Mathematical Linear Multi-objective Model with a Process of Neighbourhood Search and its Application for the Selection of an Investment Portfolio in the Mexican Stock Exchange During a Period of Debacle

2011 ◽  
Vol 3 (4) ◽  
pp. 89-99 ◽  
Author(s):  
Jose Crispin ZavalaDiaz ◽  
Ocotlan DiazParra ◽  
Jose Alberto HernandezAguilar ◽  
Joaquin Perez Ortega
Keyword(s):  
Stock Exchange ◽  
Investment Portfolio ◽  
Multi Objective ◽  
Neighbourhood Search ◽  
Objective Model ◽  
Selection Of

scholarly journals Predicción de precios futuros de acciones representativas de la Bolsa Mexicana de Valores (Índice de Precios y Cotizaciones, IPC) mediante técnicas de inteligencia artificial

2020 ◽  
Author(s):  
◽  
Juan Andrés Martínez Escobar ◽  
Román Anselmo Mora Gutiérrez
Keyword(s):  
Stock Exchange ◽  
Multiple Linear Regression Model ◽  
Relevant Information ◽  
Future Price ◽  
Compact Set ◽  
Second Phase ◽  
Multi Objective ◽  
Relevant Variables ◽  
Objective Model ◽  
Two Phases

In this work, a new methodology is presented to analyze and predict the behavior of stocks of the Mexican Stock Market based on the synergistic concatenation of non-parametric statistical strategies and multi-objective optimization models. This methodology involves two phases. The first (filtering) leverages an automated process for the analysis, evaluation, and selection of the necessary and relevant information: for the characterization of the behavior of each action. The second (the model adjustment phase) involves adapting and solving a multi-objective model for the prediction of prices of the selected stocks. The database used in this work includes the behavior of twelve significant stocks in the Mexican stock exchange in the period 2006 to 2016, the source code used is available in “http://bit.ly/396h3J1”; the data was obtained from a specialized financial markets platform for Latin America. The numerical results show that the filtering phase can identify a compact set of relevant variables with a significant influence on the future price of each stock. In the second phase, the data from 2016 is used to predict the multi-objective model, that compared with the multiple linear regression model, provides a considerable improvement in the quality of the predicted observed data. The model generated from the second phase has reliability greater than 95%.

scholarly journals The Application of genetic algorithms for the selection of WSE companies in Warsaw for the investment portfolio.

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Beata Basiura ◽  
Joanna Motyczyńska
Keyword(s):  
Genetic Algorithms ◽  
Time Window ◽  
Stock Exchange ◽  
Selection Function ◽  
Investment Portfolio ◽  
Short Processing Time ◽  
Simulation Based ◽  
Warsaw Stock Exchange ◽  
Investment Portfolios ◽  
Selection Of

Portfolio analysis is a tool particularly intended for investors. Risk assessment and risk specification make the investor able to properly diversify and offset the portfolio. Broadly speaking, there are multiple tools destined for building up an efficient set of portfolios.One of them is Markowitz’s model theory postulating building up a portfolio determined on the basis of equilibrium between expected profit level as well as accepted level of risk assessment.In the context of this paper, the objective is to shed some light on creating investment portfolios based on either Markowitz's portfolio theory or evolutionary algorithm. The simulation based methods for building up a portfolio of approximately 40-50 companies listed out in the primary marketof the Warsaw Stock Exchange using the selection function proposed in the BA thesis were presented.Portfolio profit values have been evaluated in a dynamically shifted time window. The conducted analysis showed shifts in the economy at certain periods of time. The implemented genetic algorithms smoothly handled the optimization with a relatively short processing time of the task result.

scholarly journals Optimal Selection of Portfolio in Nigerian Stock Exchange using Dynamic Programming

2020 ◽  
Vol 3 (2) ◽  
pp. 179-185
Author(s):  
MM Umar
Keyword(s):  
Dynamic Programming ◽  
High Risk ◽  
Efficient Algorithm ◽  
Stock Exchange ◽  
Expected Returns ◽  
Optimal Selection ◽  
Sample Period ◽  
Investment Portfolio ◽  
Successful Business ◽  
Selection Of

Investment in various types of assets is an exciting choice of most successful business entrepreneurs. Investors have no option than to make a holistic decision regarding the position of their wealth within the context of the portfolio. In this paper, a Dynamic Programming (DP) algorithm and Modern Portfolio Theory (MPT) were used to determine the optimal returns of investments and the risks involved. Also, the correlation between expected returns and risk of investments were analyzed. Data of four securities were collected from the Nigeria Stock Exchange, Yola, for the sample period of 2016. Dynamic programming was found to be a more efficient algorithm for determining how much to invest in each investment portfolio. Through the analysis of the investments, OANDO PLC and Nigeria Breweries were respectively selected with high optimal returns of N426000. That is, investing N5 million in OANDO PLC yields a return of N269000 and N4 million in Nigeria Breweries yields N160000. One observation about these investments is that they all have a high risk of investment.

scholarly journals Number Theory for the Selection of an Investment Portfolio: An Application to the Mexican Stock Exchange

2014 ◽  
Vol 7 (24) ◽  
pp. 5264-5270
Author(s):  
J.C. Zavala-Diaz ◽  
J. Perez-Ortega ◽  
A. Martinez-Rebollar ◽  
J.A. Hernadez-Aguilar
Keyword(s):  
Number Theory ◽  
Stock Exchange ◽  
Investment Portfolio ◽  
Selection Of
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