Investment Allocation Research Articles

US land sector mitigation investments and emissions implications

The land sector is anticipated to play an important role in achieving U.S. GHG emissions targets by reducing emissions and increasing sequestration from the atmosphere. This study assesses how much different levels of investment could stimulate land-based mitigation activities in the U.S. By applying a dynamic economic model of the land use sectors, with representation of 26 forestry and agricultural mitigation strategies across 11 U.S. regions, the study shows that annual investments of $2.4 billion could deliver abatement of around 80 MtCO2e annually. Under an optimal allocation of investments, the forestry sector and the Corn Belt are projected to receive the largest share of funds. Restricting land-based activities eligible for funds significantly reduces overall potential mitigation. For instance, if $24 billion investments are allocated only to agricultural activities, mitigation declines by 48% to 54 MtCO2e/yr over the next ten years. Finally, the level of abatement from each policy depends on the timing of implementation as the lowest cost mitigation actions are generally taken by the policy implemented first.

Foreign business exposure, policy uncertainty, and investment allocation decisions of Chinese multinational corporations

Foreign business exposure, policy uncertainty, and investment allocation decisions of Chinese multinational corporations

Mission-Guided Resilience Investment Allocation for Buildings on DoD Installations

Mission-Guided Resilience Investment Allocation for Buildings on DoD Installations

Building cybersecurity resilience: integrating defense and recovery investment strategies in an expected resilience framework

PurposeTo remain competitive in an unpredictable environment where the complexity and frequency of cybercrime are rapidly increasing, a cyber resiliency strategy is vital for business continuity. However, one of the barriers to improving cyber resilience is that security defense and accident recovery do not combine efficaciously, as embodied by emphasizing cyber security defense strategies, leaving firms ill-prepared to respond to attacks. The present study thus develops an expected resilience framework to assess cyber resilience, analyze cyber security defense and recovery investment strategies and balance security investment allocation strategies.Design/methodology/approachBased on the expected utility theory, this paper presents an expected resilience framework, including an expected investment resilience model and an expected profit resilience model that directly addresses the optimal joint investment decisions between defense and recovery. The effects of linear and nonlinear recovery functions, risk interdependence and cyber insurance on defense and recovery investment are also analyzed.FindingsAccording to the findings, increasing the defense investment coefficient reduces defense and recovery investment while increasing the expected resilience. The nonlinear recovery function requires a smaller defense investment and overall security investment than the linear one, reflecting the former’s advantages in lowering cybersecurity costs. Moreover, risk interdependence has positive externalities for boosting defense and recovery investment, meaning that the expected profit resilience model can reduce free-riding behavior in security investments. Insurance creates moral hazard for firms by lowering defensive investment, yet after purchasing insurance, expanded coverage and cost-effectiveness incentivize firms to increase defense and recovery spending, respectively.Originality/valueThe paper is innovative in its methodology as it offers an expected cyber resilience framework for integrating defense and recovery investment and their effects on security investment allocation, which is crucial for building cybersecurity resilience but receives little attention in cybersecurity economics. It also provides theoretical advances for cyber resilience assessment and optimum investment allocation in other fields, such as cyber-physical systems, power and water infrastructure – moving from a resilience triangle metric to an expected utility theory-based method.

Digging deeper: Unpacking the subnational political drivers of Chinese extractive investment in Latin America

There is a long scholarly tradition that argues that political variables figure prominently in international corporations’ decisions to invest abroad. Most of this literature has focused on the national political determinants of investment allocation. Yet, in some sectors, the bulk of this investment is local. Chief among these sectors are extractive industries where assets are endowed to different local jurisdictions. How do political factors shape extractive foreign direct investment (EFDI) in local jurisdictions? We focus on Latin America, a region of the world that is rich in natural resources, and that has recently attracted a sizable amount of Chinese investment. Drawing on a novel dataset on subnational Chinese EFDI in Argentina, Brazil, and Mexico (2002–2018), and using a logistic model with time series cross-national data we find that multilevel partisan alignment is a strong determinant of Chinese EFDI. In highly decentralized countries, Chinese EFDI is primarily targeted to subnational districts ruled by governors who are politically aligned with presidents. We illustrate the logics of this allocation with two case studies. Our findings make a valuable contribution to three growing fields of study: the subnational determinants of foreign investments, multilevel governance, and the political factors driving Chinese investments in developing countries.

