Investment Model Research Articles

Trend analysis of long-time series habitat quality in Beijing based on multiple models

This study selects Beijing from 1980 to 2020 as the research area, utilizing high temporal resolution land use data to analyze through the habitat quality module of the InVEST model. Unlike previous research, this study employs the Theil-Sen Median method and Mann-Kendall test to analyze the trend changes in habitat quality more accurately. This method has significant advantages in dealing with non-linear and non-normally distributed data over long time series, providing a more accurate and reliable analysis of habitat quality trends. Methodologically, the study first collects and organizes the land use type data of Beijing from 1980 to 2020, then uses the habitat quality module of the InVEST model to process and analyze the data of each year, assessing the impact of different land use types on habitat quality. Subsequently, the Theil-Sen Median method and Mann-Kendall test are used to analyze the time series trend of habitat quality, to identify and quantify the trend and significance of habitat quality changes. The results show that over the past 40 years, the area of construction land in Beijing has significantly expanded, leading to a compression of other types of land. The spatial distribution of habitat quality shows a clear difference between the two sides divided by a line connecting the northeast and southwest, with the west side being the area of good habitat quality and the east side being poorer. In the past 10 years, the overall habitat quality has improved, but most areas still show a decreasing trend, especially in the western and northern mountainous areas where habitat quality has significantly declined. Based on these findings, it is recommended that future urban planning and land management should pay more attention to the protection and improvement of habitat quality, especially the restoration work for areas with poor habitat quality.

Multi–Scenario Prediction of Land Cover Changes and Habitat Quality Based on the FLUS–InVEST Model in Beijing

As urbanization accelerates worldwide, understanding the impact of urban expansion on habitat quality has become increasingly critical in environmental science research. This study examines the impact of urban expansion on habitat quality in Beijing, forecasting land cover changes and ecological effects by 2030. Using CA–Markov and FLUS models, the research analyzes habitat quality from 2000 to 2030 through the InVEST model, revealing a significant urban land increase of 1316.47 km2 and a consequent habitat quality decline. Predictions for 2030 indicate varying habitat quality outcomes across three scenarios: ecological priority (0.375), natural growth (0.373), and urban development (0.359). We observed that the natural growth scenario forecasts a further decline in habitat quality, primarily due to increased low–value habitat regions. Conversely, the ecological priority scenario projects a notable improvement in habitat quality. To mitigate habitat degradation in Beijing and enhance regional habitat quality and ecological conditions, it is recommended to control urban land cover expansion, adopt effective ecological conservation policies, and systematically carry out national spatial restructuring and ecological restoration. This research provides vital decision–making support for urban planning and ecological conservation, emphasizing the need for comprehensive land cover and ecological strategies in urban development. Additionally, our findings and methodologies are applicable to other rapidly urbanizing cities worldwide. This demonstrates the broader applicability and relevance of our research, providing a framework for sustainable urban planning in diverse global contexts.

Social Needs Screening in Academic Health Systems: A Landscape Assessment.

Screening for social needs has gained traction as an approach to addressing social determinants of health, but it faces challenges regarding standardization, resource allocation, and follow-up care. The year-long study, conducted by the Association of American Medical Colleges, integrated data from conferences, surveys, and key informant interviews to examine the integration of social needs screening into health care services within Academic Health Systems (AHS). The authors' analysis unveiled eight key themes, showcasing AHS's active involvement in targeted social needs screening alongside persistent resource allocation obstacles. AHS are dedicated to efficiently identifying high-risk populations, fostering partnerships with community-based organizations, and embracing technology for closed-loop referrals. However, concerns endure about the utilization of reimbursement codes for social needs and regulatory compliance. AHS confront staffing issues, resource allocation intricacies, and the imperative for seamless integration across clinical and nonclinical departments. Notably, opportunities arise in standardized training, alignment of AHS priorities, exploration of social investment models, and engagement with state-level health information exchanges. Aligning clinical care, research pursuits, and community engagement endeavors holds promise for AHS in effectively addressing social needs.

