Mean-Variance Portfolio Optimization on Islamic Stocks by Using Non Constant Mean and Volatility  Models and Genetic Algorithm

The risk in stock market has taken an sinificant issue in investment of stock market, including Investment in some Islamic stocks. In order to minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several Islamic stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to Islamic stocks by using mean and volatility is not constant approaches. Non constant mean analyzed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analyzed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique followed by the Genetic Algorithm (GA). The expected result is to get the proportion of investment in each Islamic stock analyzed. Based on the result, we got that GA give a proportion of portfolio optimum selection with the best expected return. However, The GA has more potential candidate of solution that give the investor an alternative of their optimum portfolio selection. in this paper, we only present the best solution which has the highest fitness to the model.

Download Full-text

PORTFOLIO MODEL UNDER FRACTAL MARKET BASED ON MEAN-DCCA

Fractals ◽

10.1142/s0218348x2050142x ◽

2020 ◽

Vol 28 (07) ◽

pp. 2050142

Author(s):

WEIDE CHUN ◽

HESEN LI ◽

XU WU

Keyword(s):

Analytical Solution ◽

Correlation Analysis ◽

Capital Market ◽

Cross Correlation ◽

Investment Portfolio ◽

Cross Correlation Analysis ◽

Risk Criterion ◽

The Mean ◽

Mean Variance Portfolio ◽

Mean Variance

Under the realistic background that the capital market nowadays is a fractal market, this paper embeds the detrended cross-correlation analysis (DCCA) into the return-risk criterion to construct a Mean-DCCA portfolio model, and gives an analytical solution. Based on this, the validity of Mean-DCCA portfolio model is verified by empirical analysis. Compared to the mean-variance portfolio model, the Mean-DCCA portfolio model is more conducive for investors to build a sophisticated investment portfolio under multi-time-scale, improve the performance of portfolios, and overcome the defect that the mean-variance portfolio model has not considered the existence of fractal correlation characteristics between assets.

Download Full-text

A Mean-Variance Portfolio Selection Model with Interval-Valued Possibility Measures

Mathematical Problems in Engineering ◽

10.1155/2020/4135740 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Yunyun Sui ◽

Jiangshan Hu ◽

Fang Ma

Keyword(s):

Portfolio Selection ◽

Return Rate ◽

Selection Model ◽

Expected Return ◽

Interval Numbers ◽

Numerical Example ◽

Mean Variance Portfolio ◽

Portfolio Selection Model ◽

Mean Variance ◽

Interval Valued

In recent years, fuzzy set theory and possibility theory have been widely used to deal with an uncertain decision environment characterized by vagueness and ambiguity in the financial market. Considering that the expected return rate of investors may not be a fixed real number but can be an interval number, this paper establishes an interval-valued possibilistic mean-variance portfolio selection model. In this model, the return rate of assets is regarded as a fuzzy number, and the expected return rate of assets is measured by the interval-valued possibilistic mean of fuzzy numbers. Therefore, the possibilistic portfolio selection model is transformed into an interval-valued optimization model. The optimal solution of the model is obtained by using the order relations of interval numbers. Finally, a numerical example is given. Through the numerical example, it is shown that, when compared with the traditional possibilistic model, the proposed model has more constraints and can better reflect investor psychology. It is an extension of the traditional possibilistic model and offers greater flexibility in reflecting investor expectations.

Download Full-text

Markowitz Theory in Portfolio Optimization for Heavy Tailed Assets

Universiti Malaysia Terengganu Journal of Undergraduate Research ◽

10.46754/umtjur.v1i3.74 ◽

2019 ◽

Vol 1 (3) ◽

pp. 1-14

Author(s):

Wong Ghee Ching ◽

Che Mohd Imran Che Taib

Keyword(s):

Portfolio Optimization ◽

Composite Index ◽

Minimum Variance ◽

Optimization Techniques ◽

Expected Return ◽

Markowitz Model ◽

The Mean ◽

Heavy Tailed ◽

Mean Variance Portfolio ◽

Mean Variance

This paper aims at solving an optimization problem in the presence of heavy tail behavior of financial assets. The question of minimizing risk subjected to a certain expected return or maximizing return for a given expected risk are two objective functions to be solved using Markowitz model. The Markowitz based strategies namely the mean variance portfolio, minimum variance portfolio and equally weighted portfolio are proposed in conjunction with mean and variance analysis of the portfolio. The historical prices of stocks traded at Bursa Malaysia are used for empirical analysis. We employed CAPM in order to investigate the performance of the Markowitz model which was benchmarked with risk adjusted KLSE Composite Index. We performed a backtesting study of portfolio optimization techniques defined under modern portfolio theory in order to find the optimal portfolio. Our findings showed that the mean variance portfolio outperformed the other two strategies in terms of performance of investment for heavy tailed assets.

