Search Results (21)

Accepting a certain degree of investment loss risk is essential for long-term portfolio management. However, overconfidence bias within financial literacy can prompt excessively risky behavior and amplify susceptibility to other cognitive biases. These tendencies can undermine investment loss tolerance beyond the baseline level shaped by sociodemographic, economic, psychological, and cultural factors. This study empirically examines the association between overconfidence and investment loss tolerance, which is measured by the point at which respondents indicate they would sell their investments in a hypothetical loss scenario. Using a large-scale dataset of 161,765 active investors from one of Japan’s largest online securities firms, we conduct ordered probit and ordered logit regression analyses, controlling for a range of sociodemographic, economic, and psychological variables. Our findings reveal that overconfidence is statistically significantly and negatively associated with investment loss tolerance, indicating that overconfident investors are more prone to prematurely liquidating assets during market downturns. This behavior reflects an impulse to avoid even modest losses. The findings suggest several possible practical strategies to mitigate the detrimental effects of overconfidence on long-term investment behavior. Full article

Financial studies on the herding effect have been very popular for decades, as detecting herding behavior helps to explain price deviations and market inefficiencies. However, studying the herding effect as a single influencing factor is believed to be insufficient to explain the changes in investment behavior, as the herding effect itself may be caused by other influencing factors. In other words, the issue must be studied alongside other factors. In this study, we adopt the quantile regression model to comprehensively understand the herding effect’s influence on household investment in China, and the empirical results indicate that herding behavior leads to different investment outcomes for households in different scenarios. In this analysis, we consider a variety of household characteristics, such as income level and risk tolerance, to provide a nuanced understanding of investment behavior. Additionally, in this study, we explore the interaction between herding behavior and macroeconomic variables. Nevertheless, the results suggest that, if herding behavior can be reduced by the head of the household, profitability can be increased, or at the very least, losses can be reduced. Full article

The current range of the jaguar (Panthera onca) spans sixty degrees of latitude across eighteen countries in the Western Hemisphere and covers approximately 7,000,000 km². Throughout this geographical breadth, jaguars represent an essential component of native biological diversity, but conflict revolving around real and perceived jaguar depredation on livestock is a factor in jaguar mortality. We developed a structured questionnaire to evaluate the effectiveness of anti-depredation strategies from northern Mexico to Argentina, collecting data from 11 countries and 248 livestock operations, 194 with efficacy metrics, and 24 with benefit–cost ratios (value of the livestock losses averted/cost of the intervention). Using coarse categories, 11 intervention types were tested. Techniques effectively reducing livestock losses were documented across the entire livestock operation size (2–130,000 ha, 5–30,000 head) and biome spectrum. While the techniques varied in complexity and required levels of investment, successful reductions in depredation were achieved at all levels. We conclude that anti-depredation strategies are highly effective, and when benefits are evaluated, they surpass costs, sometimes substantially. Given the proven efficacy and cost-effectiveness of the techniques described in this paper, we advocate for broader application across the species range to increase tolerance towards jaguars and a more effective human–jaguar coexistence. Full article

This research proposes a novel arbitrage approach in multivariate pair trading, termed the Optimal Trading Technique (OTT). We present a method for selectively forming a “bucket” of fiat currencies anchored to cryptocurrency for monitoring and exploiting trading opportunities simultaneously. To address quantitative conflicts from multiple trading signals, a novel bi-objective convex optimization formulation is designed to balance investor preferences between profitability and risk tolerance. We understand that cryptocurrencies carry significant financial risks. Therefore this process includes tunable parameters such as volatility penalties and action thresholds. In experiments conducted in the cryptocurrency market from 2020 to 2022, which encompassed a vigorous bull run followed by a bear run, the OTT achieved an annualized profit of 15.49%. Additionally, supplementary experiments detailed in the appendix extend the applicability of OTT to other major cryptocurrencies in the post-COVID period, validating the model’s robustness and effectiveness in various market conditions. The arbitrage operation offers a new perspective on trading, without requiring external shorting or holding the intermediate during the arbitrage period. As a note of caution, this study acknowledges the high-risk nature of cryptocurrency investments, which can be subject to significant volatility and potential loss. Full article

