Optimal Portfolio Research Articles

Contagion effect between fuel fossil energies and agricultural commodity markets and portfolio management implications

This study investigates the dynamic spillover effects between fossil fuels (Brent oil and natural gas) and major agricultural markets (maize, soybeans, and wheat) using a time-varying parameter vector autoregression (TVP-VAR) model. Additionally, we quantify the cumulative return of the investment portfolio, optimal weights, time-varying risk coverage, and hedging effectiveness of bivariate commodity portfolios using the Minimum Variance Portfolio (MVP), Minimum Correlation Portfolio (MCP), Minimum Connectedness Portfolio (MCoP) under bearish, tranquil, and bullish market conditions. The results show that maize and natural gas are the main net receivers of spillovers, whereas wheat and Brent oil are the main net transmitters of shocks to the network, respectively. Our findings of optimal weights in multivariate portfolio suggest more weights for agri-commodities against fossil fuels in all three modes of market. In addition, the optimal weights of bivariate portfolio analysis show that the most optimal portfolio which has the highest HE value is related to natural gas pairs with agri-commodities. Moreover, depending on the status of the market; in an average and bearish modes, the highest optimal weight is related to maize/natural gas. However, in the bullish market, the highest optimal weights are related to natural gas/Brent oil pair. Furthermore, the results of optimal bivariate hedge ratios show that natural gas/wheat (wheat/maize and vice versa) portfolio is the cheapest (the most expensive) investment strategy in all modes of market and based on MVP and MCoP approaches.

The Effects of the Introduction of Volume-Based Liquidity Constraints in Portfolio Optimization with Alternative Investments

Recently, liquidity issues in financial markets and portfolio asset management have attracted much attention among investors and scholars, fuelling a stream of research devoted to exploring the role of liquidity in investment decisions. In this paper, we aim to investigate the effects of introducing liquidity in portfolio optimization problems. For this purpose, first we consider three volume-based liquidity measures proposed in the literature and we build a new one particularly suited to portfolio optimization. Secondly, we formulate an extended version of the Markowitz portfolio selection problem, named mean–variance–liquidity, wherein the goal is to minimize the portfolio variance subject to the usual constraint on the expected portfolio return and an additional constraint on the portfolio liquidity. Thirdly, we consider a sensitivity analysis, with the aim to assess the trade-offs between liquidity and return, on the one hand, and between liquidity and risk, on the other hand. In the second part of the paper, the portfolio optimization framework is applied to a dataset of US ETFs comprising both standard and alternative, often illiquid, investments. The analysis is carried out with all the liquidity measures considered, allowing us to shed light on the relationships among risk, return and liquidity. Finally, we study the effects of the introduction of a Bitcoin ETF, as an asset with an extremely high expected return and risk.

A novel portfolio optimization method and its application to the hedging problem

Abstract In this article we will propose a novel, self-financing, dynamic and path dependent portfolio trading strategy which is based on the well known principle “sell high – buy low”. Trading strategies are important also for the hedging problem selling/buying an option. The main problem of the writer of an option is how to invest the amount that she has received selling the option therefore the proposed trading strategy play an important role here. We will see that the hedging problem reduces to an optimization one and therefore the portfolio optimization and the hedging problem are closely related. We will also propose a deterministic portfolio selection method (i.e., without making any assumption-guess about the assets) and a notion of a deterministic fair price of an option.

Dynamic mean-variance portfolio selection under factor models

Utilizing insights from financial literature and empirical financial data, we introduce a comprehensive system of factor models designed to capture both return and risk dynamics. Our focus extends to addressing the multi-period mean-variance portfolio selection challenge within the framework of these proposed factor models. Through rigorous analysis, we formulate a semi-analytical optimal portfolio policy, characterized by a linear relationship with the current wealth level. The coefficients of this optimal policy are intricately linked to a specific stochastic process known as the future investment opportunity (FIO), reflecting the investor's anticipation of future investment prospects. Furthermore, empirical examination within the U.S. market context underscores the efficacy of our approach. By incorporating the factor models for return and risk, our optimal portfolio policy exhibits superior out-of-sample Sharpe ratio compared to benchmark policies.

