System portfolio selection based on GRA method under hesitant fuzzy environment

The hesitant fuzzy set(HFS) is an important tool to deal with uncertain and vague information.In equipment system portfolio selection, the index attribute of the equipment system may not be expressed by precise data;it is usually described by qualitative information and expressed as multiple possible values.We propose a method of equipment system portfolio selection under hesitant fuzzy environment.The hesitant fuzzy element(HFE) is used to describe the index and attribute values of the equipment system.The hesitation degree of HFEs measures the uncertainty of the criterion data of the equipment system.The hesitant fuzzy grey relational analysis(GRA) method is used to evaluate the score of the equipment system, and the improved HFE distance measure is used to fully consider the influence of hesitation degree on the grey correlation degree.Based on the score and hesitation degree of the equipment system,two portfolio selection models of the equipment system and an equipment system portfolio selection case is given to illustrate the application process and effectiveness of the method.

Online risk‑based portfolio allocation on subsets of crypto assets applying a prototype‑based clustering algorithm

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

Consumer choices under new payment methods

This study suggests a payment portfolio model that includes new payment methods that have emerged from the development of cryptocurrency markets and central bank digital currencies(CBDCs).Our model analyzes the optimal payment choice for consumers under various macroeconomic conditions.We determine that an individual economic agent chooses payment methods under specific conditions by incorporating policy interest rates on CBDCs and stablecoins used on cryptocurrency exchanges.We analyze the impacts of CBDCs and stablecoins on the choice of whether to use cash or deposits.We also examine how the agent changes her portfolio compositions in response to exogenous macroeconomic policies.If a government replaces cash with a CBDC,the convenience of digital currency would not affect consumer choices.The higher the government’s interest rate on CBDCs,the more consumers will use CBDCs than deposits.

An Approach of Reducing Overall Level of Export Fluctuations of the Export-oriented Countries

Overall level of export fluctuations of the export-oriented countries with rising export volume partly stem from the market failure caused by free choice of export enterprises,some government intervention thus may be necessary.To reduce the level of fluctuations of the export growth rates in these countries,this paper,taking the significant differences of the exports among various markets into account and thus using a new index named relative variance to measure the export volatility risks,proposes a model of merchandise market portfolio,a modified version of Markowitz model,available to provide explicit guidelines for the firms,the industries and even the whole country to optimize the structure of their export markets.An application of this model to the case of China’s apple is then discussed.The results show that the market share of China’s apple in 7 sub-markets should be redistributed drastically.

Portfolio Optimization Model with Transaction Costs

The purpose of the article is to formulate, under the ∞ risk measure, a model of portfolio selection with transaction costs and then investigate the optimal strategy within the proposed. The characterization of a optimal strategy and the efficient algorithm for finding the optimal strategy are given.