Статті в журналах: "National Stock Exchange NATIONAL STOCK EXCHANGE (NSE)"

1

Shah, Bansi Rajnikant. "A Comparative Study of Bombay Stock Exchange (BSE) and National Stock Exchange (NSE)." International Journal of Scientific Research 1, no. 7 (June 1, 2012): 26–31. http://dx.doi.org/10.15373/22778179/dec2012/11.

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2

SAMADDER, SWETADRI, KOUSHIK GHOSH, and TAPASENDRA BASU. "FRACTAL ANALYSIS OF PRIME INDIAN STOCK MARKET INDICES." Fractals 21, no. 01 (March 2013): 1350003. http://dx.doi.org/10.1142/s0218348x13500035.

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The purpose of the present work is to study the fractal behaviour of prime Indian stock exchanges, namely Bombay Stock Exchange Sensitivity Index (BSE Sensex) and National Stock Exchange (NSE). To analyze the monofractality of these indices we have used Higuchi method and Katz method separately. By applying Mutifractal Detrended Fluctuation Analysis (MFDFA) technique we have calculated the generalized Hurst exponents, multifractal scaling exponents and generalized multifractal dimensions for the present indices. We have deduced Hölder exponents as well as singularity spectra for BSE and NSE. It has been observed that both the stock exchanges are possessing self-similarity at different small ranges separately and inhomogeneously. By comparing the multifractal behaviour of the BSE and NSE indices, we have found that the second one exhibits a richer multifractal feature than the first one.

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3

Kotha, Kiran Kumar, and Shreya Bose. "Dynamic Linkages between Singapore and NSE listed NIFTY Futures and NIFTY Spot Markets." Journal of Prediction Markets 10, no. 2 (January 27, 2017): 1–13. http://dx.doi.org/10.5750/jpm.v10i2.1253.

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This study examines the dynamic linkages of Nifty stock index and Nifty index futures contract traded on the home market, National Stock Exchange (NSE) and on the off-shore market, Singapore Stock Exchange (SGX). The study uses daily closing prices of the Nifty index and the Nifty futures contract traded on both the exchanges for the period July 15, 2010 to July 15, 2016. The study finds a causality running from the returns of the spot market to the returns from the Nifty futures market in both the exchanges, NSE and SGX, with the help of Vector Error Correction model and Granger causality test. Variance Decomposition and Impulse Response Analysis also confirm that the spot market is the leading market in price discovery, making it the most efficient amongst others.

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4

Malhotra, Nidhi, Kamini Tandon, and Deepak Tandon. "Testing Weak Form of Efficient Market Hypothesis: Evidence from Bombay Stock Exchange (BSE) & National Stock Exchange (NSE)." Asian Journal of Research in Social Sciences and Humanities 5, no. 6 (2015): 178. http://dx.doi.org/10.5958/2249-7315.2015.00144.6.

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5

Agrawal, Ashita, Pitabas Mohanty, and Navindra Kumar Totala. "Does EVA Beat ROA and ROE in Explaining the Stock Returns in Indian Scenario? An Evidence Using Mixed Effects Panel Data Regression Model." Management and Labour Studies 44, no. 2 (April 25, 2019): 103–34. http://dx.doi.org/10.1177/0258042x19832397.

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We study a panel data of 1,700 Indian firms listed in either National Stock Exchange (NSE) or Bombay Stock Exchange (BSE) for the period 2001 to 2016 to see if economic value added (EVA) explains the annual stock returns of these Indian firms better than return on assets (ROA) and return on equity (ROE). Using mixed effect model, we find that EVA does explain the annual stock returns of these Indian firms better than ROA and ROE.

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6

Dhamija, Sanjay, and Ravinder Kumar Arora. "Initial and After-market Performance of SME IPOs in India." Global Business Review 18, no. 6 (September 4, 2017): 1536–51. http://dx.doi.org/10.1177/0972150917713081.

