Portfolio Decisions Research Articles

Optimal portfolios with sustainable assets: aspects for life insurers

Since August 2022 customers have to be asked if they are interested in sustainable investment when entering a pension contract. Hence, the provider has to be prepared to offer suitable investment opportunities. Further, the provider has to manage the new risks and chances of those assets in the whole portfolio. We therefore especially look at possible consequences for optimal portfolio decisions of a life insurer and suggest modeling approaches for the evolution of the demand and the sustainability ratings for sustainable assets. We will solve various portfolio problems under sustainability constraints explicitly and suggest further research topics. As a special feature for a life insurer, we particularly look at the role of the actuarial reserve fund and the annual declaration of its return.

An enterprise structure approach improves index-based crop portfolio decision-making

IntroductionWhile crop diversification indices are relatively simple and useful for quantifying the extent of crop diversification, they may not account for the potential differences in the types of crops grown. This study shows the need to complement crop diversification indices with an enterprise structure approach to improve index-based crop portfolio decision making.MethodsThe study uses linear regression models and nationally representative farm survey data from 7,934 farmers in Zambia. The study compares the enterprise approach and the Simpson index of diversification which is commonly used in crop diversification studies.Results and discussionWe find that complementing the enterprise structure approach with the Simpson index of diversification can increase profitability by as high as 77.89% for farmers. The cassava enterprise structure had the most returns for farmers. It had a gross margin of ZMW 3,887 per hectare and was trailed by the maize/groundnuts/cotton/rice enterprise structure with a gross margin of ZMW3,681 per hectare. These results suggest that the use of aggregation crop diversification indices, without an additional enterprise structure analysis, may obscure the necessary insights needed to practically help farmers.

Prenatal Stress and Portfolio Decisions During Adulthood

Prenatal Stress and Portfolio Decisions During Adulthood

Social Endowment Insurance and Inequality of the Household Portfolio Choice: The Moderating Effect of Financial Literacy

China implements different social endowment insurance policies between urban and rural areas which may cause differences in investment decisions of urban and rural households. Based on the 2013 to 2017 China Household Finance Survey (CHFS) data and the two-way fixed effect panel model, this study aims to investigate the effect of social endowment insurance on the inequality of household portfolio between China’s urban and rural areas and the moderating effect of financial literacy on the relationship between social endowment insurance and household portfolio. The empirical results show that (1) the participation of social endowment insurance is positively related to the possibility of holding risky financial assets and the ratio of risky financial assets; (2) the effect of social endowment insurance is strong and significant in urban areas but weak and not significant in rural areas; (3) the moderating effect of financial literacy on the relationship between social endowment insurance and household portfolio widens the gap of the household portfolio between the urban and rural areas. This paper indicates that the social endowment insurance would affect the household portfolio decisions positively and the financial literacy positively moderates the relationship between social endowment insurance and household portfolio, which just can be applied to urban households. This paper provides evidence of the inequality between the urban and rural households in China and discusses the possible causes of the urban-rural income gap.

Remanufacturing Technology Portfolio Decisions under a Discrete Choice Model

Remanufacturing Technology Portfolio Decisions under a Discrete Choice Model

Inflation, Monetary Policy, and Portfolio Decisions of U.S. Households

Inflation, Monetary Policy, and Portfolio Decisions of U.S. Households

The impact of global economic policy uncertainty on portfolio optimization: A Black–Litterman approach

This paper investigates the asymmetric impact of global economic policy uncertainty (GEPU) on global asset allocation. We employ the Double Asymmetric GARCH-MIDAS (DAGM) model to examine the asymmetric effect of GEPU shocks on long-term volatilities of global equities, bonds, commodities, clean energy and Bitcoin. The GEPU-based volatility is used as a proxy for the uncertainty of the investor’s views in the Black-Litterman (BL) framework. Empirical results show that the BL model with GEPU-based views yields higher out-of-sample risk-adjusted returns than other traditional benchmarks in most cases. The findings suggest that investors should consider the influence of GEPU when making portfolio decisions.

Measuring the effects of large-scale asset purchases: The role of international financial markets and the financial accelerator

We build and estimate a two-country structural macroeconomic model with meaningful household portfolio decisions over foreign and domestic bond holdings and financial intermediation to investigate the efficacy of large-scale asset purchases (LSAP) by a central bank. Financial intermediation between financial assets and the real economy means that asset purchases do not directly lead to increased real investment. Instead, asset purchases result in an accumulation of deposits at banks without a corresponding rise in loans to the real economy. In contrast to conventional monetary policy (CMP), positive effects of asset purchases primarily work through consumption and exports via changes in real exchange rates. We find that this is in accordance with local projection analysis performed using identified LSAP and CMP shocks. Further, historical decomposition of US data reveals that unconventional monetary policies supported real economic activity when interest rates where zero (2009–2015), but not after. However, their effect on financial markets remained significant through 2019.

