Share Market Research Articles

Blockchain for Mass Customization: The Value of Information Sharing Through Data Accuracy by Contract Coordination

This study provides the game-theoretical framework to investigate the relationship between the blockchain service and mass customization in the environment of information sharing and contract coordination. Specifically, we construct the game-theoretical models of the manufacturer and the retailer to discuss the optimal strategy of information sharing by the retailer in the case of mass customization. The result explores the conditions of information sharing for the retailer because she understands the end market information of nearby consumers. This discussion helps us to understand that the motivation of the manufacturer pays for the retailer’s construction of a blockchain system in the case of two types of products, such as a standard product and a customization product. Finally, we use the method of contract coordination to obtain the optimal strategy. Results reveal that information costs significantly impact sharing decisions, and cost-sharing contracts can incentivize retailers to share market data. This study has two main contributions. On the one hand, this study adopts the blockchain service for mass customization by supporting contract coordination, showing the technical value of avoiding false information and tampering-proof. On the other hand, although big data has the same information sharing function, this technology can’t play the role of secure data transmission. In order to increase the accuracy of information sharing, we analyze the fusion results of two technologies in the aspect of increasing the accuracy of data sharing, which better reveals the technical value.

EXPLORING RELATIONSHIP BETWEEN NIFTY, DJIA, AND SSE INDICES WITH CO-INTEGRATION AND GRANGER CAUSALITY

The global financial share market indices play a crucial role in the interconnected and dynamic landscape of international finance and considered as barometers of overall market health. The investors rely on these markets to gauge market sentiment, make informed investment decisions, and diversify portfolios. This study uses to explore the relationship between Nifty, DJIA, and SSE through econometric analysis. The data has been collected for a period of sixteen years for comprehensive investigation. The Yahoo Finance database served as the source of the collected data. The study uses EViews Software for application the Johansen Co-integration test, to evaluate if a long-term equilibrium connection exists between the indexes. To further examine the direction and intensity of the causal association between Nifty, DJIA, and SSE, the Granger Causality test is used. The results indicate that past values of Nifty contain valuable information for predicting future values of DJIA and SSE.

The Performance of Indian Banking Share Market

The performance of the Indian banking share market reflects the sector's critical role in the country's economic growth and its response to macroeconomic and policy shifts. Over the years, banking stocks have emerged as key drivers of the equity market, influenced by economic cycles, reforms, and market sentiment. Indian banking stocks have shown strong performance due to increasing credit demand, digital transformation, and financial inclusion initiatives. Private sector banks, such as HDFC Bank and ICICI Bank, have consistently outperformed due to better asset quality, customer-centric strategies, and technology adoption. Public sector banks (PSBs), after struggling with high non-performing assets (NPAs), have seen improvements due to recapitalization and bad loan resolutions. The COVID-19 pandemic initially led to a decline in banking stocks due to fears of loan defaults and an economic slowdown. However, post-pandemic recovery, supported by fiscal stimulus and accommodative policies from the Reserve Bank of India (RBI), revitalized the sector. The Russia-Ukraine conflict, inflationary pressures, and global monetary tightening caused temporary volatility but did not derail the sector's long-term growth trajectory. Despite challenges such as rising interest rates and geopolitical uncertainties, Indian banks have displayed resilience. The sector’s strong fundamentals, improved balance sheets, and focus on innovation continue to attract domestic and foreign investors. In summary, the Indian banking share market has delivered robust performance, driven by a blend of strong economic fundamentals, sectoral reforms, and increasing investor confidence.

Understanding the Indian Government’s Intentions Toward CeFi, DeFi, Cryptocurrencies, Share Market, Mutual Funds, Gold, and Fixed Deposits

This paper explores the Indian government’s stance and evolving regulatory landscape regarding various financial instruments, such as centralized finance (CeFi), decentralized finance (DeFi), cryptocurrencies, share market, mutual funds, gold, and fixed deposits. It examines the current frameworks and regulations, and how government policies are shaping each financial avenue. By analyzing the different approaches toward traditional and modern financial systems, this paper highlights the challenges and opportunities faced by the Indian financial ecosystem, particularly in the context of cryptocurrencies and DeFi.

