Optimal Investment Portfolio Research Articles

Analisis Signifikansi Pengaruh Dividend Payout Ratio, Earning Per Share dan Debt to Equity Ratio terhadap Return Saham

This study examines the influence of Dividend Payout Ratio (DPR), Earning Per Share (EPS), and Debt to Equity Ratio (DER) on Stock Returns in companies listed on the LQ45 Index of the Indonesia Stock Exchange (IDX) during the 2021–2023 period. Using a quantitative approach, this research analyzed data from annual financial reports of 22 companies selected through purposive sampling, with a total of 66 observations. Multiple linear regression analysis was employed to evaluate the relationships between variables. The results indicate that DPR and EPS have a positive and significant impact on Stock Returns, with respective p-values of 0.001. This suggests that higher dividends and improved financial performance significantly enhance stock returns. On the other hand, DER does not have a significant influence on Stock Returns. This study provides practical implications for companies to strengthen their dividend policies and financial performance to attract investors. Investors are recommended to use DPR and EPS as primary indicators in forming an optimal investment portfolio. The findings contribute to the theoretical literature on financial ratios and stock performance in developing markets and suggest opportunities for future research, including additional variables such as macroeconomic factors or market conditions, to enhance Stock Return prediction models.

Capacity investment portfolio optimization considering EV charging flexibility: A heuristic algorithm-informed system dynamics approach

Orderly charging of electric vehicles (EVs) functions as a flexible tool for peak shaving to help integrate renewable energy sources effectively. Coordinating investments in charging resources and generation resources can enhance social benefits while ensuring the charging needs of EVs are met. This study aims to optimize the long-term investment strategies for these resources using a heuristic algorithm-informed system dynamic (SD) approach. Firstly, a SD model is developed to simulate the long-term interactions among generation resources, EVs, and power demand, capturing their dynamic relationships. Then, the fixed feedback functions of investment parameters in the SD model are replaced with an optimization model, enabling parameter optimization for environmental and economic objectives. Given that the process must quantify the aforementioned long-term interrelationships with potential high-order, nonlinear feedback loops, the solution is implemented using Particle Swarm Optimization (PSO). Data from a specific province in China was used for the case study. The results show that the optimized investment portfolio reduced carbon costs and total investment by 2.4% and 22%, respectively, while total social costs decreased by 17% compared to the pre-set investment strategy. Lastly, we also conducted sensitivity analysis and algorithm comparison.

Impact of AI on Investment Decision and Financial Inclusion of Rural India: A Study on Young Rural Women Near Ranchi District

Abstract: The integration of Artificial Intelligence (AI) into the financial sector has revolutionized investment decision-making, offering a powerful tool to empower individuals in making informed choices about their financial future. This research paper explores the impact of AI on the investment decisions of rural consumers, shedding light on the transformative potential of AI in promoting financial inclusion in underserved regions. Rural consumers in emerging economies face unique challenges in accessing financial services and making sound investment decisions. The study aims to investigate how AI technologies, including machine learning algorithms and predictive analytics, can address these challenges by providing personalized investment advice, risk assessments, and portfolio optimization for rural consumers. It examines the key factors that influence the adoption and acceptance of AI-driven investment solutions among rural populations. Through a mixed-method research approach, including surveys, interviews, and data analysis, this paper analyses the application of AI in rural investment decision-making. It discusses the benefits of AI, such as improved risk management, increased financial literacy, and enhanced access to financial markets. Additionally, the research assesses potential concerns, including data privacy, ethical considerations, and the need for human oversight in AI-driven investment processes. The findings suggest that AI can bridge the financial literacy gap in rural areas and enhance the investment decision-making capabilities of rural consumers. However, this transformation requires a holistic approach, encompassing not only the development and deployment of AI technology but also regulatory frameworks and educational initiatives to ensure its responsible and equitable use. In conclusion, this research paper highlights the pivotal role of AI in democratizing access to financial opportunities for rural consumers, ultimately contributing to greater financial inclusion and economic growth in rural regions. It offers valuable insights for policymakers, financial institutions, and technology developers aiming to leverage AI to improve investment decisions and financial well-being for rural populations.

