Asset Portfolio Research Articles

Incorporating green assets in equity portfolios

We test whether the inclusion of green asset ETFs in portfolios yields better outcomes for investors. We use a mean-variance optimization framework to construct optimal portfolios with and without green assets and compare their performance over different time horizons and market conditions. Our results show that the portfolios that combine green assets with other broader market indices generate higher returns and Sharpe ratios compared to benchmark portfolios. These results survive the incorporation of transaction costs and removal of the COVID-19 period from the sample. Further, we find that the probability of outperforming the benchmark is much higher for long-horizon investors in green asset portfolios.

The impact of technical efficiency on firms' value: The case of the halal food and beverage industry in selected countries.

The market for the halal food and beverage industry sector has experienced rapid growth in recent years, which indicate excellent investment opportunities. This paper examine the effect of Technical Efficiency (TE) on firm value in 5 selected influential countries in halal food and beverage sector based on Global Islamic Economy Report 2020. Two steps estimation was used to run the data, using the Stochastic Frontier Analysis (SFA) model to determine the company's TE and panel data to test the effect of TE through firm value. The results show that Indonesia has the highest score for TE (62%), followed by Pakistan (59%), South Africa (57%), Malaysia (55%), and Singapore (52%), which means, in general, there is inefficiency in allocating resources over 38% up to 48% and needs to be improved by halal food and beverage companies in. Regarding panel data, all countries sample except Pakistan highlight that TE significantly affect company value. It indicates that the crucial part of managing efficiency can be a sign in stock market performance. The result shows that company managers should set efficiency strategies to their business process for creating sustainability and increase their value in the capital market. As for investors, this TE can be used as an indicator before choosing company stocks; if the company is efficient, then it is worthy of being one of the portfolio assets. Form the government side, the finding can help them to set appropriate policy setting to boost halal food and beverages industry such as giving subsidy or incentive to increase the efficiency ability of halal food and beverage companies and identify the industry's strength by comparing the result of TE between 5 countries.

Risk aversion and public sector employment

AbstractThis study examines whether public sector workers are more risk averse than those in the private sector and, if so, whether risk‐averse individuals self‐select themselves into the public sector or public employees become more risk averse during their career. Drawing on portfolio theory that individuals assemble their asset portfolio that maximizes expected return within an acceptable level of risk, this study analyzes 6276 South Korean employees' portfolio selection behaviors from 2000 to 2017. Findings show that public employees are likely to hold safer assets while those in the private sector tend to own riskier assets. Such behavioral heterogeneity is witnessed at the time of their first entrance into each sector and remains constant over their career. These results indicate that public employees' risk aversion is primarily a consequence of their self‐selection, but their sector affiliation also plays a role in sustaining their risk aversion.

Assessing the investment viability of Indonesia's upstream electric vehicle (EV) sector stocks amidst the COVID-19 pandemic

Amidst the global push for sustainability, the burgeoning electric vehicle (EV) industry has driven increased demand for batteries, placing the Indonesian nickel ore sector in a pivotal position due to its vast reserves. This study thoroughly examines the investment landscape of this sector, utilizing advanced portfolio optimization techniques to analyze four major nickel ore mine firms in Indonesia. Through Monte Carlo simulations, the study evaluates the optimal portfolios of risky assets, comparing their performance before and during the COVID-19 pandemic. Findings reveal a significant shift in portfolio composition during the Pandemic, reflecting investors' response to global disruptions by diversifying their holdings. Notably, the Sharpe Ratio, a risk-adjusted return measure, demonstrates an impressive increase in return relative to risk during the Pandemic, emphasizing the sector's resilience and attractiveness for investment, especially in times of economic uncertainty like the COVID-19 pandemic. The transformation in portfolio weights and the corresponding increase in risk-adjusted returns highlight the sector’s potential as a lucrative investment avenue, especially during periods of global economic uncertainty like the COVID-19 pandemic.

