Portfolio optimization of the construction sector companies in Malaysia with mean-semi absolute deviation model

Portfolio optimization is an important investment strategy to find the trade-off between the risk and return. In mean-semi absolute deviation model,semi absolute deviation is employed as risk measure while the expected return of the investors is represented by the mean return. The objective of this paper is to construct the optimal portfolio that will minimize the portfolio risk and can achieve the investors target rate of return by using the mean-semi absolute deviation model. The data of this study comprises 20 construction sector companies that listed in Malaysia stock market from July 2011 until June 2016. The results of this paper show that the constructed optimal portfolio can minimize the portfolio risk at the expected rate of return. In addition, the composition of the companies invested in the optimal portfolio is different.Keywords: portfolio risk; return; investment; investor

Portfolio Optimization of Commercial Banks- An Application of Genetic Algorithm

Portfolio optimization, in case of finance, is the trade- off between risk and return to maximize profit or return from the portfolio. Financial regulations are country specific and it depends upon the economic conditions prevailing in the country. The portfolio of a commercial bank can be constrained by regulatory prescription of exposure limits, risk weights and returns from each category of assets. Hence, optimization of return, in case of the loan portfolio, presents a challenging problem due to its large set of local extremes. In this context, Genetic Algorithm is used as a possible solution to optimize the risk-return trade-off and achieve an ideal solution for portfolio optimization. Keywords: Portfolio Management, Risk-Return Trade Off, Commercial Bankin

Portfolio selection problems in practice: a comparison between linear and quadratic optimization models

Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices

Portfolio optimisation with transaction cost

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Portfolio selection is an example of decision making under conditions of uncertainty. In the face of an unknown future, fund managers make complex financial choices based on the investors perceptions and preferences towards risk and return. Since the seminal work of Markowitz, many studies have been published using his mean-variance (MV) model as a basis. These mathematical models of investor attitudes and asset return dynamics aid in the portfolio selection process. In this thesis we extend the MV model to include the cardinality constraints which limit the number of assets held in the portfolio and bounds on the proportion of an asset held (if any is held). We present our formulation based on the Markowitz MV model for rebalancing an existing portfolio subject to both fixed and variable transaction cost (the fee associated with trading). We determine and demonstrate the differences that arise in the shape of the trading portfolio and efficient frontiers when subject to non-cardinality and cardinality constrained transaction cost models. We apply our flexible heuristic algorithms of genetic algorithm, tabu search and simulated annealing to both the cardinality constrained and transaction cost models to solve problems using data from seven real world market indices. We show that by incorporating optimization into the generation of valid portfolios leads to good quality solutions in acceptable computational time. We illustrate this on problems from literature as well as on our own larger data sets

Aplicación de la inteligencia artificial en las inversiones financieras

La aplicación de la Inteligencia Artificial (IA) en la inversión financiera es un área de investigación que ha atraído una gran atención desde los años 90, cuando se produjo un acelerado desarrollo tecnológico. Desde entonces, se han propuesto innumerables enfoques para tratar el problema de la predicción de precios en el mercado de valores, como por ejemplo la optimización de carteras, predicción bursátil mediante IA, análisis de sentimientos financieros y combinaciones que implican dos o más enfoques. Un analista de IA construido para digerir la información financiera de las empresas, la divulgación cualitativa y los indicadores macroeconómicos es capaz de batir a la mayoría de los analistas humanos en las previsiones del precio de las acciones y generar un exceso de rentabilidad en comparación con el seguimiento de los analistas humanos.Grado en Finanzas, Banca y Seguro

Twenty years of linear programming based portfolio optimization

a b s t r a c t Markowitz formulated the portfolio optimization problem through two criteria: the expected return and the risk, as a measure of the variability of the return. The classical Markowitz model uses the variance as the risk measure and is a quadratic programming problem. Many attempts have been made to linearize the portfolio optimization problem. Several different risk measures have been proposed which are computationally attractive as (for discrete random variables) they give rise to linear programming (LP) problems. About twenty years ago, the mean absolute deviation (MAD) model drew a lot of attention resulting in much research and speeding up development of other LP models. Further, the LP models based on the conditional value at risk (CVaR) have a great impact on new developments in portfolio optimization during the first decade of the 21st century. The LP solvability may become relevant for real-life decisions when portfolios have to meet side constraints and take into account transaction costs or when large size instances have to be solved. In this paper we review the variety of LP solvable portfolio optimization models presented in the literature, the real features that have been modeled and the solution approaches to the resulting models, in most of the cases mixed integer linear programming (MILP) models. We also discuss the impact of the inclusion of the real features