- author = "Tina Yu and Shu-Heng Chen and Tzu-Wen Kuo",
- title = "Discovering Financial Technical Trading Rules Using Genetic Programming with Lambda Abstraction",
- booktitle = "Genetic Programming Theory and Practice {II}",
- year = "2004",
- editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel",
- chapter = "2",
- pages = "11--30",
- address = "Ann Arbor",
- month = "13-15 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, modular genetic programming, lambda abstraction modules, higher-order functions, financial trading rules, buy-and-hold, S&P 500 index, automatically defined functions, ADF, PolyGP system, stock market, technical analysis, constrained syntactic structure, strongly typed genetic programming, STGP, financial time series, lambda abstraction GP",
- ISBN = "0-387-23253-2",
-
URL = "
http://www.cs.mun.ca/~tinayu/Publications_files/gptp2004.pdf",
-
DOI = "
doi:10.1007/0-387-23254-0_2",
- size = "20 pages",
- abstract = "We applied genetic programming with a lambda abstraction module mechanism to learn technical trading rules based on S&P 500 index from 1982 to 2002. The results show strong evidence of excess returns over buy-and-hold after transaction cost. The discovered trading rules can be interpreted easily; each rule uses a combination of one to four widely used technical indicators to make trading decisions. The consensus among these trading rules is high. For the majority of the testing period, 80percent of the trading rules give the same decision. These rules also give high transaction frequency. Regardless of the stock market climate, they are able to identify opportunities to make profitable trades and out-perform buy-and-hold.",
-
notes = "part of \cite{oreilly:2004:GPTP2}
",
