Search Results (132)

This study evaluates the risk-adjusted performance of Hospitality REITs using multi-factor asset pricing models and downside risk measures with the aim of assessing their diversification potential and crisis sensitivity. Unlike prior studies that examine REITs in aggregate, this study isolates Hospitality REITs to explore their unique cyclical and macroeconomic sensitivities. This study looks at the risk-adjusted performance of Hospitality Real Estate Investment Trusts (REITs) in relation to more general REIT indexes and the S&P 500 Index. The study reveals that monthly returns of Hospitality REITs increasingly move in tandem with the stock markets during financial crises, which reduces their historical function as portfolio diversifiers. Investing in Hospitality REITs exposes one to the hospitality sector; however, these investments carry notable risks and provide little protection, particularly during economic upheavals. Furthermore, the study reveals that Hospitality REITs underperform on a risk-adjusted basis relative to benchmark indexes. The monthly returns of REITs show significant volatility during the post-COVID-19 era, which causes return-to-risk ratios to be below those of benchmark indexes. Estimates from multi-factor models indicate negative alpha values across conditional models, indicating that macroeconomic variables cause unremunerated risks. This industry shows great sensitivity to market beta and size and value determinants. Hospitality REITs’ susceptibility comes from their showing the most possibility for exceptional losses across asset classes under Value at Risk (VaR) and Conditional Value at Risk (CvaR) downside risk assessments. The findings have implications for investors and portfolio managers, suggesting that Hospitality REITs may not offer consistent diversification benefits during downturns but can serve a tactical role in procyclical investment strategies. Full article

Measuring the small tree trunk variations during the day–night cycle, seasonal cycles, as well as those caused by the plant’s growth and health regime is a very important action in horticulture or forestry because by analyzing the collected data, assessments can be made on the health of the trees, but also on the climatic conditions and changes in a certain region. This can be performed with devices called dendrometers. This paper presents a new type of approach to these measurement types in which the trunk volume changes are highly sensitively converted into the axial stress on sensitive elements made of magnetic materials in wire form in which the giant stress impedance effect occurs. Finally, by electronic processing of the signals provided by the sensitive elements, digital words with a decimal value proportional to the diameter variations are obtained. This paper presents the operating principle, the constructive details and the experimental results obtained by testing the device in the laboratory and in-field. The proposed dendrometer, compared to those available commercially, has the advantage of good resolution and sensitivity, good immunity to temperature variations, the possibility of transmitting the result remotely, robustness and low price. Some metrological parameters obtained from the experimental testing are the following: resolution 1.6 µm, linearity 1.4%, measurement range 0 to 5 mm, temperature coefficient 0.012%/°C. Full article

This study examines the demographic and macroeconomic factors influencing housing prices in London from Q3 2014 to Q4 2022, focusing on the pre- and post-Brexit referendum periods. Using multiple regression analysis, the research evaluates the impact of interest rates, inflation, construction costs, population changes, and net migration on the housing price index (HPI) across various market segments. The findings suggest that interest rate base rates, consumer price inflation, and construction output price indices were significant predictors of housing price fluctuations. Notably, cash purchases exhibited the strongest explanatory power due to a reduced sensitivity to market changes. Additionally, London’s population was a key determinant, particularly affecting first-time buyers and mortgage-backed purchases. These results contribute to a deeper understanding of the London housing market and offer insights into policy measures addressing housing affordability and investment dynamics. Full article

Inspired by call centers, this paper models them as a constant retrial queue, with feedback and delayed vacations to balance high efficiency and low cost for service agents. After completing the service, the server randomly waits for an idle period. If customers arrive during this period, the service is provided immediately, otherwise, the server will take a vacation. We first derive steady-state probabilities and key performance measures. Then, the system cost is modeled. Particle Swarm Optimization (PSO), Ant Colony Algorithm (ACA) and Sparrow Search Algorithm (SSA) are applied to obtain the minimum system cost, respectively. To verify the correctness of the theoretical results of the system model, we simulate the model using Monte Carlo simulation to obtain the probabilities of different server states and the expected number of customers in the system, and then compare them with the theoretical values. At the same time, the sensitivity of the performance measures obtained by Monte Carlo simulation to the system parameters is also analyzed. Finally, customer behavior is analyzed, and equilibrium and socially optimal arrival rates are derived. In addition, the efficiency of the system is evaluated by examining efficiency indicators such as throughput and price of anarchy. Full article