Impact of psychological factors on investment decisions in Nepalese share market: A mediating role of financial literacy

Psychological factors such as emotional reactions, cognitive biases, and herd behavior influence investment decisions because they shape investor behavior, drive market dynamics, and affect rational decision-making. Similarly, financial literacy improves investment decisions by facilitating informed choices, minimizing biases, enhancing risk management, and promoting long-term financial planning. This study aims to examine the influence of psychological factors on investment decisions in the Nepalese share market, emphasizing the mediating role of financial literacy. Smart PLS 4.0 was used to analyze the structural relationships within the proposed theoretical model. Data were collected from the primary source using a structured questionnaire administered through a random sampling technique. The respondents included 410 active individual investors from the Nepal Stock Exchange (NEPSE). The study's findings reveal that psychological factors have a positive and significant effect on investment decisions among investors in the Nepalese stock market. Furthermore, the study revealed that financial literacy mediates the relationship between psychological factors and investment decisions by enhancing individuals' understanding and confidence, leading to more informed and rational investment choices. The results highlight the critical role of financial literacy in investment decisions in the share market. The findings indicate that investors with higher financial literacy levels are better equipped to mitigate the adverse effects of psychological biases, leading to more rational and informed investment decisions. By understanding the interplay between psychological factors and financial literacy, policymakers and financial institutions can develop targeted strategies to foster a more robust and resilient financial market in developing economies such as Nepal.

A Comparative Review on Stock Market Prediction Using Artificial Intelligence

The global financial landscape has undergone unprecedented transformations in recent decades, characterized by increased complexity, volatility, and interconnectivity. In this dynamic environment, the ability to anticipate stock market trends has become a paramount concern for investors, financial analysts, and policymakers alike. This research aims to distil insights and contribute to advanced predictive models for the dynamic global financial landscape. The exploration encompasses diverse approaches, including artificial neural networks, convolutional neural networks, LSTM, and traditional machine learning algorithms. Emphasis is placed on data pre-processing, numerical analyses, and the efficacy of LSTM models. The significance of this research lies in its synthesis of existing knowledge, offering a holistic view of methodologies and outcomes in Share Market Prediction. The model signifies a foundation for further innovation in predictive modeling, addressing real-time data challenges and dynamic market conditions. This work advances the understanding and forecasting of stock market trends.

Shared Micro-mobility Market Disturbances: the Polish Bike Sharing Market in Light of Z-score Analysis

Urban transportation has changed dramatically in the recent years through the large scale implementation of shared micro-mobility services, especially bike sharing systems (BSS) and electric scooter sharing (ESS). The COVID-19 pandemic brought further changes and uncertainty to this turbulent business environment. In 2020, the major BSS operator in Poland filed for bankruptcy claiming that its problems arise from the COVID-19 pandemic. Market reports and media speculated that BSS business, despite being publicly financed, and considered to be a of part of public transportation system, could be unsustainable in face of the competition from ESS. We used Z‑score analysis to investigate if bike sharing systems operators’ problems began before or during the COVID-19 pandemic and large scale development of ESS. Our study focuses on the Nextbike company, which held a dominant stake in the Polish BSS market. It also covers two other major Polish operators and includes a German operator for comparative analysis. To complement the quantitative findings from z‑score analysis, we have also interviewed representatives of major stakeholders, which deepened our understanding of BSS problems. The results of our research indicate that although the market was affected by the COVID-19 pandemic and growing scooter-sharing competition, the problems of the largest BSS operator were specific to this company, and the entire market was not under the risk of failure.

Wandering Drunkards Walk after Fibonacci Rabbits: How the Presence of Shared Market Opinions Modifies the Outcome of Uncertainty.

Shared market opinions and beliefs by market participants generate a set of constraints that mediate information through a not-so-unstable system of expected target prices. Price trajectories, within these sets of constraints, confirm or disprove the likelihood of participant expectations and cannot, de facto, be considered permutable, as literature has shown, since their inner structure is dynamically affected by their own progress, suggesting per se the presence of both heat and cycles. This study described and discussed how trajectories are built using different alphabets and suggests that prices follow an ergodic course within structurally similar tessellation classes. It is reported that the courses of price moves are self-similar due to their a priori structure, and they do not need to be complete in order to create the conditions, in resembling conditions, for the appearance of the well-known and commonly used Fibonacci ratios between price trajectories. To date, financial models and engineering are mostly based on the mathematics of randomness. If these theoretical findings need empirical validation, such a potential infrastructure of ratios would suggest the possibility for a superstructure to exist, in other words, the emergence of exploitable patterns.