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This article examines the initial and after-market performance of the initial public offerings (IPOs) listed on the recently launched platform for small and medium enterprises (SMEs) by the Bombay Stock Exchange (BSE), Mumbai and the National Stock Exchange (NSE). The study does find evidence of underpricing of IPOs by SMEs in line with other studies internationally. However, the level of underpricing is found to be lower than that of IPOs listed on the main board stock exchanges in India, reported by earlier studies. This may be partially due to the fact that the SME platform is at an infancy stage and has failed to attract investors’ fancy. This is reflected in a low level of oversubscription of SME IPOs at 1.35 times on average. The multivariate analysis identifies the type of offer, size of issue, promoter holding, extent of oversubscription, lead manager prestige and the stock exchange of listing as the key determinants of underpricing of SME IPOs. Post listing, these IPOs have significantly out-performed the benchmark index. The finding is inconsistent with the results of other studies on the main board exchanges where the IPOs, in general, are found to underperform the markets over a significant period of time post listing. This may partly be attributed to thin trading in these stocks and, therefore, to their lower level of liquidity. The findings have significant implications for stock-market regulators, issuers and investors.

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7

Selvam, M., G. Indhumathi, and J. Lydia. "Impact on Stock Price by the Inclusion to and Exclusion from CNX Nifty Index." Global Business Review 13, no. 1 (January 17, 2012): 39–50. http://dx.doi.org/10.1177/097215091101300103.

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Changes in an index are a regular phenomenon and they take place due to the inclusion and exclusion of stocks from the index. The inclusion or exclusion of stocks creates great impact on the value of the firm. However, these changes are simply a short-lived event with no permanent valuation effect. The present research study analyzed the impact of the inclusion into and exclusion of certain stocks from National Stock Exchange (NSE) S&P CNX Nifty index with Indian perspective. The study provides evidence on whether the announcements of Nifty index maintenance committee have any information content. This will also demonstrate the efficiency of Indian stock market with particular reference to NSE. The study revealed that on an average, no permanent effects were observed on stock prices. It is also found from the study that the NSE reacted unfavourably to the inclusion and exclusion of stocks and it is impossible to earn any excess returns where the particular stocks are included or excluded from the index.

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8

Bagade, Mr Ketan, and Prof Varsha Bhosale. "Artificial Intelligence based Stock Market Prediction Model using Technical Indicators." International Journal of Innovative Technology and Exploring Engineering 11, no. 6 (May 30, 2022): 34–39. http://dx.doi.org/10.35940/ijitee.f9915.0511622.

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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 efficient Technical Analysis (TA).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 while stock indices are less affected by these issues. In this work, architecture of project is given.As a part of prediction model the Long Short-Term Memory (LSTM), Support Virtual Machine (SVM) are used to predict future prices Stock Technical Indicators(STIs) are used to generate a buy sell signals. The project will be carried on National Stock Exchange (NSE) Stocks of India.

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9

Manickam, Tamilselvan, and R. Madhumitha. "Random Walk Investigation in Indian Market with special reference to S&P Nifty – Fifty Stocks." International Journal of Finance & Banking Studies (2147-4486) 4, no. 4 (October 21, 2015): 52–61. http://dx.doi.org/10.20525/ijfbs.v4i4.40.

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The competence of a financial system is entirely depending upon the stock market efficiency. The gradual growth of equity investor’s participation is inevitable to enrich the overall growth of emerging economies.Hence the necessity is felt to provide an empirical support to the investing community. For the purpose, this study attempts to examine the weak-form efficiency of Indian stock market – National Stock Exchange (NSE). The study has used the daily closing price of the Nifty fifty stocks from 3rdJanuary 2011 to 24thApril 2015. To test the weak form efficiency both parametric and non-parametric tests called Autocorrelation, Augmented Dicky Fuller test, and Runs Test were performed. The study reveals that 39 stocks of NSE-Nifty Fifty are found to be weak form inefficient, so that the investors can formulate trading strategies to gain abnormal returns. The Index and 10 stocks are found to be weak form efficient during the study period since the price series found to be autocorrelation existence.

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10

Shacheendran, V. "Dematerialisation of Securities in Indian Capital Market; A Paradigm Shift through Depository System." Shanlax International Journal of Commerce 8, no. 3 (July 1, 2020): 29–32. http://dx.doi.org/10.34293/commerce.v8i3.3264.

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Dematerialization has initiated a new trend in securities trading and settlement. Indian capital market has embraced technological sophistication by permitting electronic trading in securities. Depositors Act, 1996 has facilitated the setting up of depositories and dematerialization of securities. This paper attempts to evaluate the progress of dematerialization in India. For the study, data has been used that of the National Stock Exchange of India Ltd. (NSE), the largest stock exchange in India.

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11

Arasu, B. Senthil, Desti Kannaiah, Nancy Christina J., and Malik Shahzad Shabbir. "Selection of Variables in Data Envelopment Analysis for Evaluation of Stock Performance." Management and Labour Studies 46, no. 3 (May 20, 2021): 337–53. http://dx.doi.org/10.1177/0258042x211002511.

