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In the traditional portfolio models, the return rates of risky assets are usually defined as random variables or fuzzy variables. However, when some events such as financial crises or wars occur that can cause instability in financial markets, the results of these assumptions are not always satisfactory. Therefore, some scholars try to use uncertain variables to express the return rates. In this paper, a two-factor uncertain portfolio problem under optimistic value criteria is studied, where we use the optimistic value instead of expected value to describe the investment return and study the impact of economic circumstance factor and corporation's specificity factor on the return rates. Firstly, a two-factor optimistic value-variance-entropy model with background risk is constructed, in which the total investment return consists of the risky asset return and the background asset return. Then, considering the investors with two kinds of risk preferences, a three-step method is applied to convert the bi-objective optimization model. Moreover, a moment estimation method is used to process the expert-estimated data on the return rates. Finally, a numerical simulation is presented for showing the applicability of our models and solution method. • We use optimistic value to represent the return rather than expected value. • The multiple factors and background risk are considered simultaneously. • Three-step method is used to convert the model into two single-objective models. [ABSTRACT FROM AUTHOR]

The downside risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical Mean–Variance model concerning the asymmetry of returns and the risk perception of investors. This model optimization deals with a positive definite matrix that is endogenous with respect to portfolio weights. This aspect makes the problem far more difficult to handle. For this purpose, Athayde (2001) developed a new recursive minimization procedure that ensures the convergence to the solution. However, when a finite number of observations is available, the portfolio frontier presents some discontinuity and is not very smooth. In order to overcome that, Athayde (2003) proposed a mean kernel estimation of the returns, so as to create a smoother portfolio frontier. This technique provides an effect similar to the case in which continuous observations are available. In this paper, Athayde model is reformulated and clarified. Then, taking advantage on the robustness of the median, another nonparametric approach based on median kernel returns estimation is proposed in order to construct a portfolio frontier. A new version of Athayde’s algorithm will be exhibited. Finally, the properties of this improved portfolio frontier are studied and analysed on the French Stock Market. [ABSTRACT FROM AUTHOR]

In this paper, we develop a portfolio optimization method to maximize the performance of a fixed income portfolio. To achieve this aim, we define a two-step optimization problem where we firstly manage the immunization risk and then we maximize the portfolio wealth in a reward-risk framework. In the first optimization step, we create funds of bonds with constant immunization measure over time, and we propose an innovative immunization measure for bond portfolio management that leads to a more flexible immunization approach and a better trade-off between reward and risk. In the second optimization step, maximizing two performance measures on these baskets of bonds we obtain portfolio strategies that consider different investors' profiles. An empirical application to the US fixed income market during 2002-2012 period is provided. Applying the portfolio optimization method to different bond classes, we compare the results with an equity index. This ex-post analysis indicates the benefits of the proposed portfolio strategy in outperforming the benchmark and proves that capital flows to safer markets during crisis periods. [ABSTRACT FROM AUTHOR]

One of the most important problems faced by every investor is asset allocation. An investor during making investment decisions has to search for equilibrium between risk and returns. Risk and return are uncertain parameters in the suggested portfolio optimization models and should be estimated to solve the problem. However, the estimation might lead to large error in the final decision. One of the widely used and effective approaches for optimization with data uncertainty is robust optimization. In this paper, we present a new robust portfolio optimization technique for mean-CVaR portfolio selection problem under the estimation risk in mean return. We additionally use CVaR as risk measure, to measure the estimation risk in mean return. To solve the model efficiently, we use the smoothing technique of Alexander et al. (2006). We compare the performance of the CVaR robust mean-CVaR model with robust mean-CVaR models using interval and ellipsoidal uncertainty sets. It is observed that the CVaR robust mean-CVaR portfolios are more diversified. Moreover, we study the impact of the value of confidence level on the conservatism level of a portfolio and also on the value of the maximum expected return of the portfolio. [ABSTRACT FROM AUTHOR]

The paper deals with three-asset portfolio. It focuses on ordinary investor, for whom the Markowitz's theory of selection of optimal portfolio is often too difficult to use in practise. In the paper, new formulas for calculation of the weights of the assets in three-asset portfolio optimised according to the risk measured by standard deviation are being derived. The paper also deals with comparison of optimisation of two- and three-asset portfolio. Also the formulas for calculation of weights of assets in three-asset portfolio, which is optimised under the pre-defined rate of return, are derived in the paper. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the side-by-side portfolio management and the substitutability of exchange-traded funds (ETFs) and mutual funds. The study uses index ETFs and index mutual funds to measure the monthly flow of funds to each product and the demand for both investment instruments. Results show that ETF, as a newer investment product offer an alternative investment vehicle to investors who wish to break their money between two investment tracking the same index.

Hospitality stock investors, like hospitality operators and customers, are important constituents of the hospitality industry. Knowing the risk and return associated with hospitality investment and the risk-adjusted performance of hospitality stock portfolio in the capital market is of critical importance to hospitality investors. This study compares the risk-adjusted stock performances of three hospitality sectors (hotel/motel, restaurant, and casino/gaming) as three portfolios during the most recent economic downturn (2000-2003). Four portfolio performance indexes-Treynor ratio, Sharpe ratio, Jensen ratio, and Appraisal ratio-were estimated to measure their risk-adjusted performances. The results show that the casino/gaming sector performed the best, followed by the restaurant sector. The hotel/motel sector was the weakest performer among the three, mainly due to its higher risks. Our analysis suggests that the hotel/motel sector may improve its market performance via prudent growth and less reliance on debt financing. [ABSTRACT FROM AUTHOR]

• A cloud theory-based multi-objective portfolio selection model is proposed. • The crisp numerical characteristics of the cloud model theory are defined. • The acceptance and rejection functions with variable risk appetite are constructed. • A compromise programming approach is utilized to aggregate the objectives. • Several schemes as per investors' preferences regarding all objectives are devised. This study proposes a cloud theory-based multi-objective portfolio selection model with variable risk appetite, which incorporates four objectives of mean, variance, skewness, and liquidity constrained by several realistic constraints. Cloud model theory is employed to characterize the return rates and liquidity of assets due to the superiority of simultaneously capturing the ambiguity and randomness of information. The crisp numerical characteristics of the cloud model are defined to obtain the crisp form of the proposed model. To highlight and portray the investors' risk (averse-neutral-seeking) appetites, the generalized acceptance and rejection functions are modeled by using the extreme values of each objective and introducing a variable risk appetite parameter. Thus the corresponding model is transformed with the objective functions of maximizing acceptance and minimizing rejection, which is solved through the compromise programming approach. The extended model provides investors with an opportunity to adjust risk parameters according to current market status. Moreover, the preference ratio vector is introduced when optimizing, which provides investors with overall control over the preferences regarding all objectives, so that investors can derive optimal portfolios well compatible with their expectations through customized weighting schemes. A real-world empirical application is presented to demonstrate the effectiveness of the proposed model [ABSTRACT FROM AUTHOR]