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Identifying Market Behaviours Using European Stock Index Time Series by a Hybrid Segmentation Algorithm

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Abstract

The discovery of useful patterns embodied in a time series is of fundamental relevance in many real applications. Repetitive structures and common type of segments can also provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterization methodology combining a hybrid genetic algorithm and a clustering technique to automatically group common patterns from this kind of financial time series and address the problem of identifying stock market prices trends. This hybrid genetic algorithm includes a local search method aimed to improve the quality of the final solution. The local search algorithm is based on maximizing a likelihood ratio, assuming normality for the series and the subseries in which the original one is segmented. To do so, we select two stock market index time series: IBEX35 Spanish index (closing prices) and a weighted average time series of the IBEX35 (Spanish), BEL20 (Belgian), CAC40 (French) and DAX (German) indexes. These are processed to obtain segments that are mapped into a five dimensional space composed of five statistical measures, with the purpose of grouping them according to their statistical properties. Experimental results show that it is possible to discover homogeneous patterns in both time series.

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Notes

  1. Technical analysis is the science of recording, usually in graphic form, the actual history of trading (price changes, volume of transactions, etc.) in a certain stock and then deducing from that pictured history the probable future trend [34]. Consequently, technical indicators are numerical values calculated on the basis of past prices, volumes, and other market statistics and used to forecast future price movements.

  2. Trend analysis studies also include the well-known Elliott Wave Principle, Dow Theory and related vocabulary, as primary or secondary trends.

  3. Note that the first and last points of the chromosome are always considered cut points.

  4. Other statistical and temporal characteristics of each segment were tested, but the experimental results were better with these five metrics.

  5. We have considered linear trend, because of the reduced length of the segments.

  6. See https://es.finance.yahoo.com/.

  7. The stock market prices are affected by a number of factors and events, some of which directly influence stock prices while others do so indirectly (internal developments, world events...). The stock price of a company and the market in general may be affected by world events, such as wars and civil unrest, natural disasters and terrorism. Stock market prices are affected by business fundamentals, company and world events, human psychology, and much more (in general, economy, inflation, terrorism, world news...).

  8. There are 24 well-known financial patterns [2], and these are used to verify the segmentation and clustering results.

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Acknowledgements

This work has been partially subsidized by the TIN2014-54583-C2-1-R and the TIN2015-70308-REDT projects of the Spanish Ministerial Commission of Science and Technology (MINECO. Spain), FEDER funds (EU) and the P11-TIC-7508 project of the “Junta de Andalucía” (Spain). Antonio M. Durán-Rosal’s research has been subsidized by the FPU Predoctoral Program (Spanish Ministry of Education and Science), grant reference FPU14/03039.

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Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Córdoba, Rabanales Campus, Albert Einstein Building, 14071, Córdoba, Spain

    Antonio M. Durán-Rosal, Mónica de la Paz-Marín, Pedro A. Gutiérrez & César Hervás-Martínez

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  1. Antonio M. Durán-Rosal
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  2. Mónica de la Paz-Marín
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  3. Pedro A. Gutiérrez
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  4. César Hervás-Martínez
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Correspondence to Antonio M. Durán-Rosal.

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Durán-Rosal, A.M., de la Paz-Marín, M., Gutiérrez, P.A. et al. Identifying Market Behaviours Using European Stock Index Time Series by a Hybrid Segmentation Algorithm. Neural Process Lett 46, 767–790 (2017). https://doi.org/10.1007/s11063-017-9592-8

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