Assessing the profitability of intraday opening range breakout strategies

Abstract

Is it possible to beat the market by mechanical trading rules based on historical and publicly known information? Such rules have long been used by investors and in this paper, we test the success rate of trades and profitability of the Open Range Breakout (ORB) strategy. An investor that trades on the ORB strategy seeks to identify large intraday price movements and trades only when the price moves beyond some predetermined threshold. We present an ORB strategy based on normally distributed returns to identify such days and find that our ORB trading strategy result in significantly higher returns than zero as well as an increased success rate in relation to a fair game. The characteristics of such an approach over conventional statistical tests is that it involves the joint distribution of Low, High, Open and Close over a given time horizon.

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

“…Birkin and Garibaldi (2009) even showed that if the level of noise is too low, T2 models show no significant improvement on T1. A number of authors (Gençay, 1996;Finnie, 2009, 2010;Holmberg et al, 2013) suggest the use of a threshold on the predicted signals below which a trading action is not taken into consideration. This is done to reduce the effect of the underlying noise, however, at the cost of reduced trades and hence possible return.…”

“…Choudhry et al, 2012;Gradojevic and Gençay, 2013;Holmberg et al, 2013;Schulmeister, 2009;Vella and Ng, 2014a) claim a relationship between breaks in market efficiency and different levels of intraday volatility. For this reason, we consider a fifth indicator using a measure of volatility, which provides an indication of movement strength but not the direction.…”

“…We decide to take a different approach by making use of a stylised fact in financial time series that microstructure noise, in its nature, is A C C E P T E D M A N U S C R I P T more pronounced in higher frequency data (Medeiros et al, 2006). Typically this effect is reduced by using a threshold which acts as a filter on the predicted signals, below which a trading action is not taken into consideration (Gençay, 1996;Finnie, 2009, 2010;Holmberg et al, 2013). We extend the use of this method by hypothesising that this approach is effectively controlling for uncertainty (indirectly).…”

“…Another advantage of ANFIS/T2a is the fact that higher risk adjusted performance and profitability is obtained with lower number of trades (1.76% less trades). Holmberg et al (2013) claim that increased risk-adjusted performance can result from increased signal filtering (reduced trades) but at the cost of reduced overall profitability. On the contrary, our results indicate that ANFIS/T2a shows lower overall trading activity but more efficient capital allocation by instigating more trades during preferable market states and increased noise filtering.…”

“…A number of authors (e.g. Schulmeister, 2009;Zhang, 2010;Rechenthin and Street, 2013;Holmberg et al, 2013;Brogaard et al, 2014) insist that the presence of efficient pricing becomes more questionable when investigating short-lived (milli-seconds to few minutes) trades. However, Kearns et al (2010) validate the EMH in their study and argue that generating profits from aggressive high-frequency trading (HFT) is next to impossible.…”