Optimal Portfolio Composition Research Articles

The Role of Sex in the Assessment of Return and Downside Risk in Decumulation Financial Planning

This paper aims to assess the return and downside risk of a decumulation portfolio established at the retirement age of a senior, with a determined lifetime horizon differentiated by the sex of the citizen. To measure the portfolio’s return and downside risk, two ratios conditioned by seniors’ risk attitude towards portfolio failure are employed: the downside Sortino ratio and the downside risk–return ratio. Unlike other research in the field, this manuscript provides three portfolio compositions catering to different senior investment profiles: aggressive, moderate, and conservative. Additionally, it offers a decumulation horizon conditioned by the sex-specific life expectancy of the individual, instead of offering different scenarios for conducting a sensitivity analysis. Lastly, this study was conducted across three socioeconomically distinct countries: the US, Spain, and Japan. The results clearly demonstrate that both sex and nationality significantly influence the selection of the optimal decumulation portfolio composition aimed at exhausting funds by the senior’s demise.

Hedge performance of different asset classes in varying economic conditions

In the realm of long-term investment, strategic portfolio allocation is an essential tool, especially in relation to risk management and return optimisation. There are many ways to pursue optimal portfolio composition, and their effectiveness depends on many factors, including the investor’s goals, risk appetite, and investment horizon. One of the primary means of portfolio optimisation is diversification. The core idea of diversification is to maintain a diverse portfolio with weakly correlated assets that can vastly reduce portfolio exposure to different market stress factors. Diversification is a fundamental strategy in investment and portfolio management that is essential for mitigating risk and enhancing potential returns over the long term. By spreading investments across various asset classes, sectors, geographies, and investment styles, diversification helps reduce the volatility of the overall portfolio. The main subject of this study is the theoretical basis of portfolio diversification and the analysis of historical data to derive optimal strategies for using uncorrelated assets to improve portfolio performance. This paper examines the correlation dynamics between different asset classes, such as stocks, bonds, and alternative investments, and their response to changes in inflation, interest rates, and market volatility, and tests it with historical data to deduce the optimal strategies for using uncorrelated assets to improve portfolio performance. The findings of this study prove the variable relationship between asset classes under specific economic conditions. This study uses historical data to show how different asset classes can be optimally leveraged or adjusted to mitigate risks and capitalise on opportunities presented by shifting economic indicators. This reveals that the hedging benefits of equities, bonds, and gold depend greatly on interest rates, market volatility, and inflation. It also provides guidelines for investors on optimal portfolio allocation and risk management. In conclusion, dynamic portfolio management is an essential tool for reducing the portfolio’s overall volatility while maximising returns. The diversification performance of different financial asset classes depends on major economic indicators such as inflation, interest rates, and market volatility. Investors seeking to optimise their portfolios in anticipation of or in response to economic changes, aiming to maximise returns while controlling for risk, can leverage these results.

Application of Mean-variance Model in Optimizing Stock Portfolio

Constructing an optimized stock portfolio is pivotal in achieving enhanced returns with managed risks. Utilizing data from 2022, the study employed the mean-variance model to determine the optimal allocations for five prominent stocks: Intel (INTC), Johnson & Johnson (JNJ), Coca-Cola (KO), Netflix (NFLX), and Procter & Gamble (PG). In the formulations of both maximum Sharpe ratio and minimum variance portfolios, the stocks with the predominant allocations are NFLX and PG, with respective weights of 47.04% and 45.89% for the former, and 58.05% and 29.61% for the latter. Upon establishing these weightings, the cumulative returns for 2023 were calculated, revealing that the constructed portfolios notably outperformed the broader stock market. This research underscores two key observations: firstly, the NFLX and PG stocks emerge as cornerstones in the optimal portfolio composition; and secondly, these allocations, when evaluated with 2023 data, underscore their efficacy. Robustness checks, which expanded the asset pool, further validated these findings. Ultimately, this study serves as a valuable guide for investors in the financial market, presenting a structured blueprint for effective portfolio assembly.

