@inproceedings{1277397,
title = {Volatility forecasting using time series data mining
and evolutionary computation techniques},
address = {London},
author = {Irwin Ma and Tony Wong and Thiagas Sankar},
booktitle = {GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation},
editor = {Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener},
month = {7-11 July},
pages = {2262--2262},
publisher = {ACM Press},
url = {http://doi.acm.org/10.1145/1276958.1277397},
volume = {2},
year = {2007},
abstract = {Traditional parametric methods have limited success in
estimating and forecasting the volatility of financial
securities. Recent advance in evolutionary computation
has provided additional tools to conduct data mining
effectively. The current work applies the genetic
programming in a Time Series Data Mining framework to
characterise the S&P100 high frequency data in order to
forecast the one step ahead integrated volatility.
Results of the experiment have shown to be superior to
those derived by the traditional methods.},
organisation = {ACM SIGEVO (formerly ISGEC)}, publisher_address = {New York, NY, USA}, isbn13 = {978-1-59593-697-4}, notes = {GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).
ACM Order Number 910071},
keywords = {100 Applications: Poster, Real-World S&P algorithms, data economics, financial forecasting, genetic mining, programming, volatility, }
}
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