After the Fukushima nuclear disaster, the nuclear materials community has been vastly investing in accident tolerant fuel (ATF) concepts to modify/replace Zircaloy cladding material. Iron–chromium–aluminum (FeCrAl) alloys are one of the leading contenders in this race. In this study, we investigated FA-SMT (or APMT-2), PM-C26M, and Fe17Cr5.5Al over a time period of 6 months in simulated BWR environments and compared their performance with standard Zirc-2 and SS316 materials. Our results implied that water chemistry along with alloy chemistry has a profound effect on the corrosion rate of FeCrAl alloys. Apart from SS316 and Zirc-2 tube specimens, all FeCrAl alloys showed a mass loss in hydrogen water chemistry (HWC). FA-SMT displayed minimal mass loss compared to PM-C26M and Fe17Cr5.5Al because of its higher Cr content. The mass gain of FeCrAl alloys in normal water chemistry (NWC) is significantly less when compared to Zirc-2. Full article

Background: Obstructive sleep apnoea (OSA) and associated hypopnoea syndromes are chronic conditions of sleep-disordered breathing with significant sequelae if poorly managed, including hypertension, cardiovascular disease, metabolic syndrome and increased mortality. Glucagon-like peptide 1 receptor agonists (GLP-1RA) have recently garnered significant interest as a potential therapeutic, attributed to their durable effects in weight loss and glycaemic control in metabolic syndromes, such as obesity and type 2 diabetes mellitus. This has led to significant investment into companies that produce these medications and divestment from traditional gold standard methods of OSA management such as continuous positive airway pressure machines. Despite these sentiments, the impacts of these medications on OSA outcomes are poorly characterised, with no high-quality evidence at this stage to support this hypothesis. This scoping review therefore aims to address the research question of whether GLP-1RAs lead to a direct improvement in OSA and associated hypopnoea syndromes. Methods: A scoping review was performed following a computer-assisted search of Medline, Embase and Cochrane Central databases. Papers that evaluated the use of GLP-1RA medications related to sleep-disordered breathing, OSA or other sleep-related apnoeic or hypopnoeic syndromes were included. Results: Literature search and evaluation identified 9 articles that were eligible for inclusion. Of these, 1 was a study protocol, 1 was a case report, 1 was an abstract of a randomised controlled trial (RCT), 1 was a non-randomised clinical trial and the remaining 5 were randomised clinical trials of variable rigour. All studies evaluated the outcomes of GLP-1RAs in patients with diagnosed OSA or symptoms suggestive of this condition. Conclusion: This scoping review identified early evidence to suggest that GLP-1RAs may improve OSA as defined by reduction in apnoea-hypopnoea index (AHI). This evidence is however conflicting due to contradicting results demonstrated from other studies. Overall, these medications were tolerated well, with minor gastrointestinal side-effects reported in some cases. Of all included studies, the quality of evidence was low, with short lengths of follow-up to identify durable effects of these medications on OSA outcomes and identify adverse events. More rigorous, RCTs with sufficient length of follow-up are required before consideration of formalising these medications into OSA treatment guidelines, frameworks and policies are warranted. Full article

In this study, we propose three portfolio strategies: allocation based on the normality assumption, the skewed-Student t distribution, and the entropy pooling (EP) method for 14 small- and large-capitalization (cap) cryptocurrencies. We categorize our portfolios into three groups: portfolio 1, consisting of three large-cap cryptocurrencies and four small-cap cryptocurrencies from various K-means classification clusters; and portfolios 2 and 3, consisting of seven small-cap and seven large-cap cryptocurrencies, respectively. Then, we investigate the performance of the proposed strategies on these portfolios by performing a backtest during a crypto market crash. Our backtesting covers April 2022 to October 2022, when many cryptocurrencies experienced significant losses. Our results indicate that the wealth progression under the normality assumption exceeds that of the other two strategies, though they all exhibit losses in terms of final wealth. In addition, we found that portfolio 3 is the best-performing portfolio in terms of wealth progression and performance measures, followed by portfolios 1 and 2, respectively. Hence, our results suggest that investors will benefit from investing in a portfolio consisting of large-cap cryptocurrencies. In other words, it may be safer to invest in large-cap cryptocurrencies than in small-cap cryptocurrencies. Moreover, our results indicate that adding large- and small-cap cryptocurrencies to a portfolio could improve the diversification benefit and risk-adjusted returns. Therefore, while cryptocurrencies may offer potentially high returns and diversification benefits in a portfolio, investors should be aware of the risks and carefully consider their investment objectives and risk tolerance before investing in them. Full article