Habitat quality and degradation change analysis for the Sundarbans mangrove forest using invest habitat quality model and machine learning

Habitat quality and degradation change analysis for the Sundarbans mangrove forest using invest habitat quality model and machine learning

Analyzing the impacts of watershed management interventions and scenarios on soil erosion reduction using InVEST model in Yezat watershed, North West Ethiopia

ABSTRACT Land degradation is a serious problem for Ethiopia's productive capacity for land resources. To reverse these environmental dynamics, slow down soil degradation, and raise smallholder farmers’ agricultural yields, watershed management interventions have been implemented in Ethiopia since the 1980s. Thus, this study was aimed at assessing the effects of watershed management interventions on soil erosion reduction by using the InVEST model in the Yezat watershed, north-west Ethiopia. The modified ecosystem services valuation model (InVEST) was employed to evaluate soil loss reduction in response to watershed management interventions. Different watershed management intervention scenarios (baseline scenarios, afforestation scenarios, soil/stone bund scenarios, and integrated scenarios) were also applied to evaluate the effectiveness of watershed management interventions on soil loss reduction. The result of the study indicated that a high amount of mean annual soil loss (111 tons ha− 1 year−1) was observed in 2000 in the study area due to the expansion of cultivated land and built-up area at the expense of forest, shrubland, and grassland. However, the mean annual soil loss decreased from 111 tons per year in 2000 to 79 tons per year in 2021 in the study area due to different watershed management interventions. Additionally, at the baseline scenario, the estimated mean annual soil loss of the watershed was 111 tons ha-1 yr-1. Conversely, it was reduced to 49.3 ton ha-1 yr-1 in the reforestation scenario, 16.49 ton ha-1 yr-1 in the soil bund scenario, and 8.26 ton ha-1 yr-1 in the integrated scenario. The watershed covered by the very severe soil erosion severity class was reduced in all watershed management intervention scenarios. Among others, the highest soil erosion reduction was observed in the integrated (bio-physical) scenarios of watershed management interventions. Therefore, watershed management interventions and scenarios were the best mechanisms for soil loss reduction.

Cashflow-driven investment beyond expectations

ABSTRACT This paper presents a computationally tractable optimization model for cashflow-driven investment where the aim is to find asset portfolios whose future payouts cover given liability payments as well as possible. While current industry solutions are largely based on expected future cash flows, we use a stochastic optimization model that seeks portfolios that give the best possible match across time as well as scenarios. Cashflow matching across scenarios is controlled by risk aversion while the timing is driven by illiquidity of money markets. When illiquidity increases, the hedging strategy quickly shifts towards portfolios suggested by deterministic models, but significant uncertainty remains. The model can incorporate hundreds of quoted instruments in the construction of optimal hedging strategies. The hedging strategies are able to employ any statistical connections between the liabilities and publicly quoted assets. The model is solved with simple Monte Carlo approximations and off-the-shelf convex optimization software. Besides optimal hedging strategies, we find the least cost of hedging which provides a market-consistent valuation based on the current quotes and the liquidity factors as well as the views and risk preferences of the investor/regulator. The approach is illustrated by pricing and hedging defined benefit pension liabilities which depend on uncertain longevity developments and the consumer price index. The hedging strategies are constructed from 128 publicly quoted instruments including index-linked bonds and equities. We find that the optimized hedging strategies achieve lower risk at a lower cost than the strategies obtained by matching expected cashflows. The hedging-based liability valuations are robust with respect to model parameters and the additional risk reduction achieved by optimization does not add much to the overall hedging cost.

Tax Policy and Abnormal Investment Behavior

Abstract This paper studies tax-minimizing investment, whereby firms tilt capital purchases toward year-end to reduce taxes. We use this pattern to characterize how taxes affect investment behavior. We exploit variation in firm tax positions from administrative data to confirm that tax minimization causes spikes. Spikes increase when firms face financial constraints or higher option values of waiting. Cumulative investment does not completely reverse after spikes. We develop an investment model with tax asymmetries to rationalize these patterns. Both depreciation motives (later investments face lower effective tax rates) and option value motives (tax asymmetry implies time-varying opportunities to minimize taxes) are necessary to fit the data. (JEL G31, G38, H25)

Impacts of the land use transition on ecosystem services in the Dongting Lake area