Analyzing the Impact of the Threshold on Machine Learning Models for Credit Risk Prediction Using Business Intelligence

Making decisions based on predictions generated from machine learning models requires users to have a clear understanding of the mechanisms and logic behind every prediction. From one side, business users must be convinced in the ability of the models to generate correct predictions. Predictive power, expressed by the different performance measures, is not sufficient for building trust and acceptance of machine learning models. Business users need additional techniques and tools for model interpretation and evaluation of the effects from decisions based on machine learning predictions. In this research paper we propose an approach for analyzing the impact of different thresholds for converting probabilities into predictions for binomial classification machine learning models applicable for credit risk prediction in Peer-to-Peer Lending platforms. We define a set of measures to explore global and local impact on decision-making process and present different scenarios in a Business Intelligence application built in an analytical and business intelligent platform. Based on the presented results we can draw conclusions that when choosing the best model and threshold users should consider a broad set of measures not only for model accuracy, but also should consider misclassification costs, financial results, asset portfolio structure, etc.

Assessing the potential of blockchain technology for Islamic crypto assets

Purpose The rapid rise of Islamic crypto assets, underpinned by blockchain technology, has introduced a novel dimension to the Islamic financial landscape, raising questions about their potential as safe havens within emerging Islamic economies. However, the opportunities and challenges associated with this phenomenon remain insufficiently explored. In this context, this study aims to empirically investigate the extent to which blockchain technology can establish Islamic crypto assets as safe havens in equity markets within Islamic economies. Design/methodology/approach This study addresses the need for rigorous empirical analysis to understand the dynamics between Islamic crypto assets and stock markets in emerging Islamic economies, focusing on the transmission of volatility. While the evolving nature of the Islamic financial sector demands reliable data, the reliance on the most available data offers insights into the expected future trends in this emerging field. The research specifically focuses on three essential assets in the Islamic financial portfolio: OneGram Coin and X8XToken, both backed by gold and MRHB DeFi, an Islamic DeFi asset lacking gold backing. These crypto assets are compared with corresponding assets in seven stock markets of emerging Islamic economies. Using daily log returns of the Islamic crypto assets from various sources and seven Islamic stock indices. The data covers the period from December 27, 2021, to December 28, 2022, capturing the fluctuations in Islamic stocks and cryptocurrency markets during the post-COVID-19 era. This research uses advanced econometric techniques, including pairwise dynamic correlation and the DCC GARCH model. Findings The findings indicate that Islamic crypto assets exhibit distinct characteristics, with lower volatility and low correlations compared to their conventional counterparts in non-Islamic contexts. This outcome suggests that these Islamic crypto assets could potentially serve as safe havens within Islamic stock markets, offering valuable insights for various stakeholders, including investors, governments and policymakers. Research limitations/implications The findings are based on a specific set of Islamic crypto assets and may vary with a different selection. Market dynamics can also influence the relationships observed. Nevertheless, the outcomes provide valuable insights for investors, policymakers and researchers interested in the intersection of Islamic finance, cryptocurrency and technology. Originality/value In essence, this research not only unveils the potential of Islamic crypto assets as stabilizing forces but also delineates a trajectory for subsequent research endeavours within the realm of emerging Islamic Fintech, elucidating the challenges, opportunities and benefits that lie therein. With a discerning eye on circumventing the pitfalls entrenched within conventional crypto finance, this study contributes to a heightened comprehension of the transformative role that Islamic crypto assets can assume, ultimately enriching the financial resilience of Islamic economies.

Opportunities for using investments in wine for portfolio diversification: Foreign and Russian practices

Subject. This article discusses the opportunities for using investments in wine for the purpose of portfolio diversification, taking into account Russian and foreign practices. Objectives. The article aims to assess the possibilities of using collectible wine to diversify the investment portfolio. Methods. For the study, I used the mean-variance analysis, covariance and correlation analyses, modeling, and the statistical method. Results. The article presents the results of the assessment of the investment characteristics of collectible wine as a class of alternative assets. It describes the main trends in the global wine investment market and identifies the prospects and problems of the development of wine investments in Russia. Conclusions and Relevance. The article concludes that investments in collectible wine are more profitable and less risky than traditional investment instruments, and they also have a weak correlation with classic financial market assets, which makes investing in wine an effective way to diversify a portfolio. The results of the study can be used by both private and institutional investors interested in diversifying their asset portfolio using alternative investments.