Portfolio Optimization Using Machine Learning Method and Monte Carlo Simulation

Investment portfolio optimization is a crucial aspect of quantitative finance, aiming to maximize returns and minimize risks. This study focuses on optimizing investment allocation among five selected stocks (BAIC Blue Valley, BYD, Chang’ an Automobile, Kweichow Moutai, Sunwoda) over 100 days using Monte Carlo simulation and machine learning methods. This study uses the sliding window approach with the linear regression model to predict future returns and calculate the variance-covariance matrix to determine the optimal portfolio weights, leading to two key strategies: maximizing the Sharpe ratio and minimizing the risk. The results reveal that the Max Sharpe Ratio portfolio achieved a cumulative return of 0.0909, significantly outperforming the CSI 300 index’s return of -0.0348. Additionally, the Min Risk portfolio exceeded the market index with a cumulative return of 0.0062. These findings demonstrate that both the Max Sharpe Ratio and Min Risk strategies are effective in achieving a balance between risk and return and surpassing the market, offering valuable insights for investors seeking optimized portfolio allocations.

Can green bonds hedge against geopolitical risk? A cross-market connectedness analysis with portfolio implications

This study investigates whether green bonds (GBs) can hedge against geopolitical risk (GPR). This study extends the booming literature on GPR and GBs, develops a modified connectedness network model to measure the connectedness between GPR and GBs, confirms the hedging property of GBs against GPR, and becomes the first to discuss alternative hedging properties of GBs against GPR. We find evidence of market-, time-, and quantile-varying linkage between GPR and GB markets based on the time-varying Granger causality test and quantile extended joint spillover index model. We confirm via a regression model that only the GB markets in China and Japan can hedge against GPR. At the same time, GB in China remains a weak hedging and safety-haven asset simultaneously. The results remain robust for alternative proxy variables, data frequency, and model specification. Finally, the MVP approach provides superior performance while maintaining weak hedging and safety-haven properties against GPR. This study has considerable portfolio-related implications: (1) it offers an efficient hedge (i.e., GB) against GPR, (2) the heterogeneous performance of regional GB markets reminds investors to be cautious when selecting GBs assets, and (3) it encourages reasonable investment allocations on GBs to achieve a balance between profit and risk.

The Myth of Deglobalization: Multinational Corporations in an Era of Growing Geopolitical Rivalries

Globalization is past its peak, we are told. The rise of populist anti-globalization movements and the return of geopolitical rivalries among great powers in the 2010s has put an end to free-wheeling corporate global capitalism. Or has it? This article summons available data on cross-border corporate investments at the level of countries (balance of payments), firms (subsidiaries and affiliates), and corporate managers (industry surveys). It pays special attention to the period between 2015 and 2021, which spans the election of President Trump and the outbreak of the Covid-19 pandemic that have unsettled global politics. We analyze global patterns in foreign direct investment positions and in particular the evolution of investments by US corporations in China, arguably a “most likely case” for deglobalization. Our analyses find no evidence that economic cross-border integration is in decline. The global allocation of corporate investments across the world’s major economic regions has remained stable. US corporations have not notably reduced their global activities. If anything, their aggregate investment position in China has increased during the Trump administration’s trade war. Overall, the results cast empirical doubts on prominent narratives about the state of the global economy. Geoeconomic transformations in world economic infrastructures may well be underway, but they are better understood as new and adapted forms of internationalization rather than the end of globalization.

LOCAL FINANCES IN WESTERN BALKAN COUNTRIES: FOCUS ON OWN -SOURCE REVENUES AND CAPITAL INVESTMENTS

This study examines the fiscal dynamics of local governments’ finances in the Western Balkan countries, focusing on their own source revenues and capital investments, made from the municipalities. While spending for capital investment has increased in nominal terms, the share of municipal spending on capital investments has been downwardly unstable over the past 16 years. In addition to central government transfers, several significant factors contribute to the determination of municipal own-source revenues, including central and local public investment, current expenditures, and municipal development index. The study reveals that capital investment depends on its own source revenues or capital transfers from the central government, where local capital expenditure emerges as a robust determinant, exerting a stronger influence on municipal fiscal autonomy compared to central government investments. This finding underscores the importance of local capital expenditure in enhancing fiscal independence at the local level. This research emphasizes the need for a comprehensive understanding of investment allocation, development indicators, and promoting local revenue mobilization. The findings suggest that enhancing local fiscal autonomy requires a shift in policy focus towards stimulating local capital expenditure and prioritizing human development. By identifying the factors that influence municipal own-source revenues, this study provides valuable insights for local management in strengthening the financial capacities of local governments in the Western Balkan countries.