Urbanization-induced land use transitions (LUTs) result in a decline in ecosystem services, which has implications for regional ecological security. In order to explore the relationship between ecosystem services and land use transition, this paper utilizes the InVEST model, a geographically weighted regression (GWR) model, to examine the impact of land use transition on ecosystem services in the Dongting Lake area (DLA). The results showed that 1) with the change in urbanization development, the average values of land use transition intensity (LUI) in 2000, 2010, and 2020 are 237.99, 235.82, and 238.92, respectively. Land use dynamics (LUD) show a tendency to increase and then decrease, with average values of 5.58 and 5.62 for the periods 2000–2010 and 2010–2020, respectively, and the transformation of land use shows obvious spatio-temporal heterogeneity. 2) Habitat quality and carbon sequestration showed a downward trend. In contrast, food supply followed an upward trend; soil conservation (SC) and water yield (WY) services initially increased and decreased later. The overall spatial changes in habitat quality and carbon sequestration appear to be insignificant. Food supply shows significant differences in the plains compared to other areas, while soil conservation and water yield service show significant changes in places other than the DLA. 3) From 2000 to 2020, land use transition dynamics, population density, GDP density, night lighting, and transition intensity had mainly negative effects on ecosystem services. Only the Normalized Vegetation Index (NDVI) showed a positive effect on ecosystem services. The results of the research will provide valuable references for the development and implementation of spatial ecological restoration planning and land use policies in the national territory.

Ecological Zoning Management Strategies in China: A Perspective of Ecosystem Services Supply and Demand

Ecological zoning management aims to ensure ecological functions and improve environmental quality, serving as an essential means to optimize the development and protection of territorial space. However, comprehensive research on ecological zoning management that combines human needs with natural resources is still relatively scarce. In this study, we selected water yield (WY), food provision (FP), and carbon sequestration (CS) as the critical ecosystem services (ES) in China. An InVEST model, ecosystem services supply–demand index (ESI), random forest (RF), and geographically and temporally weighted regression (GTWR) were used to analyze the spatiotemporal characteristics and influencing factors of ES supply and demand, and the four-quadrant model was used to analyze the spatial matching patterns. The results showed that: (1) from 2005 to 2020, the supply and demand of WY, FP, and CS increased. Among them, WY, FP, and CS supply increased by 16.06%, 34%, and 22.53%, respectively, while demand increased by 5.63%, 12.4%, and 83.02%, respectively; (2) the supply of WY and CS follow a “high in the southeast and low in the northwest” pattern, while all of the demands exhibit a “high in the east and low in the west” pattern; and (3) the average ecosystem service supply–demand index (ESI) values for WY, FP, and CS in China are 0.45, 0.12, and −0.24, respectively, showing an overall upward trend. The study identified three dominant functional zones for WY, FP, and CS, and four classification management zones, including protection zones, conservation zones, improvement zones, and reconstruction zones. These research findings provide a scientific basis for future territorial space planning in China and the application of ecosystem service supply and demand in sustainable development.

Land-use and habitat quality prediction in the Fen River Basin based on PLUS and InVEST models

Land-use and habitat quality prediction in the Fen River Basin based on PLUS and InVEST models

The impact of land use change on carbon storage and multi-scenario prediction in Hainan Island using InVEST and CA-Markov models

The impact of land use change on carbon storage and multi-scenario prediction in Hainan Island using InVEST and CA-Markov models

CAPM: A Time-Tested Investment Model for Modern Portfolios

CAPM: A Time-Tested Investment Model for Modern Portfolios

Manual Label and Machine Learning in Clustering and Predicting Student Performance

This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a four-quadrant clustering technique. The AIM model delineates student investment into four quadrants based on their time and energy commitment to academic pursuits, distinguishing between result-oriented and process-oriented investments. The findings reveal that the four-quadrant method surpasses machine learning clustering in predictive accuracy, highlighting the robustness of manual feature engineering. The study's significance lies in its potential to guide educators in designing targeted interventions and personalized learning strategies, emphasizing the importance of process-oriented assessment in education. Future research is recommended to expand the sample size and explore the integration of deep learning models for validation.

