Optimal Portfolio Research Articles

TRANSFORMATIONAL PROCESSES IN RESOURCE-RICH COUNTRIES: FROM NATURAL RESOURCES TO INNOVATION AND TECHNOLOGY-BASED ECONOMY

This paper aims to assess the development pattern of resource-rich countries like Norway, Kuwait, Saudi Arabia, and the Republic of South Africa by analyzing their stock markets. As resource-rich countries have profound implications for the world's sustainable development, it is essential to investigate whether they transform natural resource wealth into sustained growth by reorientation from related to raw material industries to modern ones based on information and technologies. The analysis of resource-rich countries' stock markets allowed us to conclude that partially these countries are on the way to an economy dominated by intangible assets. Nevertheless, despite the declared intentions, some traditional attachments to raw materials are still present. At the same time, most companies in the optimal portfolios of these markets have a long-term nonlinear strategy. The impact of the pandemic COVID-19 turned out to be significant, but short-lived. The research was carried out based on R and Python packages.

Practical Improvements to Mean-Variance Optimization for Multi-Asset Class Portfolios

In the more than 70 years since Markowitz introduced mean-variance optimization for portfolio construction, academics and practitioners have documented numerous weaknesses in the approach. In this paper, we propose two easily understandable improvements to mean-variance optimization in the context of multi-asset class portfolios, each of which provides less extreme and more stable portfolio weights. The first method sacrifices a small amount of expected optimality for reduced weight concentration, while the second method randomly resamples the available assets. Additionally, we develop a process for testing the performance of portfolio construction approaches on simulated data assuming variable degrees of forecasting skill. Finally, we show that the improved methods achieve better out-of-sample risk-adjusted returns than standard mean-variance optimization for realistic investor skill levels.

The Optimal Portfolio of AIA Group Limiteds Investment Insurance Products Based on Markowitz Model and Index Model

This paper uses the Markowitz and Index models to look for the optimal portfolio of AIA Group Limiteds Investment Insurance Products. While most of the previous articles concerning these two models only studied theoretically, the research object of this paper is more practical and instructive. First, this papers study method is to collect ten AIA investment insurance products revenue data from 2018 to 2023 from its website. Then, process the data, such as calculating the mean, variance, etc. Markowitz and Index Model are built on the basis of these data. The constraints are then introduced to find the optimal portfolio under different conditions. After comparison, it concludes that the Index Model analyzes the risk, while the Markowitz Model portfolio has the potential to yield higher returns than the Index Model portfolio when both are subject to the same level of risk. This research is instructive for investors finding the most suitable portfolio with the highest return and AIA developing new portfolio insurance products.

Behavioral Finance: The Impact of Investor Expectation on Financial Decision-Making

Behavioral finance challenges traditional financial theories by questioning the assumption of investor rationality, highlighting the influence of cognitive biases, emotions, and psychological factors on decision-making. This deviation from conventional models like the Efficient Market Hypothesis recognizes the complexity of human decision-making in financial contexts. The field focuses on key phenomena: loss aversion, market anomalies, the framing effect, and the endowment effect. Loss aversion reveals a stronger reaction to losses than equivalent gains, influencing overly cautious decisions. Market anomalies, such as momentum and trend reversals, challenge efficient market expectations. The framing effect shows how decision-making is swayed by information presentation. The endowment effect impacts perceived asset value, affecting financial decisions. These biases significantly impact investor behavior, potentially leading to suboptimal portfolio choices. Recognizing these biases is crucial for making informed financial decisions. Behavioral finance research has profound implications, emphasizing the integration of behavioral insights with traditional models. Future studies should explore cumulative behavioral effects, conduct cross-cultural analyses, and investigate diverse market conditions for more effective investment strategies and regulations. Understanding global financial behavior through behavioral finance is essential for comprehensive insights into market dynamics and decision-making processes.

Multi-Objective Portfolio Optimization Using a Quantum Annealer

In this study, the portfolio optimization problem is explored, using a combination of classical and quantum computing techniques. The portfolio optimization problem with specific objectives or constraints is often a quadratic optimization problem, due to the quadratic nature of, for example, risk measures. Quantum computing is a promising solution for quadratic optimization problems, as it can leverage quantum annealing and quantum approximate optimization algorithms, which are expected to tackle these problems more efficiently. Quantum computing takes advantage of quantum phenomena like superposition and entanglement. In this paper, a specific problem is introduced, where a portfolio of loans need to be optimized for 2030, considering ‘Return on Capital’ and ‘Concentration Risk’ objectives, as well as a carbon footprint constraint. This paper introduces the formulation of the problem and how it can be optimized using quantum computing, using a reformulation of the problem as a quadratic unconstrained binary optimization (QUBO) problem. Two QUBO formulations are presented, each addressing different aspects of the problem. The QUBO formulation succeeded in finding solutions that met the emission constraint, although classical simulated annealing still outperformed quantum annealing in solving this QUBO, in terms of solutions close to the Pareto frontier. Overall, this paper provides insights into how quantum computing can address complex optimization problems in the financial sector. It also highlights the potential of quantum computing for providing more efficient and robust solutions for portfolio management.