Decarbonizing the transportation sector is critical for sustainable development, particularly in rapidly urbanizing countries like Turkiye. This study analyzes fuel demand elasticities for diesel, gasoline, and LPG across 12 NUTS-1 regions of Turkiye in 2022, using a panel random effects SUR approach. The model accounts for regional variation and fuel interactions, producing robust estimates that uncover significant spatial and temporal differences in consumption patterns. Uniquely, diesel demand displays a significantly positive price elasticity, challenging the conventional assumption of inelasticity. Gasoline demand is moderately price-sensitive, while LPG appears relatively unresponsive. Strong cross-price elasticities—especially between diesel and gasoline—point to substitution effects that can inform more adaptive policy frameworks. Seasonal fluctuations and Istanbul’s outsized impact also shape national trends. These findings underscore the need for differentiated region- and fuel-specific strategies. While higher gasoline taxes may effectively reduce demand, lowering diesel and LPG use will require complementary measures such as infrastructure upgrades, behavioral incentives, and accelerated adoption of alternative fuels. The study advocates for regionally adjusted carbon pricing, removal of implicit subsidies, and targeted support for electric and hybrid vehicles. Aligning fiscal tools with actual demand behavior can enhance both the efficiency and equity of the transition to a low-carbon transportation system. Full article

This paper examines the impact of various uncertainty channels on stock market returns in Saudi Arabia, with a focus on the Tadawul All Share Index (TASI). It examines factors such as Saudi-specific Geopolitical Risk, Global Oil Price Uncertainty, Climate Policy Uncertainty, and U.S. Monetary Policy Uncertainty. Using monthly data from November 1998 to June 2024 and the Local Projections (LP) methodology, the study examines how these uncertainties impact market returns across various time horizons, taking into account potential structural breaks and nonlinear dynamics. Our findings indicate significant variations in the market’s response to the uncertainty measures across two distinct periods. During the first period, geopolitical risks have a strong positive impact on market returns. Conversely, the second period reveals a reversal, with negative cumulative effects, suggesting a shift in risk–return dynamics. Oil Price Uncertainty consistently exhibits a negative impact in both periods, highlighting the changing nature of oil dependency in the Saudi market. Additionally, Climate Policy Uncertainty is becoming more significant, reflecting increased market sensitivity to global environmental policy changes. Our analysis reveals significant asymmetries in the effects of various uncertainty shocks, with Monetary Policy Uncertainty exhibiting nonlinear effects that peak at intermediate horizons, while commodity-related uncertainties exhibit more persistent impacts. These findings, which remain robust across various tests, offer critical insights for portfolio management, policy formulation, and risk assessment in emerging markets undergoing substantial economic changes. Full article

This study examines whether Chief Executive Officers (CEOs) exercise a greater proportion of their exercisable options in response to firm-specific stock price bubbles. For a sample of U.S. firms from 1992 to 2021, the study identifies stock price bubble periods using the Generalized Sup Augmented Dickey-Fuller (GSADF) method. A bubble is a statistical measure that detects an ex-post firm-specific stock price exuberance that creates abnormally high variation in stock prices arising from changes in discount rates, R&D and market liquidity. If executives have private information and can infer firm-specific bubbles, they are likely to exercise a greater proportion of their exercisable stock options during bubbles to benefit from their firms’ stock price exuberance. Using data aggregated at the CEO-year level, we find that executives are prone to exercising a larger portion of their vested stock options during market bubbles, with the aim of monetizing on the exuberance in the firm’s stock price. They leverage their expertise and their acquired price-sensitive private information to identify these bubbles. We also find that CEOs’ option exercise activity increases as the duration of the bubble increases to capture the price momentum. Full article

