Search Results (14)

We investigate whether a belief in trend continuation, often associated with the so-called “hot-hand effect,” can be endogenously triggered by personal performance feedback in a controlled financial experiment. Participants allocated funds across assets with randomly generated prices, under conditions of known probabilities and varying levels of risk. In a two-stage setup, participants were first exposed to random price sequences to learn the task and potentially develop perceptions of personal success. They then faced additional price paths under incentivized conditions. Our findings show that participants initially increased purchases following gains—consistent with a feedback-driven belief in momentum—but this pattern faded over time. When facing sustained losses, loss aversion dominated decision-making, overriding early optimism. These results highlight how cognitive heuristics and emotional biases interact dynamically, suggesting that belief in trend continuation is context-sensitive and constrained by the reluctance to realize losses. Full article

Route planning under uncertain traffic conditions requires accounting for not only expected travel times but also the risk of late arrivals. This study proposes a mean-upper partial moment (MUPM) framework for pathfinding that explicitly considers travel time unreliability. The framework incorporates a benchmark travel time to measure the upper partial moment (UPM), capturing both the probability and severity of delays. By adjusting a risk parameter ($\theta$), the model reflects different traveler risk preferences and unifies several existing reliability measures, including on-time arrival probability, late arrival penalty, and semi-variance. A bi-objective model is formulated to simultaneously minimize mean travel time and UPM. Theoretical analysis shows that the MUPM framework is consistent with the expected utility theory (EUT) and stochastic dominance theory (SDT), providing a behavioral foundation for the model. To efficiently solve the model, an SDT-based label-correcting algorithm is adapted, with a pre-screening step to reduce unnecessary pairwise path comparisons. Numerical experiments using GPS probe vehicle data from Louisville, Kentucky, USA, demonstrate that varying $\theta$ values lead to different non-dominated paths. Lower $\theta$ values emphasize frequent small delays but may overlook excessive delays, while higher $\theta$ values effectively capture the tail risk, aligning with the behavior of risk-averse travelers. The MUPM framework provides a flexible, behaviorally grounded, and computationally scalable approach to pathfinding under uncertainty. It holds strong potential for applications in traveler information systems, transportation planning, and network resilience analysis. Full article

With Generative AI (GenAI) entering medicine, understanding its decision-making under uncertainty is important. It is well known that human subjective risk appetite influences medical decisions. This study investigated whether the risk appetite of GenAI can be evaluated and if established human risk assessment tools are applicable for this purpose in a medical context. Five GenAI systems (ChatGPT 4.5, Gemini 2.0, Qwen 2.5 MAX, DeepSeek-V3, and Perplexity) were evaluated using Rheumatoid Arthritis (RA) clinical scenarios. We employed two methods adapted from human risk assessment: the General Risk Propensity Scale (GRiPS) and the Time Trade-Off (TTO) technique. Queries involving RA cases with varying prognoses and hypothetical treatment choices were posed repeatedly to assess risk profiles and response consistency. All GenAIs consistently identified the same RA cases for the best and worst prognoses. However, the two risk assessment methodologies yielded varied results. The adapted GRiPS showed significant differences in general risk propensity among GenAIs (ChatGPT being the least risk-averse and Qwen/DeepSeek the most), though these differences diminished in specific prognostic contexts. Conversely, the TTO method indicated a strong general risk aversion (unwillingness to trade lifespan for pain relief) across systems yet revealed Perplexity as significantly more risk-tolerant than Gemini. The variability in risk profiles obtained using the GRiPS versus the TTO for the same AI systems raises questions about tool applicability. This discrepancy suggests that these human-centric instruments may not adequately or consistently capture the nuances of risk processing in Artificial Intelligence. The findings imply that current tools might be insufficient, highlighting the need for methodologies specifically tailored for evaluating AI decision-making under medical uncertainty. Full article