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This study deploys data envelopment analysis (DEA) to identify the appropriate variables for the performance valuation of stocks. For this purpose, sixty-nine non-financial stocks of the Nifty 100 index of The National Stock Exchange of India Ltd (NSE) were selected as a sample for this study. We segregated the selected stocks into three groups of inputs and outputs for DEA based on fundamental indicators (financial ratios); technical indicators (momentum indicators); and both, fundamental and technical indicators. The stock performance indicators are sourced from the ACE database from financial year 2014 to 2019. The results of the study suggest that all three sets of stock performance indicators help in the identification of efficient stocks. However, stocks identified under momentum indicators are seen to have been better performing in stock return compared to the other two groups. The outcome of this study may help academicians and investors construct an effective portfolio and analyse/study its performance evaluation

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12

Sangh, Neeraj. "Static Systematic Risk Profile of Nifty 100 Stocks: A Year on Year Analysis of Beta." GIS Business 12, no. 5 (September 22, 2017): 75–83. http://dx.doi.org/10.26643/gis.v12i5.3346.

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Beta Coefficient, as a measurement statistic of systematic risk of securities, was initially explained by Sharpe as a slope of simple linear regression function using rate of return on a market index as independent variable and a securitys rate of return as dependent variable. National Stock Exchange (NSE), the leading stock exchange of India, practice this ordinary least square (OLS) regression based single index market model for disseminating beta coefficients of prominent NIFTY 100 stocks. OLS regression based index model presumes that beta coefficients of securities should remain stable for accuracy of predicted returns. Brenner and Smidt (1977) emphasized the importance of having accurate beta forecast mainly because of (i) understanding risk-return relationships in capital market theory and (ii) extensive usage of beta in making investment decisions. The objective of this paper is to examine year on year stability of beta coefficients of NIFTY 100 index stocks.

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13

Amanulla, S., and M. Thiripalraju. "Week-end Effect: New Evidence from the Indian Stock Market." Vikalpa: The Journal for Decision Makers 26, no. 2 (April 2001): 33–50. http://dx.doi.org/10.1177/0256090920010204.

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This paper tests whether the carry-forward transactions in different periods have any impact on week-end effect in the Indian stock market during the period January 1990-December 1999. This study uses the daily stock return of 82 companies traded in the Bombay Stock Exchange (BSE) and three stock market price indices, viz., BSE sensitive index, BSE national index, and S&P CNX Nifty index to investigate the weekend effect The results from the subsample period strongly support the existence of week-end effect during the period of ban on carry-forward (badld] transactions. This study also evidenced a reversal in week-end effect, i.e., positive Monday return and negative Friday return in modified carryforward transactions and revised modified carry-forward transactions. This paper further finds that there is consistent positive return on Wednesday and negative return on Tuesday due to possible impact of National Stock Exchange (NSE) on the week-end effect.

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14

Hajam, Murtaza Ali, and Mohammad Shafi. "Volatility on India’s National Stock Exchange (NSE) Effected by FIIs : An Empirical Analysis." Kuwait Chapter of Arabian Journal of Business and Management Review 9, no. 4 (December 2020): 175–82. http://dx.doi.org/10.12816/0059144.

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15

Singh, Kamaljit, and Vinod Kumar. "Dynamic linkage between nifty-fifty and sectorial indices of national stock exchange." American Journal of Economics and Business Management 3, no. 2 (March 25, 2020): 17–27. http://dx.doi.org/10.31150/ajebm.v3i2.148.

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The main objective of this paper is to analyze the trend and pattern of the Nifty-Fifty and sectorial indices. An attempt has been also made to find out the causal relationship among the Nifty-Fifty and NSE sectorial Indices. The unit root test and Granger-causality test has been applied to check the causal relationship between Nifty-Fifty and sectorial indices. The finding of the study shows that the financial service sector had performed better and followed by the banking sector among all the indices while the Pharma sector and the Realty sector were Under-performed in comparison to other indices. The Nifty-Fifty has been found less volatile in comparison to other sectorial indices however Realty sector indices show the highest volatility during the study period.

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16

Kumar, K., and Dattatray P. Gandhmal. "An intelligent indian stock market forecasting system using LSTM deep learning." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (February 1, 2021): 1082. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1082-1089.