Investment Portfolio Optimization In Infrastructure Stocks Using The Mean-VaR Risk Tolerance Model

Infrastructure a crucial role in economic development and the achievement of Sustainable Development Goals (SDGs), with investment being a key activity supporting this. Investment involves the allocation of assets with the expectation of gaining profit with minimal risk, making the selection of optimal investment portfolios crucial for investors. Therefore, the aim of this research is to identify the optimal portfolio in infrastructure stocks using the Mean-VaR model. Through portfolio analysis, this study addresses two main issues: determining the optimal allocation for each infrastructure stock and formulating an optimal stock investment portfolio while minimizing risk and maximizing return. The methodology employed in this research is the Mean-VaR approach, which combines the advantages of Value at Risk (VaR) in risk measurement with consideration of return expectations. The findings indicate that eight infrastructure stocks meet the criteria for forming an optimal portfolio. The proportion of each stock in the optimal portfolio is as follows: ISAT (2.74%), TLKM (33.894%), JSMR (3.343%), BALI (0.102%), IPCC (5.044%), KEEN (14.792%), PTPW (25.863%), and AKRA (14.219%). The results of this study can serve as a foundation for better investment decision-making.

Comparative Study of the Equal-Weight Method and Hierarchical Risk Parity in Portfolio Construction

Purpose: Portfolio optimization is a process in which the capital is allocated among the portfolio assets such that the return is maximized while the risk is minimized. Portfolio construction and optimization has long been an active research area in finance. For the portfolios with highly correlated assets, the performance of traditional risk-based asset allocation methods such as, the mean-variance (MV) method is limited because quadratic optimizers require an inversion of the covariance matrix of the portfolio to distribute weight among the portfolio assets. Methods: A possible solution to the limitations of traditional risk-based asset allocation methods can be provided by a hierarchical clustering-based Machine Learning method because it uses hierarchical relationships between the covariance of assets in the portfolio to distribute the weight, and inversion of the covariance matrix is not required. A comparison of the performance of a simple non-optimization technique called the Equal-weight (EW) method to the two optimization methods, the Mean-variance method and the HRP method, which is a machine learning method, was conducted in this research. Results: It was found that in terms of cumulative returns, the equal-weight method has outperformed several more sophisticated optimization techniques, the mean-variance method, and the HRP method. For most of the period, the Sharpe ratio of the HRP method was observed to be similar to the mean-variance method and equal-weight method. Implications: This research supports the idea that HRP is a feasible method to construct portfolios with correlated assets because the performance of HRP is comparable to the performances of the traditional optimization method and the non-optimization method.

The Comparison of Investment Portfolio Optimization Result of Mean-Variance Model Using Lagrange Multiplier and Genetic Algorithm

Investment portfolio optimization is carried out to find the optimal combination of each stock with the aim of maximizing returns while minimizing risk by diversification. However, the problem is how much proportion of funds should be invested in order to obtain the minimum risk. One approach that has proven effective in building an optimal investment portfolio is the Mean-Variance model. The purpose of this study is to compare the results of the Mean-Variance model investment portfolio optimization using Lagrange Multiplier method and Genetic Algorithm. The data used are stocks that are members of the LQ45 index for the period February 2020-July 2021. Based on the research results, there are five stocks that form the optimal portfolio, namely ADRO, AKRA, BBCA, CPIN, and EXCL stocks. The optimal portfolio generated by the Lagrange Multiplier method has a risk of 0.000606 and a return of 0.000726. Meanwhile, using the Genetic Algorithm resulted in a risk of 0.000455 and a return of 0.000471. Thus, the Genetic Algorithm method is more suitable for investors who prioritize lower risk. Meanwhile, the Lagrange Multiplier method produces a relatively higher risk, making it less suitable for investors who expect a small risk.

Optimal Portfolio Using Single Index Model (SIM) For Health Sector Stocks

Investment is one of the fund management activities with the aim of obtaining future profits. In addition to profits, investors also need to consider the risks that will be faced by diversifying. Diversification is done by forming an optimal portfolio. This research aims to determine the proportion of stocks in the optimal portfolio and calculate the expected return and risk value of the optimal portfolio. The object used to form the optimal portfolio is health sector stock group for the period January 2020 - December 2022. The method used to form the optimal portfolio is Single Index Model (SIM). The results showed that there were 6 combinations of health sector stock in the optimal portfolio, such as IRRA, PRDA, SAME, SILO, MERK, and HEAL stocks of 8.94%, 9.24%, 9.34%, 11.92%, 27.15%, and 33.41% respectively with expected return of 2.68% and a risk value of 1.85%.