Analisis Pembentukan Portofolio Optimal pada Indeks Saham LQ-45 dengan Metode Safety First Criterion

An optimal portfolio of stocks is a combination of various stock investment assets chosen to provide the maximum level of return for a specified level of risk or provide a minimal level of risk for a specified level of return. Investors form an optimal stock portfolio with the aim of minimizing the risk of investment activities. This research discusses the formation of the optimal portfolio on LQ-45 index stocks with the Safety First Criterion method. There are three criteria in the Safety First method, namely Roy Safety First, Kataoka Safety First, and Telser Safety First. The three criteria of Safety First have the main similarity in focusing on investment risk and have different objectives. The optimal portfolio with Roy Safety First criteria aims to reduce the possibility of a high level of risk. Then, the optimal portfolio with Kataoka Safety First criteria, has the goal of maximizing returns, with a level of risk determined by investors. While the optimal portfolio of Telser Safety First criteria aims to achieve the highest expected return within a predetermined risk level. The data in this study are secondary data on the weekly closing price of the LQ-45 index for the period February 2021 to January 2023, which is 105 weeks. Based on the results of the analysis, the optimal portfolio formation for risk-loving investors is the Telser criteria portfolio. This portfolio consists of ADRO, BBNI, BMRI, ITMG, and MEDC stocks. Then, the optimal portfolio for risk-averse investors is the Roy criteria portfolio consisting of ADRO, BBNI, BMRI, MDKA, and MEDC stocks.

Perbandingan Value at Risk dan Expected Shortfall pada Portofolio Optimal menggunakan Metode Downside Deviation

Portfolio formation is one of the strategies that investors can do to get the best results Portfolio formation can use the Downside Deviation method. The optimal portfolio with this method uses downside deviation and sets the return below the benchmark as a measure of risk. Every optimal portfolio certainly cannot be separated from risk. To measure risk, you can use the Value at Risk (VaR) and Expected Shortfall values. This study aims to form an optimal portfolio using the Downside Deviation method and continued by comparing the possible losses that occur from the formed portfolio using the VaR and Expected Shortfall values. The data used in this study is the daily closing price data of LQ-45 Index stocks in the banking sector in the period February-June 2023. From the stock data, data selection is carried out by selecting stocks that have a positive expected return and are normally distributed. Then, the optimal portfolio formation stage is continued using the Downside Deviation method and comparing the possible risks formed with the VaR and Expected Shortfall values. The results of this study show that the optimal portfolio with the Downside Deviation method consists of four stocks, namely with the stock codes BRIS.JK, BBRI.JK, BBNI.JK, and BBCA.JK. This study uses a case example by investing capital of Rp100,000,000 with a one-day time period and three levels of confidence, namely 90%, 95%, and 99%. Based on the comparison of the risk value of the portfolio formed using VaR and Expected Shortfall, it is shown that the possible risk with the Expected Shortfall method is greater than the VaR value. Therefore, Expected Shortfall is better in estimating the maximum risk.

Analisis Pembentukan Portofolio Optimal di Bursa Efek Indonesia dengan Menggunakan Model Markowitz

The purpose of this study is to utilize the Markowitz Model to build an optimal portfolio of stocks on the Indonesia Stock Exchange in 2019 to 2023, focusing on stocks in the IDX30 index. Based on information from KSEI, the public premium in capital market speculation continues to increase. On that basis, this study is important to be conducted in order to provide a reference for the public to invest. The IDX30 index was chosen as the subject of the study because it includes stocks with large market capitalization, high liquidity, and stable financial conditions. This study determines the optimal fund allocation for each stock and examines stocks in the IDX30 index from 2019 to 2023 that are worthy of consideration for inclusion in the optimal portfolio. In this test, it produces four stocks that become the optimal portfolio with the most extreme profits, namely ADRO 16.12%, BBCA 65.97%, BBRI 4.13%, and ICBP 13.76% with a return of 1.01% and a risk of 0.22%. In contrast, the minimum risk consists of ADRO of 5.29%, BBCA of 26.14%, CPIN of 13.91%, ICBP of 16.17%, INDF of 8%, KLBF of 0.48%, TLKM of 13.47%, and UNVR of 16.55%, with a risk of 0.1% and an expected portfolio return of 0.11%.