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<p><span>Stock market data is considered to be one of the chaotic data in nature. Analyzing the stock market and predicting the stock market has been the area of interest among the researchers for a long time. In this paper, we have stepped forward and used a deep learning algorithm with classification to predict the behavior of the stock market. LSTM deep learning algorithm is used with an optimization algorithm to formulate the hyperparameters. To further improve the accuracy of prediction the stock data is first given to a classification algorithm to reduce the number of input parameters. In this research Technical indicators are subjected to classification and deep LSTM algorithm which are both integrated to improve the accuracy of prediction. Deep LSTM hyperparameters are trained using the optimization algorithm. In this paper infosys and zensar stocks data is collected from the Indian stock market data i.e. both national stock exchange (NSE) and bombay stock exchange (BSE). The proposed approach is applied on infosys and zensar share values, the prediction accuracy obtained by employing this integrated approach of classification and LSTM has given a prominent value of MSE and RMSE as 1.034 and 1.002 respectively. </span></p>

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17

Sehgal, Sanjay, and Mala Dutt. "Domestic and International Information Linkages for the US Dollar/Indian Rupee Contracts: An Empirical Study." Management and Labour Studies 43, no. 4 (September 19, 2018): 205–33. http://dx.doi.org/10.1177/0258042x18791625.

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This study examines price discovery and volatility linkages between USD/INR spot and futures contracts in India and between USD/INR futures contracts on National Stock Exchange of India Limited (NSE), India and on three international exchanges, namely Singapore Exchange (SGX), Dubai Gold and Commodity Exchange (DGCX) and Chicago Mercantile Exchange (CME), from 29 August 2008 to 30 March 2015. Findings show that, at domestic level, the futures dominate spot in the Indian currency market; these findings are stronger than those in an earlier study, indicating improved pricing as well as hedging efficiency in the Indian currency market. At international level, NSE is dominated by both CME and DGCX in price discovery and in short-term volatility spillovers, while NSE dominates both exchanges in long-term volatility spillovers. Further, NSE dominates SGX in the international information process. The dominance of CME and DGCX over NSE may be on account of their several advantages such as longer trading hours, operations being open even after NSE has shut business, much lower trading costs as well as lower regulatory restrictions. The study provides several significant policy suggestions for improving efficiency of the Indian currency market and is also relevant for foreign portfolio investors (FPIs), domestic investors, researchers and academicians. It contributes to literature on information transmission relating to currency markets in emerging economies.

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18

Chakraborty, Jayant Kumar, and Sudeshna Sarkar. "Examining the Impact of Revised XBRL Mandate on the Performance of Firms in India." SEDME (Small Enterprises Development, Management & Extension Journal): A worldwide window on MSME Studies 48, no. 4 (December 2021): 330–40. http://dx.doi.org/10.1177/09708464211069446.

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Extensible Business Reporting Language (XBRL) is becoming increasingly popular in the area of financial accounting data. On 2 May 2018, it was announced that public listed companies (PLC) must document their reports in XBRL format at the three cross-country exchanges, namely, the Bombay Stock Exchange (BSE), National Stock Exchange (NSE) and MCX. This article examines whether such PLCs' operational performance has improved since the mandate began. The performance variables of the company are investigated. Our findings reveal that the XBRL-adopted PLC's success is mostly driven by growth (sales revenue) and size (size of assets), with the XBRL mandate having a substantial impact. The greater transparency of financial reporting information, according to our findings, may act as an external monitoring role. The government and Ministry of Corporate Affairs (MCA) should effectively understand future trends and strengthen XBRL adoption.

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19

Patni, Ity, and Nishu Gupta. "Construction of Optimal Portfolio and Selection of Stock using Fuzzy Approach." International Journal of Engineering & Technology 7, no. 3.30 (August 24, 2018): 118. http://dx.doi.org/10.14419/ijet.v7i3.30.18214.

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Stock selection methods and strategies have been the prominent area of research since long. Portfolio theory is a connotation how an intelligent bias free investor should make an optimal portfolio. The line of the work is first inclined towards construction of optimal portfolio using Sharpe-Single Index model, CAPM, Jenson’s Measure, Treynor & Sharpe Ratio. These measures consider total risk i.e. systematic and unsystematic risk and suggests a rational investor in what proportion an investment can be made to a particular stock. Further, the purpose of the work is to combine the fuzzy approach for closer representation with reference to stock selection problem in a non-linear and uncertain environment. For demonstration, data set is taken from National Stock Exchange (NSE) for a period of 6 years (1st April, 2011 to 31st March, 2017). The proposed model will serve both ranking and assigning weight procedures to the selected stocks.

