National Stock Exchange Research Articles

FACTORS INFLUENCING STOCK PRICES: AN ANALYSIS OF COMPANY PERFORMANCE METRICS ON NSE

This study examines the factors that affect NSE stock values, focusing on metrics that quantify company performance. We look at a number of ratios, including price-to-earnings (P/E), return on equity (ROE), debt-to-equity (D/E), and earnings per share (EPS), to understand how they affect stock prices. The study uses quantitative approaches like regression analysis and qualitative evaluations to find out how these measures are related to stock prices. In order to provide a comprehensive analysis of stock price patterns, we also consider market sentiment and macroeconomic factors. By showing how politicians, financial analysts, and investors perceive and react to metrics of a company's performance, the findings are helpful for all three groups. Keywords: stock prices, National Stock Exchange (NSE), company performance metrics, earnings per share (EPS), price-to-earnings ratio (P/E ratio), return on equity (ROE), debt-to-equity ratio (D/E ratio), regression analysis, market sentiment, macroeconomic factors.

A BRIEF STUDY ON STOCK MARKET

The Indian stock market has undergone a remarkable transformation over its century-long history, emerging as a dynamic and sophisticated financial ecosystem that plays a pivotal role in driving the nation's economic growth. With securities traded on prominent exchanges like the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE), the market serves as a vital platform for capital allocation and investment. Regulated by the Securities and Exchange Board of India (SEBI), it exhibits inclusivity, welcoming a diverse spectrum of participants, from retail investors to institutional funds. Despite facing challenges such as volatility and regulatory compliance, the Indian stock market remains resilient, offering ample opportunities for wealth creation and economic development. Through a comprehensive exploration of its historical evolution, regulatory framework, market participants, and performance metrics, this report aims to provide valuable insights for investors and stakeholders, shedding light on emerging trends and opportunities in the Indian financial landscape.

Initial Public Offer (IPO) And Stock Market Performance

The success of initial public offers (IPOs) in India is the subject of this master's thesis. It centres on the performance of companies following an IPO, with an emphasis on listing gains and long-term performance (in relation to the market average). With a thorough examination of one of the largest initial public offerings (LICs), the thesis makes use of data from 40 IPOs that were listed on the National Stock Exchange (NSE) between January 2022 and December 2023. In order to determine whether the initial price fluctuations are substantial and long-lasting, it computes anomalous returns.By offering insights into the variables influencing IPO performance, the study hopes to assist investors, businesses, and policymakers. This can assist investors in making well-informed investment decisions, companies in making better decisions about going public, and legislators in developing efficient laws for the Indian IPO market.Because it explicitly targets the Indian market and takes into account a number of variables influencing IPO performance, the research is novel. Both domestic and foreign participants in the Indian financial system can benefit from knowing this information. KEY WORDS: INDIAN IPO, LISTING GAINS, POST-LISTING PERFORMANCE, STOCK MARKET.

Investigation of Indian stock markets using topological data analysis and geometry-inspired network measures

Financial markets are a quintessential example of complex systems wherein strategic connections arising through the trading (buying and selling) of different stocks gives rise to dynamic correlation patterns in stock prices. Network science based approaches have been employed for more than two decades to investigate the stock market as an inferential network, with nodes representing stocks and edges as pairwise cross-correlations between stock prices. However, a recent focus has been the characterization of the higher-order structure, beyond pairwise correlation, of such networks. To this end, geometry-inspired measures (such as discrete Ricci curvatures) and topological data analysis (TDA) based methods (such as persistent homology) have become attractive tools for characterizing the higher-order structure of networks representing the financial systems. In this study, our goal is to perform a comparative analysis of both these approaches, especially by assessing the fragility and systemic risk in the Indian stock markets, which is known for its high volatility and risk. To achieve this goal, we analyze the time series of daily log-returns of stocks comprising the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE). Specifically, our aim is to monitor the changes in standard network measures, edge-centric discrete Ricci curvatures, and persistent homology based topological measures computed from cross-correlation matrices of stocks. In this study, the edge-centric discrete Ricci curvatures have been employed for the first time in the analysis of the Indian stock markets. The Indian stock markets are known to be less diverse in comparison to the US market, and hence provides us an interesting example. Our results point that, among the persistent homology based topological measures, persistent entropy is simple and more robust than L1-norm and L2-norm of persistence landscape. In a broader comparison between network analysis and TDA, we highlight that the network analysis is sensitive to the way of constructing the networks (threshold or minimum spanning tree), as well as the threshold values used to construct the correlation-based threshold networks. On the other hand, the persistent homology is a more robust approach and is able to capture the higher-order interactions and eliminate noisy data in financial systems, since it does not take into account a single value of threshold but rather a range of values.