Executive stock incentives, as an important tool for attracting talent and achieving long-term development goals, have a profound impact on the development of green innovation in enterprises. Based on this, this article uses data from Chinese listed companies from 2007 to 2020 and employs a two-way fixed effects model and a two-stage least squares method to explore the impact of executive stock incentives on green innovation in China. The study found that executive stock incentives have a significant positive impact on green innovation in enterprises. This promotional effect is more pronounced in non-state-owned enterprise groups characterized by operating losses, reduced institutional holdings, and fewer negative media reports. Mechanism analysis suggests that increasing the intensity of executive stock incentives can enhance profitability, increase tolerance for failure, and promote increased investment in green innovation. Finally, based on the conclusions, relevant policy implications are drawn. Full article

Water erosion is accelerating soil loss rates in the East Hararghe Zone due to inappropriate human activities and their complex and intertwined interactions with natural factors, particularly in sensitive agroecosystems that lack soil and water conservation (SWC) measures. Although these dynamic processes cause prolonged impacts, a comprehensive assessment of the risk of soil erosion has not yet been undertaken at the zonal level. To bridge this gap, we employed the revised universal soil loss equation (RUSLE) prediction model, along with remote sensing and geographic information systems (GIS), to estimate annual soil erosion rates, analyze the temporal-spatial patterns of erosion risk, and evaluate the potential of standard conservation practices to reduce soil loss in croplands. Total soil erosion (in millions of tonnes/year; Mt yr⁻¹) was estimated to be 9 in 1990, 14 in 2000, 12 in 2010, and 11 in 2020, with average rates of 33, 50, 44, and 39 t ha⁻¹ yr⁻¹, respectively. This suggests an overall 18% increase in soil erosion from 1990 to 2020. Over 75% of the area showed a tolerable soil loss rate (<10 t ha⁻¹ yr⁻¹) and low susceptibility to erosion risk. A mountainous landscape in the northwest presents extremely high erosion (>120 t ha⁻¹ yr⁻¹), which accounts for more than 80% of soil loss, making SWC planning a priority. Analysis of land-use land-cover change (LULCC) confirmed a higher increase in soil loss for LULCC that involved conversion to croplands, with average rates of 36.4 t ha⁻¹ yr⁻¹ (1990–2000), 70 t ha⁻¹ yr⁻¹ (2000–2010), and 36 t ha⁻¹ yr⁻¹ (2010–2020). The results have further revealed that implementing supportive practices such as terracing, stripping, and contouring could reduce average soil erosion by approximately 87%, 65%, and 29%, respectively, compared to the baseline model’s prediction. Therefore, a rigorous cost–benefit analysis is essential to design and implement optimal location-specific practices that maximize investment returns in SWC efforts and ecological restoration. However, we acknowledge the limitations of this study, associated with an empirical model that does not account for all forms of erosion, as well as reliance mainly on secondary data, which may affect the accuracy of the predicted outcomes. Full article

It is necessary to harmonize the needs of society in terms of agricultural production and land protection from various forms of degradation throughout sustainable land management. Assessing the justification of investment in sustainable management of land resources is an important step in this process. Consequently, an analysis of soil erosion risk was carried out in the suburban area of the morphological unit of the Barička river watershed, using the Revised Universal Soil Loss Equation (RUSLE) method, with the existing and projected structure of agricultural production according to the Future Agricultural Production Structure Model from the Aspect of Preserving Land Resources for Mountain Catchment Areas of Serbia (FAPSMS). The value of the existing and projected production structure from an economic aspect was also examined using dynamic economic methods. In order to assess the risk and uncertainty of investments, a sensitive analysis of dynamic methods was carried out. The results show that soil erosion losses are already below tolerance values with the existing production structure and that they could be reduced even more by applying the designed structure. Economic indicators show that the investment is justified and that it is more sensitive to changes in income. Full article

