Hasil untuk "Capital. Capital investments"

Larry H. P. Lang, E. Ofek, R. Stulz et al.

We show that there is a negative relation between leverage and future growth at the firm level and, for diversified firms, at the segment level. Further, this negative relation between leverage and growth holds for firms with low Tobin's q, but not for high-q firms or firms in high-q industries. Therefore, leverage does not reduce growth for firms known to have good investment opportunities, but is negatively related to growth for firms whose growth opportunities are either not recognized by the capital markets or are not sufficiently valuable to overcome the effects of their debt overhang.

R. Caves

D. Jorgenson, Z. Griliches

Roger C. Kormendi, Philip Meguire

Yizhong Wang, Carl R. Chen, Y. Huang

K. Bakhsh, Sobia Rose, M. Ali et al.

Platon Promyslov

We study a ruin problem for an annuity model where a fixed fraction of capital is invested in a risky asset. Under weak assumptions on jumps, the ruin probability solves a second-order integro-differential equation and decays as a power function for large initial capital.

Yeqing Zhang, Priyanka Patil, Priyanka Patil et al.

BackgroundThe Nurture Early for Optimal Nutrition (NEON) programme was designed to promote equitable early childhood development by educating mothers of South Asian origin in two boroughs (Newham and Tower Hamlets) in East London on optimal feeding, care, and dental hygiene practices. The study found that the adapted Participatory Learning and Action (PLA) approach was highly acceptable and well-received by participants, with improvements in maternal confidence, infant feeding practices, and community engagement. However, gaps in specific feeding skills and challenges such as low attendance and retention rates were noted, particularly during the COVID-19 pandemic. This study conducted a cost analysis of the NEON programme and evaluated its financial sustainability.MethodsWe conducted a financial and economic costing from the provider perspective, applying a stepdown procedure to identify costs associated with the development and implementation of the NEON programme. Estimates of total and average costs per mother are presented along with affordability assessments, expressed as a proportion of the borough’s annual child development expenditure. All costs were discounted and reported in 2022 pound sterling and in 2022 international dollars.ResultsThe total cost of NEON design and delivery was £68,165 ($INT 102,658), and the average cost per mother participating in the programme was £439($INT 661) in the face-to face arm and £407($INT 614) in the online arm. The largest contributor to the total cost was materials (50%), including NEON training manuals and intervention toolkits, vouchers for the community facilitators, and overheads, followed by staff costs (45%) and capital investments (5%). The total cost of intervention delivery in Newham accounted for around 0.047% of the borough’s annual child development expenditure, while the total intervention cost in Tower Hamlets was equivalent to 0.003% of its spending on children’s development.ConclusionThe delivery of NEON is largely within local authorities’ budget for childhood development. The unit cost is expected to decrease when sharing costs are spread across more participants and implementing systems are validated and well developed.

AZIZ UR REHMAN, ASHUROV SHAROFIDDIN , ABDUL SALAM KHAN ORAKZAI

The stability of Islamic banks is critical to the financial systems of the Gulf Cooperation Council (GCC) countries, yet the role of intellectual capital in supporting that stability remains underexplored. This study addresses the research problem of insufficient understanding of how Intellectual Capital Efficiency (ICE) specifically Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Relational Capital Efficiency (RCE)—affects the financial stability of Islamic banks. The primary objective is to empirically investigate the contribution of ICE components to enhancing financial stability, using reliable performance metrics such as the capital-to-asset ratio and net profit margin. To achieve this, the study employs a dynamic panel data approach using the hierarchical Generalised Method of Moments (GMM) estimator. This methodology is justified due to its effectiveness in handling endogeneity issues, unobserved heterogeneity, and autocorrelation in panel data. The dataset includes 37 Islamic banks operating across six GCC countries including Saudi Arabia, Kuwait, Oman, Qatar, Bahrain and United Arab Emirates from 2010 to 2021. ICE variables are introduced sequentially into the model to measure their individual and combined effects. The results reveal that HCE and SCE have a statistically significant positive impact on financial stability, while the influence of RCE is more context dependent. These findings validate the resource-based theory and provide practical insights for bank managers and policymakers, encouraging strategic investments in intellectual capital to sustain institutional resilience in competitive Islamic financial markets.