Technical Indicator based Stock Market Prediction using Recurrent Neural Network

The Indian stock market is highly volatile and complex by nature. However, notion of stock price predictability is typical, many researchers suggest that the Buy & Sell prices are predictable and investor can make above-average profits using Stock Technical Indicator (STIs).Most of the earlier prediction models predict individual stocks and the results are mostly influenced by company’s reputation, news, sentiments and other fundamental issues. In this work, architecture of project is given. As a part of prediction model the Recurrent Neural Network (RNN) Deep Learning Algorithm is used to predict future prices of Stocks Using Technical Indicators (STIs) and also buy sell signals are generated. The project will be carried on National Stock Exchange (NSE) Stocks of India.

Exploring the Use of Derivatives to Manage Financial Risks

Abstract: This research paper aims to investigate the use of derivatives in managing financial risks, with a special focus on the infrastructure sector. The study analyzes the positions taken by buyers in future and option contracts to mitigate risk exposure. The research period spans 28 days and focuses on three prominent companies listed on the National Stock Exchange (NSE): Larsen & Toubro Ltd, JSW Steel Ltd, and JMR Airports Infrastructure Ltd. The paper examines the profit and loss outcomes of derivative positions and evaluates their effectiveness in managing financial risks in the infrastructure sector.

Does Female on Board Affect M&A Participating Firms’ Performance? Empirical Evidence from India Using Dynamic Panel Data Analysis

The aim of this research is to examine the impact of gender diversity and female leadership position on the firm performance of Indian listed firms participating in mergers and acquisitions (M&A) activities. Available research has focused on a small sample of firms and considered ‘gender diversity’ as the only variable. Bearing this in mind, this study focusses on the leadership positions females hold on corporate board along with gender diversity affecting the firm performance measures for the sample of M&A participating firms, as M&As lead to major restructuring of the board. The ‘gender diversity’ is measured with the number of females present on corporate board as directors, ‘leadership position’ is measured with females holding the position either as Chief Executive Officer (CEO) or Chairperson on the board. The study employs panel data set for Indian non-financial National Stock Exchange listed M&A participating firms for the period 2013–2020. Using a system of GMM dynamic panel estimates, the study analyses the relationship between gender diversity and females’ varied leadership position on the firm performance measures, that is, return on asset, Tobin’s q. The findings reveal that there is a positive association between gender diversity and firm performance measures. Females holding the top leadership positions as either CEO or Chairperson on the board positively impact the firm performance measures. This study adds to the existing literature on gender diversity at the board level and M&A literature in the Indian context. JEL Codes: G 3, G 34, J 16

An Empirical Analysis of Investor Behavior: A Post-COVID-19 Examination of Herd Behavior in the National Stock Exchange of India (NSE)

This research paper investigates investor behavior, specifically focusing on herd behavior, in the National Stock Exchange of India (NSE) during the post-COVID-19 period, from 2020 to the present. Utilizing daily closing prices of companies listed on the Nifty50, the study employs data sourced from the NSE website. In contrast to earlier research that demonstrated herding in developing economies, this paper adopts the methodology outlined by Christie & Huang (1995) to analyze the data. The study finds no significant evidence of herding during the broader period of market stress in the NSE, suggesting a divergence from previous trends. The results suggest that Indian investors, despite operating in a volatile market environment post-COVID-19, exhibit rational behavior. Unlike some developing economies where herding behavior has been identified, Indian investors seem to rely on private information rather than imitating the actions of their peers.

A Comparative Study on Relation Between ROE and Standard Deviation of Public Sector Company National Stock Exchange