The Construction of Investment Portfolios under the Minimization of VarianceTaking the Hong Kong Insurance Market as an Example

Abstract: In the context of a decline in the Hang Sheng Index, this paper intended to study the different effect between using Markowitz Model and Index Model to invest insurance industrys stock in Hongkong, especially for those who are risk-averse. A recent ten years of adj close for 9 typical insurance stocks from Hongkong as well as the Heng sheng index has been chosen as the target data. The result illustrates that there is no significant difference between these two models. This study intends to give some reference for those who focus on Hong Kong insurance industrys investment and hate taking risk. This study find that the constraints minimal variance is the smallest at 16.832(The index model of constraint 2) and the constraints highest sharp ratio is around 0.26(The index model of constraint 4). The findings of this paper provide the investors empirical evidence on how to construct optimal investment portfolio in Hong Kong insurance market.

Modeling risky portfolios with guaranteed maximize returns under a patent race

This paper studies the patent race problem of communication enterprises investing in communication technologies, and constructs a portfolio optimization model which considers the expected returns, investment risks, and replacement costs, in order to achieve the dual goals of maximizing the net investment income of backward enterprises and minimizing the expected investment risk. Through numerical experimental analysis, the optimal investment portfolio strategy under different risk levels and the impact of different risk levels on the net income of lagging company are obtained. The research results show that due to the backward research in the first stage of the backward enterprises, when their own investment decision-making power is relatively high, they can focus on the development of self-interested key technology areas in order to achieve the victory of the patent race.

Portfolio Optimization Using Machine Learning Method and Monte Carlo Simulation

Investment portfolio optimization is a crucial aspect of quantitative finance, aiming to maximize returns and minimize risks. This study focuses on optimizing investment allocation among five selected stocks (BAIC Blue Valley, BYD, Chang’ an Automobile, Kweichow Moutai, Sunwoda) over 100 days using Monte Carlo simulation and machine learning methods. This study uses the sliding window approach with the linear regression model to predict future returns and calculate the variance-covariance matrix to determine the optimal portfolio weights, leading to two key strategies: maximizing the Sharpe ratio and minimizing the risk. The results reveal that the Max Sharpe Ratio portfolio achieved a cumulative return of 0.0909, significantly outperforming the CSI 300 index’s return of -0.0348. Additionally, the Min Risk portfolio exceeded the market index with a cumulative return of 0.0062. These findings demonstrate that both the Max Sharpe Ratio and Min Risk strategies are effective in achieving a balance between risk and return and surpassing the market, offering valuable insights for investors seeking optimized portfolio allocations.

A new portfolio approach integrating three-way decision and Encoder–Decoder network

In the stock market, it can be challenging to predict stock trends and identify stocks with investment potential due to uncertainty and risk. Accurate predictive models and reliable asset preselection methods are essential for portfolio management. This paper proposes an integration of the deep learning model, the three-way decision (3WD) theory, and the Mean-Variance (MV) method for asset preselection and optimal asset allocation. In the initial stage, an attention-based Encoder–Decoder model is used to predict asset returns and calculate volatility. It outperforms other benchmark models regarding relative error, absolute error, and directional accuracy. The subsequent stage involves selecting stocks based on expected returns and volatility characteristics. The 3WD theory is then used to identify the delayed decision set for implementing additional investment strategies to hedge portfolio risk and enhance returns. Finally, the MV model is employed for investment portfolio optimization. The proposed model is validated using a substantial dataset from the A-share market’s SSE 50 Index from January 2010 to December 2022. Results indicate that the proposed model outperforms benchmark models in terms of daily average returns, annualized Information ratio, Sortino ratio, and other aspects under certain trading costs. However, further exploration is required to establish a mechanism that limits the quantity of preselected assets to ensure its broad applicability.

A model for optimizing an investment portfolio in modern conditions

Subject. This article discusses the issues of improving the methodology for optimizing an investment portfolio in modern conditions. Objectives. The article aims to develop a model for optimizing the investment portfolio, taking into account the investor's risk profile and the limited budget. Methods. For the study, I used a comparative analysis, mathematical modeling in economics, machine learning methods, genetic algorithms, and the Python programming language. Results. The article presents the author-developed model of optimizing the investment portfolio of an enterprise, based on the symbiosis of individual elements and principles of the most effective methods of optimizing the investment portfolio, algorithms for their implementation, mathematical model and practical recommendations. Conclusions and Relevance. The combination of classical methods of investment portfolio optimization and machine learning in the model helps improve the accuracy of assessing the future return of assets and determine their weights in the investor's portfolio. The theoretical significance of the work lies in the fact that the scientific provisions that actualize the task of improving existing or developing new approaches to optimizing the investment portfolio are proved. The practical significance of the study lies in the fact that the presented model is of an applied nature, taking into account the risk profile of the investor, tactical and strategic goals of investment, and budget constraints. The results of the study may be of interest to novice stock market participants, experienced investors, IT specialists engaged in the creation of programs and robots for trading on the stock market using machine learning tools.