Optimizing the Utilization of Geothermal Water in Spa Tourism: An Investment Strategy with a Focus on Sustainability

The main subject of the paper is the effective utilization of geothermal water in a resort with the potential for spa tourism, linked to the proposal of an investment strategy. The primary objective is to suggest new development options for the destination, with a particular focus on the sustainability of the geothermal source. Two models for economic investment in area reconstruction are presented before the final proposal. The first model assesses the investment's effectiveness based on the Net Present Value (NPV), while the second model examines hydrological risks (such as a decrease in source capacity, water volume, and heat sources). This involves monitoring changes in water volume and heat sources over several years. The article's outcome aims to highlight, through a case study, the considerations for spa tourism resorts and potential risks associated with investment decisions, particularly in the context of the exhaustibility, volume, and quality of the geothermal water source.

Contextual Approach for Fundamental Law of Active Management

Active Portfolio Management is a simple yet powerful tool for portfolio selection which has became a popular strategy among individual investors. Theoretical background of active management has been established by Fundamental Law of Active Management. The main postulate of this law suggests that in order to achieve additional return alpha, investors should apply a model with higher forecasting power (IC) and should apply it to a maximum number of assets (breadth). Individual investors are attracted by this straightforward and uncomplicated logic and that is the reason why active management is so popular strategy among them. However, the original and later developed variants of the law lead to the same conclusion – for better results the forecasting model must be applied to as many as possible assets. However, this contradicts with the concentration paradox which investors meet in their investment decisions – applying the same forecasting model to maximum amount of assets results in involving unknown assets with diverse characteristics in analysis. With contextual approach we change this perspective. By applying a contextual approach to active management investors can concentrate the forecasting models to a specific group of assets with similar characteristics. We justify the existence of concentration paradox and by using different fundamental contexts to differentiate stocks in different groups, we test fundamental cross-sectional regression models as forecasting models for active investment. With data from Taiwan Stock market we prove that both ex-ante IC and realized return achieved with contextual models outperform the general portfolios which follow the standard postulate of fundamental law.

Assessment of nitrogen and phosphorus pollution based on multi-source data and the InVEST model in the Henan section of Yellow River Basin, China

Assessment of nitrogen and phosphorus pollution based on multi-source data and the InVEST model in the Henan section of Yellow River Basin, China

Assessing land-use changes and carbon storage: a case study of the Jialing River Basin, China

Land-use change is the main driver of carbon storage change in terrestrial ecosystems. Currently, domestic and international studies mainly focus on the impact of carbon storage changes on climate, while studies on the impact of land-use changes on carbon storage in complex terrestrial ecosystems are few. The Jialing River Basin (JRB), with a total area of ~ 160,000 km2, diverse topography, and elevation differences exceeding 5 km, is an ideal case for understanding the complex interactions between land-use change and carbon storage dynamics. Taking the JRB as our study area, we analyzed land-use changes from 2000 to 2020. Subsequently, we simulated land-use patterns for business-as-usual (BAU), cropland protection (CP), and ecological priority (EP) scenarios in 2035 using the PLUS model. Additionally, we assessed carbon storage using the InVEST model. This approach helps us to accurately understand the carbon change processes in regional complex terrestrial ecosystems and to formulate scientifically informed land-use policies. The results revealed the following: (1) Cropland was the most dominant land-use type (LUT) in the region, and it was the only LUT experiencing net reduction, with 92.22% of newly designated construction land originating from cropland. (2) In the JRB, total carbon storage steadily decreased after 2005, with significant spatial heterogeneity. This pattern was marked by higher carbon storage levels in the north and lower levels in the south, with a distinct demarcation line. The conversion of cropland to construction land is the main factor driving the reduction in carbon storage. (3) Compared with the BAU and EP scenarios, the CP scenario demonstrated a smaller reduction in cropland area, a smaller addition to construction land area, and a lower depletion in the JRB total carbon storage from 2020 to 2035. This study demonstrates the effectiveness of the PLUS and InVEST models in analyzing complex ecosystems and offers data support for quantitatively assessing regional ecosystem services. Strict adherence to the cropland replenishment task mandated by the Chinese government is crucial to increase cropland areas in the JRB and consequently enhance the carbon sequestration capacity of its ecosystem. Such efforts are vital for ensuring the food and ecological security of the JRB, particularly in the pursuit of the “dual-carbon” objective.

