The discovery of financial market behavior integrated data mining on ETF in Taiwan

Bo Wen Yang*, Mei Chen Wu, Chiou Hung Lin, Chiung Fen Huang, An-Pin Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In practice, many physics principles have been employed to derive various models of financial engineering. However, few studies have been done on the feature selection of finance on time series data. The purpose of this paper is to determine if the behavior of market participant can be detected from historical price. For this purpose, the proposed algorithm utilizes back propagation neural network (BPNN) and works with new feature selection approach in data mining, which is used to generate more information of market behavior. This study is design for exchange-traded fund (ETF) to develop the day-trade strategy with high profit. The results show that BPNN hybridized with financial physical feature, as compared with the traditional approaches such as random walk, typically result in better performance.

Original languageEnglish
Title of host publicationHarmony Search Algorithm - Proceedings of the 2nd International Conference on Harmony Search Algorithm, ICHSA 2015
EditorsZong Woo Geem, Joong Hoon Kim
PublisherSpringer Verlag
Pages285-294
Number of pages10
ISBN (Print)9783662479254
DOIs
StatePublished - 1 Jan 2016
Event2nd International Conference on Harmony Search Algorithm, ICHSA 2015 - Seoul, Korea, Republic of
Duration: 19 Aug 201521 Aug 2015

Publication series

NameAdvances in Intelligent Systems and Computing
Volume382
ISSN (Print)2194-5357

Conference

Conference2nd International Conference on Harmony Search Algorithm, ICHSA 2015
CountryKorea, Republic of
CitySeoul
Period19/08/1521/08/15

Keywords

  • Back-propagation neural network (BPNN)
  • Behavior discovery
  • Data mining
  • Exchangetraded fund (ETF)
  • Financial physics

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