Investment Portfolio Optimization in Renewable Energy Stocks in Indonesia Using Mean-Variance Risk Aversion Model

Climate change is a phenomenon that has been occurring for quite some time. However, the increasingly felt impacts of climate change necessitate human action to mitigate these effects. One way to address this issue is by transitioning from conventional or non-renewable energy sources to renewable energy. This step undoubtedly has implications for various aspects, such as investments. Naturally, investors are beginning to turn their attention to the field of renewable energy as a new target. Investments are inherently associated with risks and returns One approach to maximizing returns is through portfolio optimization. One well-known method in portfolio optimization is the Mean-Variance method, also known as the Markowitz method, as it was first introduced by Harry Markowitz. In this research, an optimal portfolio is generated with weights of 0.1470 for ADRO; 0.1939 for MEDC; 0.2143 for ITMG and 0.4449 for RAJA. With this composition of optimal portfolio weights, the expected return is obtained at 0.002252, and the return variance is 0.000496.

Risk Modeling, Return Forecasting, and Optimal Portfolio Selection

The main objective of this research is to form an optimal investment portfolio consisting of a number of stocks selected according to specific criteria in Amman Stock Exchange and to test the ability of a various economic models to predict the performance of this portfolio in the foreseeable future. A time series for the return of the selected portfolio and for the return of a market index are formed. A set of tests were conducted to reach a stationary time series return and, then, to follow the Box-Jenkins methodology in order to build predictive models (ARMA) and to examine the residuals of models and to model them using ARCH and GARCH models to reach the best prediction of the performance of the portfolio and the market index in the forecasted periods. The data were tracked on a daily basis for the study sample and the market index simultaneously for a period of three years. Twenty-one companies were selected in the investment portfolio distributed among several sectors. The study concluded that the formed portfolio achieved a good diversification and gave a high return in relation to the lowest possible risk according to the Sharpe scale. Also, it is concluded that the model ARMA (1,1) is the most suitable for estimating market portfolio returns and forecasting risks for the market index return, and ARMA (2,1) and the model ARMA - GARCH are the most capable one of achieving good results that can be relied upon in tracking the performance of the studied investment portfolio.

Optimal Portfolio Using Roy’s Safety-First Method on Primary Consumer Goods Sector Stocks

Before carrying out investment activities, investors need to form an optimal investment portfolio. This study aims to form an optimal portfolio in primary consumer goods sector stocks that sell the basic needs of the community so that stocks in the sector tend to be stable. The method used in forming the optimal portfolio is Roy's Safety-first method. The portfolio formed produces 6 combinations of stocks consisting of WIIM, DSNG, MRAT, CAMP, SIMP, and MBTO stocks respectively with a proportion of funds of 44.05%, 16.38%, 18.61%, 15.06%, 4.32%, and 1.59% with an expected return portfolio of 3.10% and a portfolio risk of 1.65%.

Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market.

Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors.

Quantum Technology in the Financial Sector: Econometric Analyses and Risk Management to Mitigate the Quantum Winter

In today's rapidly evolving financial landscape, methods as econometric modeling, predictive analysis, and risk analysis are becoming increasingly important. These methods offer critical tools for understanding and managing the potential of quantum technology in the financial sector. Quantum technology can achieve fast and precise results in data analysis, risk management, portfolio optimization, and market forecasting. However, to realize these possibilities, we need to overcome technical challenges as well as financial and strategic barriers. This paper discusses scientific awareness and proposed solutions to 'Quantum Winter' especially in the financial sector. Mitigating the risk of a 'Quantum Winter' is possible through steps such as building strategic partnerships, conducting risk analysis, developing specialized capabilities, securing government support, and establishing appropriate regulations. Econometric modeling, predictive analysis, and risk analysis are indispensable tools to understand the potential of quantum technology in the financial sector and to achieve sustainable success.

