Search Results (12)

This paper reexamines the question of gender differences in financial relative risk aversion using updated methods and data. Specifically, the paper revisits the 1998 work “Are women more risk averse?” by Jianakoplos and Bernasek, suggests refinements in their model in relation to the database used, namely the U.S. Federal Reserve Board’s Survey of Consumer Finances (SCF), and performs new tests on the latest SCF from 2022. The suggested refinements pertain first to an enhanced computation of wealth, which includes additional categories of assets such as 401(k)s or other thrift savings accounts, and second to the more subtle handling and consideration of specific demographic data of the SCF respondents. Unlike the original study, which also included married couples, the new study focuses exclusively on single-headed (never-married) households. This eliminates ambiguity about the actual financial decision maker in households, enabling a clearer assessment of individual gendered behavior. Following the refinements, the new tests reveal a continuing pattern of decreasing relative risk aversion; however, contrary to the 1998 findings, there is no significant gender difference in financial relative risk aversion in 2022. This study also documents that education levels strongly influence risk-taking: single women with higher education levels are more likely to hold risky assets, while for men, higher education correlates with less risk-taking. The paper concludes by informing policymakers and financial educators so as to further tailor their strategies for promoting gender equality in financial decision-making. Full article

Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims to perform a Systematic Literature Review (SLR) on robust portfolio mean-variance (RPMV) in stock investment utilizing genetic algorithms (GAs). The SLR covered studies from 1995 to 2024, allowing a thorough analysis of the evolution and effectiveness of robust portfolio optimization methods over time. The method used to conduct the SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The result of the SLR presented a novel strategy to combine robust optimization methods and a GA in order to enhance RPMV. The uncertainty parameters, cardinality constraints, optimization constraints, risk-aversion parameters, robust covariance estimators, relative and absolute robustness, and parameters adopted were unable to develop portfolios capable of maintaining performance despite market uncertainties. This led to the inclusion of GAs to solve the complex optimization problems associated with RPMV efficiently, as well as fine-tuning parameters to improve solution accuracy. In three papers, the empirical validation of the results was conducted using historical data from different global capital markets such as Hang Seng (Hong Kong), Data Analysis Expressions (DAX) 100 (Germany), the Financial Times Stock Exchange (FTSE) 100 (U.K.), S&P 100 (USA), Nikkei 225 (Japan), and the Indonesia Stock Exchange (IDX), and the results showed that the RPMV model optimized with a GA was more stable and provided higher returns compared with traditional MV models. Furthermore, the proposed method effectively mitigated market uncertainties, making it a valuable tool for investors aiming to optimize portfolios under uncertain conditions. The implications of this study relate to handling uncertainty in asset returns, dynamic portfolio parameters, and the effectiveness of GAs in solving portfolio optimization problems under uncertainty, providing near-optimal solutions with relatively lower computational time. Full article

This study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions. It considers a risk- and ambiguity-averse investor allocating resources to a risk-free asset, a market index, a green stock, and a brown stock. The study employs a robust control approach rooted in relative entropy to account for model misspecification and derive closed-form optimal investment strategies. The key contribution of this study includes demonstrating, using two sets of empirical data on asset returns and ESG ratings, the substantial influence of ambiguity on optimal trading strategies, particularly highlighting the differential effects of market, green, and brown ambiguities. As a by-product of our analytical solutions, the study contrasts ambiguity-averse investors with their non-ambiguity counterparts, revealing more cautious risk exposures with a reduction in short-selling positions for the former. Furthermore, three types of investors who employ popular suboptimal strategies are identified, together with two loss measures used to quantify their performance. The findings reveal that popular strategies, not accounting for ESG and misspecification in the model, could lead to significant financial costs, with the extent of loss varying depending on those two factors: investors’ ambiguity aversion profiles and ESG preferences. Full article

The rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo advisors gather information about clients’ preferences, financial situations, and future goals through questionnaires. Subsequently, they recommend ETF-based portfolios tailored to match the investor’s risk profile. However, these questionnaires often appear vague, and robo advisors seldom disclose the methodologies employed for investor profiling or asset allocation. This study aims to contribute by introducing an investor profiling method relying solely on investors’ relative risk aversion (RRA), which, in addition, allows for the determination of optimal allocations. We also show that, for the period under analysis and using the same ETF universe, our RRA portfolios consistently outperform those recommended by the Riskalyze platform, which may suffer from ultraconservadorism in terms of the proposed volatility. Full article

Motivated by the relatively infrequent but very large price spikes in the day-ahead and real-time energy markets operated by the Electric Reliability Council of Texas, this paper proposes an insurance that a small and risk-averse retailer in Texas (i.e., a retail electric provider (REP)) may buy to prevent financial insolvency caused by inadequate risk management. It also demonstrates the insurance’s practical design, pricing, and implementation. As participation in the REP’s procurement auction is voluntary, the insurance is mutually beneficial for the REP and the insurance seller. Hence, the proposed insurance is a newly developed wholesale market product that deserves consideration by REPs in Texas and competitive retailers elsewhere. Full article

We consider a defined-contribution (DC)-pension-fund-management problem under partial information. The fund manager is allowed to invest the wealth from the fund account into a financial market consisting of a risk-free account, a stock and a rolling bond. The aim of the fund manager is to maximize the expected utility of the terminal wealth. In contrast to the traditional literature, we assume that the fund manager can only observe the stock-price process and the interest-rate process, but the expected return rate of the stock is unobservable, following a mean-reverting stochastic process. We apply a martingale approach and Clark’s formula to solve this problem and the closed-form representations for the optimal terminal wealth and trading strategy are derived. We further present the results for the constant relative risk aversion (CRRA) function as a special case. Full article

This paper investigates how bank characteristics (market share, principal shareholders, profitability, and size), and the gender of the company’s board members, along with their supervisory abilities, influence the firm’s performance, cost of debt, and leverage. We extracted relevant data from a sample of nearly 18,300 Portuguese companies in 2020 (the pandemic year) to build our model with all the main explanatory variables; then, through the least absolute shrinkage and selection operator estimation, we reduced the variables. The robust ordinary least-squares standard-errors approach was applied by company size. Our findings allowed us to observe the crucial negative role of multiple bank relations, but only on the returns of small companies. A decrease in bank relations led to an increase in debt cost and reduced leverage across larger companies. Profitable banks generate higher company returns, mainly for small companies. Furthermore, the better-informed bank shareholders (management, institutional, or government) persuaded the banks to charge higher interest rates, resulting in a higher leverage ratio for companies of average size. Female board members tended to vote for lower debt ratios due to greater risk aversion, while the opposite was true of male board members. The supervisory capacity of the board in the area of bank relations showed a more substantial link with the increased financing costs of small companies. In brief, bank characteristics and board gender were strongly associated with the financial aggregates of companies relative to their size. This work contributes to the literature by using new bank characteristics and an original variable representing board ability to cope with bank relations. To the best of our knowledge, this is the first study to determine the association of the above characteristics in the Portuguese market relative to company size, and their impact on profitability, cost of debt, and leverage. The company board and banking systems should evaluate the impact of their decisions on corporate activity and make necessary adjustments. Full article

This article describes a class of jump-uncertain stochastic control systems, and derives an Itô–Liu formula with jump. We characterize an optimal control law, that satisfies the Hamilton–Jacobi–Bellman equation with jump. Then, this paper deduces the optimal portfolio game under uncertain stochastic financial markets with jump. The information of players is symmetrical. The financial market is constituted of a risk-free asset and a risky asset whose price process is subjected to the jump-uncertain stochastic Black–Scholes model. The game is formulated by two utility maximization problems, each investor tries to maximize his relative utility, which is the weighted average of terminal wealth difference between his terminal wealth and that of his competitor. Finally, the explicit expressions of equilibrium investment strategies and value functions for the constant absolute risk-averse and constant relative risk-averse utility function are derived by using the dynamic programming principle. Full article