The study of carbon stocks in organic compounds within terrestrial ecosystems allows us to create a pool of potential carbon farming projects. At present, it is essential to assess the economic viability of natural-based solutions in order to develop strategies to encourage small and medium enterprises (SME) and governments to address climate change through specific measures. This article is devoted to the study of the economic efficiency of afforestation projects. The purpose of this study is to evaluate the economic efficiency of the project and, based on NPV sensitivity analysis, to identify the factors affecting economic efficiency. This will make it possible to formulate directions for stimulating the development of afforestation projects using tools to improve their economic efficiency. Based on data on the number of carbon credits issued, their price, and the costs and other revenue associated with the implementation of the afforestation project, a sensitivity analysis of economic efficiency was conducted, highlighting the most significant factors. Given that different tree species are characterized by variable seedling values, planting costs, and sequestration potentials, an afforestation project with the most carbon efficient tree species was selected as a pilot project. Black alder exhibits the most optimal proportion between the volume of carbon units released and the cost of planting trees. A sensitivity analysis of the project’s net present value was conducted in order to ascertain the factors that have the most significant impact on the project’s economic efficiency. These include the discount rate based on the cost of capital and the cost of tree planting. As a result, this article makes recommendations for improving the economic efficiency of afforestation projects for SME. The government’s role in enhancing the economic efficiency of such initiatives entails reducing the cost of capital through a reduction in the key rate or the provision of subsidies for the interest rate on bank credits. An alternative approach involves the granting of subsidies for the cost of tree planting, since the effects can be seen as a series of public goods, such as the creation of recreational areas and increased biodiversity of the ecosystem. Full article

Water scarcity and pollution are critical challenges affecting agriculture and aquatic ecosystems. This study evaluates the environmental benefits of using reclaimed water (RW) for irrigation in southern Spain by applying a comprehensive cost–benefit analysis (CBA) to a water reuse project. This method allows us to assess financial feasibility and environmental externalities of RW use for irrigation, with particular focus on the reduction in eutrophication and greenhouse gas emissions. Furthermore, the proposed CBA highlights the potential of RW to provide essential nutrients for crops, reduce reliance on synthetic fertilizers, and mitigate the ecological impact of fertilizer manufacturing and transportation. Results indicate that, while the direct financial returns of RW are limited, the integration of environmental benefits significantly improves the overall economic viability of water reuse projects. Furthermore, sensitivity analyses suggest that policy measures, such as adjusted water pricing and financial incentives, could enhance the adoption of RW in agriculture. This study supports the role of RW as a sustainable alternative for irrigation, contributing to water conservation, pollution reduction, and climate resilience. Future research should focus on long-term agronomic impacts, optimized pricing models, and policy frameworks that promote water reuse as a key strategy in sustainable water management. Full article

Realized downward semi-variance (RDS) has been realized as a key indicator to measure the downside risk of asset prices, and the accurate prediction of RDS can effectively guide traders’ investment behavior and avoid the impact of market fluctuations caused by price declines. In this paper, the RDS rolling prediction performance of the traditional econometric model, machine learning model, and deep learning model is discussed in combination with various relevant influencing factors, and the sensitivity analysis is further carried out with the rolling window length, prediction length, and a variety of evaluation methods. In addition, due to the characteristics of RDS, such as aggregation and jumping, this paper further discusses the robustness of the model under the impact of external events, the influence of emotional factors on the prediction accuracy of the model, and the results and analysis of the hybrid model. The empirical results show that (1) when the rolling window is set to 20, the overall prediction effect of the model in this paper is the best. Taking the Transformer model under SSE as an example, compared with the prediction results under the rolling window length of 5, 10, and 30, the RMSE improvement ratio reaches 24.69%, 15.90%, and 43.60%, respectively. (2) The multivariable Transformer model shows a better forecasting effect. Compared with traditional econometric, machine learning, and deep learning models, the average increase percentage of RMSE, MAE, MAPE, SMAPE, MBE, and SD indicators is 52.23%, 20.03%, 62.33%, 60.33%, 37.57%, and 18.70%, respectively. (3) In multi-step prediction scenarios, the DM test statistic of the Transformer model is significantly positive, and the prediction accuracy of the Transformer model remains stable as the number of prediction steps increases. (4) Under the impact of external events of COVID-19, the Transformer model has stability, and the addition of emotional factors can effectively improve the prediction accuracy. In addition, the model’s prediction performance and generalization ability can be further improved by stacked prediction models. An in-depth study of RDS forecasting is of great value to capture the characteristics of downside risks, enrich the financial risk measurement system, and better evaluate potential losses. Full article

