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Малахова, А. А.
Старова, О. В.
Яркова, С. А.
Данилова, А. С.
Зданович, М. Я.
Зябликов, Д. В.
Кравцов, Д. И.
2021-08-13T09:28:28Z
2021-08-13T09:28:28Z
2020
Малахова, А. А. Reward-to-Variability Ratio as a Key Performance Indicator in Financial Manager Efficiency Assessment [Текст] / А. А. Малахова, О. В. Старова, С. А. Яркова, А. С. Данилова, М. Я. Зданович, Д. В. Зябликов, Д. И. Кравцов // Artificial Intelligence and Bioinspired Computational Methods. — 2020. — Т. 1225. — С. 598-613
https://link.springer.com/chapter/10.1007/978-3-030-51971-1_49
https://elib.sfu-kras.ru/handle/2311/142267
In this paper computational techniques to process financial data and to assess management efficiency are proposed. Personnel evaluation process is formalized on the basis of the proposed key performance indicators based on portfolio efficiency criteria. Personnel efficiency is assessed via the excessive portfolio return over average market performance indicators per unit of risk. Alternative measures to evaluate risk are formulated. The proposed downside risk measures are implemented into portfolio performance evaluation criteria. Comparative analysis of the introduced portfolio performance evaluation criteria is held. Case study via the Trading Organiser ‘Moscow Exchange’ is performed. The experimental results prove that the introduced portfolio performance evaluation criteria yield better results than the coefficients which do not take into account downside risk measures. It is concluded that the proposed modified ‘reward-to-variability’ ratio can be incorporated into the system of key performance indicators for assessing financial management efficiency. #CSOC1120.
Key performance indicator Portfolio performance Sharpe coefficient Reward-to-variability ratio Reward-to-volatility ratio Value at risk
Reward-to-Variability Ratio as a Key Performance Indicator in Financial Manager Efficiency Assessment
Journal Article
Published Journal Article
598-613
2021-08-13T09:28:28Z
10.1007/978-3-030-51971-1_49
Институт управления бизнес-процессами и экономики
Кафедра экономики и управление бизнес-процессами
Artificial Intelligence and Bioinspired Computational Methods
Q3
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