Overcoming Original Sin

Abstract This paper draws on newly expanded BIS government bond statistics to document how emerging market sovereigns have reduced their reliance on foreign currency denominated bonds since the emerging market crises of the 1990s. With external funding still important for emerging market governments, they have increasingly been able to borrow from foreign investors in their domestic currency. In this respect, emerging market governments are overcoming “Original Sin”. The flipside of these developments is that foreign investors increasingly bear the currency risk associated with fluctuations in emerging market exchange rates, making foreign investors’ portfolio decisions more sensitive to prevailing global financial conditions. Emerging markets thus remain vulnerable to reversals of investor sentiment in bond markets, whether or not they have outstanding debt in foreign currency.

International currency substitution and the demand for money in the euro area

Using quarterly data for the period from 1983:Q1 until 2019:Q4, we identify a stable money demand relationship for the euro area M3, with the US long-term interest rate as a regressor, together with the domestic interest rate and income. This specification is consistent with the open economy liquidity services approach to money demand, in light of which the ownership of money is separate from portfolio decisions. Our results suggest international currency substitution, pointing to interdependency of monetary policies, even in a context of floating exchange rates. In the short-run analysis, we find that excess liquidity helps predict nominal money but not the price level. In general, our findings cast doubt on the usefulness of the information content of M3 to analyse prospective inflation in the euro area. • Using quarterly data, we identify a stable money demand function for the EA M3. • We find a significant influence of the US interest rate on the EA money demand. • Results point to interdependency of monetary policies, even under float. • We find that excess liquidity helps predict nominal money, but not the price level. • Our evidence casts doubt on the usefulness of M3 to analyse prospective inflation.

An Assessment of Portfolio Managers' Awareness of Big Data Analytics (BDA) Role in Achieving Portfolio Management (PFM) Success: A Qualitative Study

As big data analytics become the most valuable resource held by all administrations, every huge organization will be investing in initiatives of big data in a succession of Portfolio Management at the next level (Grover et al., 2018). This research aims to study the extent to which portfolio managers are aware of the role of big data in the efficiency of portfolio management.This study includes a qualitative research method to assess portfolio managers' awareness of the role of big data analytics (BDA) in portfolio management success. The researcher followed a theoretical, descriptive approach. The researcher applied the onion analysis methods. The researcher surveyed many portfolio managers in several companies in the Kingdom of Saudi Arabia. Only portfolio managers were chosen because they are more concerned with achieving strategic goals than program and project managers. The researcher conducted a qualitative study by interviewing 38 company portfolio managers.The concept of BDA in this study contains four components (Volume – Veracity- velocity- variety). In this research, five main questions have been extracted to link big data analytics with portfolio management methodology as per PMI standard guidelines for managing business portfolios. The central themes and sub-themes have been extracted for conducting the interview.The results indicated that the portfolio managers' awareness of the (PMI) Portfolio Management (PFM) methodology is very high and compatible with the directions of the Kingdom of Saudi Arabia 2030. The researcher recommends future research to apply another quantitative study for a specific sector to investigate clear awareness regarding the value of using BDA in managing several portfolios. Keywords : Big Data Analytics (BDA)- Portfolio Management (PFM)- Portfolio Decision Support (PDS). DOI: 10.7176/EJBM/14-24-07 Publication date: December 31 st 2022

Examining Asymmetric Volatility Dynamism of Returns in the Infrastructure Sector in India during Covid 19: - A application of GARCH Models

Due to global shut down of economic activities and transportation, the infrastructure sector has to see a halt in operations due to disruptions in supply chain, impacting international investors as they became cautious of their investment position. The study is aimed at modelling the volatility of the returns in the infrastructure sector in India using S&P BSE Infrastructure Index during Covid-19 by applying univariate stipulations of the GARCH family of models such as GARCH (1,1), EGARCH (1,1), MGARCH (1,1). The study found the presence of asymmetric effects indicating that the arrival of Covid 19 news created more turmoil in the market. Also, significant relationship has been observed between the magnitude of variance and returns, meaning thereby the global investors, while making portfolio decisions, should emphasize that high risk implies high returns holds true for Infrastructure sector in India. The study suggests that the investors, while estimating value at risk, should consider that the infrastructure returns depict higher persistence level and the past volatility of the returns in the infrastructure sector has a significant impact on current volatility in case of BRICS economies.