Dynamic inflation hedging performance and downside risk: A comparison between Islamic and conventional stock indices

Inflation has attained the highest level in decades, with the Russian/Ukrainian war adding upward pressure on prices of energy and food. The recent war has exacerbated the already growth of consumer prices resulting from the COVID-19 and disrupted supply-chains. In a high-inflation environment where investors face inflationary pressures during their investment decision-making, it becomes increasingly important to diversify portfolios towards assets with potential inflation hedging abilities. This paper applies the dependence-switching copula and different dynamic downside risk measures to assess the role of Islamic stocks as a hedge for inflation, in comparison to their conventional counterparts. We use data for the period January 2000–October 2022, covering the periods prior to and post-COVID-19 and the war in Ukraine. We consider an investor simply wishing to hedge inflation (or a “safety first” investor) and an investor having a more ambitious target real return. For each of the investor targets, we identify the optimal portfolio composition depending on various scenarios. Prior to the pandemic and the Ukraine conflict, Islamic stock indices exhibit the best hedging properties under their bull states, extreme inflation episodes and for an investor willing just to protect against inflation. In considering the post-2020 concerns, Islamic stocks’ diversification potentials are going to be more relevant, reinforcing their strategic benefits as risk mitigation assets. But by increasing the target of real return, it is shown that the weight assigned to the Islamic equities in the portfolio is smaller relative to the conventional stocks. The fund managers and financial analysts can rely on these findings in order to help investors reach a well-diversified portfolio and achieve an optimal risk reduction in an inflationary environment.

ANALISIS SINGLE INDEX MODEL DALAM PEMBENTUKAN PORTOFOLIO OPTIMAL (STUDI SAHAM INDEKS LQ45 PERIODE 2017–2022)

<p class="Style4">This study examines the optimal portfolio composition of stocks and calculates the return and risk of the optimal portfolio formed using a single index model. The type of research used in this study is descriptive research with a quantitative approach. This research uses secondary data in the form of stock prices for the 2017-2022 period. The data analysis method used is stock analysis with a single index model. The results showed five stocks from 29 stock samples, including the optimal portfolio. Here are the optimal portfolio stocks and their composition: ADRO at 4.41%, ANTM at 10.02%, BBCA at 54.33%, BBRI at 8.65%, and CPIN at 22.59%. The portfolio formed will generate an expected portfolio return of 1.72% and a portfolio risk level of 1.20%.</p><p><strong>Key words:</strong> Expected Return, Optimal Portfolio, Risk, Single Index Model</p>

Designing Efficient Renewable Energy Portfolios for Optimal Coverage of European Power Demand under Transmission Constraints

In this paper, we consider spatially and technologically diversified portfolios of renewable energy resources (RES) on a European scale. These portfolios efficiently allocate wind and solar production capacity with a view on matching load and restricting conventional generation capacity in each country. We investigate the composition of optimal portfolios taking into account the existing cross-border transmission capacity as well as future expansion scenarios. For the purposes of our analysis, we develop several multi-criteria optimization models and apply them to a rich dataset of daily electricity load, RES capacity factors, and transmission capacity constraints for 32 European countries during the period 2010–2015. By exploring different scenarios for the composition of efficient portfolios and cross-border network connectivity, we quantify the effectiveness of the resource aggregation strategy in terms of reducing the energy production deficit or surplus in each country. Empirical evidence shows that the portfolio perspective provides a solid basis for strategic planning on the future expansion of the European grid.

Investment Portfolio Optimization with a Mean-Variance Model Without Risk-Free Assets

Investment is an allocation of money, stocks, mutual funds, or other valuable resources provided by someone at the present time and held from being used until a specified period to get a profit (return). The higher the return received, the higher the risk. This study studied the Mean-Variance investment portfolio optimization model without risk-free assets to obtain the optimum portfolio. Five shares are used, namely BMRI, AMRT, SSMS, MLPT, and ANTM. The research results obtained optimal portfolio stocks with respective weights BMRI = 0.45741; AMRT=0.17852; SSMS=0.23300; MLPT=0.08475 and ANTM=0.04632. An optimal portfolio composition produces an average return = 0.00207 and variance = 0.00020.

