Sampling frequency and the performance of different types of technical trading rules

Abstract

The predictive ability of technical trading rules has been studied in great detail however many papers group all technical trading rules together into one basket. We argue that there are two main types of technical trading rules, namely rules based on trend-following and mean reversion. Utilising high-frequency commodity ETF data, we show that mean-reversion based rules perform increasingly better as sampling frequencies increase and that conversely the performance of trend-following rules deteriorate at higher-frequencies. These findings are possibly related to noise created by high-frequency traders.

Evaluating the predictive power of intraday technical trading in China's crude oil market

“…Moreover, after investigating the profitability of more than 8000 trading rules in the Russian Rouble-US Dollar exchange market over 2000-2011, Frömmel and Lampaert (2016 find that the profitability of technical trading is subject to the variation of intraday intervals and technical trading rules are more likely to produce a profit at the shorter intraday intervals. Similar results are also found in the commodity markets as Hudson et al (2017) confirm that different families of technical trading rules perform variously across different intraday intervals. In addition, Batten et al (2018) argue via bootstrap that moving average trading rules offer predictive power in the gold market at the 5-min interval over 2008-2014. Given the increasing status of the crude oil market in global asset markets due to investors' increasing interest in using crude oil as an alternative investment vehicle, academics have long been attempting to discover whether there are predictable trends in crude oil prices and then enables technical trading rules to consistently generate profits.…”

“…Moreover, after investigating the profitability of more than 8000 trading rules in the Russian Rouble–US Dollar exchange market over 2000–2011, Frömmel and Lampaert (2016) find that the profitability of technical trading is subject to the variation of intraday intervals and technical trading rules are more likely to produce a profit at the shorter intraday intervals. Similar results are also found in the commodity markets as Hudson et al (2017) confirm that different families of technical trading rules perform variously across different intraday intervals. In addition, Batten et al (2018) argue via bootstrap that moving average trading rules offer predictive power in the gold market at the 5‐min interval over 2008–2014.…”

Technical trading and cryptocurrencies

“…Many other papers have also reported significant results for technical trading in equity markets, such as Shynkevich (2012), Han et al (2013) and Neely et al (2014). There is also evidence of significant results from technical trading in commodity futures markets (Miffre and Rallis 2007;Szakmary et al 2010;Narayan et al 2015;Han et al 2016), commodity spot markets (Batten et al 2018;Psaradellis et al 2019), bond markets (Shynkevich 2016) and commodity ETFs (Hudson et al 2017). Despite these findings, there is no clear consensus on the predictability of technical trading rules in the literature, with many papers indicating that technical trading rules do not offer any predictive power, especially once transaction costs have been accounted for (for instance Bessembinder and Chan 1998;Allen and Karjalainen 1999;Marshall et al 2008a, b;Bajgrowicz and Scaillet 2012;Yamamoto 2012;Urquhart et al 2015 ;Batten et al 2018).…”

Long Run Returns Predictability and Volatility with Moving Averages

“…In addition, Ni et al (2015) report that a combination of two MA rules (or the so-called dead cross emerges) is useful for investors. However, Hudson et al (2017) and Yamamoto (2012) conclude that MA rules are totally useless in high-frequency trading.…”