Modeling of Mean-Value-at-Risk Investment Portfolio Optimization Considering Liabilities and Risk-Free Assets

This paper aims to design a quadratic optimization model of an investment portfolio based on value-at-risk (VaR) by entering risk-free assets and company liabilities. The designed model develops Markowitz’s investment portfolio optimization model with risk aversion. Model development was carried out using vector and matrix equations. The entry of risk-free assets and liabilities is essential. Risk-free assets reduce the loss risk, while liabilities accommodate a fundamental analysis of the company’s condition. The model can be applied in various sectors of capital markets worldwide. This study applied the model to Indonesia’s mining and energy sector. The application results show that risk aversion negatively correlates with the mean and VaR of the return of investment portfolios. Assuming that risk aversion is in the 5.1% to 8.2% interval, the maximum mean and VaR obtained for the next month are 0.0103316 and 0.0138270, respectively, while the minimum mean and VaR are 0.0102964 and 0.0137975, respectively. The finding of this study is that the vector equation for investment portfolio weights is obtained, which can facilitate calculating investment portfolio weight optimization. This study is expected to help investors control the quality of appropriate investment, especially in some stocks in Indonesia’s mining and energy sector.

Ước lượng ma trận hiệp phương sai với trọng số Shrinkage cân bằng trong lựa chọn danh mục đầu tư: Nghiên cứu thực nghiệm trên thị trường chứng khoán Việt Nam

Portfolio optimization based on the process of estimating the covariance matrix is one of the new approaches. In this article, the author has conducted an analysis and evaluation of various covariance matrix estimators to measure their effectiveness in investment activities. The results of the empirical study on over 400 listed stocks on the Vietnamese stock market from January 2011 to January 2024 have shown that the covariance matrix estimator with balanced shrinkage weight yields significantly superior results across all three portfolio performance metrics: Sharpe ratio, Sortino ratio, and Calmar ratio, compared to the other seven estimators divided into three groups including the LW Shrinkage method group, the factor model group, and the traditional method group. The study’s findings also aim to further encourage investors to expand research on covariance matrix estimation in selecting optimal investment portfolios.

Optimization of investment portfolios of Chinese commodity futures market based on complex networks

Futures trading in developing countries is now attracting more attention since investors may easily generate more excess return compared to the markets in developed countries, especially in Chinese market. In this paper, we analyzed the relationship between the centrality of commodity in the Chinese commodity futures market network and the optimal weight of each commodity in a portfolio, empirically examined the market systemic factors and commodity idiosyncratic factors that affect the centrality of commodity, and evaluated the effect of network structure on the optimization of commodity portfolio selection under the mean-variance framework. We found that the commodities with high network centrality are often related to industrial products with high volatility and small portfolio weights. We put forward a kind of commodity futures investment strategy based on this network and results showed that cumulative yield is better than other benchmark portfolios. The main contribution of this paper is to apply complex network theory to optimize futures portfolio selection by establishing the relationship between portfolio weight and commodity centrality in Chinese market, which is still under explored.

Research on cryptocurrency portfolio based on Markowitz model

In recent years, the booming growth of the cryptocurrency market has garnered significant attention from a diverse range of investors with the overall market capitalization experiencing continuous expansion. This surge in popularity, however, is accompanied by challenges arising from the market's inherent high volatility and complexity. The increasing diversity of available assets presents investors with a complex landscape to navigate. The quest to build an optimal investment portfolio has become a focal point, particularly as market participants seek strategies that not only capitalize on the market's potential for substantial returns but also effectively manage the inherent risks. In this paper, nine cryptocurrencies have been chosen to construct the portfolios, which include BTC, Ether, Tether, Binance, ripple, USDC, ADA, DOGE and TRX. The data selected comprises the daily price of the above assets from November 1, 2020, to October 9, 2023. This paper presents a comprehensive framework for constructing and optimizing cryptocurrency investment portfolios, by employing the Markowitz model and Sharpe ratio analysis. We examined these nine selected digital currencies to determine the optimal investment allocation. Our findings suggest that the highest sharpe ratio is achieved when each investment cryptocurrencies (according to the order above) allocates 0, 0.003, 0.762, 0.038, 0.005, 0.172, 0, 0.014 and 0.007, respectively, to each cryptocurrency. And the lower risk portfolio is obtained when each cryptocurrencies accounted for 2.740e-20, 6.315e-20, 0.5, 0, 0.001, 0.499, 0, 1.419e-04 and 4.210e-20, respectively. This study contributes to the sustainable development of the cryptocurrency market by providing valuable insights for financial institutions, policymakers, and other stakeholders. However, limitations of this research come from the small number of selected digital currencies and the exclusion of risk-free assets such as government bonds. Future research should consider expanding the sample size and incorporating risk-free rates to enhance the applicability and reliability of investment strategies. In conclusion, this research offers investors a practical framework to navigate the complexities of the cryptocurrency market and optimize their investment decisions for long-term value appreciation.