This study investigates the relationship between Return on Equity (ROE) and Standard Deviation (SD) of public sector companies listed on the National Stock Exchange (NSE). ROE serves as a measure of profitability, reflecting a company's ability to generate earnings from shareholders' equity, while SD represents the volatility or risk associated with the stock returns of a company. Understanding the interplay between these two metrics is crucial for investors seeking to optimize their investment portfolios by balancing risk and return. Over a five-year period, this comparative study analyzes the ROE and SD of public sector companies on the NSE, aiming to uncover any significant correlations between these metrics. By conducting statistical analysis, including correlation and regression analysis, we seek to identify patterns and trends that can provide valuable insights into the financial performance and risk profiles of public sector companies. The study is motivated by the need to offer empirical evidence and insights that can assist investors in making informed decisions regarding their investments in public sector companies on the NSE. By examining the relationship between ROE and SD, we aim to provide investors with a deeper understanding of the risk-return trade off inherent in these companies, thus enabling them to better manage their investment strategies and portfolios. Through the findings of this study, investors can gain insights into the financial health and risk characteristics of public sector companies on the NSE, facilitating more informed investment decisions. Additionally, the study contributes to the existing body of research on financial performance and risk management in the context of government-owned enterprises, offering valuable implications for both investors and policymakers.

Corporate Social Responsibility: Impact on Firm Performance for an Emerging Economy

Corporate Social Responsibility (CSR) was usually referred to as a concept where companies initiate voluntary action towards social and environmental concerns in the context of business operations related to the stakeholders of the company prior to the CSR Act 2013 in India. Post-2013, the voluntary initiative was replaced by regulatory guidelines to address social and environmental concerns. The CSR applicability–investment gap was used as a base concept in this study with instrumental theory; the study offers a strategic perspective of CSR and how organizations emphasized maximizing stakeholders’ value. In order to further investigate the effect of CSR on corporate financial performance (CFP) through the measure of shareholders’ value, i.e., the return on equity (ROE), the study used the sample from the National Stock Exchange (NSE)-Nifty-100 indexed companies of Emerging Economy—India for a span of fourteen years (2009–2023). The vast majority of research in this domain is conducted in developed countries; the research gap is filled by this study by considering India and drawing samples from multiple industries. The empirical model was developed by using panel data regression, where the dependent variable was ROE, and the independent variables were earning per share (EPS), log total income (LTI), CSR applicability/profit after tax (CRSAPPPAT), and CSR investment/profit after tax (CSRIPAT). The findings also highlighted the CSR applicability and investment of the firms during pre- and post-Sustainable Development Goal (SDG) periods. The same was also analyzed for the firms committed to CSR and not committed to CSR. The results indicated that there is no significant impact of the CSR/ESG initiatives (applicability and investment) on the ROE of the firms. The performance could be better if the companies minimize the CSR/ESG promise–performance gap through effective communication with stakeholders.

Financial Behavior in Personal Investment: Influence of Psychological Factors on Investment Decision

In the ever-evolving realm of financial markets, understanding the underlying psychological dynamics shaping investor behavior is paramount. Financial behavior refers to the choices and actions people make when managing their own cash across a range of investment vehicles, including stocks, bonds, mutual funds, and real estate. It includes components that are vital in determining the results of investments, including risk tolerance, investment objectives, decision biases, emotions, and financial literacy. Achieving long-term investment success and financial security requires an understanding of and ability to control one's own financial behavior. An extensive research model has been painstakingly built to clarify the complex connections between these psychological variables and the process of making investing decisions. This study examines the impact of three psychological aspects on investment decisions made by 220 investors who trade on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE): information asymmetry, problem framing, and risk propensity. Effective investing strategies necessitate an awareness of the interplay between psychological biases and cognitive processes, as these aspects influence personal investment decisions. By using a quantitative research methodology, information was gathered by sending surveys to investors who were actively trading stocks on the BSE and NSE platforms. Regression analysis and correlation studies are two statistical analytic approaches that were used to investigate the connections between investing decisions and psychological aspects. The study's conclusions add to the corpus of knowledge by illuminating the complex dynamics of financial decision-making and the part played by psychological variables. Furthermore, the knowledge gained from this study has important ramifications for financial advisors, investors, and legislators that aim to improve investor welfare and encourage well-informed stock market decision-making.

Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis

This study presents a comprehensive comparative analysis of deep learning models for stock market forecasting using data from two prominent stock exchanges, the National Stock Exchange (NSE) and the New York Stock Exchange (NYSE). Four deep neural network architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)—were trained and tested on NSE data, focusing on Tata Motors in the automobile sector. The analysis included data from sectors such as Automobile, Banking, and IT for NSE and Financial and Petroleum sectors for NYSE. Results revealed that the deep neural network architectures consistently outperformed the traditional linear model, ARIMA, across both exchanges. The Mean Absolute Percentage Error (MAPE) values obtained for forecasting NSE values using ARIMA were notably higher compared to those derived from the neural networks, indicating the superior predictive capabilities of deep learning models. Notably, the CNN architecture demonstrated exceptional performance in capturing nonlinear trends, particularly in recognizing seasonal patterns within the data. Visualizations of predicted stock prices further supported the findings, showcasing the ability of deep learning models to adapt to dynamic market conditions and discern intricate patterns within financial time series data. Challenges encountered by different neural network architectures, such as difficulties in recognizing certain patterns within specific timeframes, were also analyzed, providing insights into the strengths and limitations of each model.