Download Full-text

Application of semi-deviation as a proxy for the expected return estimation in the Croatian equity market

Croatian Review of Economic Business and Social Statistics ◽

10.2478/crebss-2019-0002 ◽

2019 ◽

Vol 5 (1) ◽

pp. 9-20

Author(s):

Denis Dolinar ◽

Davor Zoričić ◽

Zrinka Lovretin Golubić

Keyword(s):

Stock Market ◽

Portfolio Management ◽

Risk Measures ◽

Equity Market ◽

Expected Return ◽

Deviation Measure ◽

Out Of Sample ◽

The Mean ◽

Risk Parameters ◽

Mean Variance

AbstractIn the field of portfolio management the focus has been on the out-of-sample estimation of the covariance matrix mainly because the estimation of expected return is much more challenging. However, recent research efforts have not only tried to improve the estimation of risk parameters by expanding the analysis beyond the mean-variance setting but also by testing whether risk measures can be used as proxies for the expected return in the stock market. In this research, we test the standard deviation (measure of total volatility) and the semi-deviation (measure of downside risk) as proxies for the expected market return in the illiquid and undeveloped Croatian stock market in the period from January 2005 until November 2017. In such an environment, the application of the proposed methodology yielded poor results, which helps explain the failure of the out-of-sample estimation of the maximum Sharpe ratio portfolio in earlier research in the Croatian equity market.

Download Full-text

Dynamic Risk Management of Investment Portfolio by Futures Contracts

Regaining Global Stability After the Financial Crisis - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-5225-4026-7.ch008 ◽

2018 ◽

pp. 147-162

Author(s):

Alexander K. Kerimov ◽

Oleg I. Pavlov

Keyword(s):

Risk Management ◽

Moving Average ◽

Management Strategies ◽

Minimum Variance ◽

Futures Contracts ◽

Investment Portfolio ◽

Expected Return ◽

Dynamic Risk ◽

Dynamic Risk Management ◽

Risk Management Strategies

This chapter is devoted to the dynamic risk management of the investment portfolio using future contracts. The number of futures for each portfolio asset, which is determined by portfolio effectiveness and acceptability of risk at each step, serves as a control parameter. The authors define effective portfolios as the ones of the minimum variance with the expected return greater than or equal to the specified value. Risk is measured by the probability of losing a certain part of the portfolio value. Effective adaptive strategies of portfolio risk management are proposed and their comparative analysis is carried out on a concrete example. In order to determine risk management strategies, the authors implement simple methods of volatility forecasting and correlation of relative changes of price data based on exponential moving average.

Download Full-text

Provisions for bank deposit withdrawals and portfolio selection

International Journal of Financial Engineering ◽

10.1142/s2424786319500373 ◽

2020 ◽

Vol 07 (01) ◽

pp. 1950037

Author(s):

Ryle S. Perera

Keyword(s):

Portfolio Selection ◽

Closed Form Solution ◽

Efficient Frontier ◽

Optimal Investment ◽

Form Solution ◽

Investment Portfolio ◽

Efficient Manner ◽

Selection For ◽

Mean Variance Portfolio ◽

Mean Variance

The primary economic function of a bank is to redirect funds from savers to borrowers in an efficient manner, while increasing the value of the bank’s asset holdings in absolute terms. Within the regulatory framework of the Basel III accord, banks are required to maintain minimum liquidity to guard against withdrawals/liquidity risks. In this paper, we analyze a continuous-time mean-variance portfolio selection for a bank with stochastic withdrawal provisioning by relating the reserves as a proxy for the assets held by the bank. We then formulate an optimal investment portfolio selection for a banker by constructing a special Riccati equation as a continuous solution to the Hamilton–Jacobi–Bellman (HJB) equation under mean-variance paradigm. We obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier. The aforementioned modeling enables us to formulate a stochastic optimal control problem related to the minimization of the reserve, depository, and intrinsic risk that are associated with the reserve process.

Download Full-text

Extended Mean - Variance Portfolio Optimization Model: A Comparative Study Among Swarm Intelligence Algorithms

International Journal of Accounting and Financial Reporting ◽

10.5296/ijafr.v9i2.14601 ◽

2019 ◽

Vol 9 (2) ◽

pp. 184

Author(s):

R. K. Jena

Keyword(s):

Portfolio Optimization ◽

Swarm Intelligence ◽

Portfolio Risk ◽

Expected Return ◽

Monthly Data ◽

Bee Colony ◽

Portfolio Optimization Model ◽

Mean Variance Portfolio ◽

Portfolio Selection Model ◽

Mean Variance

Portfolio optimization is one of the important issues in the effective management of investment. There is plenty of research in the literature addressing these issues. Markowitz’s primary portfolio selection model is a more suitable method to solve the model for obtaining fairly optimum portfolios. But, the problem of portfolio optimization is multi-objective in nature that aims at simultaneously maximizing the expected return of the portfolio and minimizing portfolio risk. The computational complexity increases with an increase in the total number of available assets. Therefore heuristic methods are more suitable for portfolio optimization in compare to deterministic methods. This research compares three well-known swarm intelligence algorithms (e.g. Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC)) for portfolio optimization. The Sharpe ratio was used as one of the important criteria for this comparison. PSO outperformed other algorithms in portfolio optimization experiments. The results were also showed that the portfolios which were made of monthly data had performed better than the yearly data.

Download Full-text

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

International Journal of New Media Technology ◽

10.31937/ijnmt.v5i1.679 ◽

2018 ◽

Vol 5 (1) ◽

pp. 41-46

Author(s):

Rosalina Rosalina ◽

Hendra Jayanto

Keyword(s):

Neural Network ◽

Graph Theory ◽

Wavelet Transform ◽

Stock Market ◽

Discrete Wavelet Transform ◽

Stock Price ◽

Moving Average ◽

Discrete Wavelet ◽

Stock Market Forecasting ◽

Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

Download Full-text