Mathematical Methods in Determining Spatial Development Priorities for The Industrial Sector at The Level of The Iraqi Governorates: A Study of Quantitative Measures to Determine Investments in Industrial Urbanization

The levels of development exhibit significant disparities across countries and even among different parts of the same governorates. The major focus of many planners and decision makers was to investigate and analyze the regional variation in development levels. The study aims to examine the regional disparities in investment within the industrial sector throughout the governorates of Iraq. This analysis is not just based on quantitative indicators, but also takes into account the specific investment requirements of each governorate. To overcome this issue, we do the following actions: A mathematical model is constructed to elucidate the regional differences in investment amongst governorates. This model use factor analysis to quantify the association between various variables studied. Utilize the Ridge Regression Model to assess the influence of the factors on the geographical distribution of investment. The study aimed to determine the allocation of investment in the industry sector among the Iraqi governorates. The priorities for distribution were as follows: ''Al-Muthanna'', ''Missan'', ''Wasit'', ''Salahuddinni'', ''Thi-Qar'', ''Diala'', ''Kerbala'', ''Kirkuk'', ''Al-Qadisiya'', ''Al-Anbar'', ''Al-Najaf'', ''Babil'', ''Nineueh'', ''Basrah'', and Baghdad. This allocation aimed to achieve a balanced regional development. The value of the challenge coefficient (R^2= 0.84 and the correlation coefficient) (R= 0.91), which indicates the strength of the effect of the economic variables on the investment sector. This value is the spatial distribution of industrial investments among the governorates, and this is confirmed by the decrease in the value of (MSE) as it reached 3319.41, which confirms the increase in the efficiency of the model at (k= 0.016).

Modified fragility functions for offshore wind turbines considering soil-structure interaction subjected to wind, wave, and seismic loads

Investment allocation for offshore wind turbines (OWT) as an important class of structures is typically carried out through supporting decision-making approaches utilizing some fragility functions. This study attempts to deliver fragility functions for OWTs on monopile foundations accounting for soil-structure interaction (SSI) effects. Simultaneous wind, wave, and earthquake loads were considered probabilistically by adjusting their occurrence hazard levels for predefined damage states in diverse performance levels. The designated damage states in this study are defined based on collapse probability and some targeted performance levels which could be very straightforward to distinguish. The damage state detection is based on rotation in the connection section of the tower’s transition part to the foundation, which perceptibly reveals the effects of SSI on fragility functions. The expected results comprise modified fragility functions accounting for SSI effects contributing to less median spectral acceleration, more evidently rotational demands, further dispersions, and a subsequent dominant increase in the probability of exceeding performance limit states. Considering operational performance level, the most applied design performance level for turbines as an important class of structures, not considering the SSI effects could noticeably underestimate the demands and lead to high-risk decisions.

Is return on investment the appropriate tool for healthcare quality improvement governance?

PurposeIn this article, we outline our views on the appropriateness and utility of Return on Investment (ROI) for the evaluation of the value of healthcare quality improvement (QI) programmes.Design/methodology/approachOur recent research explored the ROI concept and became the genesis of our viewpoint. We reflect on our findings from an extensive research project on the concept of ROI, involving a multidisciplinary global systematic literature review, a qualitative and Delphi study with mental healthcare leaders from the United Kingdom National Health Service. Research participants included board members, clinical directors and QI leaders. Our findings led to our conclusions and interpretation of ROI against the broad QI governance. We discuss our views against the predominant governance frameworks and wider literature.FindingsROI is in-line with top-down control governance frameworks based in politics and economics. However, there is evidence that to be of better utility, a tool for the assessment of the value of QI benefits must include comprehensive benefits that reflect broad monetary and non-monetary benefits. This is in-line with bottom-up and collaborative governance approaches. ROI has several challenges that may limit it as a QI governance tool. This is supported by wider literature on ROI, QI as well as modern governance theories and models. As such, we question whether ROI is the appropriate tool for QI governance. A more pragmatic governance framework that accommodates various healthcare objectives is advised.Practical implicationsThis article highlights some of the challenges in adopting ROI as a QI governance tool. We signal a need for the exploration of a suitable QI governance approach. Particularly, are healthcare leaders to be perceived as “agents”, “stewards” or both. The evidence from our research and wider literature indicates that both are crucial. Better QI governance through an appropriate value assessment tool could improve clarity on QI value, and thus investment allocation decision-making. Constructive discussion about the utility and appropriateness of ROI in the evaluation of healthcare QI programmes may help safeguard investment in effective and efficient health systems.Originality/valueThe article raises awareness of QI governance and encourages discussions about the challenges of using ROI as a tool for healthcare QI governance.

