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An online portfolio selection algorithm using clustering approaches and considering transaction costs

Khedmati, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1016/j.eswa.2020.113546
  3. Publisher: Elsevier Ltd , 2020
  4. Abstract:
  5. This paper presents an online portfolio selection algorithm based on pattern matching principle where it makes a decision on the optimal portfolio in each period and updates the optimal portfolio at the beginning of each period. The proposed method consists of two steps: i) sample selection, ii) portfolio optimization. First, in the sample selection, clustering algorithms including k-means, k-medoids, spectral and hierarchical clustering are applied to discover time windows (TW) similar to the recent time window. Then, after finding the similar time windows and predicting the market behavior of the next day, the optimum function along with the transaction cost is used in the portfolio optimization step in which, four algorithms including KMNLOG, KMDLOG, SPCLOG and HRCLOG are proposed for this purpose. The presented algorithms are applied on 5 different datasets with different characteristics including different markets, stocks, and time periods, and their performance has been evaluated. The results show that the provided algorithms in this paper, have better performance in terms of efficiency compared to the algorithms provided in the literature. © 2020 Elsevier Ltd
  6. Keywords:
  7. Algorithmic trading ; Clustering ; Data mining ; Online portfolio selection ; Commerce ; Costs ; Financial data processing ; Financial markets ; Hierarchical clustering ; Pattern matching ; Clustering approach ; Market behavior ; Online portfolios ; Optimal portfolios ; Portfolio optimization ; Sample selection ; Time windows ; Transaction cost ; K-means clustering
  8. Source: Expert Systems with Applications ; Volume 159 , November , 2020
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0957417420303705