- author = "Maria Gaila and Vassilios Vassiliadis and Nikolaos Kondakis and George Dounias",
- title = "Genetic {Programming}-based trading system: An application on the {NASDAQ} 100 stock index",
- booktitle = "IMAEF-2012",
- year = "2012",
- editors = "Spyridon Symeonides and Nikos Benos and Yorgos Goletsis",
- address = "Ioannina, Greece",
- month = "21-22 " # jun,
- organisation = "Department of Economics of the University of Ioannina",
- keywords = "genetic algorithms, genetic programming, artificial intelligence, technical indicators, trading system",
-
URL = "
http://mde-lab.aegean.gr/images/stories/docs/CC79.pdf",
- size = "2 pages",
- abstract = "Nowadays, the vast amount of socio-economic and market information play an important role in the formation of any financial market's characteristics and overall behaviour. As a consequence, the uncertainty and complexity of the financial markets immensely increase. Based on the aforementioned, a crucial task for potential traders is to identify market trends and detect potential investment opportunities. What is more, individually traditional trading strategies based on technical indicators, such as certain statistical and econometric forecasting methods, have proven inadequate to adapt to the rapidly evolving market conditions. Conversely, when combining such indicators, there is a higher possibility of more promising results. The field of Artificial Intelligence provides a range of metaheuristic algorithms for dealing with complex tasks, as the above mentioned. Specifically, in this study an intelligent algorithm based on the principles of Darwinian evolution, namely Genetic Programming, is proposed. The main aim of the study is to combine a number of technical indicators and other financial heuristics, with the use of Genetic Programming, in order to detect potential market signals for trading. One of the main characteristics of Genetic Programming is its ability to manipulate complex technical rules/heuristics in a way that optimizes the investors expected outcome. The proposed trading system is applied to the NASDAQ 100 stock index. Particularly, the dataset comprises daily adjusted closing prices of the stock index, for the period January 1985 to December 2011. Regarding the experimental set-up, the entire dataset is divided into three sub-periods: training, validation and forecasting (trading) interval. The algorithmic trading system is applied to the training interval in order to provide a number of technical rules. The quality (fitness) of these rules is then tested in the validation period, based on the criterion of profit maximization. Finally, the fittest rule is applied to the forecasting time period, which consists of unknown data.",
-
notes = "broken Jan 2024
http://www.econ.uoi.gr/imaef2012/programme.php
broken Jan 2024 http://mde-lab.aegean.gr/research-material",