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20

Singh, Amit Kumar, and Shivani Kalra. "Short Run Underpricing of Initial Public Offerings (IPOs) in the National Stock Exchange (NSE)." Ramanujan International Journal of Business and Research 4, no. 1 (December 12, 2019): 223–47. http://dx.doi.org/10.51245/rijbr.v4i1.2019.157.

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21

Singh, Amit Kumar, and Shivani Kalra. "Short Run Underpricing of Initial Public Offerings (IPOs) in the National Stock Exchange (NSE)." Ramanujan International Journal of Business and Research 4, no. 1 (December 12, 2019): 223–47. http://dx.doi.org/10.51245/rijbr.v4i1.2019.157.

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22

Thappa, Sankar. "A study on the performance in national stock exchange (NSE) sectoral indices of India." Journal of Global Information and Business Strategy 14, no. 1 (2022): 50–62. http://dx.doi.org/10.5958/2582-6115.2022.00007.8.

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23

Sinha, Paritosh Chandra. "Herd Behaviour: How Decisive is the Noise in the NSE and BSE Stock Markets?" GIS Business 11, no. 6 (November 15, 2016): 01–16. http://dx.doi.org/10.26643/gis.v11i6.3381.

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Do investors in the stock markets act/react on true information or noise? Do they believe on their own information or simply herd? The study seeks to explore these typical research queries from the behavioral finance perspectives. In particular, it develops a new theory of herding behavior and extends the models of Banerjee (1992) and Bikhchandani, Hirshleifer, and Welch (1992). The study also empirically tests the same on the Indian context with the high frequency intraday trading data for the real trade-time or time-stamp, trade-volume, and trade-price of ten sample scripts listed for their trading in both markets - the Bombay Stock Exchange (BSE) and the National stock Exchange (NSE). The study contributes to the literature with original findings. It shows that investors in the two Indian stock markets show crowd of positive and negative herding as well significantly and there is huge noise along with information in the markets equilibrium pricing mechanism.

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24

Fattah, Doaa A., Amany A. Naim, Abeer S. Desuky, and Mervat S. Zaki. "AutoKeras and particle swarm optimization to predict the price trend of stock exchange." Bulletin of Electrical Engineering and Informatics 11, no. 2 (April 1, 2022): 1100–1109. http://dx.doi.org/10.11591/eei.v11i2.3373.

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The stock price varies depending on time, so stock market data is time-series data. The prediction of the trend of a stock price is a more interesting topic for investors to take an investment decision in a specific stock. Prediction of stock price always depends on machine learning algorithms. In this work, optimizing deep neural network (DNN) is used for predicting if the close price is reached to the profit which is determined by the investor or not and improve the prediction accuracy. Particle swarm optimization (PSO) and auto machine learning (AutoML) are used as optimizers with DNNs. The methods are applied to data of nine companies in Indonesia and National Stock Exchange (NSE) of India. The data is got from yahoo finance. Based on the experimental results, AutoML of deep learning proved to have the best accuracy rate, which is varying from 81 percent to 92 percent across all companies, and the accuracy after optimizing DNNs using PSO is varying from 73 percent to 82 percent across all companies.

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25

Kumar, Rakesh. "Examining the Dynamic and Non-linear Linkages between Crude Oil Price and Indian Stock Market Volatility." Global Business Review 18, no. 2 (March 16, 2017): 388–401. http://dx.doi.org/10.1177/0972150916668608.

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The present study is an attempt to examine the dynamic impact of crude oil price variations in the international market on the Indian stock market volatility. For the purpose, the study uses crude oil monthly price expressed in dollar per barrel, Bombay Stock Exchange (BSE)-listed index BSE Sensex and National Stock Exchange (NSE)-listed CNX Nifty prices for the period from January 2001 to December 2014. GARCH (1,1) model with net crude oil price change as exogenous variable is used to estimate the impact of net oil price change in international market on the conditional volatilities of both the indices. The findings report that net oil price change has a significant impact upon the conditional volatility of both the indices. These findings show that investors redesign their portfolios in response to crude oil price variations in the international market. They can use crude oil price as an important exogenous variable in forecasting models of stock returns and risk in the Indian stock market.