Ecological Function Zoning Framework for Small Watershed Ecosystem Services Based on Multivariate Analysis from a Scale Perspective

A thorough comprehension of distribution features of ecosystem services (ESs) and the influencing mechanisms can offer scientific guidance for the ecosystem management of small watersheds. We analyzed the spatial distribution patterns, interrelationships, and service hotspots of ESs across pixel and administrative scales using a multi-level statistical analysis approach in the Ashi River Basin. Through the quantitative calculation of the InVEST model, the findings revealed a decrease in soil retention, water yield, total nitrogen and phosphorus export, carbon storage, and habitat quality, while an increase in food production was observed during the period from 1995 to 2015. Through the geographical detector, the spatial heterogeneity of most individual ESs was influenced by land use patterns. Through redundancy analysis, terrain factors had the highest contribution rate to the integral ESs. Socio-economic factors and climate factors also drove the ESs’ amount and spatial distribution. At the pixel scale and administrative scale, there were distinctions in the correlations between all ESs, reflected by the fact that the relationships between ESs at the administrative scale were generally weaker and no more significant than at the pixel scale. Based on the number and distribution of hotspots in ESs and the clustering results of influencing factors, the framework of ecosystem zoning was constructed. This basin was divided into three ecological zones, and the management policies were formulated according to the ecological environment. This study clarifies the internal and mutual connection between ESs and influencing factors across two scales, thus contributing to the advancement of management strategies for ecological conservation and socio-economic development within the context of small watersheds.

Water yield forecast in Shandong Province based on InVEST model

A better understanding of the effects of land use change and climate change on water yield is highly important for water resource planning and sustainable management. Land use changes in Shandong Province were analyzed over recent years. The PLUS Model was applied to forecast the land use layout for 2032, with adjustments made based on actual conditions and utilizing various CMIP6 scenarios for precipitation and actual evapotranspiration. The 2032 Water Yield was quantified using the InVEST Model at both grid and administrative scales, and its temporal-spatial characteristics and spatial correlation were analyzed. The findings revealed several key points: first, a decrease of 16.92% in arable land area, along with diminishing trends in grassland and unused land, and an increase of 33.92% in built-up land area, accompanied by growth in forest and water area. Second, the water yield decreases from southeast to northwest in Shandong Province, with significant inter-annual variations. Third, future predictions suggest that the annual water yield generally increases during flat water years, with offshore areas exhibiting greater water yield depth compared to inland areas. Rizhao is projected to have the highest water yield depth, while Binzhou and Dongying are expected to have the lowest depths, with a spatially positive correlation between them.

An Investigation of Predictability of Traders' Profitability Using Deep Learning

Trading in the financial market is a daunting task in spite of the attracting increase of the daily turnover of the Forex financial market from 6.5 trillion USD in 2022 to approximately 7.5 trillion USD in 2024. About 80% of retail investors lose money. However, to minimize the risk of losses, investors explore the possibility of profitable trading by resorting to social trading. In social trading of the financial market, the performance statistics and performance charts of traders with diverse trading strategies, methods and characteristics are showcased by the financial market brokers to enable investors decide on which trader’s signal to adopt or copy for profitable investment. However, investors are often faced with the problem of choosing a set of profitable traders among thousands with different past hypothetical results, in spite of the provision of traders’ performance ranking, made available by the brokers. The investors have serious concern on the stability, sustainability and predictability of a trader’s future performance which will eventually determine the investors profit or loss if the trader’s signals are copied or followed. This paper applies three deep learning models: the multilayer perceptron, recurrent neural network and long short term memory for the prediction of traders’ profitability to provide the best model for investment in the financial market, and reports the experience. The results of the study show that recurrent neural network performs best, followed by long short term memory while multilayer perceptron yields the least results for the prediction. These three models yield a mean squared error of 0.5836, 0.7075 and 0.9285 respectively in a test scenario for a trader.