The increasing penetration of distributed renewable energy sources introduces challenges in maintaining balance within power systems. Civic energy initiatives offer a promising solution by decentralizing balancing responsibilities to local areas, with energy clusters serving as an example of such communities. This article proposes a novel mixed-integer linear programming (MILP) model for optimizing the energy mix within a cluster, addressing both planned balancing (day-ahead) and unplanned real-time adjustments. The proposed approach focuses on mid-term decision-making, including the integration of additional wind energy sources into the cluster and the procurement of new demand-side response (DSR) contracts, that allow for short-term planned and unplanned balancing. While increased wind energy enhances the system’s renewable capacity, it also raises operational stiffness, whereas DSR contracts provide the flexibility necessary for effective system balancing. The model incorporates risk aversion by employing Conditional Value at Risk (CVaR) as a risk measure, enabling a nuanced evaluation of trade-offs between cost and risk. The interactive framework allows decision-makers to tailor solutions by adjusting confidence levels and assigning weights to cost and risk metrics. A representative numerical example, based on a typical energy cluster in Poland, illustrates the model’s applicability. This case study demonstrates that the model responds intuitively to varying decision-maker preferences and can be efficiently solved for practical problem sizes. Full article

Global energy production is undergoing a transition from fossils to renewables. At the same time, the Norwegian Oil Fund has grown exponentially in size and is now a major global investor. These events in combination are likely to impact the dynamics of the Norwegian krone. Concurrently, the persistent weakening of the Norwegian krone (NOK), hitting record low exchange rates against the major currencies, is sparking national and international interest. Using updated data, we find that oil prices and global asset prices are both important drivers of EURNOK returns. However, we find that the relative importance changed following the 2015 oil price decline, whereafter asset prices became more significant. Furthermore, we observe an impact of investor risk aversion, suggesting that the krone is no longer a safe-haven currency. Full article

Bitcoin Pricing Kernels (PKs) are estimated using a novel data set from Deribit, the leading Bitcoin options exchange. The PKs, as the ratio between risk-neutral and physical density, dynamically reflect the change in investor preferences. Thus, the PKs improve the understanding of investor expectations and risk premiums in a new asset class. Bootstrap-based confidence bands are estimated in order to validate the results. Investors are heterogeneous in their risk profiles and preferences with respect to volatility and investment horizon. The empirical PKs turn out to be U-shaped for short-dated instruments and W-shaped for long-dated instruments. We find that investors are willing to pay a substantial risk premium to insure themselves against short-term price movements. The risk premium is smaller for longer-dated instruments and their traders are risk averse. The shape of the empirical PKs reveals the existence of a time-varying risk premium. The similarity between the shape of empirical PKs for Bitcoin and other markets that represent aggregate wealth shows that Bitcoin is becoming an established asset class. Full article

The allocation of pension funds has important theoretical value and practical significance, which improves the level of pension investment income, achieves the maintenance and appreciation of pension funds, and resolves the pension payment risk caused by population aging. The asset allocation of pension funds is a long-term asset allocation problem. Thus, the long-term risk and return of the assets need to be estimated. The covariance matrix is usually adopted to measure the risk of the assets, while calculating the long-term covariance matrix is extremely difficult. Direct calculations suffer from the insufficiency of historical data, and indirect calculations accumulate short-term covariance, which suffers from the dynamic changes of the covariance matrix. Since the returns of main assets are highly autocorrelated, the covariance matrix of main asset returns is time-varying with dramatic dynamic changes, and the errors of indirect calculation cannot be ignored. In this paper, we propose a novel Black–Litterman model with time-varying covariance (TVC-BL) for the optimal asset allocation of pension funds to address the time-varying nature of asset returns and risks. Firstly, the return on assets (ROA) and the covariance of ROA are modeled by VARMA and GARCH, respectively. Secondly, the time-varying covariance estimation of ROA is obtained by introducing an effective transformation of the covariance matrix from short-term to long-term. Finally, the asset allocation decision of pension funds is achieved by the TVC-BL model. The results indicate that the proposed TVC-BL pension asset allocation model outperforms the traditional BL model. When the risk aversion coefficient is 1, 1.5, and 3, the Sharp ratio of pension asset allocation through the TVC-BL pension asset allocation model is 13.0%, 10.5%, and 12.8% higher than that of the traditional BL model. It helps to improve the long-term investment returns of pension funds, realize the preservation and appreciation of pension funds, and resolve the pension payment risks caused by the aging of the population. Full article