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26

Amuthan, R. "Co relating NIFTY 50 Index Trend’s impact on NSE’s Sector based Indices Growth Momentum in Post COVID-19 led Indian Economy with Special reference to NIFTY Bank, NIFTY Consumer Durables, NIFTY IT and NIFTY Pharma Indices using Arithmetic Modelling." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 5, 2021): 2184–89. http://dx.doi.org/10.17762/turcomat.v12i6.4824.

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The NIFTY 50 is the flagship index on the National Stock Exchange of India Ltd. (NSE). The Index tracks the behavior of a portfolio of blue chip companies, the largest and most liquid Indian securities. It includes 50 of the approximately 1600 companies traded (listed & traded and not listed but permitted to trade) on NSE, captures approximately 65% of its float-adjusted market capitalization and is a true reflection of the Indian stock market. This study probed in to the correlation between NIFTY 50 and NIFTY Bank, NIFTY 50 and NIFTY Consumer Durables, NIFTY 50 and NIFTY IT and NIFTY 50 and NIFTY Pharma Indice.

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Pandey, Ram Prakash. "A Study on Performance Evaluation and Computation of Stock Market Index with Special Reference to BSE and NSE." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 1023–46. http://dx.doi.org/10.22214/ijraset.2023.55292.

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Abstract: The Stock market index in India play a lcrucial roe in the country’s economic growth and development. The Investors, policymakers, and market participants closely monitor the performance of stock market indices. In general, people tend to be unaware of Indian indices, their weights, and their composition patterns. This research paper aims to analyze the performance of stock market indices and focuses on important indexes from the Bombay Stock Exchange and the National Stock Exchange while taking into account their development, calculation method, and historical value. This Study is mainly based on three Objectives and data selected and synthesizes for the year 2001 to 2022, due to base year limitations of various indices. By utilizing quantitative analysis methods and examining relevant economic indicators, this paper seeks to provide valuable insights into the Indian stock market Indices and its role as a barometer of economic health. This article makes it simple for people to understand the number of companies that make up the important indices, their weight in the indexes, their performance on a sectoral basis, and other methodologies. Finally, this paper attempts to provide a brief overview of various index which is traded in Indian Stock market.

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Pandey, Ram Prakash. "A Study on Performance Evaluation and Computation of Stock Market Index with Special Reference to BSE and NSE." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 1023–46. http://dx.doi.org/10.22214/ijraset.2023.55292.

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Abstract: The Stock market index in India play a lcrucial roe in the country’s economic growth and development. The Investors, policymakers, and market participants closely monitor the performance of stock market indices. In general, people tend to be unaware of Indian indices, their weights, and their composition patterns. This research paper aims to analyze the performance of stock market indices and focuses on important indexes from the Bombay Stock Exchange and the National Stock Exchange while taking into account their development, calculation method, and historical value. This Study is mainly based on three Objectives and data selected and synthesizes for the year 2001 to 2022, due to base year limitations of various indices. By utilizing quantitative analysis methods and examining relevant economic indicators, this paper seeks to provide valuable insights into the Indian stock market Indices and its role as a barometer of economic health. This article makes it simple for people to understand the number of companies that make up the important indices, their weight in the indexes, their performance on a sectoral basis, and other methodologies. Finally, this paper attempts to provide a brief overview of various index which is traded in Indian Stock market.

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29

Sen, Abhibasu, and Karabi Dutta Choudhury. "On the co-movement of crude, gold prices and stock index in the Indian market." International Journal of Financial Engineering 07, no. 03 (September 2020): 2050036. http://dx.doi.org/10.1142/s242478632050036x.

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The nonlinear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week’s closing exchange rate. Though a good correlation was obtained during some period, but as a whole no such cointegration relation can be found out. Using the Discrete Wavelet Analysis, the data was decomposed and the presence of Granger Causal relations was tested. Unfortunately, no significant relationships are being found. We then studied the Wavelet Coherence of the two pairs, namely NSE-Nifty & Gold and NSE-Nifty & Crude. For different frequencies, the coherence between the pairs have been studied. At lower frequencies, some relatively good coherence have been found. In this paper, we report for the first time the co-movements between Crude Oil, Gold and Indian Stock Market Index using Wavelet Analysis (both Discrete and Continuous), a technique which is most sophisticated and recent in market analysis. Thus, for long-term traders they can include gold and/or crude in their portfolio along with NSE-Nifty index in order to decrease the risk (volatility) of the portfolio for the Indian Market. But for short-term traders, it will not be effective, not to include all the three in their portfolio.