Application of Mean-variance Model of 11 Industries in Chinese A Share Market

Application of Mean-variance Model of 11 Industries in Chinese A Share Market

Current Situation, Problems and Countermeasures of the Spirits Sector in the Chinese A Share Market

Current Situation, Problems and Countermeasures of the Spirits Sector in the Chinese A Share Market

Impact of Psychological Factors on Investment Decisions in Nepalese Share Market: A Mediating Role of Financial Literacy

The Nepalese stock market is characterized by emerging market dynamics and notable volatility. It is influenced by factors such as regulatory developments, economic conditions, and investor sentiment, shaping its growth trajectory and investor behavior. This study examines the influence of psychological factors on investment decisions within the Nepalese share market, emphasizing the mediating role of financial literacy. To achieve the stated objective, data were collected from the primary source using a structured questionnaire administered through random sampling technique. The respondents comprised 410 individual investors inside the Kathmandu Valley. The SmartPLS 4.0 was used to analyze the structural relationships within the proposed theoretical model. The findings of this study validated the set hypotheses that psychological factors have positive and significant effect on investment decisions among the investors in Nepalese share market. In the context of Nepal, where the share market is characterized by volatility and emerging market dynamics, these psychological biases often lead to suboptimal investment choices. Findings indicate that investors with higher financial literacy levels are better equipped to mitigate the adverse effects of psychological biases, leading to more rational and informed investment decisions. The study highlights the critical need for enhanced financial education programs to empower investors and improve overall market efficiency. By understanding the interplay between psychological factors and financial literacy, policymakers and financial institutions can develop targeted strategies to foster a more stable and resilient investment environment in Nepal.

Marketing of Management: Share Market, Potential Market, Competitor Business and Promotion (Literature Review)

Literature review research on marketing management: market share, potential market, competitor business, and promotion is a scientific literature article in the scope of marketing management science. The purpose of this literature research is to build a hypothesis regarding the influence between variables that can later be used for further research in the scope of marketing management. The approach used in this study is descriptive qualitative. The data collection technique uses literature studies. The data used in this descriptive qualitative approach comes from previous research that is relevant to this study and is sourced from academic online media such as the Scopus Emerald Journal, Sage, WoS, Sinta Journal, DOAJ, EBSCO, Google Scholar and digital reference books. The results of this literature review article are: 1) Market Share influences Marketing Management; 2) Potential Market influences Marketing Management; 3) Competitor Business influences Marketing Management; and 4) Promotion influences Marketing Management.

Analysing the Mediating Influence of Share Markets on the Relationship between GDP Growth and Macroeconomic Variables in MENA and Developed Countries

This study examined the relationship between GDP growth and different macroeconomic variables including foreign direct investment, gross fixed capital formation, government expenditure on education, consumption expenditure, GDP per person employed, exchange rate, market capitalization, and inflation in case of MENA and developed countries. Furthermore, it tried exploring the mediating role of the share market in the relationship between economic development and various macroeconomic variables MENA & developed countries. The study has used panel data from the period of 2002-2021. The data has been taken from multiple sources i.e. World Development Indicators (WDI) by The World Bank, International Financial Statistics (IFS), several economic surveys of respective countries and Central Bank’s publications. The study reveals mixed order of integration among variables. Consumption expenditure is stationary in both panels, while expenditure on education is non-stationary. Real effective exchange rate is stationary under both panels, while GDP per employed persons is non-stationary. GDP growth rate, inflation, and market capitalization are stationary for both developed and Mena countries. However, gross fixed capital formation is stationary at first difference. The correlation matrix results show a weak correlation between consumption expenditure and educational expenditure, with a 9 percent negative correlation. For Mena countries, the study found a negative association between consumption expenditure and educational expenditure, with a 23% correlation. Real effective exchange rate is negatively associated with foreign direct investment, GDP per employed person, gross fixed capital formation, inflation, and market capitalization. For developed countries, the coefficient of FDI increases GDP growth rate by 97%, while gross fixed capital formation decreases GDP growth rate by 10.2 units due to 1 unit increase in gross fixed capital formation. Consumption expenditures increase GDP growth rate by 2% due to 1 unit increase, while inflation increases it by 5.9 units. Exchange rate is also positively related with GDP growth rate, with a 1% increase in exchange rate boosting it by 29%. The study found partial mediation of market capitalization in developed countries and uni-directional causality in Mena countries. Keywords: Market Capitalization, Mediation Analysis, Mena Countries, Economic Growth, Panel Data Analysis

