Big Investment Research Articles

Impact of Emerging Technologies on Higher Education

New technologies like Generative AI and Immersive Reality are set to change higher education dramatically. They offer unique ways to improve learning outcomes, boost creativity, and provide better access to quality education. But, incorporating these technologies also comes with challenges, such as ethical issues, the need for big financial investments, and environmental concerns. This paper explores how Generative AI and Immersive Reality affect higher education, highlighting their benefits and the obstacles to their adoption

Performance of water quality simulation model for lifting drainage water joints and mixing zone determination

Limited water resources with gradual increase in water demand led to higher dependence on drainage water as one of the non-conventional water resources in Egypt. However, there was no precise approach for using such resource. The practices ranged between stopping lifting drainage water to main canals at many locations due to the water quality degradation in the drains and pure dependence on polluted drainage water by farmers. This implies the importance of applying the mathematical models that provide precise and flexible alternative for the dependence on the drainage water. This procedure could save the big investments that were used in the lifting stations while mitigating the environmental hazards. Cornell Mixing Zone Expert System (CORMIX) model is one of these mathematical simulation models. The study used surface discharge sub-model (CORMIX3) to define the mixing zone between the lifted drainage water from Mehalet Rough drain and the freshwater in Mit Yazid canal, by investigated biochemical oxygen demand (BOD) and total dissolved solids (TDS). The simulation results verified that the two investigated parameters met WQ standards for the Egyptian law 48/1982. BOD standard value was met after 448.36 m, in 723 s. TDS standard value was met after 4.46 m, in 7.8 s. This was far ahead of the first municipal station regardless low quality of these parameters in the drain. This is the first time to apply this model in the irrigation sector in Egypt, and the results were promising for defining the precise approach to reuse the drainage water in Egypt.

Big data service investment strategy for low-carbon supply chains with reference effect

Big data service investment strategy for low-carbon supply chains with reference effect

Portfolio Optimization Strategies: New Approaches Based on Machine Learning Forecasting

This study provides an in-depth discussion and comprehensive review of the latest applications of machine learning techniques in the field of portfolio optimization. The article begins with an overview of traditional portfolio optimization theory and its limitations, and then focuses on how machine learning predictive models, which have flourished in recent years, can provide new perspectives and tools for solving the problems of non-linearity, dynamics and uncertainty in investment decision-making. This paper provides a detailed overview of the application practices of various machine learning algorithms (e.g., deep learning, reinforcement learning, integrated learning, etc.) in the areas of asset return prediction, risk assessment, and optimal weight allocation, and analyses their advantages and challenges compared to traditional methods. The analysis of relevant research cases reveals the significant effect of machine learning predictions in increasing expected portfolio returns, reducing risk exposure, and achieving effective diversification. The study also explores possible future trends and potential research directions for machine learning in portfolio optimization, highlighting the importance of combining domain knowledge with big data-driven intelligent investment decisions. This review aims to provide financial scholars and practitioners with a new way of thinking about portfolio optimization, and to promote the combination of theoretical research and practical operation in the fields of financial engineering and investment management, so as to achieve more accurate and efficient investment decisions.

Assessing the Role of China’s Investment on Youth Empowerment in South Africa

China is indeed the biggest investor in South Africa but it is very important to understand the effects that its investment has on the youth of South Africa. This study aims to analyse whether China's investment empowers the youth of South Africa through entrepreneurial skills, access to advanced technology, and provisions of scholarships. The findings of the study reveal that there is a level of inconsistency when it comes to China empowering the youth of South Africa. The study, therefore, recommends that China's relationship with South Africa should prioritize the youth of South Africa and be consistent in do so to contribute to their development as individuals.

Lubrizol plans big Indian investment

Lubrizol plans big Indian investment

Artificial intelligence and big data tokens: Where cognition unites, herding patterns take flight

Artificial intelligence (AI) and big data tokens have emerged as unique investment options, garnering interest due to their connectedness with other assets and financial markets. Utilizing Chang et al. (2000)'s cross-sectional absolute deviation (CSAD) model, we investigate static and time-varying herding in the AI and big data token markets. This research contributes to the growing discourse on AI and big data token investment through the lens of behavioral finance, with a particular focus on examining investor herding. The study's findings confirm market-wide herding of AI and big data tokens. The results suggest that investors exhibit herding in up markets, low volatility, and low volume days. Conversely, anti-herding is more prevalent in down markets, high volatility, and high volume days. Our analysis shows that herding is time-varying and emerges during a crisis period. The finding carries robust regulatory and policy implications to mitigate systemic risk and safeguard investor interests, ensuring market stability and resilience. The provided insights offer a valuable understanding of investors’ behavior across various market scenarios.