Mapping the Conceptual and Intellectual Structure of the Investor’s Financial Behaviour: A Bibliometric Analysis

Examining the diverse roles played by various kinds of investors in the stock market is crucial, because of their differing behaviours while making investment decisions. This study reviewed the academic literature on investor behaviour to acquire insights into the current status, developments and significant gaps with the help of bibliometric analysis. The research methodology involved selecting articles from the Scopus database and conducting performance analysis using VOSviewer software for science mapping. The study retrieved 313 research articles using the PRISMA protocol. It identified influential articles, journals, authors and five major themes (inefficiency and bubbles in the stock market, technological advancement in the financial market, behavioural aspect in investment decisions, prospect theory and cognitive bias and stock trading behaviour), adding to the body of knowledge on investor behaviour. The findings also revealed that there has been a consistent rise in research in this area. In addition, scientific mapping techniques, such as bibliographic coupling, showed that the majority of the literature is focused on the connection between asset price models, behavioural biases, investor sentiment and portfolio choices. Finally, the research directions identified in this study could serve as a guide for future research.

A theory of net capital flows over the global financial cycle

We develop a theory to account for the relationship between global asset price changes and net capital flows. We show empirically that countries that have a net debt of safe assets experience a rise in net outflows of safe assets (i.e. pay off safe asset debt) when global asset prices fall. This is accomplished through a rise in total net outflows (an increase in net savings) and a drop in net outflows of risky assets (the net sale of foreign risky assets). We develop a multi-country portfolio choice model that can account for these facts. The theory relies on cross-country heterogeneity in the share of an investor’s portfolio invested in risky assets. A global drop in risky asset prices changes relative wealth across countries due to this heterogeneity, which leads to changes in net flows of safe and risky assets. The model is applied to 20 advanced countries and calibrated to reflect observed cross country heterogeneity of net foreign asset positions of safe and risky assets. The implications of the calibrated model for net capital flows are quantitatively consistent with the data.

Safety assessment of cryptocurrencies as risky assets during the COVID-19 pandemic

This study examines the safe-haven properties of cryptocurrencies during the COVID-19 pandemic by exploring various cryptocurrency allocation strategies and their impact on downside risk compared to holding only the stock market index. Spanning from 2019 to 2021, the analysis includes seven cryptocurrencies—Bitcoin, Ethereum, Litecoin, Dash, Maker, Monero, and NEO—and six stock market indexes—SP500, FTSE, CAC40, IBEX, AEX, and DJIM. The research introduces a portfolio optimization approach focused on mitigating significant losses during crises, employing Value at Risk (VaR), Modified Value at Risk (MVaR), and Conditional Value at Risk (CVaR) metrics across different portfolio compositions. Findings highlight several key insights: higher cryptocurrency allocations correlate with increased downside risk, with none of the studied cryptocurrencies acting as a safe haven for international equity indexes during the COVID-19 pandemic. Relative MVaR exceeds CVaR across most combinations, and Maker demonstrates the highest downside risk, while Bitcoin and Litecoin exhibit the lowest risk levels based on CVaR and MVaR, respectively. Integration of the FTSE index is associated with peak risk levels, whereas incorporating DJIM and AEX shows reduced risk exposure. The study contributes novel insights into cryptocurrency risk dynamics during the COVID-19 pandemic, offering valuable implications for risk management strategies in investment portfolios.

An Investigation on Quantum-Inspired Algorithms for Portfolio Optimization Across Global Markets

An Investigation on Quantum-Inspired Algorithms for Portfolio Optimization Across Global Markets

Risk tolerance towards research and development investment: the role of firm size and technology intensity

ABSTRACT This research examines how risk tolerance towards research and development (R&D) investment varies by firm size and technology intensity using a case study of four groups of manufacturing firms (i.e. large high-tech firms, small high-tech firms, large low-tech firms, and small low-tech firms) in South Korea. Our research contributes to the literature by offering the first empirical application quantifying different risk tolerances towards R&D investment of firms of varying sizes and technology intensities using total factor productivity and optimal portfolio frontiers. We find that the risk tolerance towards R&D investment is proportional to firm size, and high-tech firms tend to be more risk tolerant than low-tech firms given the same firm size. Furthermore, we find that large low-tech firms have a greater tolerance for risk than small high-tech firms when the present guaranteed return is lower, but risk tolerance is reversed as the current guaranteed return increases. These findings could be used to inform R&D project funding decisions in particular, our finding of higher risk tolerance of small high-tech firms with higher present guaranteed return suggests that government subsidies for R&D investment may function as the present guaranteed return for small high-tech businesses.

