TAILIEUCHUNG - A First Application of Independent Component Analysis to Extracting Structure from Stock Returns

This forecast is designed to support policy-makers through providing a view on future vehicle numbers, ages, types and the potential implications for costs going forward. This work will also help to guide suppliers and Government, including the Cabinet Office and the Department of Business, Innovation and Skills, regarding the size and nature of the ‘pipeline’ of future rolling stock construction, potential for life extension, and requirements for future re-engineering to meet technical and customer requirements. . | A First Application of Independent Component Analysis to Extracting Structure from stock Returns Andrew D. Back Brain Science Institute The Institute of Physical and Chemical Research RIKEN Andreas s. Weigend Leonard N. stern School of Business New York University November 1997 Working Paper Series Stern IS 97-22 Center for Digital Economy Research Stem Schoo of Business Working Paper LS-97-22 Working Paper IS-97-22 Information Systems Leonard N. Stern School of Business New York University Forthcoming in International Journal of Neural Systems Vol. 8 1997 Special Issue on Data Mining in Finance http www. stern. nyu. edu aweigend Research Papers ICA A First Application of Independent Component Analysis to Extracting Structure from Stock Returns Andrew D. Back Brain Science Institute The Institute of Physical and Chemical Research RIKEN 2-1 Hirosawa Wako-shi Saitama 351-0198 Japan . go. jp absl back Andreas s. Weigend Department of Information Systems Leonard N. Stern School of Business New York University 44 West Fourth Street MEC 9-74 New York NY 10012 USA WWW. stern. nyu. edu aweigend Abstract. This paper discusses the application of a modern signal processing technique known as independent component analysis ICA or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components ICs . This can be viewed as a factorization of the portfolio since joint probabilities become simple products in the coordinate system of the ICs. We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories i infrequent but large shocks responsible for the major changes in the stock prices and ii frequent

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