Increased demand for sustainable timber products has resulted in large investments in agroforestry in Australia, with plantations growing various Pinus species, selected to suit a plantation’s environment. Juvenile Pinus species have a low fire tolerance. With Australia’s history of wildfires and the likelihood of climate change exacerbating that risk, the potential for a total loss of invested capital is high unless cost-effective targeted risk minimisation is part of forest management plans. Based on the belief that the understory profiles within the juvenile plantations are a major factor determining fuel hazard risks, an accurate assessment of these profiles is required to effectively mitigate those risks. At present, assessment protocols are largely reliant on ground-based observations, which are labour-intensive, time consuming, and expensive. This research project investigates the effectiveness of using geospatial analysis of drone-derived photographic data collected in the commercial pine plantations of south-eastern Queensland as a cost-saving alternative to current fuel hazard risk assessment practices. Understory composition was determined using the supervised classification of orthomosaic images together with derivations of canopy height models (CHMs). The CHMs were subjected to marker-controlled watershed segmentation (MCWS) analysis, isolating and removing the plantation pine trees, enabling the quantification of understory fuel profiles. The method used proved highly applicable to immature forest environments with minimal canopy closure, but became less reliable for close canopied older plantations. Full article

Due to the COVID-19 pandemic, a very low interest rate policy was economically applied in Korea, and various investment activities through loans were activated. Real estate and stock prices rose rapidly, and many people became involved in stock investments because of economic instability. However, hastily started investment behavior resulted in economic loss and addictive behavior in stocks. The phenomenon of using stock investment to satisfy individual sensation seeking or addictive dependence on stocks due to lowered life satisfaction expectancy can become a serious social problem. However, the improvement of distress tolerance and the ability to endure pain despite frequent stock price fluctuations or lowered life satisfaction expectancy would be good alternatives to prevent stock addiction tendency. Therefore, the purpose of this study is to test the moderating effect of distress tolerance on the effect of adults’ sensation seeking and life satisfaction expectancy in stock addiction tendencies. The participants were 272 adults with stock investment experience. As a result, distress tolerance significantly moderated the positive effect of sensation seeking on stock addiction tendency. In addition, life satisfaction expectancy did not significantly increase in the group with high distress tolerance even if life satisfaction expectancy was lowered. These results suggest that stock addiction can be prevented by enhancing distress tolerance. Full article

This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle’s low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods. Full article

Distributed electric power production by small-scale customers is increasing continuously. Photovoltaic production is a popular method of producing self-energy for customers. Additionally, power systems require more flexibility when weather-dependent renewable energy production increases. Small-scale customers can increase the self-consumption of self-produced energy by using batteries or a demand response operation. However, batteries require high investment, and demand response operations induce a loss of comfort. Customers who heat their buildings using electric heaters are a good target for demand response operations because their heating can be controlled with limited changes in the indoor temperature. The demand response potential of a building can be defined by simply using customer load profiles and knowledge of the outdoor temperature. Any other information is not required in the proposed novel method. A tolerable variation in indoor temperature corresponds to considerably smaller battery capacity, though it is still a significant amount. With an optimally sized photovoltaic system, it is possible to use both methods simultaneously to increase self-consumption. Maximal benefits can be attained from both methods if the battery system is used as a primary control and the demand response is used as a secondary control. The defined novel method for determining the demand response potential of small-scale customers can also be used when estimating the flexibility of a large customer group. Small-scale customers together can provide significant flexible capacity when their electrical heating is centrally controlled. Full article

In this paper, a techno-economic investigation of a small-scale solar water pumping system combined with power generation is conducted numerically. Irrigation and power production for a typical small-size citrus farm located in southern Iran is simulated. The system consists of monocrystalline photovoltaic panels (CS3K-305MS, 305 W), absorbent glass material batteries (8A31DT-DEKA, 104 Wh), inverters (SMA Sunny Boy 2.0, 2000 W), and a pumping storage system. The key concepts of water shortage probability (WSP) and loss of power supply probability (LPSP) are used in conjunction with users’ tolerances and sizing of the system. A genuine MATLAB code was developed and validated before the simulations. A specific electricity consumption pattern for a rural home and a variable irrigation water profile were considered. The main objective of the study is to size a system that provides both electricity for domestic use of a home as well as the energy required for running the irrigation pumps with respect to investment cost, LCOE, WSP, and LPSP. The main findings of the research are that LPSP and WSP threshold tolerances can have a preponderant effect on the cost and sizing of the system. Interestingly, results reveal that there is a minimum variation of the capital expenditure (CAPEX) versus the number of PV panels. For the optimal configuration, the study indicates that shifting from an LPSP of 0% to 3% (or about ten days of potential yearly shortage) makes the LCOE drop by about 55%, while the WSP decreases by about 36%. Full article