Vague Expert Information/Recommendation in Portfolio Optimization-an Empirical Study

Marcin Bartkowiak , Aleksandra Rutkowska


In a real market, the quantity of information and recommendations is constantly increasing. However, recommendations are often in linguistic form and no one recommendation is based on a single piece of information. Predictions of individuals and their confidence can vary greatly. Thus, a problem arises concerning different (disjointed or partially coherent) vague opinions of various experts or information from multiple sources. In this paper, we introduce extensions of the Black—Litterman model with linguistic expressed views from different experts/many sources. The study focuses on empirical analysis of proposed fuzzy approach results. In the presented modification every expert presents its opinion about particular assets according to intervals, and then an experton for each asset is built. In the portfolio optimization, we use aggregated views presented by interval, which is the mean value of the experton built on particular views. In an empirical study, we built and tested 10,000 portfolios based on recommendation from EquityRT, which was made by 14–49 experts monthly between November 2017 and June 2019 for the 29 biggest companies from the US market and different sectors. The annual average return from portfolios is 9.5–11.8%, depending on the width of the intervals and additional constraints. This approach allows people to formulate intuitive views and view the opinions of a group of experts.
Author Marcin Bartkowiak (WIiGE / KMS)
Marcin Bartkowiak,,
- Department of Applied Mathematics
, Aleksandra Rutkowska (WIiGE / KMS)
Aleksandra Rutkowska,,
- Department of Applied Mathematics
Journal seriesAxioms, ISSN 2075-1680, (N/A 20 pkt)
Issue year2020
Publication size in sheets0.55
Keywords in Polishmodel Blacka-Littermana, expertony, optymalizacja portfela, liczby rozmyte
Keywords in EnglishBlack–Litterman model; experton; portfolio optimization; fuzzy number
Languageen angielski
Score (nominal)20
Score sourcejournalList
ScoreMinisterial score = 20.0, 16-04-2020, ArticleFromJournal
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