Fuzzy Candlesticks Forecasting Using Pattern Recognition for Stock Markets

  1. Rodrigo Naranjo 1
  2. Matilde Santos 1
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Libro:
International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings
  1. Manuel Graña (coord.)
  2. José Manuel López-Guede (coord.)
  3. Oier Etxaniz (coord.)
  4. Álvaro Herrero (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Editorial: Springer Suiza

ISBN: 978-3-319-47364-2,  3-319-47364-6,  978-3-319-47363-5,  3-319-47363-8

Año de publicación: 2017

Páginas: 323-333

Congreso: International Conference on Computational Intelligence in Security for Information Systems (9. 2016. San Sebastián)

Tipo: Aportación congreso

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Resumen

This paper presents a prediction system based on fuzzy modeling of Japanese candlesticks. The prediction is performed using the pattern recognition methodology and applying a lazy and nonparametric classification technique, k-Nearest Neighbours (k-NN). The Japanese candlestick chart summarizes the trading period of a commodity with only 4 parameters (open, high, low and close). The main idea of the decision system implemented in this article is to predict with accuracy, based on this vague information from previous sessions, the performance of future sessions. Therefore, investors could have valuable information about the next session and set their investment strategies.

Fuente de los datos: Dialnet