Dynamic (Mis)allocation of Investments in Solar Energy

Dynamic (Mis)allocation of Investments in Solar Energy

Evaluating Regional Potentials of Agricultural E-Commerce Development Using a Novel MEREC Heronian-CoCoSo Approach

In terms of both economy and sustainability, rural areas can greatly benefit from adopting E-commerce. The Chinese government is currently devoting significant efforts to developing agricultural E-commerce. However, one of the most significant problems is the lack of effective tools for evaluating regional potentials in this regard, possibly leading to inappropriate policymaking, investment allocation, and regional planning. To address this issue, this study proposes a novel and effective method for evaluating regional potentials for agricultural E-commerce development, integrating the method based on the removal effects of criteria (MEREC), Heronian mean operator, and combined compromise solution (CoCoSo) method. The method’s effectiveness is then tested and confirmed in the Chinese city of Yibin through an evaluation of its ten regions. The results suggest that such a method is robust, objective, and able to consider indicator interactions effectively. By applying this method, regional agricultural E-commerce development potentials can be thoroughly evaluated and ranked. This study contributes to the literature by providing new analytical techniques for agricultural studies, as well as by supporting political and investment decision-making for governments and E-commerce practitioners in the agriculture sector.

Does environmental regulation promote enterprise environmental protection investments? A quasi-natural experiment from river chief system in the Yangtze river delta of China

The investment decisions of enterprises are affected by environmental regulations designed to protect the environment, so environmental regulations may change companies' investment behavior in environmental protection. This study focuses on the River Chief System (RCS)1, an innovative environmental regulation related to river governance which officially launched in China in 2014. Based on data collected from heavy-polluting companies in the Yangtze River Delta, we use the difference-in-differences model (DID Model)2 and focus on RCS's impacts on micro-environmental protection investments. Our findings reveal that the RCS is conducive to expanding the scale of enterprises' environmental protection investments. Industrial structural upgrades appear to have a masking effect wherein the one-sided pursuit of industrial structural upgrades may slow economic growth and cause enterprises to reduce the scale of environmental investments. We recommend that the allocation of environmental investment should be based on the characteristics of local markets and public participation, and maintain a balance between secondary and tertiary industries, government and business incentives.

The Impact of the Cryptocurrency Market on Islamic vs. Conventional Stock Returns: Evidence from Gulf Cooperation Council Countries

The rapid rise and widespread global adoption of cryptocurrencies in recent years has fundamentally transformed the international financial landscape, with digital assets increasingly being recognized for their potential to influence the stability and performance of traditional capital markets. Against this backdrop, this study aims to empirically investigate the impact of cryptocurrency returns on Islamic vs. conventional stock returns in Gulf Cooperation Council (GCC) countries. The salient distinctions between Islamic and conventional stock markets include fundamental differences in principles, investment allocations, and risk profiles, underscoring the importance of examining the impact of cryptocurrency returns on these distinct equity segments. Daily data were collected from stock indices in five GCC countries over the period 2016–2019, including two sub-periods: before and after the 2017 crypto crash. Pooled OLS, fixed effects, random effects, and generalized linear models (GLMs) were used to analyze the data collected during the study. With the GCC increasingly focusing on cryptocurrency markets, there is growing concern about these markets’ potential impact on regional stocks. This study addresses the important questions of whether the impacts of the cryptocurrency market on Islamic vs. conventional stock markets differ throughout the GCC region and how these impacts have evolved since the crypto crash period. The findings reveal that cryptocurrency returns had a negative impact on both GCC Islamic and conventional stock market returns for the full sample period (2016–2019), and the negative effect was far more pronounced for conventional stocks. For the two sub-periods before and after the crash, only the cryptocurrency market and conventional GCC stocks remained negatively correlated, while the cryptocurrency market and the GCC Islamic stock markets became uncorrelated. Thus, for the calmer sub-periods before and after the crypto crash, the rise in cryptocurrency returns may have enticed GCC investors away from conventional stocks, perhaps resulting in a decline in their investment in these stocks. Meanwhile, those who invest in Islamic stocks may not be exposed to this temptation.

