Profitability of technical stock trading: Has it moved from daily to intraday data, страница 15

•  These technologies enable traders to apply technical models on intraday data frequencies which further increases the speed of

transactions. As a consequence, the persistence of price trends on the basis of intraday data rises, feeding back upon the profitability of “fast” technical models.

Under these conditions, it becomes progressively more difficult to form expectations about the fundamental price equilibrium and, hence, to speculate rationally. The results of this study fit well into this hypothetical picture. They suggest that technical stock trading on the basis of intraday data can be considered a profitable and, hence, rational adaptation to inherently unstable asset markets.

Acknowledgements

The author wants to thank two anonymous referees for the many valuable suggestions which improved the paper considerably. He is also extremely grateful to Eva Sokoll for the statistical assistance and to Michael D. Goldberg for the valuable comments and illuminating discussions. Special thanks go to Markus Fulmek who wrote the program for testing the performance of technical trading systems. Financial assistance from the Anniversary Fund of the Österreichische Nationalbank (Austrian National Bank) is gratefully acknowledged (Project 8860).

References

Achelis, S. B. (2001). Technical Analysis from A to Z, Second Edition New York: McGraw-Hill.

Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47, 1731–1764.

Chan, L., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. Journal of Finance, 51, 1681–1711.

Chang, P. H. K., & Osler, C. L. (1999). Methodical madness: Technical analysis and the irrationality of exchange-rate forecasts. The Economic Journal, 109, 636–661.

Chang, Y. H., Metghalchi, M., & Chan, C. C. (2006). Technical trading strategies and crossnational information linkage: The case of Taiwan stock market. Applied Financial Economics, 16, 731–743.

Cheung, Y. W., & Chinn, M. D. (2001). Currency traders and exchange rate dynamics: A survey of the US Market. Journal of International Money and Finance, 20, 439–471.

Cheung, Y. W., & Wong, C. Y. P. (2000). Survey of market practitioners' views on exchange rate dynamics. Journal of International Economics, 51, 401–419.

Cheung, Y. W., Chinn, M. D., & Marsh, I. W. (2004). How do UK-Based foreign exchange dealers think their market operates? International Journal of Finance and Economics, 9, 289–306.

DeBondt, W. F. M., & Thaler, R. H. (1985). Does the stock market overreact? Journal of Finance, 40, 793–805.

DeBondt, W. F. M., & Thaler, R. H. (1987). Further evidence on investor overreaction and stock market seasonality. Journal of Finance, 42, 557–581.

Deel, R. (2000). The strategic electronic day trader. New York: John Wiley & Sons.

Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25, 23–49.

Fernandez-Rodriguez, F., Gonzalez-Martel, C., & Sosvilla-Rivero, S. (2000). On the profitability of technical trading rules based on artificial neural networks: Evidence from Madrid stock market. Economic Letters, 69, 89–94.

Fernandez-Rodriguez, F., Gonzalez-Martel, C., & Sosvilla-Rivero, S. (2005). Optimization of technical trading rules by genetic algorithms: Evidence from the Madrid stock market. Applied Financial Economics, 773–775.

Frydman, R., & Goldberg, M. D. (2007). Imperfect Knowledge Economics: Exchange Rates and Risk. Princeton, New Jersey: Princeton University Press.