An efficient and effective approach for mining a group stock portfolio using mapreduce

Chun Hao Chen, Chao-Chun Chen, Yusuke Nojima

研究成果: Article

1 引文 (Scopus)

摘要

Portfolio optimization is always an attractive topic for research. In our previous approach, we proposed a method for mining a group stock portfolio that used grouping genetic algorithms. The derived group stock portfolio represents stocks in the same group that may have similar properties; consequently, a variety of stock portfolios could be offered to investors. However, the evaluation process used by this previous approach is time consuming when the number of stocks or groups increases. To address this problem, the map-reduce technique is considered. Map-reduce is a well-known approach for speeding up the mining process. This paper proposes a map-reduce-based approach to mine groups of stock portfolios and speed up the evolution process while still achieving results as similar as possible to the previous approach. Here, a chromosome represents a mapper number, a group number, a stock part and a portfolio part. Utilizing the mapper number, the chromosomes in a population are divided into subsets and sent to respective mappers, while the reducers execute fitness evaluation and genetic operations. The evolution process is repeated until the terminal conditions are reached. Finally, experiments on a real dataset are conducted to demonstrate the efficiency of the proposed approach.

原文English
頁(從 - 到)S217-S232
期刊Intelligent Data Analysis
21
發行號S1
DOIs
出版狀態Published - 2017 一月 1

指紋

MapReduce
Mining
Chromosomes
Chromosome
Genetic algorithms
Process Mining
Portfolio Optimization
Evaluation
Grouping
Fitness
Experiments
Speedup
Genetic Algorithm
Subset
Demonstrate
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

引用此文

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An efficient and effective approach for mining a group stock portfolio using mapreduce. / Chen, Chun Hao; Chen, Chao-Chun; Nojima, Yusuke.

於: Intelligent Data Analysis, 卷 21, 編號 S1, 01.01.2017, p. S217-S232.

研究成果: Article

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