Investment allocation in an adjustment-cost production technology framework for two-stage network structures

Effective resource allocation can assist organizations in utilizing infinite resources, thereby improving efficiency and productivity. For an organization, investment is an essential production resource, and how to appropriately allocate investment is one of the most important decisions they need to make. This study aims to rationally allocate investment across each stage of two-stage network structure under two scenarios. We first propose general investment allocation model with adjustment costs to efficiently allocate investments across each stage. Then, given a fixed total investment, we use the DEA-based fixed cost allocation method to achieve the optimal allocation across stages, and we also incorporate two cooperative and noncooperative relationships between two stages. Additionally, capital stock efficiency is defined to analyze the relationship between optimal and actual capital stocks, clarifying the degree to which the optimal capital stock is reached. Finally, the proposed approach is applied to 14 A-share listed companies in China, and suggestions on the optimal amount of investment allocation and capital stock required are provided to facilitate organizational production.

Rapid production forecasting for heterogeneous gas-condensate shale reservoir

In the upstream oil and gas industry, production forecasting is crucial for decision making, investment allocation, reservoir management, and field development. While traditional methods like empirical models and numerical simulators have been employed for production forecasting, their limitations become pronounced when dealing with large, heterogeneous reservoirs. Though traditional simulators provide detailed and accurate models of the reservoir, they can be computationally expensive and time consuming. Machine-learning based production forecasting can offer a faster yet accurate approach that is useful in scenarios where quick estimates are needed or thousands of scenarios need to be investigated within an optimization and history-matching workflow. We propose a machine learning-driven workflow that integrates a massive geomodel compression followed by neural-network-based regression to rapidly forecast the production of a well in a large, complex reservoir. The proposed workflow accounts for spatial heterogeneities in porosity, permeability, and fluid saturation as well as uncertainties in induced and natural fracture properties and their distributions. The newly developed geomodel compression followed by neural-network-based rapid production forecasting reduces the computation time to generate a 5-year forecast per realization by an order of 6. Overall, the newly developed rapid production forecasting per realization takes approximately 0.0003 s as compared to 1037 s or 17 min per realization for a traditional simulator.

Large shareholders' stock selling and corporate performance: Evidence from China

This paper investigates whether and how large shareholders' stock selling influences corporate performance and conducts an empirical study using the data of Chinese A-share listed companies. The findings reveal that large shareholders' stock selling has a significant negative impact on corporate performance, primarily manifested through two paths that are intensifying agency conflicts and increasing financing constraints. Moreover, the negative effect is more pronounced in enterprises with more intense product market competition, non-state-owned enterprises, enterprises with lower ownership concentration, and those with higher equity balance degree. Further analysis confirms that cash dividends significantly mitigate the negative impact of large shareholders' stock selling on corporate performance, while share pledging significantly exacerbates the negative effect. Our findings suggest that governments should regulate the stock selling of large shareholders based on internal and external enterprise environments, while international investors should consider market information such as stock selling, dividends and pledging when optimizing their investment allocations.

The Best Investment Scenario for Hajj Fund Management in Indonesia

The Hajj Fund Management Agency (Badan Pengelola Keuangan Haji [BPKH]) is an institution responsible for managing Hajj funds in Indonesia. Through the investment mechanism, the BPKH’s benefit value can reduce the burden of prospective pilgrims in covering the cost of organizing the Hajj pilgrimage (Biaya Penyelanggaran Ibadah Haji [BPIH]). This study examined the combination of optimal portfolio construction in two situations, namely, without using regulatory restrictions and with government regulatory restrictions. The results of a linear programming approach with the Markowitz method revealed that investment allocation without regulatory restrictions forms an optimal portfolio to maximize Sharpe with a moderate scenario. Likewise, investment allocation based on regulations forms a portfolio with the same goal, namely maximizing the Sharpe ratio. This is in accordance with BPKH’s investment characteristics, which consider acceptable levels of return and risk, as well as BPKH’s risk profile, namely low to moderate. This research is expected to be useful for BPKH, especially in compiling investment portfolios to produce the optimal value of benefits and create sustainability in Hajj finance.