Search Results (88)

The purpose of this study is to propose a novel approach for measuring productivity changes in decision-making units (DMUs) over time and evaluating the performance of each DMU under uncertainty in terms of progress, regression, and stagnation. To achieve this, the Malmquist productivity index (MPI) and the data envelopment analysis (DEA) models are extended, and a new productivity index capable of handling uncertain data are introduced through a robust optimization approach. Robust optimization is recognized as one of the most applicable and effective methods in uncertain programming. The implementation and calculation of the proposed index are demonstrated using data from 15 actively traded stocks in the petroleum products industry on the Tehran stock exchange over two consecutive years. The results reveal that a significant number of stocks exhibit an unfavorable trend, marked by a decline in productivity. The findings highlight the efficacy and effectiveness of the proposed robust Malmquist productivity index (RMPI) in measuring and identifying productivity trends for each stock under data uncertainty. Full article

Reforestation is widely promoted as a nature-based solution for climate change, yet its unintended consequences, such as deforestation leakage, remain under-investigated. This study provides empirical evidence of reforestation-induced leakage in the Brazilian Amazon, using municipality-level panel data from 2000 to 2023 and spatial Durbin panel models to estimate both the magnitude and spatial reach of agricultural displacement. Despite the positive local effects of reforestation projects, we found a significant displacement of deforestation to the vicinity of municipalities. We estimated a statistically significant deforestation leakage effect of approximately 12% from the reforested area, due to the agricultural displacement of cattle ranching activities. Spatial spillovers are strongest within a 150 km radius and within two years after reforestation onset. Sensitivity tests using alternative spatial weight matrices, including distance decay and land rent-weighted specifications, confirm the robustness of these findings. Livestock intensification, proxied by cattle stocking rates, does not significantly mitigate displacement effects, challenging assumptions about land sparing benefits. These results suggest that current carbon market protocols (e.g., Verra, ART-TREES) may improve their leakage analysis to avoid under- or over-estimating net carbon benefits. Incorporating spatial econometric evidence into offset methodologies and reforestation planning can improve climate policy integrity and reduce unintended environmental trade-offs. Full article

This study examines stock borrowing costs in Japan’s centralized lendable stock market, focusing on differences between ‘hard-to-borrow’ and ‘easy-to-borrow’ stocks over six months of daily data. This study employs a comprehensive methodology to examine metrics such as the short interest ratio, borrowing costs, institutional ownership, price-to-book value ratio, and new stock borrowing patterns. Regression models are utilized to explore the relationships between these factors and borrowing costs. The findings reveal that ‘hard-to-borrow’ stocks are associated with higher short interest ratios, borrowing costs, price-to-book ratios, and turnover but exhibit lower institutional ownership compared to ‘easy-to-borrow’ stocks. Notably, institutional ownership negatively correlates with borrowing costs across both categories, while the short interest ratio positively correlates with borrowing costs only for ‘hard-to-borrow’ stocks. Contrary to expectations, ‘hard-to-borrow’ stocks do not underperform despite elevated borrowing expenses, suggesting that these costs do not deter short selling activities in the Japanese market. The findings of this study offer key implications for investors and regulators. For investors, understanding the factors influencing borrowing costs aids in optimizing short-selling strategies. For regulators, the results highlight the role of centralized lendable stock markets in enhancing pricing efficiency without hindering trading activities. Full article

Investigating the stability of and fluctuations in the energy market has long been of interest to researchers and financial market participants. This study aimed to analyze the Chinese energy market, focusing on its volatility and response to financial tensions. For this purpose, data from eight major financial companies, which were selected based on their market share in Shanghai’s and Shenzhen’s financial markets, were collected from January 2014 to December 2023. In this study, stock prices and trading volumes were used as the key variables to build bootstrap-based minimum spanning trees (BMSTs) using ratio mutual information (RMI). Then, using the sliding window procedure, the major network characteristics were derived to create an anomaly-detection tool using the multivariate exponentially weighted moving average (MEWMA), along with the Brent crude oil price index as a benchmark and a global oil price indicator. This framework’s stability was evaluated through stress testing with five scenarios designed for this purpose. The results demonstrate that during periods of high oil price volatility, such as during the turbulence in the stock market in 2015 and the COVID-19 pandemic in 2020, the network topologies became more centralized, which shows that the market’s instability increased. This framework successfully identifies anomalies and proves to be a valuable tool for market players and policymakers in evaluating companies that are active in the energy sector and predicting possible instabilities, which could be useful in monitoring financial markets and improving decision-making processes in the energy sector. In addition, the integration of other macroeconomic factors into this field could strengthen the identification of anomalies and be considered a field for possible research. Full article