This paper attempts to understand the dynamic interrelationships and financial asset capabilities of Bitcoin by analysing several aspects of its volatility vis-a-vis other asset classes. This study aims to analyse the volatility dynamics of the returns of Bitcoin. An asymmetric GARCH model (EGARCH) is used to investigate whether Bitcoin may be useful in risk management and ideal for risk-averse investors in anticipation of negative shocks to the market (leverage effect). This paper also examines Bitcoin as an investment and hedge alternative to gold as well as NSE NIFTY using a multivariate DCC GARCH model. DCC GARCH models are also used to check whether correlation (co-movement) between the markets is time-varying, examine returns and volatility spillovers between markets and the effect of the outbreak of COVID-19 in India on the investigated markets. The results show that given the supply of Bitcoin is fixed, low returns realisation is equivalent to excess supply over demand wherein investors are selling off Bitcoin during bad times. The positive co-movement between Bitcoin and gold during the COVID-19 outbreak shows that investors perceived Bitcoin as a relatively safe investment. However, overall analysis shows that Bitcoin was not considered a safe hedge and an investment option by Indian investors during the study period. Full article

Agricultural production accounts for 64.2% of the Vietnam’s Mekong Delta. However, this sector has to face damage risks, especially from the natural disasters, such as flood, drought, severe soil salinity, pests, and erosion, which might factor into the farmers’ risk attitude and their decision-making relative to investment in production activities. This study analyzes the factors influencing the risk attitudes of the rice farmers, based on evidence from the Vietnamese Mekong Delta. The data were collected through face-to-face interviews and experimental games with 145 rice farmers. An ordered probit regression model was applied to estimate how the factors affected the rice farmers’ risk attitudes. The risk-neutral farmers comprised 53.72% of farmers in the survey, while 31.72% and 15.15% were risk-preferred and risk-averse farmers. The study results indicated that age, number of rice crops per year, household assets, income from rice production, and credit accessibility were the main factors affecting the farmers’ risk attitudes. The results suggest that the financial incentives’ policies to compensate for losses in uncertain conditions and increase the household income, diversification of income sources, and improving the accessibility of formal credit might be useful to increase farmers’ willingness to accept the risks of investing in better profitability projects and gaining a higher income. Full article

This paper investigates the optimal investment strategy for a defined contribution (DC) pension plan during the decumulation phase which is risk-averse and pays close attention to inflation risk. The plan aims to maximize the expected constant relative risk aversion (CRRA) utility from the terminal real wealth by investing the fund in a financial market consisting of an inflation-indexed bond, an ordinary zero coupon bond and a risk-free asset. We derive the optimal investment strategy in closed-form using the dynamic programming approach by solving the related Hamilton-Jacobi-Bellman (HJB) equation. The results reveal that, with any level of the parameters, an inflation-indexed bond has significant advantage to hedge inflation risk. Full article

Innovations that initiate new technology cycles, i.e., radical innovations, bring tremendous value to Society and build for the companies that deploy them sustainable competitive advantages. However, large firms have typically been relatively inefficient at accessing from academia or technology start-ups such technological leaps. Indeed, most multiyear and multimillion dollar academia-industry partnerships have historically not resulted in any acceleration of the rate of deployment of game-changing innovations, which empirically proceeds in 25 year cycles, such as for example the expansion of the scope of the pharmaceutical industry from small molecules to biologics, or, projecting into the future, to siRNA or therapeutic stem cell technologies. Syndicated innovation venturing is a new strategic partnering concept described here that brings together actors from different economic segments in a non zero-sum game as a means to facilitate seed-funding, with the aim to de-risk technologies while reducing initial financial exposures. A case study in the pharmaceutical industry suggests that alleviating this hurdle may provide an appropriate environment to improve the dynamics of academic technology transfer to the commercial phase. By contributing to the de-risking of the creation of novel biotechnology businesses, this novel mechanism could help speed up the commercialization of emerging technologies on a large scale. At a time when knowledge-based firms such as pharmaceutical companies attempt to revisit their innovation models to advance science, in spite of an environment of increasing risk-aversion, such responses could tilt the balance in favor of disruptive products and sustained corporate financial performance by removing common barriers to radical innovation deployment. Full article