Navigating Risk Aversion and Regret

This study investigates the distinctive modeling of regret utility when compared with common utility. I also introduce the interplay between common utility and regret utility. Using this model, I examine the differences in decision making, which encompasses issues such as risk sharing and principal–agent dilemmas. Regret utility is set so that its risk aversion shows common utility’s prudence (i.e., downside risk aversion). This paper reveals, both qualitatively and quantitively and with a concrete model, that regret utility leads to a more balanced and optimal ratio of agent payouts to outputs compared with common utility, meaning when major outputs are kept by principal, there are relatively larger agent payouts, and when major outputs are kept by the agent, there are relatively smaller agent payouts. This means that regret makes a more balanced distribution, and regret utility is more conservative (not biased). In addition, preliminary empirical research was performed in which people were asked risk preference or averseness questions, and their risk averseness was calculated by using the CRRA (Constant Relative Risk Aversion) utility function. The regret condition leads to a more conservative attitude. Furthermore, the regret model can be used in other areas, like in conservative investment portfolio optimization.

Optimal selection of manufacturing enterprise financialization based on simulated annealing investment portfolio theory

The global market competition is becoming increasingly fierce, and manufacturing enterprises need to invest and expand. However, the traditional financial optimization of manufacturing enterprises has faced problems such as low efficiency and inaccurate search for the optimal solution, which has made manufacturing enterprises likely to face financial risks. The investment portfolio can enable enterprises to obtain the maximum profit on a certain risk level, or reduce their investment risk on a certain return level as far as possible. If the combinatorial optimization is realized, it can be applied to the optimal selection of manufacturing enterprises’ financialization. This article analyzed the respective characteristics of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithms, and analyzed the combination of GA and SA algorithms to solve the optimal investment portfolio through GA-SA algorithm, thereby helping manufacturing enterprises to make the optimal choice for financialization. The experimental results of this article indicated that the GA-SA algorithm solved the problem of GA algorithm easily falling into local optima, SA algorithm’s initial temperature and generation mechanism, and improved the efficiency of finding the optimal solution. Meanwhile, the experimental results showed that the average optimal solutions of Genetic Algorithm, Simulated Annealing algorithm, and GA-SA algorithms for 36 stock portfolios in Enterprise 1 were 69, 69, and 107, respectively. The average optimal solutions of the three algorithms for 36 stock portfolios in Enterprise 2 were 73, 90, and 112, respectively. This proves that the number of optimal solutions searched by GA-SA algorithm is higher than that of GA and SA algorithm, and also proves that it is effective to use GA-SA algorithm to optimize investment portfolio and help manufacturing enterprises to make optimal financial choices.

Extreme time-frequency connectedness across U.S. sector stock and commodity futures markets

This study investigates the extreme time-frequency return connectedness between ten U.S. sectors and commodities from January 2014 to May 2023. Using quantile time-frequency measures, we find the following: Firstly, the total connectedness estimates are more sensitive at shorter frequencies than at longer ones. Secondly, the study reveals varied degrees of contagion during crisis periods. Notably, during COVID-19 (Russia-Ukraine conflict), the contagion is driven by short-term (long-term) shocks, specifically during the bearish (bullish) phase. Thirdly, quantile connectedness measures depict intense correlations around market extremes, underlining dynamic net return-contagion with tailored risk strategies. The shifts in shock transmission roles during bearish and bullish scenarios, along with evolving dynamics across time-frequency horizons, emphasize substantial interconnectedness within the network. Our findings suggest limited diversification scope under extreme market conditions, informing investment decisions, risk management, and portfolio optimization.

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.

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.

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%.