It is well-believed that most trading activities tend to herd. Herding is an important topic in finance. It implies a violation of efficient markets and hence, suggests possibly predictable trading profits. However, it is hard to test such a hypothesis using aggregated data (as in the literature). In this paper, we obtain a proprietary data set that contains detailed trading information, and as a result, for the first time it allows us to validate this hypothesis. The data set contains all trades transacted in 2019 by all the brokers/dealers across all locations in Taiwan of all the equities (stocks, warrants, and ETFs). Given such data, in this paper, we use swarm intelligence to identify such herding behavior. In particular, we use two versions of swarm intelligence—Boids and PSO (particle swarm optimization)—to study the herding behavior. Our results indicate weak swarm among brokers/dealers. Full article

Coralligenous bioconstructions are a key Mediterranean ecosystem for their associated biodiversity and role in the blue carbon cycle. They are also sensitive to environmental alterations (e.g., climate change) and other anthropic impacts related to coastal anthropization (e.g., fishing activities). Marine-coastal zone protection, conservation programs and management strategies are essential to guarantee a good ecological status of the coralligenous habitat. In this context, environmental and ecosystem accounting are useful tools to measure natural capital stocks and ecosystem service flows associated with marine ecosystems, conveying their importance in scientific and policy contexts. Indeed, the importance of marine ecosystems is often overlooked due to the difficulty of expressing their value in common units, making it challenging for decision-makers to explore trade-offs between conservation and exploitation of marine ecosystems. In this study, a biophysical and trophodynamic environmental accounting model was used to assess the biophysical value of natural capital stocks of the coralligenous habitat in three Marine Protected Areas (MPAs) of the Campania Region (Southern Italy): Punta Campanella, Santa Maria di Castellabate, and Costa degli Infreschi e della Masseta. The natural capital value per unit area associated with the coralligenous habitat ranged from 2.44 × 10¹² to 4.72 × 10¹² sej m⁻² for Santa Maria di Castellabate and Punta Campanella, respectively. Despite the different intensive values of natural capital calculated for the MPAs, there were no significant differences both in the biomass values of the taxonomic groups and in the biomass-based Shannon diversity index. Additionally, the biophysical values were also converted into monetary units, with the aim of facilitating the understanding of the importance of natural stocks in socio-economic and political contexts. The economic equivalent of natural capital value refers to the total extent of the coralligenous habitat and ranged from about EUR 1 to 15 million for Costa degli Infreschi e della Masseta and Santa Maria di Castellabate, respectively. The results of this study could be useful for local managers and policy makers and may make them more likely to achieve biodiversity conservation and sustainable development goals in MPAs. This is the first study devoted to the assessment of natural capital value of coralligenous habitats. Future studies could complement the results of this study with biophysical and economic assessments of ecosystem service flows generated by coralligenous habitats, focusing on the role they play in human well-being. Full article

The performance of foreign investors relative to domestic investors has been a subject of mixed evidence. While foreign investors are often perceived to underperform due to an information disadvantage, they are also known for their aggressive trading and superior performance in initiated orders. We provide further clarity on this issue. Specifically, by analyzing over five million transactions on the Jakarta Stock Exchange, our findings reveal that foreign investors consistently outperform domestic investors in terms of both annualized returns and profit amounts. Further investigation attributes this outperformance to the higher sophistication of foreign investors, who demonstrate superior stock-picking abilities and effective growth investing strategies. Full article

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes. Full article

We revisit, and document new facts regarding, the financialization of U.S. energy markets in 2000–2010. We show that, after controlling for macroeconomic factors and physical energy market fundamentals, the strength of energy markets’ co-movements with the U.S. stock market is positively related to the energy paper market activity of hedge funds that trade both asset classes. This relation weakens when credit risk is elevated. We find, in contrast, no link with the aggregate positions of commodity index traders in energy futures markets. Our findings have implications for the ongoing debate regarding the financialization of commodities. Full article

