Portfolio Decisions Research Articles

Research on Autonomous Reconstruction Method for Dependent Combat Networks

Cascade effects arising from the interoperability of weapon systems in a dynamic battlefield environment make the combat system-of-systems (CSoS) portfolio decision a problem. In this article, we propose an integrated CSoS autonomous decision-making method framework for solving the autonomous planning problem of CSoS. Specifically, we employ a functional dependency network analysis method to capture the interdependencies between heterogeneous equipment and construct a dependent combat network (DCN). The network structure is considered as a decision action space, the CSoS formation process is considered as a portfolio decision process of the combat network, and then, an approximate dynamic programming method is employed to obtain the optimal policy function for CSoS. Finally, the validity of the proposed method is verified through a DCN case study. The results indicate that the CSoS reconstructed according to the optimal policy function restores an average of 20.2% of the lost operational capability under four attack scenarios. This study provides a technical method for autonomous planning and decision making of CSoS.

Stock market liquidity during crisis periods: Australian evidence

AbstractLiquidity is an important characteristic of financial markets, affecting portfolio decisions and priced risk. During periods of market turmoil, such as occurs during financial crisis, investors have an elevated need for cash and so understanding how liquidity differs during those periods is important. We examine how stock market liquidity was impacted by two crises with distinct origins, the global financial crisis (GFC) and the COVID‐pandemic. Our sample includes the S&P/ASX200 constituents for the period January 2005–December 2020. We find that the Australian stock market is less liquid during both crisis periods; spreads are wider, depth is lower, and price impact is larger (stock prices move a lot in response to small amounts of volume). Although the magnitude of the liquidity change is greater at the onset of COVID, the duration of the impact is longer during the GFC, resulting in a larger average effect. While trading volume declines during the GFC, it increases during COVID. Our results are robust to alternate liquidity proxies, methodologies and crisis period identification, and generally applicable across stock sectors.

Is it a boy or a girl? Newborn gender and household portfolio decisions

AbstractThis paper analyzes the role of newborn gender in household investment decisions. Parenting a new baby is associated with a reduction of the share of financial wealth held as cash and an increase in risky investments. The reallocation is however gender‐heterogeneous: the increase in the share of both total and financial wealth allocated to risky assets when parenting girls is reduced for households parenting boys. The effect is driven by the first child. Parents of newborn girls hold riskier portfolios because they make financial decisions influenced by their expectations on the autonomy and financial independence of newborns in adulthood.

Using investor utility to determine portfolio choice with REITs

This article examines the decision of individual investors to allocate a portion of their existing investment portfolios to REITs. It first derives the risk preferences of investors represented by their benchmark portfolios of stocks and bonds. Such risk preferences are then used for portfolio decisions regarding REITs. The analysis shows that investors with lower risk aversion tend to have a more substantial stock component in their benchmark portfolio and will obtain higher risk-return benefits from adding REITs. In addition to the theoretical analysis, the article provides a practical solution to evaluate the benefit of investing in REITs.

Behavioral biases and the rational decision-making process of financial professionals: significant factors that determine the future of the financial market

PurposeThis paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on well-developed financial markets, and little is researched about financial professionals, such as institutional investors, portfolio managers, investment advisors, financial analysts, etc., in emerging markets.Design/methodology/approachAn expert-validated questionnaire measure four prominent behavioral biases and Indian financial professionals' rational decision-making process. The final sample consists of 274 valid responses using the purposive sampling technique. IBM SPSS and AMOS structural equation modeling (SEM) software are used to build measurement and structural models, multivariate analysis including regression, factor analysis, etc.FindingsThe results provide empirical insights into the relationship between behavioral biases and the decision-making process. The results suggest that the structural path model closely fits the sample data. The presence of behavioral biases indicates that financial professionals' forecasting and decision-making is not always rational but bounded rational or irrational due to these factors. Furthermore, these biases (except overconfidence bias) have a markedly significant and positive relationship with irrational decision-making.Research limitations/implicationsIt is critical to eradicate these psychological errors, but awareness and attentiveness toward behavioral biases may help financial professionals to make informed decisions. Investors can improve their portfolio decisions and investments by recognizing their judgment errors and focusing on specific investment strategies to mitigate the impact of these biases. It is necessary to incorporate behavioral insights while developing training techniques for financial professionals. Rules of thumb, visual tools, financial coaching and implementing social-cultural elements in training programs enable financial professionals to develop simple, engaging, appealing and customized approaches for their clients.Originality/valueThis novel study is the first of this kind of research that examines the relationship between financial professionals' behavioral biases and rational decision-making process. This study significantly and remarkably provides insights into irrationality in financial professionals' decision-making.