Market Capitalization and Its Impact on Equity Valuation a Mixed Method Study of The Indian Capital Market

An investor must perform research on a stock before investing in it. It is critical for the intelligent investor. As a result, stock prices have long piqued the interest of investors. Researchers have worked hard to identify the elements that influence stock prices and so returns. In the current study, 300 companies listed on India's National Stock Exchange (NSE) were divided into three categories based on market capitalization. The study relied on both primary and secondary data to reach its findings. Logistic regression is used to examine secondary data in the form of historical financial information. The outcomes of secondary data analysis are then confirmed using primary data gathered through interviews with domain experts. The study also revealed the importance of the new variables PEG ratio and economic value added (EVA) in Indian scenario to predict the market price of large cap companies. The study's findings can help market participants such as fund managers, stocks analysts, portfolio managers, retail investors, and high-net-worth individuals. The findings of the study will also contribute to the current body of knowledge on equity valuation.

Comparative Analysis of Returns and Standard Deviations of Automotive Companies’ Stocks: A Five-Year Study (2019-2023)

Abstract In this study, returns and standard deviation of stock of automotive companies listed on the National Stock Exchange(NSE) are compared during a five-year period, from 2019-2023.The goal of the research is to help investors in the automotive sector balance risk and return by offerings insightful information. Using a descriptive research design, secondary data from journals, papers, and the NSE website are used. The data is analyzed using statistical methods including standard deviation and returns, The goals are to compare the accomplishments of automotive companies, evaluate risk tolerance, look at the average returns, and suggest investments for investors. The results provide investors looking to maximize their investment returns in automotive sector with practical advice.The examination looks at how the stocks of automotive companies performed on the NSE between 2019 and 2023. The goal is to support decision-making with knowledge. Secondary data was gathered using a descriptive design from multiple sources. To interpret the data, statistical methods like returns, and standard deviation are used. Examining average returns, assessing risk, and suggesting stocks to investors are some of the main goals. The findings are intended to offer helpful suggestions for investments in the healthcare industry.

Investigating Risk and Return Dynamics in Equities of Selected 10 Firms: A Five-Year Study (2019-2023)

This five-year study (2019-2023) delves into the intricate dynamics of risk and return within equity markets, focusing on ten selected firms listed on the National Stock Exchange (NSE). Through meticulous analysis, leveraging existing literature and empirical evidence, the research aims to empower stakeholders with evidence-based insights to optimize portfolio performance and manage risks effectively. Employing descriptive research methodology, secondary data was collected from various sources including the NSE website, publications, and journals. Statistical tools such as standard deviation and correlation matrices were utilized to assess risk and return profiles. The findings reveal diverse performance trajectories among the selected companies, with some exhibiting stable returns while others experiencing significant fluctuations. The scatter plot analysis highlights a potential positive correlation between risk and return, underscoring the importance of considering both factors in investment decisions. Additionally, the correlation matrix heatmap aids in identifying pairs of companies with complementary return patterns, facilitating portfolio diversification.The study recommends cautious consideration of the risk-return tradeoff, emphasizing the need for investors to align investment strategies with their risk tolerance and financial objectives. By bridging the gap between theory and practice, this research contributes to a deeper understanding of equity market dynamics, providing actionable insights for investors to navigate volatile market conditions effectively.

Does ESG disclosure really influence the firm performance? Evidence from India

In this paper, we examine the influence of ESG disclosure scores on firm performance of companies listed in the National Stock Exchange (NSE). The study uses 167 sample firms from 2010 to 2020. We capitalise overall ESG disclosure scores taken as a proxy to measure the effect of sustainability disclosure on firm performance. Further, we consider the return on assets (ROA), return on equity (ROE), Tobin’s Q, and Price Earnings ratio (P/E ratio) as firm performance measures. We employ dynamic panel data regression analysis to examine the influence of ESG disclosures on performance of the firm. In order, to address the endogeneity issues, we apply Generalised Method of Moments (GMM) model. The study results show a positive relationship between ESG disclosure and firm performance. It suggests that companies’ desire to enhance their performance need to pay more attention towards sustainability disclosures.

