The Application of Conflict Measure to Estimating Incoherence of Analyst's Forecasts about the Cost of Shares of Russian Companies
Author(s) -
Andrey G. Bronevich,
Alexander Lepskiy,
Henry Penikas
Publication year - 2015
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2015.07.079
Subject(s) - measure (data warehouse) , axiom , computer science , conflict analysis , set (abstract data type) , function (biology) , shapley value , investment (military) , econometrics , investment banking , value (mathematics) , monotone polygon , operations research , mathematical economics , actuarial science , economics , data mining , mathematics , conflict resolution , finance , game theory , political science , machine learning , geometry , evolutionary biology , biology , politics , law , programming language
This paper is devoted to modern approaches to the estimation of external conflict in the theory of evidence based on axioms. The conflict measure is defined on the set of beliefs obtained from several sources of information. It is shown that the conflict measure should be a monotone set function with respect to sets of beliefs. Some robust procedures for evaluation of conflict measure that are stable to small changes in evidences are introduced and discussed. The analysis of conflict among forecasts about the value of shares of Russian companies of investment banks is presented. In this analysis the conflict measure estimates inconsistency of recommendations of investment banks, while the Shapley values of this measure on the set of evidences characterize the contribution of each investment bank to the overall conflict. The relationship between conflict and precision of forecasts is also investigated
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