hunting search algorithm

Asset return is associated with uncertainty and always occurs during unexpected fluctuations in economic, social and political conditions, and so forth. In return on assets such as stocks. Fuzzy logic can be one of the best options for modelling asset returns. For this purpose, a rule based fuzzy expert system has been developed to support investment managers in their mid term investment decisions. Considering the non linearity of the portfolio selection problem and its NP Hard, the performance of the proposed fuzzy system is evaluated by the information of 157 companies that have been active in Tehran Stock Exchange between 2008 to 2018 using of Fuzzy Genetic and Fuzzy Hunting Search Algorithms. The system's performance in terms of risk taking and duration of investment was comparable to average market returns. Besides, the performance of the proposed fuzzy system for the risk averse investor in the short run yields good results.

This paper presents a new meta-heuristic solution to find the efficient frontier using the mean-variance approach. Portfolio optimization problem is a quadratic programming model and، changes to NP-hard if the number of assets and constraints has increased، and it cannot be solved using common mathematical methods in a reasonable time. Therefore، a heuristic or meta-heuristic algorithm should be used that is the appropriate solution. This paper optimizes portfolio using a new meta-heuristic algorithm called hunting search algorithm. To determine the strengths and precision of proposed algorithm، a case study is designed using Iran stock market data from 1/3/1389 to 1/3/1390 for big thirty companies. The proposed algorithm finds the efficient frontier precisely and in timely manner. To determine abilities of the algorithm، two verified examples، Hang Sang 31 and Dax100 are also solved with it. Results show that hunting search algorithm has a high speed and high accuracy in order to solve portfolio optimization problems، and it can be used to find the efficient frontiers in various portfolio optimization problems.