standard ARMA-ARCH models are estimated for time series of returns and bootstrap methods are used to decide if the models can explain the observed trading statistics. The models provide a reasonable description but there is evidence from the trading rule methodology that standard models sometimes fail to describe the dynamics of the indices and prices. Several comparisons are made: between an index and the stock prices that define the index, between spot levels and futures prices for indices, and between UK and US indices.

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  • Stephen Taylor, 2000. "Stock index and price dynamics in the UK and the US: new evidence from a trading rule and statistical analysis," The European Journal of Finance, Taylor & Francis Journals, vol. 6(1), pages 39-69.
    • Handle: RePEc:taf:eurjfi:v:6:y:2000:i:1:p:39-69
    DOI: 10.1080/135184700336955
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    Cited by:

    1. Stephen J. Taylor, 2003. "Discussion of On the Existence of Visual Technical Patterns in the UK Stock Market," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(1‐2), pages 295-297, January.
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    3. Terence Tai-Leung Chong & Sheung Tat Chan, 2008. "Structural Change in the Efficiency of the Japanese Stock Market after the Millennium," Economics Bulletin, AccessEcon, vol. 7(7), pages 1-7.
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    5. Ślepaczuk Robert & Sakowski Paweł & Zakrzewski Grzegorz, 2018. "Investment Strategies that Beat the Market. What Can We Squeeze from the Market?," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(4), pages 36-55, December.
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    7. Metghalchi, Massoud & Chen, Chien-Ping & Hayes, Linda A., 2015. "History of share prices and market efficiency of the Madrid general stock index," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 178-184.
    8. Dan Anghel, 2013. "How Reliable is the Moving Average Crossover Rule for an Investor on the Romanian Stock Market?," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 5(2), pages 089-115, December.
    9. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    10. Enoch Cheng & Clemens C. Struck, 2019. "Time-Series Momentum: A Monte-Carlo Approach," Working Papers 201906, School of Economics, University College Dublin.
    11. Benjamin R. Auer, 2021. "Have trend-following signals in commodity futures markets become less reliable in recent years?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 533-553, December.
    12. Shynkevich, Andrei, 2013. "Time-series momentum as an intra- and inter-industry effect: Implications for market efficiency," Journal of Economics and Business, Elsevier, vol. 69(C), pages 64-85.
    13. Robert Ślepaczuk & Grzegorz Zakrzewski & Paweł Sakowski, 2012. "Investment strategies beating the market. What can we squeeze from the market?," Working Papers 2012-04, Faculty of Economic Sciences, University of Warsaw.
    14. Strobel, Marcus & Auer, Benjamin R., 2018. "Does the predictive power of variable moving average rules vanish over time and can we explain such tendencies?," International Review of Economics & Finance, Elsevier, vol. 53(C), pages 168-184.

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