Search Results for « Technical trading rules »

In this paper we utilize White's Reality Check bootstrap methodology (White (1999)) 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.

We use a genetic algorithm to learn technical trading rules for the S&P 500 index using daily prices from 1928 to 1995. After transaction costs, the rules do not earn consistent excess returns over a simple buy-and-hold strategy in the out-of-sample test periods. The rules are able to identify periods to be in the index when daily returns are positive and volatility is low and out when the reverse is true. These latter results can largely be explained by low-order serial correlation in stock index returns.

This paper studies simple moving average trading strategies employing daily price data on the eleven most-traded cryptocurrencies in the 2016–2018 period. Our results indicate a variable moving average strategy is successful when using the 20 days moving average trading strategy. Specifically, excluding Bitcoin the technical trading rule generates an excess return of 8.76% p.a. after controlling for the average market return. Our results suggest that cryptocurrency markets are inefficient.

In this paper, we present new evidence on the profitability and statistical significance of technical trading rules in the foreign exchange market. We utilize a new data base, currency futures contracts for the period 1976–1990, and we implement a new testing procedure based on bootstrap methodology. Our results suggest that simple technical trading rules have very often led to profits that are highly unusual. Splitting the entire sample period into three 5-year periods reveals that on average the profitability of some trading rules declined in the latest period although profits remained positive (on average) and significant in many cases. (JEL F31, F47, G15).

The focus of this paper is to determine the profitability of technical trading rules by evaluating their ability to outperform the naïve buy-and-hold trading strategy. Moving average cross-over rules, filter rules, Bollinger Bands, and trading range break-out rules are tested on the S&P/TSX 300 Index, the Dow Jones Industrial Average Index, NASDAQ Composite Index, and the Canada/U.S. spot exchange rate. After accounting for transaction costs, excess returns are generated by the moving average cross-over rules and trading range break-out rules for the S&P/TSX 300 Index, NASDAQ Composite Index and the Canada/U.S. spot exchange rate. Filter rules also earn excess returns when applied on the Canada/U.S. spot exchange rate. The bootstrap methodology is used to determine the statistical significance of the results. The profitability of the technical trading rules is further enhanced with a combined signal approach.