Darya N. Veselova, Mikhail Yu. Ponomarev

Aim. To explore participation of “Gazprom” PJSC in Russian public-private partnership projects. Methods: institutional approach, document analysis, case study, generalization and synthesis. Results. The article examines specific examples of the implementation of the capital participation model and the contractual public-private partnership of “Gazprom” PJSC. The model of participation in capital is the priority model, which is confirmed by the state’s controlling stake in the company and is expressed in significant state support for “Gazprom” PJSC by guaranteeing an advantageous position in the high-yield market, providing large loans from banks with state participation, ensuring attractive tax conditions for work in promising segments of the economy and concluding profitable contracts with public authorities. However, the company has to take on social obligations in the form of gasification of whole country, which to a certain extent harms its own economic efficiency. At the same time, “Gazprom” PJSC can be both a public and a private partner in PPP projects. Conclusions. The key problem for “Gazprom” PJSC remains the commitments it has made to gasify the country, which require investments from the company and are characterized by low profitability. The ways to solve this problem may be to involve other gas companies in the implementation of gasification projects within the framework of PPP; to ensure the profitability of gasification projects for “Gazprom” PJSC itself by providing the latter with tax breaks or increasing export supplies of gas to the Asia-Pacific market, which will also provide access to gas for regions of Siberia and the Far East; to reduce the gasification program by optimizing energy consumption and developing alternative sources such as liquefied natural gas and liquefied petroleum gas.

Hira Bashir, Durr e Shahwar Khan, Asia Karim

This article examines the growing influence of Chinese investments in Latin America, focusing on Venezuela and Argentina. It explores how these countries benefit from Chinese capital and how this investment impacts their economies. Venezuela has relied on oil-backed loans to sustain its political system, worsening its economic challenges. In contrast, Argentina has channeled Chinese investment into infrastructure, including hydroelectric dams, nuclear plants, railway modernization, and energy diversification projects. The study highlights that while infrastructure investment can enhance economic stability and reduce reliance on further loans, both countries remain vulnerable due to their dependence on raw material exports. Despite these challenges, the study underscores that with institutional improvements and a shift toward diversification, such investments can contribute to sustainable growth in both nations. A qualitative research approach using secondary sources was employed to analyze the long-term implications of Chinese investments in these two economies.  

Shuochen Bi, Wenqing Bao, Jue Xiao et al.

With the continuous development of artificial intelligence technology, using machine learning technology to predict market trends may no longer be out of reach. In recent years, artificial intelligence has become a research hotspot in the academic circle,and it has been widely used in image recognition, natural language processing and other fields, and also has a huge impact on the field of quantitative investment. As an investment method to obtain stable returns through data analysis, model construction and program trading, quantitative investment is deeply loved by financial institutions and investors. At the same time, as an important application field of quantitative investment, the quantitative investment strategy based on artificial intelligence technology arises at the historic moment.How to apply artificial intelligence to quantitative investment, so as to better achieve profit and risk control, has also become the focus and difficulty of the research. From a global perspective, inflation in the US and the Federal Reserve are the concerns of investors, which to some extent affects the direction of global assets, including the Chinese stock market. This paper studies the application of AI technology, quantitative investment, and AI technology in quantitative investment, aiming to provide investors with auxiliary decision-making, reduce the difficulty of investment analysis, and help them to obtain higher returns.

Hambur Wang

Air pollution has emerged as a serious challenge for China, posing a threat to public health and hindering the progress of sustainable economic development. In response to air pollution and other environmental issues, the Chinese government introduced a new Environmental Protection Law in 2015. This paper investigates the impact of the new Environmental Protection Law's implementation and corporate Environmental, Social, and Governance (ESG) investments on air pollution and public subjective well-being. Using panel data at the macro level, we employ a difference-in-differences (DID) model, with Chinese provinces and municipalities as units of analysis, to examine the combined effects of the new Environmental Protection Law and changes in corporate ESG investment intensity. The study evaluates their impacts on air quality and public subjective well-being. Findings indicate that these policies and investment behaviors significantly improve public subjective well-being by reducing air pollution. Notably, an increase in ESG investment significantly reduces air pollution levels and is positively associated with enhanced well-being. These results underscore the critical role of environmental legislation and corporate social responsibility in improving public quality of life and provide empirical support for promoting sustainable development in China and beyond.

