Data Mining Tool for Decision Support in Stock Market

Sung-Dong Kim

Abstract

Stock investors want to make continuous profits in stock market. They have to choose profitable stocks and to follow the appropriate trading policy to achieve their goal. It is difficult for individual investors to determine what to buy and when to buy and sell. This paper proposes a data mining tool for stock investors’ decision support by recommending profitable stocks and proposing the trading policy. The proposed tool provides three functions: stock data management, stock price prediction model generation by applying the machine learning algorithms and the investment simulation for seeking the profitable trading policy. Users can generate and test the stock price prediction model by selecting their own technical indicators, simulate the trading and select the best trading policy through the evaluation of the trading results.

References

  1. C. F. Tsai and S. P. Wang. 2009. Stock Price Forecasting by Hybrid Machine Learning Techniques. Proceedings of the International MultiConference of Engineers and Computer Scientists. Vol. 1. 755-760.
  2. J. R. Quinlan. 1993. C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers.
  3. K. S. Kannan, P. S. Sekar, M. M. Sathik, and P. Arumugam. 2010. Financial Stock Market Forecast using Data Mining Techniques. Proceedings of the International MultiConference of Engineers and Computer Scientists.
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Paper Citation


in Harvard Style

Kim S. (2013). Data Mining Tool for Decision Support in Stock Market . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 473-477. DOI: 10.5220/0004240804730477


in Bibtex Style

@conference{icaart13,
author={Sung-Dong Kim},
title={Data Mining Tool for Decision Support in Stock Market},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={473-477},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004240804730477},
isbn={978-989-8565-39-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Data Mining Tool for Decision Support in Stock Market
SN - 978-989-8565-39-6
AU - Kim S.
PY - 2013
SP - 473
EP - 477
DO - 10.5220/0004240804730477