Bell, Peter N (2013): New Testing Procedures to Assess Market Efficiency with Trading Rules.

PDF
MPRA_paper_46701.pdf Download (419kB)  Preview 
Abstract
This paper presents two computational techniques and shows that these techniques can improve tests for market efficiency based on profit of trading rules. The two techniques focus on interval estimates for expected profit per trade, in contrast to the standard approach that emphasizes point estimates for profit per trade (Daskalakis, 2013; Marshall, Cahan, & Cahan, 2008). The first technique uses confidence intervals to determine if the expected profit is significantly different from zero. The second technique uses movingwindow resampling, a procedure of drawing subsamples that overlap and move incrementally along a time series, to determine if the expected profit is sensitive to sample selection. The paper develops formal testing criteria based on each technique and uses simulation to establish existence results about the tests for efficiency: the standard approach can give false negative results and the new tests can give correct negative or correct positive results. Using a random walk, I show situations where the standard approach incorrectly determines that a market is inefficient whereas the new techniques do not make this error; the standard approach can be fooled by randomness of profit. Using a mean reverting process and a trading rule designed to exploit mean reversion, based on Bollinger bands, I show that the new techniques can correctly recognize an inefficient market. Since the new testing procedures can correctly identify an efficient or inefficient market, with an error rate discussed in the paper. These results support Fama’s (1970) position that trading rules can form the basis for the theory of efficient markets. This definition of an efficient market in terms of trading profit is timely given the current dominance of algorithmic trading in secondary markets.
Item Type:  MPRA Paper 

Original Title:  New Testing Procedures to Assess Market Efficiency with Trading Rules 
Language:  English 
Keywords:  Efficient market, trading rule, expected profit, testing procedure, confidence interval, moving window, resampling, random walk, mean reversion 
Subjects:  C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling D  Microeconomics > D8  Information, Knowledge, and Uncertainty > D84  Expectations ; Speculations G  Financial Economics > G0  General G  Financial Economics > G1  General Financial Markets > G14  Information and Market Efficiency ; Event Studies ; Insider Trading 
Item ID:  46701 
Depositing User:  Peter N Bell 
Date Deposited:  05 May 2013 05:53 
Last Modified:  30 Mar 2016 02:21 
References:  Arthur, B. (1994). Inductive Reasoning and Bounded Rationality, American Economic Review, 84(2), 406–411. Bell, P. (2010). Introduction to the Profit Surface (MPRA Working Paper). Retrieved from http://mpra.ub.unimuenchen.de/26812/ Bollinger Capital Management (2013). Bollinger Bands  Tutorial. Retrieved from http://www.bollingerbands.com/services/bb/ Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the stochastic properties of Stock Returns, Journal of Finance, 47, 1731–1764. ClarkJoseph, A. (2013). Exploratory Trading. (Unpublished job market paper). Harvard University, Cambridge MA. Chu, C. S. (1995). Time series segmentation: A sliding window approach, Information Sciences, 85(1), 147–173. Daskalakis, G. (2013). On the efficiency of the European carbon market: New evidence from Phase II, Energy Policy, 54, 369–375. Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, 25(2), 383–417. Gençay, R. (1998). Optimization of technical trading strategies and the profitability in security markets, Economics Letters, 59, 249–254. Hettmansperger, T.P., & Sheather, S.J. (1986). Confidence intervals based on interpolated order statistics, Statistics & Probability Letters, 4(2), 75–79. Kendrick, D. (1993). Research Opportunities in Computational Economics, Computational Economics, 6, 257–314. Kurz, M. & Motolese, M. (2011). Diverse beliefs and time variability of risk premia, Economic Theory, 47(2), 293–335. Levich, R., & Thomas, L. (1993) The significance of technical–trading rules profits in the foreign exchange market: A bootstrap approach, Journal of International Money and Finance, 12(5), 451–474. Lo, A., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation, The Journal of Finance, 55(4), 1705–1765. Marshall, B. R., Cahan, R. H., & Cahan, J. M. (2008). Can commodity futures be profitably traded with quantitative market timing strategies? Journal of Banking & Finance, 32, 1810–1819. Malkiel, B. (2003). The Efficient Market Hypothesis and Its Critics, The Journal of Economic Perspectives, 17(1), 59–82. Neyman, J. (1937). Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 236(767), 330–380. Samuelson, P. (1965). Proof That Properly Anticipated Prices Fluctuate Randomly, Industrial Management Review, 6(2), 41–49. Shasha, D., & Zhu, Y. (2004). High Performance Discovery in Time Series: Techniques and Case Studies. New York: Springer. Skouras, S. (2001). Financial returns and efficiency as seen by an artificial technical analyst, Journal of Economic Dynamics & Control, 25, 213–244. Strategic Economic Decisions (2010). Home  Strategic Economic Decisions. Retrieved from http://www.sedinc.com/ Sullivan, R., Timmerman, R., Timmerman, A., & White, H. (1999). Data–Snooping, Technical Trading Rule Performance, and the Bootstrap, The Journal of Finance, 54(5), 1647–1691. Ziliak, S.T. & McCloskey, D.N. (2007). The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. Ann Arbor: University of Michigan Press. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/46701 