The Influence of Increased Financial Flexibility on Small Accounting Firms' Public Audit Client Portfolios: Evidence from the Paycheck Protection Program

SUMMARY In this study, we examine whether increased financial flexibility afforded by the Paycheck Protection Program (PPP) affects small accounting firms’ public audit client portfolio decisions. We find some evidence suggesting that accounting firms receiving PPP loans exhibit greater public audit client selectivity. Specifically, accounting firms receiving PPP loans exhibit an increased likelihood of auditor switching and engage new clients that exhibit less financial and auditor business risk but greater potential for profitability relative to departing clients. Relative to both departing and continuing clients, new clients of firms receiving PPP loans exhibit some evidence of lower audit risk. Collectively, the results provide some evidence to suggest that the increased financial flexibility afforded through the PPP loans provided smaller accounting firms an opportunity to selectively manage their public audit client portfolio. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M42; M48.

Bayesian predictive decision synthesis

Abstract Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing, and combining inferences from sets of models. Bayesian predictive decision synthesis (BPDS) advances conceptual and theoretical foundations, and defines new methodology that explicitly integrates decision-analytic outcomes into the evaluation, comparison, and potential combination of candidate models. BPDS extends recent theoretical and practical advances based on both Bayesian predictive synthesis and empirical goal-focused model uncertainty analysis. This is enabled by the development of a novel subjective Bayesian perspective on model weighting in predictive decision settings. Illustrations come from applied contexts including optimal design for regression prediction and sequential time series forecasting for financial portfolio decisions.

Automotive Product Portfolio Design from the Perspective of Energy Sustainability: Multicriteria Decision-Making Based on Lotka–Volterra MCGP Model

Low energy consumption and green transformation of automobile product portfolio is the trend of the times. Automotive manufacturers make product portfolio decisions by setting multiple criteria such as fuel consumption, sales, and volume. It is also important to take into account the symbiotic interaction effects between automotive products. In order to achieve the above research objectives, this paper constructs the Lotka–Volterra MCGP model to make multicriteria decisions on automobile product portfolio design from the perspective of energy sustainability with BMW Brilliance is taken as an example to illustrate the process of using the multicriteria model. The empirical analysis successively measures product growth using the logistic model, analyzes the symbiotic relationship of product portfolios using the Lotka–Volterra model, and finally performs multicriteria evaluation using the MCGP model. In order to verify the reliability of the model, this paper verifies the robustness of the model from the perspectives of parameter dynamics, system boundaries, and model scalability. The results of empirical analysis and robustness analysis show that the Lotka–Volterra MCGP model proposed in this paper is applicable to the multicriteria decision-making of automobile product portfolio design from the perspective of energy sustainability.

Asymmetric effects of market uncertainties on agricultural commodities

The interplay between energy and agricultural commodities and the responses of agricultural commodities to global uncertainty factors have been receiving increasing attention due to the pivotal contributions of commodity markets to the world economy. This study investigates the asymmetric effects of oil, market volatility and sentiment, economic policy uncertainty, and geopolitical risk on agricultural commodities in a nonparametric quantile-on-quantile regression framework. The sample used for empirical analysis spans January 03, 2013, to October 21, 2022, and includes ten agricultural commodities (canola, cocoa, coffee, corn, feeder cattle, lean hogs, live cattle, orange juice, sugar, and wheat), crude oil, crude oil implied volatility (OVX), market sentiment (VIX), economic policy uncertainty of the US (USEPU), and geopolitical risk (GPR). The empirical findings suggest that crude oil fails to serve as either a hedge or safe-haven for agricultural commodities. Meanwhile, OVX and VIX serve as potential hedges against agricultural commodity shocks. At elevated levels of USEPU, agricultural commodities reveal only weak hedging capacities against the USEPU-driven downside risks and mostly act just as diversifiers of economic policy uncertainty effects. However, they could hedge against the crystallisations of the downside geopolitical risks. These findings are important for effective regulation policies, risk management, and portfolio decisions across bullish, bearish, and normal market conditions.

