Working Paper: CEPR ID: DP1976
Authors: Ryan Sullivan; Allan Timmermann; Halbert White
Abstract: In this paper we utilize White's Reality Check bootstrap methodology (White (1997)) to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the first time, the paper presents a comprehensive test of performance across all technical trading rules examined. We consider the study of Brock, Lakonishok and LeBaron (1992), expand their universe of 26 trading rules, apply the rules to 100 years of daily data on the Dow Jones Industrial Average and determine the effects of data-snooping.
Keywords: technical trading rules; bootstrap methods; financial performance; datasnooping
JEL Codes: G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| technical trading rules (F14) | valuable economic signals (D46) |
| data snooping (C52) | misleading conclusions regarding effectiveness of trading rules (G14) |
| data snooping (C52) | performance of trading rules (G18) |
| trading rules (F14) | outperforming benchmark (1897-1986) (P17) |
| out-of-sample performance (1987-1996) (C52) | probability of best-performing rule not outperforming benchmark (C52) |
| transaction costs or short-sale constraints (G19) | previous successes of trading rules (F10) |
| shorter moving averages (C41) | best-performing trading rules (F14) |