An Empirical Analysis of the Impact of Financial Leverage on the Performance of Second-Level Banks in Albania

The financial scene in Albania, has seen some significant changes over time, shaped by different market situations and rules that put specific limits on financial choices. In today’s world, where globalization and competition are running high, making smart financial moves is crucial for a company’s success. Most studies on traditional finance have been mostly focused on analyzing how a company’s everyday performance is connected with the amount of debt. These studies try to investigate the complex relationship between financial leverage and the factors that make a company successful. Deferent researchers have been looking into the basic details of financial leverage, focusing on theories like the pecking order, which suggests that companies Fprefer using their own money rather than borrowing from outside. Despite having an assembly of theories, there’s no one answer for how a company should balance its money or which financial moves guarantee success. Every business is different, dealing with unique challenges in its way. Understanding how a company acquires its funding and where it originates plays a crucial role for businesses aiming to economize while trying to achieve substantial growth. This highlights how important it is to set up a money plan that looks out for the company’s interests. Interestingly, in a market where there’s no tax on money moves, the way a company sets up its money doesn’t seem to change how much it’s worth. Businesses have room to refine how much debt they carry, both in the short and long term, based on big investment decisions and the need for quick cash. The exact effect of leverage depends on several variables, such as the firm’s risk appetite, the state of the economy, and the dynamics of the industry. To summarize, the main goal of this empirical investigation is to clarify the relationship between financial leverage and the performance curve of Albania’s second-level banks throughout the ten years from 2012 to 2022. This investigation seeks to clarify if financial leverage methods were modified by Albanian banks, especially second-level banks, in response to changing market dynamics and regulatory changes, and how these modifications affected the banks’ overall performance.

Uncertainty analysis of the electric vehicle potential for a household to enhance robustness in decision on the EV/V2H technologies

Electric vehicles (EVs) and vehicle-to-home (V2H) technologies are regarded as promising technologies for optimizing household energy management, particularly as valuable resources for optimizing the management of distributed photovoltaic (PV) generation. However, the potential of EVs and V2H for households is closely related to their usage behavior. As EVs and V2H technologies are big investments for a family, especially for families in rural area, their benefits for households need to be analyzed in an uncertain environment to obtain robust results and mitigate investment risks for households. To achieve this, this study builds a 7-parameter stochastic model to simulate random driving patterns and employs a mixed-integer linear programming model to derive the optimal solution for the household energy system. Subsequently, the potential value of EVs and V2H technologies for a household is assessed based on three key performance indicators: economic viability, carbon emissions reduction, and energy and power autonomy. The findings of this study demonstrate that the EV is a viable technology for significantly reducing household energy expenditures, lowering household carbon emissions, and enhancing household energy and power autonomy. Furthermore, the V2H technology can provide additional benefits by further reducing carbon emissions as well as increasing the autonomy of the household energy system. However, the impact of V2H technologies on household energy expenditures is limited. The above conclusions still hold when the EV travel behavior stochasticity is taken into account, but an unexpected finding is that EV travel behavior stochasticity has almost no effect on the economy and carbon emission indicators, while significantly affecting the autonomy of the household energy system. Further investigation uncovers that the external charging price plays a critical role in determining the potential of EV + V2H technology as both stationary and mobile energy storage for households. Additionally, it is observed that the impact of EV travel behavior stochasticity on household energy expenditures becomes pronounced as the external charging price decreases, which can assist families in making reasonable decisions on configuring emerging technologies, like EV and V2H.

Persistence of disaggregate energy RD&D expenditures in top-five economies: Evidence from artificial neural network approach

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies.

Big data application and corporate investment decisions: Evidence from A-share listed companies in China

Based on a natural language processing (NLP) method and annual reports from A-share listed companies in China from 2011 to 2020, this paper constructs a big data index for the corporate level and studies the impact of big data on corporate investment decisions. The study finds that big data significantly improves corporate investment decisions. From the perspective of index decomposition, data mining and mobile service are found to significantly improve corporate investment efficiency, while algorithm development and intelligent manufacturing mainly enhance the corporate investment level. The study also notes that heterogeneity may exist due to differences in financial, innovation, and legal environments. In terms of mechanisms, big data can positively influence corporate investment decisions by improving R&D investment and reducing transaction costs.