Abraham Ramos Torres, Laura N Montoya

Artificial intelligence (AI) is rapidly transforming the global economy, and Latin America is no exception. In recent years, there has been a growing interest in AI development and implementation in the region. This paper presents a ranking of Latin American (LATAM) countries based on their potential to become emerging powers in AI. The ranking is based on three pillars: infrastructure, education, and finance. Infrastructure is measured by the availability of electricity, high-speed internet, the quality of telecommunications networks, and the availability of supercomputers. Education is measured by the quality of education and the research status. Finance is measured by the cost of investments, history of investments, economic metrics, and current implementation of AI. While Brazil, Chile, and Mexico have established themselves as major players in the AI industry in Latin America, our ranking demonstrates the new emerging powers in the region. According to the results, Argentina, Colombia, Uruguay, Costa Rica, and Ecuador are leading as new emerging powers in AI in Latin America. These countries have strong education systems, well-developed infrastructure, and growing financial resources. The ranking provides a useful tool for policymakers, investors, and businesses interested in AI development in Latin America. It can help to identify emerging LATAM countries with the greatest potential for AI growth and success.

Tian Tian, Ricky Cooper, Jiahao Deng et al.

This paper introduces a novel methodology for index return forecasting, blending highly correlated stock prices, advanced deep learning techniques, and intricate factor integration. Departing from conventional cap-weighted approaches, our innovative framework promises to reimagine traditional methodologies, offering heightened diversification, amplified performance capture, and nuanced market depiction. At its core lies the intricate identification of highly correlated company clusters, fueling predictive accuracy and robustness. By harnessing these interconnected constellations, we unlock a profound comprehension of market dynamics, bestowing both investment entities and individual enterprises with invaluable performance insights. Moreover, our methodology integrates pivotal factors such as indexes and ETFs, seamlessly woven with Hierarchical Risk Parity (HRP) portfolio optimization, to elevate performance and fortify risk management. This comprehensive amalgamation refines risk diversification, fortifying portfolio resilience against turbulent market forces. The implications reverberate resoundingly. Investment entities stand poised to calibrate against competitors with surgical precision, tactically sidestepping industry-specific pitfalls, and sculpting bespoke investment strategies to capitalize on market fluctuations. Concurrently, individual enterprises find empowerment in aligning strategic endeavors with market trajectories, discerning key competitors, and navigating volatility with steadfast resilience. In essence, this research marks a pivotal moment in economic discourse, unveiling novel methodologies poised to redefine decision-making paradigms and elevate performance benchmarks for both investment entities and individual enterprises navigating the intricate tapestry of financial realms.

Erik Ekström, Yerkin Kitapbayev, Alessandro Milazzo et al.

We study a model of irreversible investment for a decision-maker who has the possibility to gradually invest in a project with unknown value. In this setting, we introduce and explore a feature of "learning-by-doing", where the learning rate of the unknown project value is increasing in the decision-maker's level of investment in the project. We show that, under some conditions on the functional dependence of the learning rate on the level of investment (the "signal-to-noise" ratio), the optimal strategy is to invest gradually in the project so that a two-dimensional sufficient statistic reflects below a monotone boundary. Moreover, this boundary is characterised as the solution of a differential problem. Finally, we also formulate and solve a discrete version of the problem, which mirrors and complements the continuous version.

