Research on Quantitative Trading Strategy Based on Neural Network Algorithm and Fisher Linear Discriminant
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Trifan, Emanuela, 2004. "Entscheidungsregeln und ihr Einfluss auf den Aktienkurs," Darmstadt Discussion Papers in Economics 131, Darmstadt University of Technology, Department of Law and Economics.
- Chiarella, Carl & Ladley, Daniel, 2016. "Chasing trends at the micro-level: The effect of technical trading on order book dynamics," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 119-131.
- Sid Ghoshal & Stephen J. Roberts, 2018. "Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting," Papers 1807.03192, arXiv.org, revised Jul 2018.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014.
"Forecasting the Equity Risk Premium: The Role of Technical Indicators,"
Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2010. "Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules," Working Papers 2010-008, Federal Reserve Bank of St. Louis.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2011. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Working Papers CoFie-02-2011, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
- Adam Karp & Gary Van Vuuren, 2019. "Investment Implications Of The Fractal Market Hypothesis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 1-27, March.
- Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
- Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000.
"Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation,"
Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
- Andrew Lo & Harry Mamaysky & Jiang Wang, 1999. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Computing in Economics and Finance 1999 402, Society for Computational Economics.
- Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," NBER Working Papers 7613, National Bureau of Economic Research, Inc.
- Lijun Wang & Haizhong An & Xiaohua Xia & Xiaojia Liu & Xiaoqi Sun & Xuan Huang, 2014. "Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, May.
- Dan Anghel, 2013. "How Reliable is the Moving Average Crossover Rule for an Investor on the Romanian Stock Market?," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 5(2), pages 089-115, December.
- Vaihekoski, Mika, 2008. "History of finance research and education in Finland : the first thirty years," Research Discussion Papers 18/2008, Bank of Finland.
- Andreas Krause, 2009. "Evaluating the performance of adapting trading strategies with different memory lengths," Papers 0901.0447, arXiv.org.
- Shynkevich, Andrei, 2013. "Time-series momentum as an intra- and inter-industry effect: Implications for market efficiency," Journal of Economics and Business, Elsevier, vol. 69(C), pages 64-85.
- Shangkun Deng & Kazuki Yoshiyama & Takashi Mitsubuchi & Akito Sakurai, 2015. "Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 49-89, January.
- Fernando Fernández-Rodríguez & Christian González-Martel & Simón Sosvilla-Rivero, "undated". "Optimisation of Technical Rules by Genetic Algorithms: Evidence from the Madrid Stock Market," Working Papers 2001-14, FEDEA.
- He, Xue-Zhong & Li, Kai, 2015. "Profitability of time series momentum," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 140-157.
- Isakov, Dusan & Marti, Didier, 2011. "Technical Analysis with a Long-Term Perspective: Trading Strategies and Market Timing Ability," FSES Working Papers 421, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Lukas Menkhoff & Mark P. Taylor, 2007.
"The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis,"
Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
- Menkhoff, Lukas & Taylor, Mark P., 2006. "The Obstinate Passion of Foreign Exchange Professionals : Technical Analysis," The Warwick Economics Research Paper Series (TWERPS) 769, University of Warwick, Department of Economics.
- Menkhoff, Lukas & Taylor, Mark P., 2006. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Hannover Economic Papers (HEP) dp-352, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- Menkhoff, Lukas & Taylor, Mark P., 2006. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Economic Research Papers 269739, University of Warwick - Department of Economics.
- Liu, Xiaojia & An, Haizhong & Wang, Lijun & Jia, Xiaoliang, 2017. "An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms," Applied Energy, Elsevier, vol. 185(P2), pages 1778-1787.
- Manahov, Viktor & Hudson, Robert & Hoque, Hafiz, 2015. "Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 85-98.
- Hsu, Po-Hsuan & Hsu, Yu-Chin & Kuan, Chung-Ming, 2010. "Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 471-484, June.
More about this item
Keywords
neural network algorithm; fisher linear discriminant; quantitative trading;All these keywords.
JEL classification:
- R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
- Z0 - Other Special Topics - - General
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:ijefaa:v:9:y:2017:i:2:p:133-141. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.