Big Data Analytics Maturity Model for SMEs

Small and medium-sized enterprises (SMEs) are the backbone of the global economy, constituting 90% of all businesses. Despite being widely adopted by large businesses who have reported numerous benefits including increased profitability and increased efficiency and a survey in 2017 of 50 Fortune 1000 and leading firms’ executives indicated that 48.4% of respondents confirmed they are achieving measurable results from their Big Data investments, with 80.7% confirming that they have generated business. Big Data Analytics is adopted by only 10% of SMEs. The paper outlines a review of Big Data Maturity Models and discusses their positive features and limitations. Previous research has analysed the barriers to adoption of Big Data Analytics in SMEs and a scoring tool has been developed to help SMEs adopt Big Data Analytics. The paper demonstrates that the scoring tool could be translated and compared to a Maturity Model to provide a visual representation of Big Data Analytics maturity and help SMEs to understand where they are on the journey. The paper outlines a case study to show a comparison to provide intuitive visual model to assist top management to improve their competitive advantage.

Effect of Public Debt on Economic Growth in Nigeria with or without Domestic Investment

The paper examines the effect of public debt on economic growth in Nigeria with or without domestic investment between 1981 and 2020 using a technique of Dynamic Least Square (DOLS). The findings of the study reveal that public debt in Nigeria retards economic growth through reduction in the level of investment. This indicates the possibility that the current levels of public debt in the Nigeria might not have been reducing the volume of growth but have the tendency to create a poorer macroeconomic and uncertain climate for investment. The implication is that the nonlinear relationship between debt and economic growth is a reality. It is suggested that there should be big push investment into all sectors of the economy that serve as engine of growth to the nation and whenever public debt is acquired, domestic investment must be given priority. Keywords: Effect, Public Debt, Economic Growth, Domestic Investment, Nigeria

Gender diversity, education diversity and big data analytics investments - A test of moderation

Purpose: This study examines the influence of education diversity in moderating the association between gender diversity and big data analytics investments. Design/methodology/approach: A multimethod approach is employed where the data (161 responses) are drawn from two quantitative data sources. The primary data are obtained from the questionnaire survey, and the secondary data are gathered from the annual reports. Multiple linear regression analysis is used as the analytical method. Findings: This study finds that gender diversity is positively and significantly associated with big data analytics investments. Next, the association between gender diversity and big data analytics investments is negatively moderated by education diversity. The association between gender diversity and big data analytics investments is weakened when the education diversity is higher. Originality: This study is one of the first to examine the intervening processes of how gender diversity affects big data analytics investments using education diversity as a moderating variable. This study is novel in its approach to providing empirical evidence and examining the moderating effect of education diversity. Practical implications: This study shows that gender diversity facilitates big data analytics investments. The findings help policymakers encourage the female director’s role in strategic decision-making. The negative moderating effect of education diversity on gender diversity and big data analytics investments association implies that firms may face challenges accessing resources. The findings help firms promote open debates and effective communication in strategic decision-making to leverage education diversity.

Big data applications in portfolio management: A review of techniques and strategies

In the ever-evolving landscape of finance, the advent of big data has ushered in a paradigm shift, transforming the art and science of portfolio management from its traditional moorings to a future where data reigns supreme. This paper delves into the intricate dance between big data and portfolio management, aiming to demystify the journey from historical practices to the forefront of technological innovation in finance. Through a meticulous examination, the study sets out to map the evolution of portfolio management in the digital age, scrutinize the technological foundations essential for big data analytics, and explore the symbiotic relationship between big data capabilities and investment strategies. Employing a systematic literature review as its methodological backbone, the research navigates through the complexities of integrating big data into portfolio management, offering a panoramic view of the current landscape and the challenges that lie ahead. The findings illuminate the transformative impact of big data analytics, highlighting its potential to enhance decision-making, optimize risk assessment, and unearth new investment opportunities. However, the journey is fraught with challenges, including data privacy concerns, ethical considerations, and the need for continuous technological adaptation. In conclusion, the study advocates for a proactive embrace of big data, urging portfolio managers to cultivate a culture of innovation, ethical vigilance, and lifelong learning. As the financial sector stands on the brink of a data-driven era, this paper calls for a strategic reorientation towards the integration of big data analytics in portfolio management, paving the way for a future where informed decision-making and strategic foresight are paramount.

Parenthood intentions of 16–18-year-olds in England: a survey of school students

There is a need to understand teenagers’ views towards having children to better tailor sexuality and reproductive health education. We conducted a survey of parenthood intentions for 16–18-year-old students. The survey was anonymous, contained open-ended and multiple-choice questions and was live from 10 May 2021 to 18 July 2022. The sample size was n = 931. Quantitative data were analysed descriptively and using chi-squared tests; qualitative data were analysed thematically. The majority of students (64%) indicated they wanted to have children in the future, with 49% desiring to have two children. Students who opted against having children cited the turbulent state of the world, concerns about parenthood, and, for many females, negative associations to pregnancy and childbirth. Many students felt concern about the possibility of having children in the future (45%), expressing concerns about their ability to have healthy children and worries about the lives their children might lead. Six themes were identified in their responses to the question ‘What are your concerns about the possibility of having children in the future?’: Fears; Self-doubt; Health and wellbeing; Big investment; Hinderance to personal aspirations; and Non-inclusive LGBTQ+ education. We conclude by making recommendations as to how school reproductive health and sex education might be improved.