Return and volatility connectedness between agricultural tokens and us equity sectors

This study investigates the return and volatility interconnectedness between emerging digital assets, specifically agricultural tokens, and U.S. equity sectors using the TVP-VAR model. Analyzing data from August 7, 2020, to January 2, 2024, the findings indicate modest interconnections between agricultural tokens and U.S. sectors, with time-varying behavior. Notably, return interconnectedness is generally stronger than volatility, except at the sample period's outset, where volatility dominates. Return spillovers predominantly drive the connectedness system, though agricultural tokens uniquely act as net recipients of both return and volatility spillovers, while U.S. equity sectors mainly transmit spillovers. Optimal portfolio analysis, utilizing portfolio weights and hedge ratios, reveals that incorporating agricultural tokens offers portfolio diversification benefits and enhances hedging performance. Investors are advised to frequently adjust portfolios to maximize diversification and hedging gains. These findings provide significant portfolio implications for policymakers, market participants, and investors.

The investment grade and funds' portfolio allocation in Greek assets

The importance of investment funds for the global economy has increased in the aftermath of the global financial crisis. In the present paper, we focus on the case of Greece and the developments in investment funds’ portfolios, with a special emphasis on the period before the sovereign credit rating upgrade of Greece to investment grade. By means of a differences-in-differences estimator, we find that, in the aftermath of the change in Greece’s sovereign credit rating outlook to positive by the rating agency Standard and Poor’s (S&P), investment funds increased their holdings of Greek sovereign bonds in relation to other comparable euro area sovereign bonds. Next, in a dynamic panel data model setup we find that this increase in investment funds’ positions in Greek government bonds (GGBs) explains about 80% of the reduction in Greek sovereign bond spreads. Our results highlight the strong association between investment funds’ portfolio allocation and the underlying assets’ credit ratings, and provide incentives for continuing reforms that may lead to rating upgrades, as a means of increasing demand for Greek sovereign bonds and controlling the cost of debt. This is especially important when the monetary policy environment becomes tighter and interest rates, as well as the cost of funding, increase.

Order Statistics and Actuarial Measures from Powered Inverse Rayleigh Distribution

Nashaat [25] introduced the powered inverse Rayleigh (PIR) distribution. It provides a better fit other than (inverse Rayleigh, Rayleigh, and Weibull) distributions. The moments of order statistics and recurrence relations for the single and double moments have been established. The computation of the means and variances are enumerated. These computations can be trulyinteresting and applied in numerous domains of study. Moreover, cumulative entropy (C.E.) and actuarial measures (A.M.) are also calculated to address the uncertainty in portfolio optimization. The usages of C. E. and A.M. are widespread in many real-word applications specifically in physical sciences and insurance science.

Portfolio Optimization Strategies for the Common Investor

Abstract: Financial Analytics is a key field to making investment decisions and has been developed only recently. It involves analytical thinking, computational knowledge and creative thinking about investment strategies. There are many ways to invest money. Some people invest them in Gold, some in real estate, some create a fixed deposit, some in the stock market. Some people diversify their money into different sectors of the market and invest to get an optimal profit by understanding the market. This kind of investment is called portfolio management. Unlike gambling, understanding of the portfolio and its management can actively minimize the risks associated with investment. In the proposed research we tried to study the different strategies of investing in the different sectors and building a portfolio, with the express aim of creating optimal diversified portfolios. Our methods include optimizing the weights of different investment instruments in different sectors, with implementations carried out in Python and using time-series Python libraries. We have focussed on three different investment strategies in particular: (1) Genetic algorithms help us to divide the portfolio (chromosome) into the genes (sectors) and understand it in a better way. Exploiting the existing algorithms for optimizing fitness functions in genetic algorithms, we have implemented one strategy for optimizing portfolios; (2) Efficient frontier analysis from Markowitz theory helps in investing and creating our own portfolio. We developed some of the strategies that help in displaying portfolio allocation and efficient frontier graphs; (3) We have also implemented a new criterion in vogue in the financial markets today, the “Kelly Criterion” preferred by many investors all over the world. We used real time financial stock data to validate and test our functions. All the strategies developed are modular and we also show a proof-of-concept web interface for one of the developed strategies. It is hoped that our work enables investors in making better decisions about where to park their money and converting their wealth into high-yield portfolios.