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30

Agrawal, Tarunika Jain, Sanjay Sehgal, and Rahul Agrawal. "Disruptive Innovations, Fundamental Strength and Stock Winners: Implications for Stock Index Revisions." Vision: The Journal of Business Perspective 24, no. 3 (June 14, 2020): 356–70. http://dx.doi.org/10.1177/0972262920928890.

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Globally, disruptions driven by technological advancements are visible in the form of unicorns and declining lifespan of the index constituents. Sectors such as information technology, financial services, energy, consumer goods and automobile are found to be more prone to disruptive innovation. Assessing the financial strength of the incumbents is crucial to assess their strength to endure disruption. We construct a fundamental strength index (FSI) using 11 financial performance measures covering 7 key attributes, namely profitability, efficiency, solvency, liquidity, net investments, pursuance of innovation and entry barriers, over the 5-year period 2014–2019. FSI helps in categorizing stocks of National Stock Exchange (NSE) 200 universe as ‘A’ being the fundamentally strongest and ‘C’ being the weakest. Potential crossovers can take place between ‘C’ category stock in Nifty 50 (Next 50) and ‘A’ category stock belonging to the Next 50 (Nifty Midcap 100). The results show that the disruptor’s portfolio (Next 50 stocks) outperforms the incumbent’s portfolio (Nifty 50 constituents) with a return of 1.61 per cent vs 0.47 per cent. A similar observation holds true for the Next 50 and Nifty Midcap 100, with the disruptor’s portfolio surpassing the incumbent’s portfolio (return of 2.59% vs 0.44%). The study has significant implications for the policymakers, investors, companies and academicians.

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Wanaputra, Farisan, and Siti Nurwahyuningsih Harahap. "Analisis pengaruh XBRL terhadap kinerja perusahaan di India." Jurnal Ekonomi dan Bisnis 21, no. 2 (October 29, 2018): 219–40. http://dx.doi.org/10.24914/jeb.v21i2.1686.

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Penelitian ini bertujuan untuk menganalisis pengaruh adopsi XBRL terhadap kinerja perusahaan, khususnya Return on Assets (ROA) sebagai ukuran kinerja akuntansi dan Market to Book Ratio (MTB) sebagai ukuran kinerja pasar. Penelitian ini menggunakan sebanyak 100 sampel perusahaan yang terdaftar pada National Stock Exchange (NSE) maupun Bombay Stock Exchange (BSE) pada tahun 2008 hingga 2013. Metode pengambilan sampel yang digunakan adalah metode purposive sampling. Pengujian hipotesis dilakukan dengan menggunakan metode Ordinary Least Square. Hasil penelitian menunjukkan bahwa adopsi XBRL berpengaruh positif terhadap kinerja perusahaan karena peningkatan transparansi informasi mendorong perusahaan untuk meningkatkan kualitas laporan keuangan dan kinerjanya. Regulator pasar modal dapat menggunakan hasil penelitian ini bahwa adopsi XBRL dapat meningkatkan kinerja sehingga perusahaan termotivasi untuk mengadopsi XBRL.

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Yadav, Naveen Ramesh, Hirak Dasgupta, and Rashmy Moray. "Short Term Performance Analysis of IPOs in India." Asian Journal of Empirical Research 8, no. 11 (December 3, 2018): 392–403. http://dx.doi.org/10.18488/journal.1007/2018.8.11/1007.11.392.403.

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The research is aimed at the short term IPO returns that are issued on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). The evaluation of IPO is done on the basis of the returns generated on the day of issue, 10 and 30 days after the day of issue. The significance of this paper can be realized from the fact whether the return generated in short term in comparison to the market are more or less i.e. the IPO has outperformed the index as a benchmark. The study includes a sample of 28 IPO’s issued from the year 2013 to 2015. The results showed that the mean % performance of IPO on the day of issue, 10 and 30 days after the day of issue is 9%, 10% and 10% respectively for NSE and 8%, 9% and 14% respectively for BSE. The above results are also supported with relative valuation index which thus brings us to a very important conclusion that the Indian Stock market provides significant returns within 30 days from the date of issue of share.

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Bhuva, Krunal K., and Vijay H. Vyas. "Expiry day Impact on return on Indian Stock market (NSE)- an Empirical Study." Journal of Management and Science 1, no. 3 (December 30, 2013): 402–9. http://dx.doi.org/10.26524/jms.2013.45.