Crop residue burning (CRB) poses a serious threat to the climate, soil fertility, human health and wellbeing, and air quality, which increases mortality rates and slumps agricultural productivity. This study conducts a pan-India analysis of CRB burning based on the spatial characteristic of crop residue management practices and analyzes the linkage among health, agriculture value addition, and regional finance using the simultaneous equation to find the causality and panel quantile regression for direct effect and intergroup difference. We discuss some of the alternative crop residue management practices and policy interventions. Along with in situ management, this paper discusses ex situ crop residue management (CRM) solutions. The ex situ effort to manage crop residue failed due to the scarcity of the supply chain ecosystem. Force of habit and time constrain coupled with risk aversion have made farmers reluctant to adopt these solutions. Our results show that financial viability and crop residue have bidirectional causality; therefore, both the central and state governments must provide a financial solution to lure farmers into adopting residue management practices. Our analysis shows that framers are likely to adopt the management solution (farmers have some economic benefits) and are reluctant to adopt the scientific solution because the scientific solution, such as “pusa decomposer”, is constrained by the weather, temperature, and humidity, and these parameters vary throughout India. 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

Cancer-bearing mice are at risk of developing anxiety, pain, or malaise. These conditions may not only harm welfare but could also undermine data quality and translational validity in studies to develop therapeutic interventions. We aimed to establish whether, or at what point mice developing lung cancer show these symptoms, what measures can best detect their onset, and if data quality and animal welfare can be enhanced by using non-aversive handling (NAH). Welfare was monitored using various daily methods. At the beginning and end of the study, we also scored behaviour for general welfare evaluation, recorded nociceptive thresholds, and applied the mouse grimace scale (MGS). Cancer caused a decline in daily welfare parameters (body weight, and food and water consumption) beginning at around 4 days prior to euthanasia. As cancer progressed, rearing and walking declined to a greater extent in cancer-bearing versus control mice, while grooming, inactive periods, and MGS scores increased. A decline in nest building capability and food consumption provided a particularly effective means of detecting deteriorating welfare. These changes suggested a welfare problem arose as cancer developed, so similar studies would benefit from refinement, with mice being removed from the study at least 4 days earlier. However, the problem of highly varied tumour growth made it difficult to determine this time-point accurately. There were no detectable beneficial effects of NAH on either data quality or in terms of enhanced welfare. Full article

This study assesses the effect of fund-level and systemic factors on the performance of mutual funds in the context of changing market conditions. A Markov regime-switching model is used to analyze the performance of 33 South African equity mutual funds from 2006 to 2019. From the results, fund flow and fund size exert more predictive influences on performance in the bearish state of the market than in the bullish state. Fund age, fund risk, and market risk were found to be the most significant factors driving the performance of active portfolios under time-varying conditions of the market. These variables exert more influence on fund performance under bearish conditions than under bullish conditions, emphasizing the flight-to-liquidity assets phenomenon and risk-aversion behavior of fund contributors during unstable conditions of the market. Consequently, fund managers need to maintain adequate asset bases while implementing policies that minimize dispersions in fund returns to engender persistence in performance. This study provides novel perspectives on how the determinants of fund performance change with market conditions as portrayed by the adaptive market hypothesis (AMH). Full article

This paper examines the role of non-cash flow factors over correlation jumps in financial markets. Utilizing time-varying risk aversion measure as a proxy for investor sentiment and the cross-quantilogram method applied to intraday data, we show that risk aversion captures significant predictive power over realized stock-bond correlation jumps at different quantiles and lags. The predictive relation between correlation jumps and time-varying risk aversion is found to be asymmetric, as we detect a heterogeneous dependence pattern across different quantiles and lag orders. Our findings underline the importance of non-cash flow factors over correlation jumps, highlighting the role of behavioral factors in optimal portfolio allocations and the effectiveness of diversification strategies. Full article

This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models. Full article

Agricultural production faces several types of risk, and risk management tools vary by place, season, and crop type. Most farmers use multiple risk-minimizing tools to reduce the effects of various hazards. However, previous research has overlooked the potential connections between different risk management tool utilization decisions. This study examines farmers’ decisions of adopting risk management tools (contract farming and precautionary savings) and investigates the impacts of various factors on farmers’ risk management decisions by using bivariate and multinomial probit models. The study was carried out in four different agro-ecological regions of Bangladesh with 350 farmers chosen through multistage stratified random sampling procedures. The findings revealed that the farmers’ decisions towards adopting risk management tools are correlated, and the adoption of one risk management tool may induce farmers to adopt other risk management tools at that time. Moreover, the results revealed that age, education, income, and land ownership are the major factors affecting the adoption of risk management tools, and most farmers are risk-averse in nature. Both models provide interpretation and information for the development of a better understanding of the current situation of rural farm households, which may serve as a platform for policymakers who are anticipating appropriate risk management tools for the farmers. Full article