Credible transition plans for coal power sector: current disclosure framework and considerations for transition finance

The zero-carbon transition of the coal power industry comes with substantial economic costs, both in terms of the cost of stranded assets and the innovation and application of carbon reduction, zero-carbon, and decarbonization technologies. Mobilizing social capital through transition finance is crucial to support companies in their efforts to reduce carbon emissions. The lack of high-quality data and consistent, comparable disclosure frameworks hinders proper due diligence and strategic planning, presenting a major obstacle to financing the transition. To identify the mismatches between existing disclosure frameworks for transition plans and the needs of transition finance, we conducted a systematic review of various transition disclosure frameworks, such as TCFD (Task Force on Climate-Related Financial Disclosures), CA100+ (Climate Action 100+) and TPT (Transition Plan Taskforce), and compared them with the information requirements of financial institutions for assessing coal phase-out plans of production entities. The results indicate that the ex-ante assessment of transition finance requires disclosure frameworks to be further expanded and aligned in areas such as technological distinctions and data quality to meet the demands of pricing, risk management, new product innovation, and disclosure of financial institutions’ carbon footprints in their asset portfolios. Moreover, the implementation of the current disclosure standards by coal power enterprises may encounter dual constraints from soft regulatory practices and the external business environment, resulting in the emergence of the “green silence” phenomenon. The inconsistency between disclosure frameworks may also give rise to regulatory arbitrage, leading to a distorted evaluation of the transition process of coal power enterprises by the capital market, resulting in potential price distortions and credit risks.

A Distributed Virtual-Machine Placement and Migration Approach Based on Modern Portfolio Theory

Virtual machine placement and migration (VMPM) are key operations for managing cloud resources. Considering the large scale of cloud infrastructures, several proposals still fail to provide a comprehensive and scalable solution. A variety of approaches have been used to address this issue, e.g., the modern portfolio theory (MPT). Originally formulated for financial markets, MPT enables the construction of a portfolio of financial assets in order to maximize profit and reduce risk. This paper presents a novel VMPM approach applying MPT and incremental statistics computation for VMPM decision-making so as to maximize resource usage while minimizing under and overload. Extensive simulation experiments were conducted using CloudSim Plus, relying on synthetic data, PlanetLab and Google Cluster traces. Results show that the proposal is highly scalable and largely reduces computational complexity and memory footprint, making it suitable for large-scale cloud service providers.

Enhancing portfolio resilience during crisis periods: Lessons from BRICS indices and multi asset strategies

This paper uses Markowitz’s mean-variance model to construct an investment portfolio incorporating multiple assets – BRICS equity indices, Gold, crude oil, bonds, and cryptocurrencies. The optimally created risky portfolios outperform alternative portfolio optimization methods – the naive portfolio and the equal risk contribution portfolio; and established indices – the S&P 500 and the MSCI Emerging Equity Index in terms of metrics – adjusted Sharpe ratio, modified Sharpe ratio, and the modified Value at Risk. The findings are validated across different periods, including the COVID-19 period and the Russian invasion of Ukraine, including various in and out of sample periods. The findings highlight the benefits of portfolio diversity, mainly using BRICS indices, Gold, and Brent Crude oil, and challenge the notion of limited diversification benefits in BRICS indices found in previous studies. This paper further suggests the potential of emerging market bonds ETF as a diversification option during turbulent economic periods and highlights the limitations of cryptocurrencies in optimizing multi asset portfolios. By adopting the recommended multi asset portfolios, investors can enhance their risk-return trade-offs and achieve superior performance compared to the S&P500 and MSCI emerging indices. Lastly, the paper recommends future research opportunities in measuring portfolio performance and hedging strategies considering risk-adjusted return measurements, transaction expenses, and dynamic rebalancing techniques.

Balancing Portfolios with Metals: A Safe Haven for Green Energy Investors?

This study investigates the relationship between energy metals and precious metals to assess their suitability as safe haven assets in clean energy investment portfolios. This study aims to conduct an effect analysis of the events that occurred during the years 2020 and 2022, characterized by substantial investments in the field of clean energy. The analysed period encompasses the period from 13 July 2018 to 11 July 2023. The study is carried out in multiple stages with the aim of investigating a highly tumultuous period in the global economy. To assess long-term relationships, the econometric methodology proposed by Gregory and Hansen will be employed. The research shows a positive association between energy metals (excluding nickel futures) and clean energy indexes, suggesting their potential as secure investments for green investors diversifying their portfolios. Additionally, the study confirms the reliability of precious metals, such as gold, silver, and platinum as safe havens for clean energy stock indexes. These findings highlight the stability that both energy and precious metals can offer within clean energy portfolios during market volatility, emphasizing their value in such investment strategies. In brief, this study affirms that energy and precious metals are invaluable pillars in the structure of clean energy portfolios, offering unwavering support during market turbulence.

