A Fuzzy Approach to Selecting Top Performing Stocks
Date Issued
2010
Date
2010
Author(s)
Lin, Ai-Chi
Abstract
With advance in information technology, a large amount of financial reports can be accessed easily. Analyzing those financial reports can help investors to select investment targets and plan their investment strategies. In this thesis, we propose an effective method to select top performing stocks. The proposed method consists of four phases. First, we extract financial activity phrases and financial ratios from each financial report and transform it into a feature vector. Second, we utilize the affinity propagation (AP) algorithm to group similar feature vectors into clusters, and identify exemplar of each cluster. Third, we use the fuzzy k-nearest neighbors (FKNN) algorithm to compute membership degrees towards each class for a feature vector. Finally, we use these fuzzified feature vectors as references to rank new feature vectors and select top performing stocks from the ranked list. Since we utilize the AP algorithm to reduce the chance for the FKNN algorithm to choose bad references when fuzzifying the membership degrees of a feature vector, the proposed method provides a good channel to select top performing stocks. The experimental results show that the proposed method outperforms the SVM method in terms of average trading profit.
Subjects
affinity propagation algorithm
fuzzy k-nearest neighbors algorithm
stock price prediction
financial report
Type
thesis
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