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.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Vision and Pattern Recognition
- Artificial Intelligence