VaR Estimation with Quantum Computing Noise Correction Using Neural Networks

In this paper, we present the development of a quantum computing method for calculating the value at risk (VaR) for a portfolio of assets managed by a finance institution. We extend the conventional Monte Carlo algorithm to calculate the VaR of an arbitrary number of assets by employing random variable algebra and Taylor series approximation. The resulting algorithm is suitable to be executed in real quantum computers. However, the noise affecting current quantum computers renders them almost useless for the task. We present a methodology to mitigate the noise impact by using neural networks to compensate for the noise effects. The system combines the output from a real quantum computer with the neural network processing. The feedback is used to fine tune the quantum circuits. The results show that this approach is useful for estimating the VaR in finance institutions, particularly when dealing with a large number of assets. We demonstrate the validity of the proposed method with up to 139 assets. The accuracy of the method is also proven. We achieved an error of less than 1% in the empirical measurements with respect to the parametric model.

Probability Spaces Identifying Ordinal and Cardinal Utilities in Problems of an Economic Nature: New Issues and Perspectives

Prevision bundles identifying expected returns on risky assets are established. A probability space associated with risky assets is defined. In this research work, the optimization principle is based on the notion of distance. This is because problems of an economic nature are not handled in an axiomatic or intrinsic way, but they are investigated with regard to a given coordinate system. The latter is shown to be invariant. The notion of mathematical expectation applied to summarizing both monetary values and utilities is treated. Such a notion is extended to study portfolios of financial assets. Objective conditions of coherence connected with the notion of mathematical expectation are extended. Rational behaviors towards risk are based on them. A model representing diagrams considered inside the same coordinate system is shown. Such a model identifies as many optimal choices as pair comparisons it is possible to take into account in order to obtain a multilinear measure. The latter is the expected return on a specific portfolio of financial assets.

Distribution Transformer Remaining Useful Life Estimation Considering Electric Vehicle Penetration

A major portion of a power system's asset portfolio comprises distribution transformers on residential premises. The rapid and massive acceptance of electric vehicles is posing challenges for distribution transformers to operate over their expected lifespan. This work proposes a four-layer framework to assess the real-time and anticipated aging of a distribution transformer and estimate the remaining useful life of a distribution transformer. The first layer stores residential smart meter data to be utilized for the kVA load estimation of a distribution transformer in the second layer. The performance of two powerful forecasting tools, i.e., Time Series Decomposition and Hidden Markov Model, is compared in the third layer. The historical and forecast data, along with the distribution transformer's thermal parameters, are used for its remaining useful life assessment. Numerical validation is conducted on real-world data utilizing electricity consumption and ambient temperature of fifteen households in London, Ontario, Canada. This work also includes the penetration of the most popular electric vehicles in Canada, along with service drop cable data and practical secondary distribution circuit configuration.

Variation Approach and Principle of Maximum in Theory of Dynamic Systems

The article shows dynamics of value figures on market of digital finance assets. The research studies methods of the sensitivity theory in tasks of optimal management of non-linear systems for automated monitoring and controlling the portfolio of digital finance assets and crypto-currencies. The authors study process modeling in economic systems, set the task of optimal management by the system of non-linear differential equations and target functionality, describe the variation method of task resolving, the principle of invariability of non-linear systems and possibilities to model economic systems provided by the solution. The approach to investigating dynamics of economic systems based on the principle of maximum was analyzed. The article shows possible application of the approach for resolving optimization tasks and for methods of management with split and continuous character in conditions of uncertainty typical of economic systems. Functions of non-linear sensitivity are identified. The use of the sensitivity theory for tasks of optimal management of non-linear systems was studied, illustrated by estimating the vector of unknown parameters of dynamic system. The authors analyze the condition in tasks of economic modeling with regard to uncertainty caused by social and psychological reasons. The material provided in the article shows possibilities of using the method of sensitivity analysis for tasks of system decomposition and related transformations in systems of differential equations.