Interconnectedness of Health and Economy in Post-COVID-19: A Systems Thinking Approach to Synergies, Trade-Offs, and Policy Responses in Nepal.

This study addresses the economic challenges posed by the Coronavirus Disease 2019 (COVID-19) pandemic, with a specific focus on Nepal. Employing a Systems Thinking approach, the study develops a comprehensive model that integrates macroeconomic interventions, exploring the trade-offs and synergies among various economic sectors. The interconnectedness of health, economic, and policy domains highlights the need for a holistic understanding of the pandemic's effects. An exploratory research design was used. The model introduces three primary subsystems-import, local demand, and local production-illustrating their interdependence. The causal loop diagram depicts the implications of fiscal and monetary policies on the economy, emphasizing the synergy and trade-offs resulting from policy interventions. Findings reveal the significant economic impact of the pandemic on Nepal, leading to its negative growth rate in FY 2020/21, the first time Nepal has experienced this in the last two decades. The results show that both intended and unintended consequences are observed following the adoption of fiscal and monetary policy that aimed at minimizing the spread of virus suggesting a complex nature of relationship among the policy variables. The fiscal policy induced negative consequences on the increasing loan, slow revenue growth, while positive feedbacks are observed on increased investment opportunities, and boosting of local economy. The monetary measures yielded unintended consequences on the rising land prices, surge in share market. The study concludes by emphasizing the crucial role of informed policymaking in navigating the complex landscape, offering insights for creating a resilient and sustainable post-pandemic future.

인공지능 융합교육 수업 사례 연구

Objectives This study aimed to develop an AI convergence education lesson case that can be applied to integrated curriculum for lower elementary school students, and to analyze its effectiveness by applying it to actual lessons. Methods For this purpose, AI-based educational lessons were developed in accordance with the ADDIE model and subsequently implemented over 16 sessions with 20 first-grade students from an elementary school in Daegu. In order to assess the efficacy of the aforementioned lessons, a range of methodologies were employed, including the evaluation of learning outcomes, surveys on lesson satisfaction, in-depth interviews with students, an analysis of students' diaries, and the examination of teacher's field notes. Results The results of the study indicated that integrating AI content within the framework of subject-centered convergence education in elementary schools can significantly enhance students' understanding of AI, including its principles and applications. The lessons, which were developed around themes such as the “Community, Autumn, and South Korea” taught in the first grade of elementary school, led to high levels of satisfaction among students. In particular, the projects “Creating a Sharing Market Stall,” “Getting Acquainted with Autumn,” and “Promoting Traditional Korean Foods” not only facilitated a deeper understanding of AI but also enabled students to become proficient in using AI tools. Furthermore, these activities contributed to the development of students' creative thinking and problem-solving skills, thereby demonstrating the potential of AI convergence education for supporting comprehensive learning outcomes in young learners. Conclusions The results of this study have demonstrated the potential of AI convergence education within elementary education. In order to implement AI convergence education programmes effectively for lower-grade elementary students, it is necessary to develop educational materials, enhance the technical capabilities of AI tools, and establish institutional support measures.