A comparative study of fuzzy multi-objective investment project portfolio selection and optimization based on financial return and different risk measurements

Competitiveness in the global market is getting more intense. Due to resource and budget constraints, firms need to achieve their expected goals and satisfy all investment constraints under uncertainty. Selecting the set of projects among other candidates to get the most efficient portfolio requires a lot of attention from the Decision Makers (DMs) as this consideration no longer relies purely on the financial term. This problem becomes a multi-objective problem under uncertainty where the financial return and risk from uncertainty are required into the trading off consideration. Due to the financial uncertainty, the chance-constrained programming has been employed in this study for defuzzifying and solving uncertain optimization problems at a specified confidence level that is defined by the DMs. Then, various kinds of investment or financial risk measures, Lower-Semi Variance Index (LSVI), the absolute deviation with the expected FNPV, and the absolute mean-Conditional Value at Risk (CVaR) gap are provided in the selection of such risk measures to show their differences in characteristics and performances in the obtained results. Since, such problems can consist of many project candidates and complex constraints, which may grow beyond the application of the exact optimization approach, a meta-heuristic method, Genetic Algorithm (GA), is introduced to optimize this problem through designing and constructing a decision support tool for the investment portfolio selection and optimization. The applicability of the proposed comparative approach and the constructed tool are illustrated through examples.

Feasible Strategy Analysis of BYDs Portfolio Optimization Based on Markowitz Model under the Dual Carbon Goal

Feasible Strategy Analysis of BYDs Portfolio Optimization Based on Markowitz Model under the Dual Carbon Goal

Testing the Weak Efficient Market Hypothesis of Chinese Stock Market--Based on the Market Performance of Representative Stock Investment Portfolios in the HS300 Index

Chinas domestic stock market has developed very quickly overall in the more than 30 years since it was established, but information effectiveness in the Chinese stock market still faces multiple problems, so testing the weak efficient market hypothesis of the Chinese stock market is a good way to test its information quality and give investors relevant advice. This article selected representative stocks from the HS300 index, gathered the stocks historical data, used the index model to construct an optimal portfolio, and used another part of historical data to test if the portfolio has continuous excess return compared with the market portfolio and risk-free asset. The article finds excess return for the optimal portfolio during a long period and cannot prove that the Chinese stock market is weak and efficient. This result will provide a certain theoretical basis for investors who use technical analysis for stock investment and may also indicate that many issues still need to be addressed in the Chinese stock market, such as information asymmetry, high transaction costs, and insufficient regulation.

Investment modeling between energy futures and responsible investment

This research examines the hedging and diversification ability of energy futures (crude oil, heating oil, natural gas, gasoline, carbon) concerning investment in four different segments of responsible investment. Wavelet coherence and DCC-GARCH are used to inspect the association and volatility transmission between them. Conditional correlation, optimum portfolio weights and hedge ratios are also calculated for real-time data and decomposed series to analyze the optimum portfolio structure. Our analysis reveals that energy futures are more efficient as a hedge to responsible investment during the short-run period. Natural gas is the perfect hedge against all the segments of responsible investment. Each segment of energy futures can hedge investments in sustainability stocks and green bonds. Carbon and Gasoline are not the perfect hedge for ESGL and GCEI but can be used as a cheap hedge. Our study will help responsible investors to hedge their portfolio risk using related assets, countering their familiarity bias.

Research on the Optimal Strategy of Investment Portfolio Based on Markowitz Model

Nowadays, investment is becoming increasingly common. Under the globalization of the economy, investors are given more investment opportunities and choices. Investors need to select excellent assets and allocate the selected asset portfolio on weight during the investment process. The mean-variance model proposed by Markowitz plays an important guiding role in investment and risk management. This model can effectively evaluate investors portfolio risk and return decisions and significantly impact their decision-making choices. This study uses the data of the annual average return and variance from 11 risky assets and 1 risk-free asset in about 20 years to construct an investment portfolio in a fixed group based on the Markowitz Model. The study calculates the changes in the allocation of the maximum Sharpe ratio and the minimum variance portfolios under three different constraints and one condition with the addition of risk-free assets, respectively, and then analyzes changes in the outcome of data. The capital allocation lines are introduced to analyze the outcome of an investment portfolio with risk-free assets. Compared with the changes from three constraints, reasons are explained why these changes happen under three constraints. Then, the above limitations are taken as a premise, and the study proposes recommendations for the optimal investment portfolio selection for different investors according to the investment cycle, the preference of investors based on the situation of different investors, and market constraints. The study guides investors future investment decisions.

A robust ordered weighted averaging loss model for portfolio optimization

In this paper we will propose a Robust Ordered Weighted Averaging (ROWA) optimization model to find a portfolio according to different attitudes towards risk of a decision maker. The rationale of our model is supported by the idea of measuring risk through conditional means of losses from a database of past returns. The way in which these means of extreme losses affect to the final decision may be different according to the risk perception of the decision maker (scenarios). In this context, a compromise portfolio is identified to reconcile the admisible risk attitudes of a decision maker according to different paradigms. We will also link the robustness of the proposed solution with its efficiency from a multi-criterion decision making viewpoint (Pareto optimality). Both concepts have been connected previously in the literature in different contexts. The paper ends with an extensive numerical experiment in order to check the applicability of our model to real data on six financial markets.