Foreign investors, rebalancing trades, and increases in U.S.-Japan stock market correlations

ABSTRACT Foreign investors’ transaction volume in the Tokyo Stock Exchange has grown steadily in the past three decades, and Japanese stocks have become a familiar instrument for international diversification. Rebalancing trades in a local national market by foreign institutional investors holding multi-country asset portfolios, often heavily represented by U.S. stocks, amplify the national market’s stock index co-movements with the U.S. This paper examines the extent to which greater participation of foreign investors is associated with increases in the unconditional correlation of daily returns between the U.S. (S&P 500) and Japan (TOPIX). A Gaussian copula marginal regression model is applied to the monthly series of unconditional correlations. We find that the growth in foreign investors’ transaction share of the Tokyo market’s turnover approximately accounts for the rise in U.S.-Japan correlations for 1990–2019. Foreign investors’ trades improve the informational efficiency of local national markets. Increased correlations lead to smaller potential gains from international diversification, however.

Portfolio Optimization for New Energy Sector in the Chinese Capital Market

Portfolio optimization is now playing a key role in the financial sector, investors turn to portfolio optimization to make trade-offs between the expected return and risk. Based on the background of the rapid development of new energy in China. The purpose of this paper is to conduct an asset allocation analysis in the field of the new energy sector. This paper selected nine representative companies from the sector, then this paper analyzed the asset portfolio weights using a mean-variance model, and conducted portfolio tests using real data to obtain the cumulative portfolio returns. The results show that BYD accounts for the largest share in both the maximum Sharpe ratio model and the minimum variance model. And besides, the evaluation of the portfolio weights shows that the maximum Sharpe ratio model performs best. These findings can be used as a reference for investors who are interested in the new energy sector.

International effects of quantitative easing and foreign exchange intervention

A two-country general equilibrium model is developed to study the global consequences of quantitative easing and foreign exchange intervention. The model incorporates financial frictions such as limited commitment, differential pledgeability of assets as collateral, and a low supply of collateralizable assets. Due to differential asset pledgeability, financial intermediaries acquire different asset portfolios particular to their home country. Quantitative easing can reduce long-term nominal interest rates, mitigate financial frictions globally, and depreciate the currency of the country that supplies more pledgeable assets. The international effects of foreign exchange intervention depend on the implementing country. If implemented by the country that supplies more pledgeable assets, such intervention can ease financial frictions and enhance welfare globally.

Stock price index prediction based on SSA-BiGRU-GSCV model from the perspective of long memory

PurposeBecause the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.Design/methodology/approachThe authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.FindingsThree critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.Practical implicationsThe empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.Social implicationsIf the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.Originality/valueAlthough long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

NATURAL HEALING ASSETS EVALUATION SCIENTIFIC APPROACHES

Topicality. The existing conceptual and methodological support was analyzed. Main of them is proposed by international institutions regarding accounting and valuation of natural assets. Arising disproportions in the process of natural healing assets accounting and the applying possibility of the modern approaches of it accounting are considered.Aim and tasks. The aim of the article is to determine the main international trends in the assessment of natural healing assets. The tasks of the article are the systematization of international approaches and the legal basis for the of healing assets accounting, the determination of directions for the natural healing assets evaluation as an element of blue growth, and the identification of the main contradictions in the institutional and legislative and regulatory provisions regarding the management of natural healing assets.Materials and Methods. The theoretical base of the article was developed using the method of theoretical generalization to define the concept of natural healing assets. Environmental and economic assessment approaches were used to identify accounting trends.Research results. The article proves that the income received by the state in the process of extracting natural-healing assets is not income in the pure sense, but is a rearrangement of the portfolio of natural-healing assets, namely the moment when assets from a physical state acquires a monetary equivalent. Income, at the same time, is investment and mutually beneficial cooperation with business entities, which depends on the institutional capacity of countries to diversify.Conclusion. Disproportions have been revealed regarding the accounting of natural healing assets, such as those that are taken into account only in interaction with medical and health services. International achievements in the assessment of natural healing assets were summarized into a single methodological basis for their assessment in monetary terms. We note that today natural healing assets in international practice are calculated only in the form of services provided at resorts or spa centers for treatment, recovery and rehabilitation. Also, disproportions arise in the absence of classification of natural healing assets as basic products, which complicates the process of accounting and evaluation of natural healing assets.