Carbon emissions pose a significant challenge to sustainable development, particularly for China, which is the world’s largest emerging economy and is under pressure to achieve carbon neutrality and reduce emissions amid escalating human activities. The variation in economic development levels and carbon sequestration capacities among its provinces poses a significant hurdle. However, previous research has not adequately examined this dual discrepancy from the perspective of spatial heterogeneity, resulting in a lack of differentiated management of forest carbon sinks across diverse regions. Therefore, to mitigate this discrepancy, this study presents an assessment methodology that analyzes over 100 types of natural and plantation forests using forest age and biomass expansion factors. This study presents a model that can significantly support the efforts of both China and the whole world to achieve carbon neutrality through the improved management of forest carbon sinks. This approach facilitates the assessment of carbon offsets required to meet reduction targets, the development of a provincial framework for carbon intensity and sequestration, and the exploration of their potential for trading markets. Analysis is conducted using MATLAB. Key achievements of this study include the following: (1) The collection of a comprehensive carbon stock dataset for 50 natural and 57 plantation forest types in 31 provinces from 2009 to 2018, highlighting the significant role of new forests in carbon sequestration. (2) The development of a provincial carbon status scoring system that categorizes provinces as carbon-negative, carbon-balancing, or carbon-positive based on local forest sink data and carbon credit demand. (3) The formulation of the carbon intensity–carbon sink assessment (CISA) model, which suggests that provinces with middle- to upper-middle-level economies may have a prolonged need for carbon sink credits during their peak carbon phase. Furthermore, the results show that carbon trading may benefit Guangxi and Yunnan, but may also bring opportunities and risks to Hunan and Hubei. To address regional imbalances, this study advocates tailored policies: carbon-negative and carbon-balancing provinces should enhance carbon sink management, while carbon-positive provinces must focus on energy structure transformation to achieve sustainable development goals. Full article

Intensive economic and human activities present challenges to the carbon storage capacity of terrestrial ecosystems, particularly in arid regions that are sensitive to climate change and ecologically fragile. Therefore, accurately estimating and simulating future changes in carbon stocks on the northern slope economic belt of Tianshan Mountains (NSEBTM) holds great significance for maintaining ecosystem stability, achieving high-quality development of the economic belt, and realizing the goal of “carbon neutrality” by 2050. This study examines the spatiotemporal evolution characteristics of the NSEBTM carbon stocks in arid regions from 1990 to 2050, utilizing a combination of multi-source data and integrating the Patch-generating Land use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models. Additionally, an attribution analysis of carbon stock changes is conducted by leveraging land use data. The findings demonstrate that (1) the NSEBTM predominantly consists of underutilized land, accounting for more than 60% of the total land area in the NSEBTM. Unused land, grassland, and water bodies exhibit a declining trend over time, while other forms of land use demonstrate an increasing trend. (2) Grassland serves as the primary reservoir for carbon storage in the NSEBTM, with grassland degradation being the leading cause of carbon loss amounting to 102.35 t over the past three decades. (3) Under the ecological conservation scenario for 2050 compared to the natural development scenario, there was a net increase in carbon storage by 12.34 t; however, under the economic development scenario compared to the natural development scenario, there was a decrease in carbon storage by 25.88 t. By quantitatively evaluating the land use change in the NSEBTM and its impact on carbon storage in the past and projected for the next 30 years, this paper provides scientific references and precise data support for the territorial and spatial decision making of the NSEBTM, thereby facilitating the achievement of “carbon neutrality” goals. Full article

Land use serves as a connecting link between human activities and the natural ecology of the surface; under the multi-objective background of national policies and dual-carbon tasks, land use transformation is studied and simulated in multiple scenarios, and carbon stock changes are analyzed based on future land use to explore the path for a region to achieve multi-objective coordination. Drawing upon land use data from 2000 to 2020 in Lintao County, Gansu Province, we conducted an in-depth analysis of the dynamics governing land use transformation. Subsequently, employing the FLUS (Future Land Use Simulation) model, we simulated the projected land use for Lintao County in 2035 under various scenarios. Furthermore, we utilized the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model to assess the change in carbon stock within the study area under each scenario. These analyses aim to furnish a robust scientific foundation for future land use planning endeavors in Lintao County. The conclusions are as follows: (1) The land use transition in Lintao County from 2000 to 2020 showed the strongest motivation for construction land growth, with continued rapid growth in the scale of urban land and other construction land and relatively slow growth in the land for rural settlement areas, while cropland and water areas continued to decrease, forest land grew slowly, the magnitude of land use change exhibited a higher intensity in river townships compared with mountainous townships. (2) The simulation results of cropland protection scenario (CPS), ecological protection scenario (EPS), economic development scenario (EDS), and comprehensive development scenario (CDS) in 2035 are better. Among them, the CDS, which considers various types of higher-level strategic requirements and can compensate for the single-goal nature of the single-demand scenario, demonstrates a higher level of rationality in the land use pattern. (3) The total carbon stock in descending order is the EPS, CDS, EDS, and CPS. Among these, the CDS is at a higher level of total carbon stock, and the changes in carbon stock in each land use site are more balanced, which is an ideal carbon stock state and a scenario more in line with multi-objective coordination. Full article