FORMATION OF OPTIMAL STOCK PORTFOLIO USING THE SINGLE INDEX MODEL IN THE COVID-19 PANDEMIC

This study examines and analyzes how the impact of Covid-19 on the formation of an optimal portfolio using the Single Index Model and measuring optimal portfolio performance using the Sharpe index, Treynor index and Jensen index. The sample data in this study uses stocks listed on the Indonesia Stock Exchange, especially those listed on the LQ45 Index, MNC36 Index, IDX30 Index and Bisnis27 Index from March 1, 2020 to May 31, 2022. The purpose of this study is to analyze the optimal portfolio composition, analyze portfolio returns and risks. optimally, as well as analyzing the performance of stock portfolios formed during the Covid-19 pandemic. The results of the study show that the optimal portfolio formed in the LQ45 index is 20 stocks, the MNC36 index is 12, the IDX30 index is 11 shares, the Bisnis27 index is 14 shares. The optimal portfolios that are formed all produce an expected portfolio return that is greater than the level of risk, and the expected return of the portfolio is higher than the markets expected return and also higher than the risk-free return. The results of the analysis of portfolio performance calculations for each LQ45 Index, MNC36 Index, IDX30 Index and Bisnis27 Index show that the most optimal stock portfolio performance during the Covid-19 pandemic is the portfolio formed from LQ45 index shares, this index means that it is good and deserves to be investment options for investors. Evaluation of the optimal portfolio formed results in realized returns greater than expected returns and also greater than risk-free returns. This study shows that the formation of an optimal portfolio using the Single Index Model provides positive results during the Covid-19 era. KEYWORDS: single index model, portfolio formation, optimal portfolio, portfolio evaluation, Covid-19

Multiobjective Model Predictive Control for portfolio optimization with cardinality constraint

Model Predictive Control has been shown to be adequate to solve portfolio optimization problems because of its ability to perform the dynamic readjustment of the portfolio considering the market expectations. To consider both wealth and risk and real issues imposed by the financial market, this work proposes a Multiobjective Model Predictive Control strategy with cardinality constraints, besides transaction costs, self-financing, and upper and lower limits. The objective functions are the expected portfolio wealth and the expected Variance and Conditional Value at Risk as the portfolio risk measures. The optimization is performed by a multiobjective genetic algorithm, with operators proposed to control the number of assets in each portfolio and respect the prediction horizon perspective. Finally, an insightful case study is designed using the Brazilian Stock Exchange data in 2019 and 2020. An in-sample analysis explores the relationship between prediction horizon length, cardinality, optimal portfolio composition, risk-free asset, and objective functions on performance. An out-of-sample analysis considers the cumulative wealth, Sharpe ratio, maximum Drawdown, and the monthly accumulated return. Numerical experiments indicate that the proposed strategy outperforms the myopic portfolio selection, beats the primary Brazilian benchmark, a modified Markowitz model, and some top Brazilian investment funds even in crisis times like during the COVID-19 pandemic.

Metoda za odabir odgovarajućih perioda ulaganja za ostvarivanje profita od arbitraže i objašnjenje pojma "stock returns"

The current paper presents theoretical and experimental evidence to justify the need for paying attention to the investment horizon. Therefore, a criterion called the 'Safest Investment Horizon' (SIH) is utilized to select the appropriate investment horizon. To compute this quantity, a ratio called the 'Safest Investment Ratio' (SIR) is calculated, and the relationship between these criteria and arbitrage opportunities, along with methods for making an arbitrage profit through selecting an appropriate time horizon are discussed. Afterward, by applying this method for real-life data, the presence of arbitrage opportunities at different time horizons is confirmed. Furthermore, the effects of the time horizon on optimal portfolio composition are described. Finally, it is shown that these criteria outperform some of the conventional variables in CAPM, the 3-factor, and the 5-factor models for explaining stock returns and using SIH or SIR as a new variable increases the explanatory power of these models.

Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix

Investing in the stock sector, investors often face risk problems. Usually, forming an investment portfolio is done to minimize risk. In this research, investment portfolio optimization is discussed. The data analyzed are 8 shares traded on the capital market in Indonesia through the Indonesia Stock Exchange (IDX). Optimization is performed using the Mean-Absolute Deviation model with the singular covariance matrix to determine the optimal weights. The results of portfolio optimization Mean-Absolute Deviation model with singular covariance matrix method, was obtained optimal portfolio weights that is of 17.22% for BBCA shares; 26.64% for TKIM shares; 9.96% for BBRI shares; 9.96% for BBNI shares; 8.70% for BMRI shares; 3.75% for ADRO shares; 6.52% for GGRM shares; and 17.25% for UNTR shares. Where the optimal portfolio composition is obtained the expected rate of return (expected return) of 0.18% with a portfolio risk level (standard deviation) of 0.07%.

Portfolio selection with mental accounts: An equilibrium model with endogenous risk aversion

Abstract In Das et al. (2010), an agent divides his or her wealth among mental accounts that have different goals and optimal portfolios. While the moments of the distribution of asset returns are exogenous in their normative model, they are endogenous in our corresponding positive model. We obtain the following results. First, there are multiple equilibria that we parameterize by the implied risk aversion coefficient of the agent’s aggregate portfolio. Second, equilibrium asset prices and the composition of optimal portfolios within accounts depend on this coefficient. Third, altering the goal of any given account affects the composition of each portfolio.

Several Extended Mean-variance Fuzzy Portfolio Selection Models Based on Possibility Theory

Classical Mean-Variance model pioneered by Markowitz 1952 has proposed the model to be one of the most important researches in modern finance. However the classical Markowitz’s Mean-Variance method does not match to represent the practicality of the uncertainties in stock market problem in portfolios. Seven types of extended mean-variance method of portfolio selection model based on the possibilitic return and possibilistic risk is presented in this paper. The returns of assets obey trapezoidal and pentagonal fuzzy numbers. The objective of this paper is to compare the optimal portfolio composition and performance using trapezoidal, pentagonal fuzzy numbers by considering whether the data is assumed normal, using real distribution and follow the trading day situations. A numerical example is presented to illustrate the usability of the model. The result will provide on how the investors could improve on their investment strategy.

Dynamic Commodity Portfolio Management: A Regime-switching VAR Model

This article examines the impact of dynamic economic states on commodity portfolio performance by using Markov regime-switching vector autoregression (MRS-VAR) framework. Temporal behaviour of different assets in portfolio has been studied using weekly dataset. Empirical evidence finds prevalence of regime-switching phenomenon in all assets suggesting their state-dependent behaviour. Results of the study confirm that a pair of asset classes exhibits different levels of correlations under different economic scenarios. Optimal asset allocation under each regime has been investigated and the results provide evidence of differential optimal commodity portfolio composition in distinct economic states. The results of the study provide interesting insights for investors and portfolio managers. By examining the switching points in different asset classes, they can rebalance their portfolio which will help them in better risk management and enhancing risk–return performance.

Portfolio composition and critical line: a methodological approach

The purpose of this paper is to illustrate, at least pedagogically, composition of an efficient portfolio. Principally, two scenarios are examined. In the first we intend to minimise the portfolio variance and achieve a desired level of return. To do so, we find the optimal weights using Lagrange multiplier method. Short sales of securities are allowed. This implies that negative weights can be found. In the second case, we obtain the optimal portfolio composition, considering that weights cannot be negative. This suggests that short sales of securities are not allowed, and the Kuhn-Tucker system is used. Results are examined in the light of the investor's risk tolerance, and reveal that an investor who chooses an aggressive investment is focused more on return rather than risk. Conversely, when the investor's risk tolerance decreased, funds were invested more in stocks with both lower return and risk.