Uncertain Public R&D Project Portfolio Selection Considering Sectoral Balancing and Project Failure

In order to promote scientific and technological innovation and sustainable development, public funding agencies select and fund a large number of R&D projects every year. To guarantee the performance of the resulting project portfolio and the government’s investment benefits, the decision maker needs to select appropriate projects and determine a reasonable funding amount for each selected project. In the process of project selection, it is necessary to consider the balance of funding allocated to different scientific sectors as well as the failure probability of the projects in future execution, so that the expected performance of the project portfolio is maximized as much as possible. In view of this, we propose and study the uncertain public R&D project portfolio selection problem considering sectoral balancing and project failure. We formulate a stochastic programming model for the problem to support the portfolio decisions of the funding agencies. We also transform the model into an equivalent deterministic second-order cone programming model that can be directly solved by exact solvers. We generate datasets reflecting different scenarios through simulation and perform computational experiments to validate our model. The impacts of various factors (i.e., the number of project proposals, project failure probability, the upper limit of the budget allocated to each project, and the decision maker’s tolerance for project failure) on the project portfolio performance are analyzed.

Analysis of the frequency dynamics of spillovers and connectedness among Islamic and conventional banks and their determinants: evidence from Gulf Cooperative Council (GCC) markets

ABSTRACT The study investigates the return spillovers across 20 Islamic and 34 conventional banks among GCC markets (UAE, Saudi Arabia, Bahrain, Qatar, Kuwait) over the period 2005–2022 based on Dieobold and Yilmaz (2014) and Barunik and Krehlik (2018) methods. The outcomes interestingly reveal that the spillover between markets is time-varying, asymmetric, and crisis-sensitive. Moreover, short-oriented spillovers dictate the long-oriented spillovers, while long-oriented spillovers establish the major chunk of the total return spillovers. The results of the DY and BK approach show a weak connectedness between all Islamic banks rather than conventional banks of GCC. Subsample analysis of COVID-19 and GFC strengthens the total and short-oriented spillovers more than long-term spillovers. The global financial stress is exposed with the most substantial coherence, increasing the connectedness of Islamic banks in the short and long-oriented markets compared to conventional banks. The results of the study have practical implications for bankers, central banks, Islamic banks, policymakers, international economic institutions, banking investors, FIIs, DIIs, and academia. Additionally, the current findings can be guiding forces for many investors across the world to take their portfolio decision by leveraging Islamic banks’ securities.

Portfolio decisions of primary energy sources and economic complexity: The world's large energy user evidence

Sustainable energy systems are sensitive to economic complexity, i.e., the combination of knowledge, innovation, and productivity, since it affects the countries' portfolio decisions of primary energy sources, shaping geopolitics, and contributing to global energy security. This research assesses the impact of economic complexity on the performance of two energy systems measurements, e.g., diversification of primary energy demand (D.P.E.D) and non-carbon-based fuel portfolio (N.C.F.P), controlling for energy intensity, energy prices, resource supply diversity, and CO2 emissions in a panel of 25 large energy-using countries during 1998–2018. The findings support the long-run and causal relationships across energy systems using the panel cointegration methods and dynamic panel models. Specifically, economic complexity's statistically significant and positive effect on D.P.E.D and N.C.F.P is detected. Moreover, the contribution of N.C.F.P to total energy demand is more elastic than D.P.E.D when the shares of economic complexity and the control variables increase. Results also indicate that the cyclical movements of economic complexity are not related to energy systems fluctuations across large energy-consuming economies. Consequently, the role of economic complexity in sustainable energy systems is a necessary condition to overcome the barriers in achieving (i) resource abundance and equitability and (ii) a non-carbon-based fuel portfolio for large energy consumers.

On the Predictive Value of the (Shadow) Real Interest Rate for the Realized Volatility of Gold-Price Returns

We use a quasi-out-of-sample forecasting experiment to study the predictive value of a short-term real interest rate for the volatility of gold-price returns. To this end, we use monthly U.S. data for the sample period from 1990/1 to 2022/2, and we study a standard effective-federal-funds-based real interest rate as well as a shadow real interest rate, which accounts for the recent extended zero-lower-bound period. We find that the real interest rate has predictive value for the subsequent realized volatility, and this predictive value turns out to be stronger in several specifications of our forecasting experiment for the shadow real interest rate than for the standard real interest rate. We evaluate the predictive value of forecasts in terms of an asymmetric loss function. Because gold is considered as a safe-haven asset, our results provide some important implications for portfolio decisions of investors.