This paper develops a dynamic model of tender offers in which there is trading on the target’s shares during the takeover, and bidders can freeze out target shareholders (compulsorily acquire remaining shares not tendered at the bid price), features that prevail on almost all takeovers. We show that trading allows for the entry of arbitrageurs with large blocks of shares who can hold out a freezeout—a threat that forces the bidder to offer a high preemptive bid. There is also a positive relationship between the takeover premium and arbitrageurs’ accumulation of shares before the takeover announcement, and the less liquid the target stock, the stronger this relationship is. Moreover, freezeouts eliminate the free-rider problem, but front-end loaded bids, such as two-tiered and partial offers, do not benefit bidders because arbitrageurs can undo any potential benefit and eliminate the coerciveness of these offers. Similarly, the takeover premium is also largely unrelated to the bidder’s ability to dilute the target’s shareholders after the acquisition, also due to potential arbitrage activity. Full article

We explore a multi-asset jump-diffusion pricing model, combining a systemic risk asset with several conditionally independent ordinary assets. Our approach allows for analyzing and modeling a portfolio that integrates high-activity security, such as an exchange trading fund (ETF) tracking a major market index (e.g., S&P500), along with several low-activity securities infrequently traded on financial markets. The model retains tractability even as the number of securities increases. The proposed framework allows for constructing models with common and asset-specific jumps with normally or exponentially distributed sizes. One of the main features of the model is the possibility of estimating parameters for each asset price process individually. We present the conditional maximum likelihood estimation (MLE) method for fitting asset price processes to empirical data. For the case with common jumps only, we derive a closed-form solution to the conditional MLE method for ordinary assets that works even if the data are incomplete and asynchronous. Alternatively, to find risk-neutral parameters, the least-square method calibrates the model to option values. The number of parameters grows linearly in the number of assets compared to the quadratic growth through the correlation matrix, which is typical for many other multi-asset models. We delve into the properties of the proposed model, its parameter estimation using the MLE method and least-squares technique, the evaluation of VaR and CVaR metrics, the identification of optimal portfolios, and the pricing of European-style basket options. We propose a Laplace-transform-based approach to computing Value at Risk (VaR) and conditional VaR (also known as the expected shortfall) of portfolio returns. Additionally, European-style basket options written on the extreme and average stock prices or returns can be evaluated semi-analytically. For numerical demonstration, we examine a combination of the SPDR S&P 500 ETF (as a systemic risk asset) with eight ordinary assets representing diverse industries. Using historical assets and options prices, we estimate the real-world and risk-neutral parameters of the model with common jumps, construct several optimal portfolios, and evaluate various basket options with the eight assets. The results affirm the robustness and efficiency of the estimation and evaluation methodologies. Computational results are compared with Monte Carlo estimates. Full article

It is of great significance to accurately assess the carbon sink capacity and trend of forest ecosystems on a regional scale, which is a key step to realizing sustainable forest management and carbon sink. Based on several remote sensing datasets, this study analyzes the dynamic characteristics of forest carbon stock in the Yangtze River Basin and its response to environmental factors using the Mann–Kendall nonparametric test, correlation analysis, and BP neural network during 2005–2020. The results show that forest carbon stock in the Yangtze River Basin shows a fluctuating upward trend, with an average annual growth rate of 0.91%. Forest carbon stock in western high-altitude areas and areas with high human activity in the east showed a downward trend, while the central plains showed a stable growth trend. In the vast plains of the Yangtze River Basin, a suitable drought degree (−0.5 < SPEI < 0.5) is helpful to the accumulation of forest carbon reserves. In the future, rich forest resources should be fully developed to promote synergy between environmental protection and economic development from the perspective of developing green carbon trading, such as the carbon-sink forestry projects of CCER. Full article