What Do We Know About the Profitability of Technical Analysis
By Cheol Park, Scott H. Irwin. 2007
611 citations recorded by the source corpus.
Subfields: Accounting, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/cd3e4d517bcb9953979734e35c85dc6ff8b88432
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Accounting
- K. Lim
- R. Brooks
- L. Gutierrez
- Pedro Cortesão Godinho
- Luis Pedro Mendes
- Georgios Sermpinis
- Charalampos Stasinakis
- Christian L. Dunis
In Finance
- K. Lim
- R. Brooks
- Wei Luo
- Jae Hyun Kim
- L. Gutierrez
- Pedro Cortesão Godinho
- Luis Pedro Mendes
- Georgios Sermpinis
In Management Science and Operations Research
- K. Lim
- R. Brooks
- L. Gutierrez
- Pedro Cortesão Godinho
- Luis Pedro Mendes
- Georgios Sermpinis
- Jae Hyun Kim
- Charalampos Stasinakis
Citations in the corpus
- The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature (2011, background): “…(2003) and Schwert (2003) scrutinize those studies reporting evidence of statistically significant predictable patterns in stock returns; (3) Park and Irwin (2007) review the evidence on the profitability of technical trading rules in a variety of speculative markets, including 66 stock…”
- Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests (2013, background): “1 The second group of studies examines the profitability of trading strategies based on past returns such as technical trading rules (see the survey paper by Park and Irwin, 2007 ), momentum and contrarian strategies (see references cited in Chou et al , 2007 ).”
- The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market (2009, methodology): “Our analysis uses daily spot exchange rate data from the Federal Reserve H.10 Statistical Release and interest rates from the Bank for International 2Park and Irwin (2005) use variable (segmented) transaction costs in their futures market study.”
- A Random Forests Approach to Predicting Clean Energy Stock Prices (2021, neutral): “Second, regression based approaches for predicting stock prices or approaches relying solely on technical indicators provides mixed results (Park and Irwin 2007) but machine learning (ML) methods appear to offer better accuracy (Shah et al. 2019; Ghoddusi et al. 2019; Khan et al. 2020; Atsalakis…”
- Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects (2014, background): “Park and Irwin (2007) report that the number of successful trading studies of foreign exchange rates increase rapidly after 2000, although there are some earlier signs of marginal FX profitability (Lee and Mathur, 1996).”
- A Forex trading system based on a genetic algorithm (2012, methodology): “…market (Menkhoff and Taylor 2007; Neely et al. 2009), and there is evidence that, at least until the early 1990s, it generated significant excess returns (Sweeney 1986; Levich and Thomas 1993; LeBaron 1999, 2002; Olson 2004; Park and Irwin 2007; Schulmeister 2008; Neely et al. 2009; among others).”
- Speculative bubbles in agricultural commodity markets (2013, methodology): “Using a bootstrap method computing the finite sample probability distribution of the tests recently proposed in Phillips and Yu (2009b) and Phillips et al. (2011), we find that there was evidence of explosiveness in the future prices of wheat, corn and rough rice in 2007–2008.”