research-article

Stock Prediction by Searching for Similarities in Candlestick Charts

Authors:

Chih-Fong Tsai,

Zen-Yu QuanAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 5, Issue 2

Article No.: 9, Pages 1 - 21

https://doi.org/10.1145/2591672

Published: 01 July 2014 Publication History

Abstract

The aim of stock prediction is to effectively predict future stock market trends (or stock prices), which can lead to increased profit. One major stock analysis method is the use of candlestick charts. However, candlestick chart analysis has usually been based on the utilization of numerical formulas. There has been no work taking advantage of an image processing technique to directly analyze the visual content of the candlestick charts for stock prediction. Therefore, in this study we apply the concept of image retrieval to extract seven different wavelet-based texture features from candlestick charts. Then, similar historical candlestick charts are retrieved based on different texture features related to the query chart, and the “future” stock movements of the retrieved charts are used for stock prediction. To assess the applicability of this approach to stock prediction, two datasets are used, containing 5-year and 10-year training and testing sets, collected from the Dow Jones Industrial Average Index (INDU) for the period between 1990 and 2009. Moreover, two datasets (2010 and 2011) are used to further validate the proposed approach. The experimental results show that visual content extraction and similarity matching of candlestick charts is a new and useful analytical method for stock prediction. More specifically, we found that the extracted feature vectors of 30, 90, and 120, the number of textual features extracted from the candlestick charts in the BMP format, are more suitable for predicting stock movements, while the 90 feature vector offers the best performance for predicting short- and medium-term stock movements. That is, using the 90 feature vector provides the lowest MAPE (3.031%) and Theil’s U (1.988%) rates in the twenty-year dataset, and the best MAPE (2.625%, 2.945%) and Theil’s U (1.622%, 1.972%) rates in the two validation datasets (2010 and 2011).

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Index Terms

Stock Prediction by Searching for Similarities in Candlestick Charts
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Decision support systems

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 5, Issue 2

July 2014

82 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/2659230

Editor:
Hsinchun Chen
University of Arizona, USA

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Publication History

Published: 01 July 2014

Accepted: 01 February 2014

Revised: 01 October 2013

Received: 01 March 2013

Published in TMIS Volume 5, Issue 2

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Huang WWang HWang S(2024)A structural VAR and VECM modeling method for open-high-low-close data contained in candlestick chartFinancial Innovation10.1186/s40854-024-00622-610:1Online publication date: 5-Mar-2024
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