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Effective application of improved profit-mining algorithm for the interday trading model.

Yu-Lung Hsieh1, Don-Lin Yang1, Jungpin Wu2

  • 1Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhwa Road, Taichung 40724, Taiwan.

Thescientificworldjournal
|April 2, 2014
PubMed
Summary
This summary is machine-generated.

Traditional association rule mining fails in financial markets. This study introduces novel profit-mining algorithms, offering investors rules for profit, risk, and winning rates, enhancing trading decisions.

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Area of Science:

  • Financial data mining
  • Algorithmic trading strategies

Background:

  • Association rule mining is widely used but ineffective for financial markets.
  • Traditional methods lack metrics crucial for investors, such as profit, risk, and winning rates.

Purpose of the Study:

  • To develop and present effective profit-mining algorithms tailored for financial market trading.
  • To provide investors with actionable trading rules incorporating profit, risk, and winning rate.

Main Methods:

  • Proposed novel profit-mining algorithms based on an interday trading model.
  • Detailed the inner workings of the improved profit-mining algorithm.
  • Conducted experiments using real-world trading data to validate the approach.

Main Results:

  • The developed algorithms generate profit rules with explicit profit, risk, and winning rate information.
  • Experimental results demonstrate the practicality and effectiveness of the profit-mining approach.
  • The algorithms show good performance across various financial datasets.

Conclusions:

  • The proposed profit-mining algorithms represent a significant advancement for financial market analysis.
  • This approach offers a more suitable framework for investors seeking to optimize trading strategies.
  • The study highlights the potential of profit-mining in the nascent field of financial data analysis.