Godfrey J. Kweka

Electricity is an important ingredient for development; however, inadequate electricity supply and its frequent fluctuations adversely affect the productivity and profits of small and medium enterprises in sub-Saharan Africa (SSA). In turn, the adverse effects pose challenges to economic growth and subsequently narrow further the low tax base in the region. Information regarding the macroeconomic effects of electricity fluctuations on the tax base in SSA is limited, thus calling for a detailed and refined study of this nature to analyse the effect of electricity fluctuations on the tax base in SSA. A bias-corrected linear dynamic estimator is employed for the analysis using a panel dataset for 41 SSA countries from 2000 to 2022. The results show that electricity consumption is positively related to the tax base in SSA while electricity fluctuation creates fiscal losses in terms of narrowing the tax base. Specifically, gross capital formation and informal economic activities are adversely affected by electricity fluctuations. This is a dramatic dampening effect that requires policy attention. The results indicate that the African governments in SSA need to increase investments in (including renovation of) the electricity infrastructures and diversify sources of energy into visible and tangible levels. This is because unreliable supply of electricity denies these countries the benefit of digital transformation, especially internet access. Sustaining the pace of stable and reliable electricity is paramount for economic growth and the growth of tax revenue in SSA countries. The article offers a highlight in energy policy review to include reliability as a prime concern for elevating economic growth and tax base in SSA countries.

Anatolii Tsynka, Serhii Illiash, Volodymyr Zelenovskyi

Introduction. The article deals with the issues of expanding the regulatory and technical base of the road sector for the construction of roads with cement concrete pavement. The article covers the issues of improving the existing and developing new requirements for the construction of cement concrete pavements with pin-connected slabs, reinforced slabs with pin connection, with continuous reinforcement and using rolled cement concrete mixtures using modern technologies. Improvement of the national regulatory framework is carried out on the basis of the analysis of existing legal acts, regulatory documents, technical literature related to the objects of standardization and, of course, taking into account the requirements of safety and environmental protection and the factors and features established by the current martial law in Ukraine. The work concerns the developed national standards for the installation of cement concrete pavements, namely those innovations that have already been agreed upon and are planned to be submitted to the National Standardization Body for adoption in the near future. Problem statement. Implementation of a unified technical policy to ensure the reliability and durability of roads, increase the efficiency of capital investments, implementation of scientific and technical achievements of science and best domestic and international practices in the practice of construction of roads with cement concrete pavement, issues of saving material, labor, financial and energy resources, improving the quality of construction work, labor safety and health conditions, rational use of land and other types of natural resources. The fact of constant growth in the intensity of heavy vehicles traffic, which is associated with the transportation of heavy machinery, agricultural products, metallurgical products, etc., significantly affects the reduction of the service life of roads with non-rigid pavement. This necessitates the search for alternative solutions for the construction of more durable pavement structures, one of which is the improvement of existing and introduction of new technologies for the construction of cement concrete pavements.

Oleksandr Romanko, Akhilesh Narayan, Roy H. Kwon

In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation of the output. Therefore, we take an alternative approach. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Subsequently, we compared various portfolio optimization strategies that utilized this AI-generated trading universe, evaluating those against quantitative portfolio optimization models as well as comparing to some of the popular investment funds. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio. But when stocks selection by ChatGPT is combined with established portfolio optimization models, we achieve even better results. By blending strengths of AI-generated stock selection with advanced quantitative optimization techniques, we observed the potential for more robust and favorable investment outcomes, suggesting a hybrid approach for more effective and reliable investment decision-making in the future.

Khashayar Mahani, Farhad Angizeh, Mohsen A. Jafari

Energy storage is inherently a flexible asset that can be used to reduce renewable energy curtailment and the congestion at its host network, enhance system resilience, and provide ancillary services at peak times. But the cost of technology still hampers the large-scale adoption of storage in power distribution networks. With EV parking lots included in its asset portfolio, a city can take advantage of the power stored in the parked EVs without major capital investments. In this article, we formulate the operation of an EV parking lot from the viewpoint of its owner (i.e., a city or a private entity). The lot works as a market aggregator with operational uncertainties stemming from: (i) random arrival and departure of vehicles, (ii) the SoC of EV batteries at the times of arrival and departure, and (iii) willingness of EV owners to participate. The risks from these uncertainties and market prices of ancillary services impact the bottom line of the lot owner’s revenue. For EV owners the excessive up and down cycles of battery is offset by discount offered by the lot owner. We provide an illustrative example and a roadmap to extend this model to take the holistic view of a power distribution network.