A Decision Support System for Project Portfolio Management in Construction Companies

Project portfolio management requires a systematic process that comprises assessment of portfolio risk and expected profitability, as well as strategic fit of individual projects with company objectives. After a needs analysis based on literature findings and surveys with experts, in this study, a process model and a tool, COPPMAN (COnstruction Project Portfolio MANagement), were developed to support project portfolio decisions in construction companies. COPPMAN was developed in collaboration with construction professionals. Different from previous studies, it incorporates a portfolio risk and strategic fit assessment model considering project dependencies and integrates knowledge of previous, on-going and potential projects to estimate value of alternative project portfolios. COPPMAN was implemented in a construction company and evaluated as a useful tool due to its features such as knowledge integration, forecasting of portfolio profitability and recommendation of strategies as well as its visualization features. Research design and findings can be used for development of similar tools in other project-based industries.

A robust multicriteria clustering methodology for portfolio decision analysis

We consider a portfolio decision problem in which a subset of projects is selected to form a portfolio by dealing with uncertain multiple criteria evaluations, decision makers’ preferences and real-world constraints. Over the last years, many methods have been developed with the aim of maximizing the sum of multicriteria scores of projects selected for the final portfolio. In this paper, unlike the existing literature, we propose a new robust multicriteria clustering methodology that enables to group the best ranked projects into a new kind of cluster (so-called optimal portfolio) that complies with the given constraints. With this aim, a new Integer Programming (IP) model as an extension of the K-medoids clustering technique is combined with the PROMETHEE method. Specifically, we first apply PROMETHEE for multicriteria evaluation of the individual projects and then the two main outputs of PROMETHEE, preference matrix and net flows, are used in the IP model to generate clusters of projects with the given resource constraints. Herein, to alleviate the problem without the influence of all K clusters on the final results, we focus on generating two clusters, with the selected projects in the best one forming the portfolio. In developing this model, we also introduce portfolio quality constraints to ensure the proper distribution of “good” evaluations among all considered criteria. We then enhance this combined model by embedding it into SMAA framework to consider the inherent uncertainties. As a large number of potentially optimal portfolios are obtained through the SMAA simulation, both project and portfolio-level robustness indices are computed in order to help decision makers to identify the most robust and stable portfolio. Our methodology is validated using the data from a bridge maintenance program.

Green transition, investment horizon, and dynamic portfolio decisions

This paper analyzes the implications of investors’ short-term oriented asset holding and portfolio decisions (or short-termism), and its consequences on green investments. We adopt a dynamic portfolio model, which contrary to conventional static mean-variance models, allows us to study optimal portfolios for different decision horizons. Our baseline model contains two assets, one asset with fluctuating returns and another asset with a constant risk-free return. The asset with fluctuating returns can arise from fossil-fuel based sectors or from clean energy related sectors. We consider different drivers of short-termism: the discount rate, the nature of discounting (exponential vs. hyperbolic), and the decision horizon of investors itself. We study first the implications of these determinants of short-termism on the portfolio wealth dynamics of the baseline model. We find that portfolio wealth declines faster with a higher discount rate, with hyperbolic discounting, and with shorter decision horizon. We extend our model to include a portfolio of two assets with fluctuating returns. For both model variants, we explore the cases where innovation efforts are spent on fossil fuel or clean energy sources. Detailing dynamic portfolio decisions in such a way may allow us for better pathways to empirical tests and may provide guidance to some online financial decision making.

Quantifying the extreme spillovers on worldwide ESG leaders' equity

With the increasing consensus about the severity of climate change, environmental, social, and governance (ESG) philosophy has been rising and remarkably integrated into green investors' portfolio decisions. This paper applies the novel quantile-based VAR spillover index methods and examines the spillovers among worldwide ESG leaders' equity markets under different market states. Overall, the significant spillovers among global ESG leaders' equity can be captured. The Northern American and E.U. markets are the main risk transmitters to the global ESG investment market. Based on the quantile connectedness analysis, the spillovers among worldwide ESG leaders' equity are more significant for extreme market states than the normal states. Meanwhile, the downward tail risk spillover is larger than the upper. Thus, this paper obtains an asymmetric “U” shape curve to describe the total spillovers across different quantiles. The dynamic estimation also supports the asymmetric characteristics of the cross-market spillover effects and suggests external events could shape the pattern of dynamic connectedness. The potential determinants of total and net extreme spillovers are also investigated to help increase investors' awareness of ESG risk management decisions. Our findings eventually provide a crucial understanding of global ESG investment and indicate some targeted investment implications.