Abstract— Portfolio turnover has taken an important place in portfolio management because of its impact on the trading cost. There are few methods to assess the probability of a portfolio turnover, the most famous one being the representation with signed graphs and even fewer ways to extract from a range of assets the portfolio with the smallest probability of turnover. We use the signed graph method combined to several optimization algorithms to solve this problem. We demonstrate the efficiency of our methods with the data provided by an American asset management company and show how it is possible to extract from a range of assets the portfolio with the lowest turnover probability.
Index Terms— Graph theory, portfolio turnover management, metaheuristic algorithms.
C. Perina is with the Department of Mathematics and Computer Science, Faculty of Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, United Kingdom C. Perina is also with Department of Mathematics, Institut National des Sciences Appliquées de Rouen, France (e-mail: cedrik.perina@insa-rouen.fr)
N Buckley and A. K. Nagar are with the Department of Mathematics and Computer Science, Faculty of Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, United Kingdom.
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Cite: C. Perina, N. Buckley, and A. K. Nagar, " Application of Metaheuristics Algorithms and Signed Graphs to Portfolio Turnover Management," International Journal of Innovation, Management and Technology vol. 8, no. 2, pp. 161-165, 2017.