Abstract
In portfolio management, stock selection and evaluation can be based on a variety of financial attributes over a period of time. It has been shown recently by Irukulapati et al. that long term portfolio management strategy using attribute selection and combinatorial fusion can not only achieve better results than individual attributes but also exceed the performance of the Russell 2000 index. In this paper, we propose a method to compute the attribute scoring system using weighted average by recency (AR) giving more weight to scores at the time closer to the present. We then show, by market testing, that our results perform better than that of Irukulapati et al. in a majority of cases as well as the Russell 2000.
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Wang, X., Ho-Shek, J., Ondusko, D., Frank Hsu, D. (2019). Improving Portfolio Performance Using Attribute Selection and Combination. In: Esposito, C., Hong, J., Choo, KK. (eds) Pervasive Systems, Algorithms and Networks. I-SPAN 2019. Communications in Computer and Information Science, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-030-30143-9_5
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DOI: https://doi.org/10.1007/978-3-030-30143-9_5
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