Working Capital Management and Profitability: With Special Evidence from Agriculture Companies Listed on the National Stock Exchange

The study attempted to analyze the relationship between working capital management (WCM) and the profitability of agriculture companies. The data were collected from four agricultural companies listed on the National Stock Exchange during 2013-2021. The results showed that RCC and CCC had a significant negative relationship with the ROA of agriculture companies. The negative relationship implied that profitability was lower if the cash conversion cycle was longer. The study concluded that a reduction in the receivables collection cycle and cash conversion cycle could lead to an enhancement in the profits of the agriculture company. The findings also highlighted the significant positive impact of ICC and PDC on the ROA of agriculture companies. It implied that longer the duration of the inventory conversion period and payment to suppliers led higher profitability for the companies. Therefore, to maintain a specific level of inventories for proper working capital management and improved profitability, agricultural companies should invest in current assets with the help of a financial institutional support system. Policymakers could develop regulations to encourage the amount of current assets in the working capital of agricultural companies, leading to profitability.

Corporate Investment and Economic Policy Uncertainty (EPU) Index Evidence from India

The author examined the relationship between the capital expenditure to total assets (CETA) ratio and the economic policy uncertainty (EPU) index, as well as a few firm-specific financial and two macroeconomic variables in the Indian context. Logistic Regression and quantile regression are used to evaluate secondary data from 50 listed companies on India's National Stock Exchange (NSE) from 2014 to 2023 (January to December). The author found that as uncertainty increases, the sample companies' investment in assets decreases and vice versa. The study's originality is the use of EPU index data in an Indian context, which has rarely been done in previous studies. The findings have consequences for the promoter in understanding the impact of uncertainty in investment decisions, as well as for the government in reducing uncertainty in order to create a more stable economic environment.

Comparative Analysis of Returns and Standard Deviations of Healthcare Companies’ Stocks: a Five-year Study (2019-2023)

This research conducts a comparative analysis of returns and standard deviations of healthcare companies’ stocks listed on the National Stock Exchange (NSE) over a five-year period from 2019 to 2023. The study aims to provide valuable insights for investors in the health sector, assisting them in balancing risk and return in their investment decisions. Secondary data sourced from the NSE website, publications, and journals is utilized, employing a descriptive research design. Statistical tools such as returns, beta, and standard deviation are used to analyze the data. The objectives include examining mean returns, assessing risk levels, contrasting achievements among healthcare corporations, and recommending suitable securities for investors. The findings offer actionable insights for investors aiming to optimize their investment outcomes within the healthcare industry. The investigations examines to the comparisons between healthcare companies’ stocks on NSE from 2019 to 2023. The intent is to assist with making informed decisions. Secondary data was collected from various sources, utilizing a descriptive design. Statistical tools such as returns; beta and standard deviation are applied to interpret the information. Key objectives include examining mean returns, evaluating risk, and recommending securities for investors. The results aim to provide useful recommendations for investment within the healthcare sector!.

“Impact of ESG disclosure on firm performance and cost of debt: Empirical evidence from India”

Despite several research evaluating the connection between sustainability and the bottom line of firms, the existing research is inconclusive. In addition, most studies focus on multinational firms based in the United States or Europe, while research in the context of emerging economies is scant. This paper endeavours to investigate the strength of the association between Environment, Social and Governance (ESG) disclosure scores and the financial performance of 272 Indian firms listed on National Stock Exchange for the period 2015 to 2021 by employing panel data regression techniques. The results indicate that organisations having better ESG performance outperform their industry peers financially and are valued more by the market. Moreover, the data indicates that financial markets for lenders consider ESG disclosure when determining creditworthiness. In accordance with this, businesses can examine their operations and become more aligned with eco-friendly methods. Even though ESG disclosure appears to be associated with improved financial success, this is not the case across all industries and during COVID-19 pandemic. This study is relevant due to the growing significance of sustainability and the role of emerging nations in international commerce. It helps managers prioritize resource allocations to ESG-related activities that may affect financial performance differently across industry sectors, thus adding to the literature on the financial effects of ESG disclosures, particularly industry-specific aspects. This research represents a pioneering effort by assessing the impact of ESG disclosure on the profitability, market value, and cost of financing of Indian firms using both aggregation and disaggregation methods.