ANALYSIS OF OPTIMAL PORTFOLIO FORMATION ON IDX30 INDEXED STOCK WITH THE MEAN ABSOLUTE DEVIATION METHOD

In investing in stocks, an investor must be able to form a stock portfolio to obtain optimal results. Factor analysis is one way to select stocks to form a portfolio. Factor analysis with Principal Component Analysis (PCA) extraction is used to summarize many variables into new smaller factors by producing the same information. The new factor formed is called a portfolio. This study aims to form an optimal portfolio using the Mean Absolute Deviation (MAD) method, which is an alternative to Markowitz optimization, and assess the stock portfolio's performance using the Sharpe index. This research uses IDX30-indexed stocks because the stocks in this index have high market capitalization and liquidity. The data used in this study are daily close stock price data on the IDX30 index from September 20, 2022, to September 20, 2023. The data used is secondary data obtained from the official website https://finance.yahoo.com/. From the analysis, three stock portfolios were obtained. With MAD optimization, the investment weight of each stock is obtained namely, in the first portfolio, the investment weight of AMRT shares is 21.95%, BBCA shares are 30%, BBNI shares are 18.05%, and BBRI shares are 30%. In the second portfolio, the investment weight of AKRA shares is 34.03%, BRPT shares are 40%, and MEDC shares are 25.97%. In the third portfolio, the investment weight of BMRI shares is 50%, and INDF shares are 50%. By measuring the performance of the Sharpe index, the optimal portfolio is obtained in the second portfolio with an expected return portfolio of 0.155% and a portfolio risk of 1.927%.

Robust Hydropower Planning Balances Energy Generation, Carbon Emissions and Sediment Connectivity in the Mekong River Basin

AbstractWe present a framework for strategic dam planning under uncertainty, which includes GHG emissions mitigation as a novel objective. We focus on the Mekong River Basin, a fast‐developing region heavily relying on river‐derived ecosystem services. We employ a multi‐objective evolutionary algorithm to identify strategic dam portfolios for different hydropower expansion targets, using process‐related and statistical models to derive indicators of sediment supply disruption and GHG emissions. We introduce a robust optimization approach that explores variations in optimal portfolio compositions for more than 5,000 state‐of‐the‐world configurations, regarding sediment origins and trapping and GHG emissions. Thus, we can rank dam projects' attractiveness based on their frequency of inclusion in optimal portfolios and explore how uncertainty affects these rankings. Our results suggest that developing dams in the upper Mekong would be a more robust option for near‐term development than, for example, the lower Mekong and its tributaries, for both environmental and energy objectives. Our work presents a novel approach to better understand the basin‐scale cumulative impacts of dam development in high‐uncertainty, data‐scarce contexts like the Mekong Basin.

Empirical Analysis of Portfolio Allocation During Market Turbulence Based on Efficient Frontier

The global financial markets have experienced considerable turmoil due to the COVID-19 pandemic, in this context, portfolio management becomes critically important. This paper employs Monte Carlo Simulation to determine Efficient Frontier of a portfolio comprising fourteen US technology stocks in the period of COVID-19 pandemic by python, which also allocates portfolios for Minimum Volatility and Maximum Sharpe Ratio, along with their key parameters. According to this study, Maximum Sharpe Ratio portfolio outperforms Minimum Volatility portfolio in terms of cumulative return. However, its important to note that Maximum Sharpe Ratio portfolio also experiences a greater maximum drawdown. Additionally, this paper visually depicts the optimal allocation of asset weights across different levels of risk aversion using a chart. Furthermore, it identifies NVDA as the optimal choice for investors who have low degree of risk-aversion due to its high volatility and expected return. Conversely, IBM and CSCO emerge as the preferred choices for risk-averse investors, as they effectively mitigate the portfolios entire risk.

Analysis of optimal portfolio formation using the single index model approach on Bisnis-27 index shares on the Indonesia Stock Exchange for the 2018-2022 period

Based on data from the Indonesian Central Securities Depository (KSEI), the number of investors in the Indonesian stock market is increasing, indicating the popularity of stocks as an investment instrument. This study aims to examine the returns and risks of stocks in the optimal portfolio, compared to those not under the optimal portfolio, using the Single Index Model. Secondary data from the Bisnis-27 index were used for the analysis. The results show that returns differ significantly between stocks in the optimal portfolio and those that are not. However, these risks were found to be insignificant. The determination of the optimal portfolio in this study can be used as a reference by investors when making investment decisions.