The Fairy Tale of Unicorns

Unicorns have become symbols of entrepreneurial success and innovation-friendly’ countries. Therefore, understanding the process of diffusion of these highly valued startups has important implications for business, investors and policymakers. However, the existing literature lacks a description of the diffusion and forecast of these types of ventures. We address this gap by analyzing the diffusion process of unicorns in 8 countries (USA, China, India, UK, Germany, France, Netherlands, and Sweden) and 3 industries (Fintech, Health, and Transportation) using both 3-parameters and 4-parameters Gompertz and Logistic models. Our diffusion analyses reveal that the Gompertz model is suited for analyzing unicorn diffusion in China, whereas the Logistic model is appropriate for analyzing unicorn diffusion in the rest of the countries studied. We also find that India has the highest estimated speed of diffusion (97%) while the USA has the highest saturation level of unicorns (6241). Our forecasting analyses indicate that all the countries selected in our study, except the USA, will achieve their saturation level of unicorn diffusion around 2030. Further, results regarding unicorn diffusion by sectors show that the Logistic model is suited for analyzing unicorn diffusion in all the sectors studied. Results of forecasting analyses indicate that while the Transportation and Health sectors are expected to achieve the maximum level of unicorn diffusion in 2030, the Fintech sector need ten years more to reach this level. These findings are useful for planning, regulation, policy formulation, and portfolio decision.

A structured methodology for essential medicines lists and health emergency stockpiles: Experience with the Emergency Medicines Buffer Stock in the United Kingdom

IntroductionFormularies of essential medicines, such as Essential Medicines Lists (EMLs) and health emergency stockpiles, are intended to be always available, including in emergency situations, acting as important tools for access to medicines. The Emergency Medicines Buffer Stock (EMBS) in the United Kingdom (UK) was a stockpile of critical medicines managed by the UK Department of Health and Social Care (DHSC). We propose a new methodology for selecting and including medicines in EMLs and health emergency stockpiles and empirically apply it for selecting medicines in the case of the UK EMBS. MethodsWe used Multi-Attribute Value Theory and Portfolio Decision Analysis to develop a three-phase methodological framework for medicines selection, involving: (i) the decision context definition and selection of evaluation criteria, (ii) the therapeutic area prioritisation, and (iii) the medicines value-for-money evaluation and product selection. The EMBS application took place in 2018–2019 and focused on therapeutic area prioritisation, involving primary data collection through expert interviews (n = 4), a workshop with DHSC decision-makers (n = 13), and an online survey with National Clinical Directors and relevant experts (n = 24). A Monte Carlo simulation supported therapeutic area prioritisation using the British National Formulary (BNF) classification. FindingsTwo criteria sets were selected for i) therapeutic area prioritisation, reflecting the value concerns of population need and shortage severity, and ii) medicines evaluation, reflecting magnitude of clinical benefit and supply vulnerability, among others.Primary evidence was collected for “national need” and “shortage severity”, based on which a “population health loss” index was developed. A total of 51 therapeutic areas were ranked using their index value while assessing the robustness of the ranking. The top ranked therapeutic area was antisecretory drugs and mucosal protectants, closely followed by diabetes drugs. ConclusionsThe methodological application generated a ranking of therapeutic areas based on expected “population health loss” index, while addressing evidence uncertainty. The methodology can be adapted for other EMLs and emergency stockpile contexts to inform medicines selection.

