Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data

Abstract

ElsevierCervelló Royo, RE.; Guijarro Martínez, F.; Michniuk, K. (2015). AbstractThis study introduces a new approximation of the flag price pattern recognition. We develop a trading rule which provides positive risk-adjusted returns for intraday data of the Dow Jones Industrial Average Index. In order to mitigate the data snooping problem we use a data set of more than 90,000 observations, results are reported over 96 different configurations of the trading rule parameters. Furthermore, results are examined over 3 non-overlapping sub-periods. The trading rule provides positive results for all the configurations.

Testing the Predicting Ability of Technical Analysis Classical Patterns in the Egyptian Stock Market

“…Generally, we could conclude that the classical technical patterns can significantly predict the direction of the price movements which supports the studies of many researchers such as: (Brock et al 1992;Bessembinder and Chan 1995;Zapranis and Tsinaslanidis 2012;Cervello-Royo et al 2015). The results indicated that the actual returns of classical patterns significantly exceed the expected returns, this means that classical patterns can predict the direction of the future price movements rather than accurately predicting exact price targets.…”

“…Fang et al (2014); Fama and Blume (1966) and Jensen and Bennington (1970);Fama (1965Fama ( , 1970 However, a growing body of literature has been recognized recently that technical analysis could generate abnormal returns. For example (Zapranis & Tsinaslanidis 2012;Cervello-Royo;Guijarro & Michniuk 2015) for classical price patterns, (Marshall & Cahan 2005;Gustafsson 2012;Ülkü & Prodan 2013;Boboc & Dinică 2013;Baetje & Menkhoff 2016;Chen;Su & Lin 2016;El-Ansary & Atuea 2017) for technical indicators and (Brock;Lakonishok & LeBaron 1992;Bessembinder & Chan 1995) for both technical patterns and indicators. There was a common conclusion that returns achieved using technical rules significantly exceeded the returns generated through buy and hold strategy and that technical rules can help in predicting future returns.…”

Comparative Analysis of Investment Decision Models

“…Analizuojant netiesiškus duomenis, akcijų kainai prognozuoti dažnai taikoma netiesinė regresija ar neuronų tinklai (Enke, Mehdiyev 2013). Taip pat buvo ištobulinti techninės analizės modeliai, kurie įtraukia ekspertų vertinimo sistemas ir kitas informacijos apdorojimo formas (CervellóRoyo et al 2015). Sprendimų paramos sistemos pripažintos kaip naudingos priemonės, padedančios jų naudotojams planuoti ir priimti sprendimus, be to, gali būti taikomos skirtingose srityse (Klein, Methlie 2009;Martinez et al 2009;Stasytytė 2011Stasytytė , 2012.…”

The use of technical analysis indicators in the Russian stock market

“…Среди них стоит выделить труды Александра Элдера «Как играть и выигрывать на бирже» 1 и «Трейдинг с доктором Элдером» 2 , «Технический анализ товарных и финансовых рынков» 3 Анны Эрлих, «Трейдинг. Дополнительное измерение принятия решений» 4 и «Практическое использование волн Эллиотта в трейдинге» 5 Валерия Сафонова, «Как увидеть деньги на экране монитора» 6 и «Кому светят японские свечи?» 7 Вениамина Сафина, а также многих других отечественных и зарубежных экономистов и финансистов (Володин, Кулагина, 2013;Володин, Янбаева, 2013;Жиркин, 2011;Заяц, 2013;Кашина, 2014;Кудряшов, 2009;Лебедева, Павлов, 2009;Соколова, Кузьмина, Тохтабаев, 2007;Трегуб, 2005;Трушанина, 2015;Яковлева, 2013;Cervelló-Royo, Guijarro, Michniuk, 2015;Chavarnakul, Enke, 2008;Chen, Huang, Lai, 2009;Hoffmann, Shefrin, 2014;Ko, Lin, Su, Chang, 2014;Marshall, Cahan, 2005;Pau, 1991;Teixeira, Oliveira, 2010;Yamaguchi, 1989;Yamamoto, 2012;Wang, Chan, 2007;Zhu, Zhou, 2009). …”