This study explores the feasibility parameters of a potential investment plan for injecting “green” hydrogen into the existing natural gas supply network in Greece. To this end, a preliminary profitability optimization analysis was conducted through key performance indicators such as the cost of hydrogen and the socio-environmental benefit of carbon savings, followed by break-even and sensitivity analyses. The identification of the major impact drivers of the assessment was based on the examination of a set of operational scenarios of varying hydrogen and natural gas flow rates. The results show that high natural gas capacities with a 5% hydrogen content by volume are the optimal case in terms of socio-economic viability, but the overall profitability is too sensitive to hydrogen pricing, rendering it unfeasible without additional motives, measures and pricing strategies. The results feed into the main challenge of implementing commercial “green” hydrogen infrastructures in the market in a sustainable and feasible manner. Full article

The Smart Choice logo (SCL), as an encouraging form of front-of-package nutrition labeling (FOPNL), helps consumers to choose low-oil, -salt, and -sugar mooncakes during the Mid-Autumn Festival. It is widely acknowledged that nutrition knowledge contributes to nutrition label use, but there has been little research on whether it helps enhance consumers’ willingness to pay (WTP). Our study aims to fill this gap by investigating 630 randomly selected Chinese adults from Jilin, Inner Mongolia, Shaanxi, Shandong, Henan, Sichuan, and Guangdong. The semi-double-bounded dichotomous choice contingent value method was selected to measure their WTP for pre-packed mooncakes with the SCL at 20 different premium levels, ranging from 0% to 95% of the price per unit. It was found that the respondents’ WTP decreased by 0.7% as the premium level increased by 1%, and the WTP of people from South China, those who were obese, and those with a high income was not sensitive to changes in premium. Nutrition knowledge played a negative moderating role, and the probability of the premium levels affecting WTP decreased by 1.0% for each 1 point increase in the nutrition knowledge level. These findings highlight the potential implications associated with SCL promotion and differentiated mooncake pricing, as well as the supply of healthier Chinese holiday foods. Full article

This paper investigates the valuation of vulnerable exchange options with two underlying assets that follow a two-factor volatility model. We employ a reduced-form model incorporating a Poisson process with stochastic intensity. The proposed reduced-form model depends on a stochastic intensity process that is guaranteed to remain positive and includes both systemic and idiosyncratic risks. Using measure change techniques and characteristic functions, we obtain an explicit pricing formula for vulnerable exchange options within the proposed framework. We also provide numerical examples demonstrating the sensitivity of option prices to significant parameters. Full article

Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error metrics such as the Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). However, these metrics present challenges including sensitivity to outliers (notably MSE and RMSE) and scale dependency, which complicates comparisons across different models. Additionally, traditional metrics sometimes yield values that are difficult to interpret across various problems. Consequently, there is a need for a metric that consistently reflects regression model performance, independent of the problem domain, data scale, and outlier presence. To overcome these shortcomings, this paper introduces a new regression accuracy measure based on the Hassanat distance, a non-convex distance metric. This measure is not only invariant to outliers but also easy to interpret as it provides an accuracy-like value that ranges from 0 to 1 (or 0–100%). We validate the proposed metric against traditional measures across multiple benchmarks, demonstrating its robustness under various model scenarios and data types. Hence, we suggest it as a new standard for assessing regression models’ accuracy. Full article

Soil electrical conductivity has an important influence on the growth and development of plants. The existing real-time soil electrical conductivity detection device is affected by temperature, inconvenient to use, expensive, etc.; therefore, based on the classical four-terminal method of soil electrical conductivity detection principle, in this study, we aim to improve the limitations of the constant current source, selecting the high-performance integrated chip AD5941, optimizing the detection circuit and probe structure, improving the achievability of the detection circuit, and designing a type of in situ on-line real-time access to a soil electrical conductivity detection device, and improve the detection accuracy by temperature compensation. In this paper, dynamic performance, steady state performance, radial sensitivity range, and calibration test are carried out for the soil electrical conductivity detection prototype. The test results show that the dynamic response speed of the prototype is less than 50 ms, the steady state error is not more than ±2%, and the radial measurement sensitivity range is 8~10 cm. A comparison with the commercial sensor shows that the linear fit of the two measurements reaches 0.9995, and the absolute error ranges from −61.40 µS/cm to 23.90 µS/cm, with a relative error range of −1.94~1.86%. It shows that the performance of the two sensors is comparable, but the quality/price ratio of the prototype is much higher than that of the commercialized product. In this study, it is demonstrated that a high-precision, low-cost, and easy-to-use in situ online soil electrical conductivity detection device can be provided for agricultural and forestry production. Full article