Prediction of foreign exchange market states with support vector machine

Kei Shioda, Shangkun Deng, Akito Sakurai

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

This paper proposes a method to give an early warning of an abrupt change of price in a foreign exchange market. Volatility is a quantification of how much a value moves in a time series. It is now customary to assume that volatility of foreign exchange markets is time-varying. Intuitively we observe that there are at least two states or regimes: one is with low volatility and the other is with high volatility. Under high volatility regime, there are chances of high returns but with very high risks. For many nonprofessional traders, the high volatility regimes are periods that they loose with high probability. We believe that giving an early alert of starts of high volatility regimes is beneficial for many nonprofessional traders and for the foreign exchange markets. There are many studies to predict volatility of foreign exchange market by using ARCH or GARCH model with possibly hidden Markov model to represent regimes. We, though, focused on prediction of volatility levels by using machine learning techniques so that we get a good prediction. We particularly focused on support vector machine that learns sequences of volatility levels estimated by hidden Markov model and makes prediction of the level. We performed numerical experiments on real data and obtained good performance.

本文言語English
ホスト出版物のタイトルProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
ページ327-332
ページ数6
DOI
出版ステータスPublished - 2011 12月 1
イベント10th International Conference on Machine Learning and Applications, ICMLA 2011 - Honolulu, HI, United States
継続期間: 2011 12月 182011 12月 21

出版物シリーズ

名前Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
1

Other

Other10th International Conference on Machine Learning and Applications, ICMLA 2011
国/地域United States
CityHonolulu, HI
Period11/12/1811/12/21

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 人間とコンピュータの相互作用

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