Navigating the double-edged sword: how does big data affect firm innovation under different investment combinations?

ABSTRACT Although big data has been claimed to revolutionise the way firms innovate, there is still a lack of knowledge about how big data affects firm innovation under different investment combinations. Drawing on the resource-based view, organisation learning theory, and organisation change theory, we introduce big data into a Kline-Rosenberg chain-linked model to propose that firms harness information value from big data to improve innovation output while organisation-wide adjustments increase innovation risk. Based on the data collected from Chinese listed firms, our empirical results reveal that combining big data and R&D investment can improve innovation output and reduce innovation risk. In contrast, combining big data and IT investment significantly increases innovation risk. The finding that overinvesting in IT enhances firm innovation risk contributes novel insights to the literature by contradicting the belief that investment is beneficial to innovation.

Big Data, Artificial Intelligence, and Financial Literacy: Exploring their Combined Influence on Investment Behavior among Chinese Household

The investing behavior of Chinese families is undergoing a dramatic transition in the context of the digital financial era, impacted by factors such as big data use, AI adoption, financial literacy, digital literacy, and risk aversion. Although prior research has offered useful insights into certain components, a thorough examination of their linked dynamics has been lacking. The purpose of this research was to look into how big data usage, AI adoption, financial literacy, digital literacy, and risk aversion influence investment behavior among Chinese households. Additionally, it aimed to learn more about how risk aversion and digital literacy function as mediators in these relationships. A questionnaire-based survey of 370 Chinese families was employed as part of the quantitative research methodology. The study employed AMOS to find the relationship between variables. The research found that big data usage, AI adoption, financial literacy, and digital literacy significantly and favorably influenced Chinese households' investment behavior. It was discovered that digital literacy mediated the linkages between the adoption of technology and investment decisions. Furthermore, risk aversion reduced the effects of financial literacy and big data usage on investment behavior. This study added to the body of knowledge by providing a comprehensive framework that incorporates several aspects impacting investment behavior. It shed insight into the complicated dynamics of technology uptake and literacy, as well as their impact on investment decisions. The study went beyond individual components to investigate their interactions, resulting in a more complex view of modern investment behavior. This study has broad-ranging effects that will help investors, financial institutions, governments, educators, and researchers. The focus on a particular setting and self-reported data are two important constraints that must be acknowledged. Future studies can investigate longitudinal dynamics and cross-cultural variances to further our understanding of investment behavior in the digital age.

The impact of big tech corporate venture capital investments on innovation: Evidence from the equity investment market

Big technology companies have undeniably left an indelible mark on society, shaping it with their groundbreaking products and services. However, concerns have arisen regarding their market dominance and the potential stifling of competition and innovation, resulting in the formation of ominous “kill zones.” As a response to these concerns, regulatory scrutiny has intensified. To contribute to a better understanding of this issue, this paper introduces a distinctive dataset encompassing the years from 2010 to 2018, drawing from the Chinese equity investment market. We conduct an empirical analysis to examine the impact of corporate venture capital investments made by tech giants on the innovation of the recipient firms. Our results unveil a positive and notable influence of these investments on innovation, particularly in the domain of substantial innovations such as invention patents. Additionally, we delve into the underlying mechanisms propelling this effect, identifying that the innovation incentives of tech behemoths primarily stem from the infusion of financial resources, technological synergies, and a wealth of diversified knowledge accrued through joint investments. These findings bear significant implications for policymakers and regulators who must delicately balance the scales of competition and innovation within the technology sector.

Research on Big Data and Urban Agglomeration Development in High-Speed Railway

The introduction of high-speed railway has significantly enhanced transportation services for cities along its route, offering greater convenience to passengers and commuters. Through analysis of passenger flow data and economic data from cities along the high-speed railway line, we find the apparent siphon effect caused by the introduction of high-speed railway on larger cities. However, for medium and small cities, high-speed railway further weakens their attractiveness, leading to capital and labour outflow. This article provides an analysis of the impact of high-speed railway on cities along its route, utilizing passenger big data and investment data. It also offers recommendations for small and medium-sized cities to optimize their tourism resources and develop unique industries, aiming to mitigate the economic risks associated with the introduction of high-speed railway.