A multidimensional market analysis method using level-velocity-momentum time-series vector space

Shin Ito, Yasushi Kiyoki

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Numerous stock market analysis methods have been proposed from simple moving average to the use of artificial intelligence such as neural networks and Bayesian networks. In this paper, we introduce a new concept and a methodology that enable predictability of asset price movement in the market by way of inference from the past data. We use schema to describe an economic instance, and a set of schema in time series to describe the flow of economic instances in the past. Within the schema, we introduce a concept of velocity and momentum to effectively characterize the dynamic nature of the market. We compare the current and the past instances to identify resemblance and take inference as a predictive capability of future asset price movement.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXV
PublisherIOS Press
Pages158-173
Number of pages16
Volume260
ISBN (Electronic)9781614993612
ISBN (Print)9781614993605
DOIs
Publication statusPublished - 2014 Jan 14

Fingerprint

Vector spaces
Time series
Momentum
Economics
Bayesian networks
Artificial intelligence
Neural networks
Financial markets

Keywords

  • Market
  • Momentum
  • Multidimensional
  • Prediction
  • Schema
  • Time-series
  • Vector space analysis
  • Velocity

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

A multidimensional market analysis method using level-velocity-momentum time-series vector space. / Ito, Shin; Kiyoki, Yasushi.

Information Modelling and Knowledge Bases XXV. Vol. 260 IOS Press, 2014. p. 158-173.

Research output: Chapter in Book/Report/Conference proceedingChapter

Ito, Shin ; Kiyoki, Yasushi. / A multidimensional market analysis method using level-velocity-momentum time-series vector space. Information Modelling and Knowledge Bases XXV. Vol. 260 IOS Press, 2014. pp. 158-173
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