Analysis of Stock Market Trends for Market Capitalization and Securities Management: An approach to Nepalese Stock Market

Attraction towards share market is increasing day by day. Relevance for guiding people towards purchase and sale of share is also increasing everywhere in the world. Nepal is a poor country having well developed share market, but some sort of hurdles and technical disparities are still creating problems in operation of this market. Increasing economic crisis for last two years has made this market critical. Decrease in price of share is caused by increasing losses in business and industry at present. The main objective of the study is to examine the growth trend of the Nepalese securities market. It helps to analyze the trend of Nepalese stock market in terms of market capitalization, number of transaction, number of securities traded and number of securities listed and to examine the space of primary market growth in Nepal. this work is motivated by the works of (see[6-31]).

Survey on Stock Market Prediction using Deep Learning

The share market is a very unpredictable entity that is extremely sensitive to different sorts of criteria. Substantial research has been done in the field of financial markets projection to discover the many characteristics and elements connected with price volatility. The routine movements of the financial markets is exceedingly turbulent and dynamic. It is impacted by a large number of underlying elements and intricate interactions, resulting in a substantial percentage of non linearity. According to certain beliefs on the behavior of the financial markets, stock exchange progression is entirely unpredictable. Therefore, to achieve effective prediction of the stock markets through the use of machine learning approaches a number of related works have been analyzed. This analysis has allowed for the development of a useful and highly effective methodology that will be discussed in the upcoming editions of this research article. Key Words: K Nearest Neighbors, Long Short Term Memory, Stock Market Prediction.

Pengaruh Corporate Governance Terhadap Nilai Pasar Saham di Pasar Modal

This research aims to determine the influence of corporate governance on share market value in the capital market. The research results show that corporate governance has a significant influence on stock market value. The results of the analysis show that corporate governance has a significant positive influence on stock market value. The results of this research contribute to capital market theory and practice, as well as providing useful information for investors and capital market policy developers, and overall, the implementation of good corporate governance practices can increase the company's credibility and value in the eyes of investors, thereby increasing stock prices. higher.

USER PERCEPTION TOWARDS STOCK PRICE PREDICTION USING LINEAR REGRESSION MODEL

Stock market prediction is a critical area of research with profound implications for investors, financial analysts, and policymakers. This study explores the efficacy of linear regression analysis within the domain of business analytics for forecasting stock market movements. Leveraging historical stock prices and relevant economic indicators, we employ a rigorous methodology to construct and evaluate predictive models using linear regression techniques. Technological advancement increases the study on stock and share market industry. Decision making is enhanced by various statistical and machine learning algorithms. Enormous research work has been concentrated on the feature prediction of stock prices based on historical prices and volume. Performance measures are analyzed in this work with S&P 500 Index using statistical methods in Python environment. Results obtained in this study are superior than the existing methods. The conventional methods for financial market analysis are based on linear regression. This paper focuses on the best independent variables to predict the closing value of the stock market. This study is used to determine specific factors which are providing the most impact on the prediction of the closing price.

Equity Analysis on IT Sector Stocks

The investment in share market is highly rewarding but highly risky. The concept of analysis comes into picture when decision must be made on choosing a particular company's shares for investment. Analysis includes fundamental analysis. Fundamental analysis is a method of forecasting the future price movements of a financial instrument based on economic, political, environmental, and other relevant factors and statistics that will affect the basic supply and demand. This paper examined fundamental analysis of IT sector of the selected companies from the Information Technology sector, since IT sector is one of the most upcoming sectors in the Indian Market Secondary Data of last 5 year (2019-2023). The sample of the stocks for the purpose of collecting secondary data has been selected based on Random Sampling. For analysis the monthly share prices of the last 5 financial years is collected for ten leading IT companies. for the study through fundamental analysis is carried out by tools like Economic analysis, Industrial analysis and Company Analysis Key ratios include ROA, ROI, ROE, EPS, DPS, dividend payout ratio, dividend yield, and P/E ratio. The stocks are chosen in an unbiased manner and each stock is chosen independent of the other stocks chosen. The major findings are all IT sector companies performing very well and their fundamentals are very strong based on P/E ratio, EPS and Payout ratio. It is recommended to the investor that to invest in IT sector company for long term will be benefited. Key Words: IT Sector, Fundamental Analysis, Investment, Equity, Market [1] Student, B. V. Patel Institute of Management, Uka Tarsadia University [2] Associate Professor, B. V. Patel Institute of Management, Uka Tarsadia University