The heterogeneous relationship of owner-occupied and investment property with household portfolio choice

The dual nature of property as both a consumption and investment good presents a challenge for household portfolios. Prior theoretical literature predicts a constraint imposed by property on investment decisions, and empirical studies support this notion. However, previous research often overlooks investigating the heterogeneity of this constraint and fails to differentiate between owner-occupied and investment property. Building on a stochastic control model, we analyse the UK’s Wealth and Assets Survey panel and find that along the distribution of how household allocate their wealth a one percentage point (pp) increase in the share of owner-occupied property in the total portfolio is associated with a 0.07 pp decrease in the share of stocks in liquid assets. However, this association varies significantly based on the value of the owner-occupied property share. For low values of the owner-occupied property share, the association with stockholdings is negligible. As the share of owner-occupied property increases, the negative association with stockholdings becomes more pronounced: when the owner-occupied property share reaches 90%, a further 1 pp increase corresponds to a 0.14 pp decrease in the share of stocks in liquid assets. By contrast, buy-to-let property shows no significant relationship with stockholdings, supporting the idea that the constraint on portfolio decisions is primarily driven by the role of property as a consumption good.

US partisan polarization and households' portfolio decisions

AbstractThis paper uses a panel survey (micro‐level) dataset to explore the impact of US partisan polarization on households' portfolio decisions. The results document that households significantly reduce their market investments in risky assets due to stronger partisan polarization conditions. The findings survive certain robustness checks, while they clearly illustrate the rising effect of political uncertainty on such decisions. Such results could carry substantial implications since they suggest that partisan polarization uncertainty acts as a negative externality in this type of portfolio choice decisions.

Foreign bias in institutional portfolio allocation: The role of social trust

We study the role of social trust in the equity allocation decisions of global investors using a large sample of institutionally managed portfolios of 8,088 investors from 33 countries over the 2000 through 2017 period. The negative relationship between social trust and foreign bias suggests that institutional investors from high-social trust countries are less prone to underinvesting in foreign equity. Our results provide credence to an information-based explanation, indicating that social trust reduces foreign bias by compensating for the lack of information about foreign stock markets. Moreover, the effect of social trust on foreign bias is stronger if host-country institutions are weak, while it vanishes when the host country is characterized by strong institutions. The informal institution of social trust compensates for the lack of well-functioning formal country-level institutions in international portfolio decisions. Finally, the allocation effect resulting from social trust is different from “blind” trust. The portfolios of high-trust investors exhibit higher cross-country diversification and an enhanced portfolio risk-return trade-off.

Portfolio Optimization Using Markowitz Model on Sri-Kehati Index

This thesis investigates the portfolio optimization process using the Markowitz model on the SRI-KEHATI index, an esteemed sustainable investment index. The study aims to explore the potential advantages of incorporating environmental, social, and governance (ESG) factors into portfolio construction. By leveraging historical financial data and reliable ESG metrics, this research develops optimized portfolios that strike a balance between risk and return while adhering to the sustainability criteria of the SRIKEHATI index. The methodology encompasses the collection of credible ESG data and financial information for the constituents of the SRIKEHATI index. The Markowitz model is subsequently employed to analyze the risk and return characteristics of each asset within the portfolio. Through the application of optimization algorithms, the study seeks to identify the optimal asset allocation that maximizes risk-adjusted returns, taking into account the ESG criteria outlined by the SRI-KEHATI index. The outcomes of this research provide valuable insights into the effectiveness of portfolio optimization techniques within the realm of sustainable investing. By considering both financial metrics and ESG factors, investors can construct portfolios that align with their sustainability objectives while optimizing risk and return. The findings shed light on the performance of the optimized portfolios and compare them with conventional approaches, thereby demonstrating the potential benefits of integrating ESG considerations into portfolio decision-making. Additionally, this study examines the practical implications associated with implementing sustainable portfolio strategies based on the SRI-KEHATI index. Overall, this thesis contributes to the expanding body of knowledge on sustainable investing and portfolio optimization, specifically focusing on the SRI-KEHATI index. It provides valuable insights for investors, asset managers, and policymakers interested in sustainable investment strategies. Furthermore, it offers a framework for incorporating ESG considerations into the portfolio construction process using the Markowitz model, thereby aiding in the development of more robust and sustainable investment portfolios.