Portfolio composition and critical line: a methodological approach

The purpose of this paper is to illustrate, at least pedagogically, composition of an efficient portfolio. Principally, two scenarios are examined. In the first we intend to minimise the portfolio variance and achieve a desired level of return. To do so, we find the optimal weights using Lagrange multiplier method. Short sales of securities are allowed. This implies that negative weights can be found. In the second case, we obtain the optimal portfolio composition, considering that weights cannot be negative. This suggests that short sales of securities are not allowed, and the Kuhn-Tucker system is used. Results are examined in the light of the investor's risk tolerance, and reveal that an investor who chooses an aggressive investment is focused more on return rather than risk. Conversely, when the investor's risk tolerance decreased, funds were invested more in stocks with both lower return and risk.

An Optimal Stock Market Portfolio Proportion Model Using Genetic Algorithm

To reduce the amount of loss due to investment risk, an investor or stockbroker usually forms an optimal stock portfolio. This technique is done to get the maximum return of investment on shares to be purchased. However, in forming a stock portfolio required a fairly complex calculations and certain skills. This work aims to provide an alternative solution in the problem of forming the optimal and efficient stock portfolio composition by designing a system that can help decision making of investors or stockbrokers in preparing stock portfolio in accordance with the policy and risk investment. In this work, determination of optimal stock portfolio composition is constructed by using Genetic Algorithm. The data used in this work are the 4 selected stocks listed on the LQ45 index in 2017. Meanwhile, the calculation of profit and loss rate utilizes a single index model theory. The efficiency of the algorithm has been examined against the population size and crossover and mutation probabilities. The experimental results show that the proposed algorithm can be used as one of solutions to select the optimal stock portfolio.

Does low leverage minimise the impact of financial shocks? New optimisation strategies using Islamic stock screening for European portfolios

This study embodies a preliminary endeavour at analysing the impact of leverage on portfolio behaviour, with specific reference to return and volatility, in the European stock markets, using the debt ratio as one of the important benchmarks for Islamic stock screening. Given the focus of Islamic stock screening on the debt ratio, we use data from 320 firms for eight European countries which were classified according to their level of debt and size. For this, the portfolio optimisation based Mean-Variance Efficient Frontier (MVEF), the Sharpe Ratio and the Capital Market Line (CML) were employed. Our findings tend to demonstrate that, under shocks, high leverage worsens the portfolio return, volatility, and value at risk. The results further point out that optimal portfolio composition is obtained through a high proportion of low debt funds in the case of two separate equity funds, of low debt and high debt portfolios respectively. The systematic risk of several portfolio strategies is further explored with regards to a benchmark of European index and market-wide, return and volatility shocks.

Stable Value Funds Performance

Little in the scholarly economics literature is directed specifically to the performance of stable value funds, although they occupy a leading place among retirement investment vehicles. They are currently offered in more than one-third of all defined contribution plans in the USA, with more than $800 billion of assets under management. This paper rigorously examines their performance throughout the entire period since their inception in 1973. We produce a composite index of stable value returns. We next conduct mean-variance analysis, Sharpe and Sortino ratio analysis, stochastic dominance analysis, and optimal multi-period portfolio composition analysis. Our evidence suggests that stable value funds dominate (on average) two major asset classes based on a historical analysis, and that they often occupy a significant position in optimized portfolios across a broad range of risk aversion levels. We discuss factors that contributed to stable value funds’ past performance and whether they can continue to perform well into the future. We also discuss considerations regarding whether or not to include stable value as an element in target date funds within defined contribution pension plans.

Risk Premia and the Preference for Asymmetric Gambles

Recent research has studied the impact of asymmetry on cost of capital estimation, performance evaluation, and optimal portfolio composition. This study examines the impact of skewness preference on the risk premium of a gamble. The three-moment analogue of the Pratt-Arrow risk premium is derived for asymmetric gambles that are not necessarily actuarially neutral. It is seen that the standard Pratt-Arrow risk premium is biased down (up) for an actuarially neutral gamble that has positive (negative) skew.