Ioannis Psaradellis

This study investigates the debatable success of technical trading rules, through the years, on the trending energy market of crude oil. In particular, the large universe of 7846 trading rules proposed by Sullivan, Timmermann, and White (1999. “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap.” The Journal of Finance 54 (5): 1647–1691. doi:10.1111/0022-1082.00163), divided into five families (filter rules, moving averages, support and resistance rules, channel breakouts, and on-balance volume averages), is applied to the daily prices of West Texas Intermediate (WTI) light, sweet crude oil futures as well as the United States Oil (USO) fund, from 2006 onwards. We employ the k-familywise error rate (k-FWER) and false discovery rate (FDR) techniques proposed by Romano, J. P., and M. Wolf. (2007. “Control of Generalized Error Rates in Multiple Testing.” The Annals of Statistics 35 (4): 1378–1408. doi:10.1214/009053606000001622) and Bajgrowicz, P., and O. Scaillet. (2012. “Technical Trading Revisited: False Discoveries, Persistence Tests, and Transaction Costs.” Journal of Financial Economics 106 (3): 473–491. doi:10.1016/j.jfineco.2012.06.001) respectively, accounting for data snooping in order to identify significantly profitable trading strategies. Our findings explain that there is no persistent nature in rules performance, contrary to the in-sample outstanding results, although tiny profits can be achieved in some periods. Overall, our results seem to be in favor of interim market inefficiencies.

In this study, a Krill-Herd Support Vector Regression (KH-vSVR) model is introduced. The Krill Herd (KH) algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. The KH optimizes the SVR parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading three commodity exchange traded funds on a daily basis over the period 2012–2014. The inputs of the KH-vSVR models are selected through the model confidence set from a large pool of linear predictors. The KH-vSVR’s statistical and trading performance is benchmarked against traditionally adjusted SVR structures and the best linear predictor. In addition to a simple strategy, a time-varying leverage trading strategy is applied based on heterogeneous autoregressive volatility estimations. It is shown that the KH-vSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the leverage strategy is found to be successful.