Technical Trading Rules in Emerging Stock Markets
Literature reveals that many investors rely on technical trading rules when making investment decisions. If stock markets are efficient, one cannot achieve superior results by using these trading rules. However, if market inefficiencies are present, profitable opportunities may arise. The aim of this study is to investigate the effectiveness of technical trading rules in 34 emerging stock markets. The performance of the rules is evaluated by utilizing White-s Reality Check and the Superior Predictive Ability test of Hansen, along with an adjustment for transaction costs. These tests are able to evaluate whether the best model performs better than a buy-and-hold benchmark. Further, they provide an answer to data snooping problems, which is essential to obtain unbiased outcomes. Based on our results we conclude that technical trading rules are not able to outperform a naïve buy-and-hold benchmark on a consistent basis. However, we do find significant trading rule profits in 4 of the 34 investigated markets. We also present evidence that technical analysis is more profitable in crisis situations. Nevertheless, this result is relatively weak.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057659Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2114
 Lo, A. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective, Journal of Portfolio Management, 30, 15-29.
 McKenzie, M.D. (2007). Technical Trading Rules in Emerging Markets and the 1997 Asian Currency Crises, Emerging Markets Finance and Trade, 43, 46-73.
 Marshall, B., Cahan, R.C., Cahan, J.M. (2010) Technical Analysis Around The World, working paper, Massey University New Zealand.
 White, H. (2000). A Reality Check for data snooping, Econometrica, 68, 1097-1126.
 Sullivan, R., Timmermann, A., White, H. (1999). Data snooping, technical trading rule performance, and the bootstrap, Journal of Finance 54, 1647-1691.
 Hansen, P.R. (2005). A test for Superior Predictive Ability, Journal of Business & Economic Statistics, 23, 365-380.
 Hsu, P.H., Hsu Y.-C., Kuan, C.H. (2010). Testing the Predictive Ability of Technical Analysis Using a New Stepwise Test without Data Snooping Bias. Journal of Empirical Finance, 17:3, 471-484.
 Fama, E. (1970). Efficient capital markets: a review of theory and empirical work, Journal of Finance, 25, 25-52.
 Jensen, M.C. (1968). The performance of mutual funds in the period 1945-1964, Journal of Finance, 23, 389-416.
 Malkiel, B.G. (1973). A Random Walk Down Wall Street, New York, Norton, 1-428.
 Malkiel, B.G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17, 59-82.
 Li, W., Wang, S.W. (2007). Ownership Restriction, Information Diffusion Speed, and the Performance of Technical Trading Rules in Chinese Domestic and Foreign Shares Markets, Review of Pacific Basin Financial Markets and Policies, 10:4, 585-617.
 Chen, C-W., Huang, C-S., Lai, H-W. (2009). The impact of data snooping on the testing of technical analysis: An empirical study of Asian stock markets. Journal of Asian Economics, 20, 580-591.
 Lukac, L.P., Brorsen, B.W., Irwin, S.H. (1998). A Test of Futures Market Disequilibrium using Twelve Different Technical Trading Systems, Applied Economics, 20, 623-639.
 Brock, W., Lakonishok, J., LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance, 47, 1731-1764.
 Gunasekarage, A., Power, D.M. (2001). The Profitability of Moving Average Trading Rules in South Asian Stock Markets, Emerging Markets Review, 2, 17-33.
 Fifield, S., Power, D., Sinclair, D. (2005). An analysis of trading strategies in eleven European stock markets, European Journal of Finance, 11, 531-548.
 Bessembinder, H., Chan, K. (1998). Market Efficiency and the Returns to Technical Analysis. Financial Management, 27, 5-17.
 Timmermann, A., Granger, C.W.J. (2004). Efficient market hypothesis and forecasting, International Journal of Forecasting, 20, 15-27
 Hansen, P.R., Lunde A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20, 873-889.
 Marshall, B., Qian, S., Young, M. (2009). Is technical analysis profitable on US stocks with certain size, liquidity or industry characteristics?, Applied Financial Economics, 19, 1213-1221.
 Lo, A., McKinlay, C. (1988). Stock Do Not Follow Random Walks, Review of Financial Studies (Spring), 41-66.
 Fama, E., French, K. (1988). Permanent and Temporary Components of Stock Prices, Journal of Political Economy, 96:2, 246-274.
 Papathanasiou, S., Samitas A. (2010). Profits from Technical Trading Rules: The Case of Cyprus Stock Exchange, Journal of Money, Investment and Banking, 13, 35-43.
 Hsu, P.H., Kuan, C.H. (2005). Re-examining the profitability of technical analysis with data snooping checks. Journal of Financial Econometrics, 3, 606-628.
 Park, C.H., Irwin, S.H. (2010). A Reality Check on Technical Trading Rule Profits in the U.S. Futures Market. Journal of Futures Markets,30:7, 633-659.
 Munck, N.H. (2005). When Transactions Went High-Tech: A Cross-Sectional Study of Equity Trading Costs in the Light of More Sophisticated Trading Systems, SSRN Working Paper.
 Diebold, F.X., Mariano, R.S. (1995). Comparing Predictive Accuracy, Journal of Business and Economic Statistics, 13, 253-263.
 West, K.D. (1996). Asymptotic Inference About Predictive Ability, Econometrica, 64, 1067-1084.
 Politis, D.N., Romano, J.P. (1994). The Stationary Bootstrap. Journal of the American Statistical Association, 89, 1303-1313.
 Politis, D.N., White, H. (2004). Automatic Block-Length Selection for the Dependent Bootstrap, Econometric Reviews, 23:1, 53-70.