A ranking approach for robust portfolio decision analysis based on multilinear portfolio utility functions and incomplete preference information

Uncertainties are often present in typical portfolio decision analysis (PDA), and it is prudent to conduct a robust PDA to identify project portfolios that perform reasonably well across the entire range of uncertain parameter values. Previous research on robust PDA and applications has almost entirely relied on additive portfolio preference functions, with little research on robustness under multilinear portfolio utility functions (PUFs). A ranking approach for robust PDA is proposed based on multilinear PUFs, incomplete preference information, and the algorithm for accelerating stochastic multicriteria acceptability analysis (SMAA). First, the initial set of portfolios is screened through various constraints to produce a smaller set of desirable portfolios. Second, several types of incomplete preference information are thought to be elicited with regard to multilinear PUFs, including preference information on coefficients, portfolios, and uncertainties on preference elicitation techniques. Third, the SMAA decomposition algorithm is implemented as a ranking approach to perform robust PDA on the basis of multilinear PUFs, incomplete preference information, and potentially numerous portfolios in the set of desirable portfolios. Finally, the approach is applied to a case to validate its effectiveness, and comparisons among additive, multiplicative, and multilinear PUFs in terms of decision recommendations are performed.

Dynamic interlinkages between cryptocurrencies, NFTs, and DeFis and optimal portfolio investment strategies

PurposeThis study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.Design/methodology/approachThis work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.FindingsThis study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.Research limitations/implicationsThis study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.Originality/valueThe contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

Dependence structure between crude oil and BRICS bond markets prior to and during the COVID-19 pandemic

ABSTRACT This study investigates the return and volatility spillovers between BRICS bonds and oil price volatility in the pre-COVID and during COVID periods by employing the bivariate EGARCH model, cross-quantilogram approach, and wavelet coherence framework. The findings are mixed when comparing the volatility transmissions with various nations and in two periods since variations in return and volatility spillovers between oil prices and bond market returns have co-moved through time. In particular, they increased during the COVID-19 outbreak, which supports the idea that financial market integration intensified during the crisis. Additionally, oil prices have a positive impact on bond markets in Russia and South Africa in both periods, while there are weak and negative relationships in China, India, and Brazil in pre-COVID and pandemic periods. This information has consequences for risk management and portfolio decisions for investors with a substantial position in the BRICS bonds market.

The Use of Credit Ratings in the Delegated Management of Fixed Income Assets

Investment mandates of fixed income funds constrain managers’ portfolio decisions, often employing credit ratings to classify asset risk. We categorize U.S. and European fixed income funds’ mandates using textual analysis and measure the use of ratings. Over the past two decades, despite the weaknesses of ratings revealed in the global financial crisis, ratings use has increased significantly. Since 2010, the fraction of funds not using ratings in any way has fallen by almost half in both the United States and Europe. By 2020, 94% of U.S. funds and 65% of European funds used ratings. These patterns fit agency-based models of investment mandates and point to a lack of practically useful alternatives. This paper was accepted by Tomasz Piskorski, finance. Funding: This work was supported by the Nasdaq Nordic Foundation. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2023.4848 .

Home bias and local equity portfolio decisions of Chinese insurance institutional investors.

Our paper explores whether Chinese insurance companies exhibit a local preference in deciding their equity portfolios and the incentive of this preference. Our research finds that Chinese insurance institutional investors significantly tilt to invest in local firms geographically close to them, and local investments do not significantly outperform non-local investments. The results indicate that the behavioral aspect of home bias, rather than information advantage, play a more significant role in deciding equity holdings of China's insurance companies. Additionally, local equity preference is more pronounced in dialect-segmented areas and life insurance firms. This paper incorporates hometown identification into the analysis framework of insurance companies' portfolio decision-making and enriches the research of their investing strategies from the perspective of behavioral finance.