Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/32035
Title: Is it possible to predict long-term success with k-NN? Case study of four market indices (FTSE100, DAX, HANGSENG, NASDAQ)
Authors: Shi, Y.
Gorban, A. N.
Yang, T. Y.
First Published: 2014
Presented at: 2nd International Conference on Mathematical Modeling in Physical Sciences 2013 (IC-MSQUARE 2013)
Publisher: IOP Publishing
Citation: Journal of Physics: Conference Series 490 (2014) 012082
Abstract: This case study tests the possibility of prediction for 'success' (or 'winner') components of four stock & shares market indices in a time period of three years from 02-Jul-2009 to 29-Jun-2012.We compare their performance ain two time frames: initial frame three months at the beginning (02/06/2009-30/09/2009) and the final three month frame (02/04/2012-29/06/2012).To label the components, average price ratio between two time frames in descending order is computed. The average price ratio is defined as the ratio between the mean prices of the beginning and final time period. The 'winner' components are referred to the top one third of total components in the same order as average price ratio it means the mean price of final time period is relatively higher than the beginning time period. The 'loser' components are referred to the last one third of total components in the same order as they have higher mean prices of beginning time period. We analyse, is there any information about the winner-looser separation in the initial fragments of the daily closing prices log-returns time series.The Leave-One-Out Cross-Validation with k-NN algorithm is applied on the daily log-return of components using a distance and proximity in the experiment. By looking at the error analysis, it shows that for HANGSENG and DAX index, there are clear signs of possibility to evaluate the probability of long-term success. The correlation distance matrix histograms and 2-D/3-D elastic maps generated from ViDaExpert show that the 'winner' components are closer to each other and 'winner'/'loser' components are separable on elastic maps for HANGSENG and DAX index while for the negative possibility indices, there is no sign of separation.
DOI Link: 10.1088/1742-6596/490/1/012082
ISSN: 1742-6588
eISSN: 1742-6596
Links: http://iopscience.iop.org/1742-6596/490/1/012082/
http://hdl.handle.net/2381/32035
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (CC BY 3.0) (http://creativecommons.org/licenses/by/3.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Appears in Collections:Published Articles, Dept. of Mathematics

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