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Derivative products are alleged to have a sharp affect on the stock market in various ways ever since their inception in June 2000. Currently, derivative trading constitutes approximately 90% of the total turnover of the NSE (National Stock Exchange). Launching of derivatives and their expiration (last Thursday of every month) in the Indian stock market has been perceived to have direct corollary on the return, volatility, efficiency and marketability of the stock market. This paper tries to analyze empirically the expiration day effect of stock derivatives on underlying securities. This study tests the presence of the last Thursday of the montheffect on stock market volatility by using the S&P 500 market index during the period of January 2012 and December 2012 and sample companies which are trading on derivative market. The findings show that the last Thursday of the month effect on stock market volatility is not present in volatility and return equations.

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Islam, Khalid Ul, and M. M. Goyal. "Examining the Fisher Effect in Short and Long Run: A Study of NSE Sectoral Indices." GIS Business 12, no. 5 (October 16, 2017): 20–29. http://dx.doi.org/10.26643/gis.v12i5.3341.

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The belief that stock market provides hedge against inflation has been put to test by many researchers over the past few decades. The present study aims at testing the Fisher effect in the Indian context. We have used monthly data, from July 2006 to June 2016, of the National Stock Exchange sectoral indices and consumer price index. The ordinary least square regression and Johansen cointegration approach have been used to test whether or not Indian sectoral indices provide hedge against inflation in short and long run respectively. The weak exogenity test under VECM has been used to establish the hedge hypothesis in the Indian stock market. The present study has established results in support to the hedge hypothesis that stock market provides hedge against inflation.

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Keshari Jena, Sangram, and Ashutosh Dash. "Does contract size matter for price discovery and risk management in stock index futures?" Investment Management and Financial Innovations 13, no. 3 (August 23, 2016): 62–74. http://dx.doi.org/10.21511/imfi.13(3).2016.05.

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In an effort to increase the liquidity and accessibility to the investors, National Stock Exchange of India (NSE) had reduced contract size of its Nifty index futures two times from 200 to 100 and, subsequently, to 50 units. How does this change in contract size of index futures impact the informed and hedge based trading, thereby contributing to the twin objectives of price discovery and risk management, respectively? VAR model is applied to daily return volatility, volume and open interest to study the impact. Significant feedback relationship between volume and volatility following the reduction in contract size establishes the informational trading and price discovery. However, no causality from volatility to open interest implies contract size is not a determinant of hedging. But significant causality from open interest to volatility is establishing the non-informational and liquidity trading. So stock exchanges should consider the appropriate lot size before going for introducing new futures contract

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KUMAR, K. KIRAN, and SHREYA BOSE. "HEDGING EFFECTIVENESS OF CROSS-LISTED NIFTY INDEX FUTURES." Global Economy Journal 19, no. 02 (June 2019): 1950011. http://dx.doi.org/10.1142/s2194565919500118.

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This paper investigates the hedging effectiveness of cross-listed Nifty Index futures and compares the performance of constant and dynamic optimal hedging strategies. We use daily data of Nifty index traded on the National Stock Exchange (NSE), India and cross-listed Nifty futures traded on the Singapore Stock Exchange (SGX) for a period of six years from July 15, 2010 to July 15, 2016. Various competing forms of Multivariate Generalised Autoregressive Conditional Heteroscedasticity (MGARCH) models, such as Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation (DCC), have been employed to capture the time-varying volatility. The results clearly depict that dynamic hedge ratios outperform traditional constant hedge ratios with the DCC–GARCH model being the most efficient with maximum variance reduction from the unhedged portfolio.

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Khanra, Sayantan, and Sanjay Dhir. "Creating Value in Small-cap Firms by Mitigating Risks of Market Volatility." Vision: The Journal of Business Perspective 21, no. 4 (October 25, 2017): 350–55. http://dx.doi.org/10.1177/0972262917733166.

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Extant research has explored numerous ideal approaches to predict and anticipate the unpredictability in stocks to mitigate business risks. This article attempts to offer an important insight on creating values in terms of financial returns dodging the risks associated with the market volatility in emerging market economies by exploring the context of National Stock Exchange (NSE), India. The study establishes that Small-cap companies, which are included in NSE Small 100 index, are less inclined to be impacted by the market volatility index (NVIX) compared to the Large-cap companies and Mid-cap companies that are under respective Broad Market Indices. Furthermore, this article examines 64 Small-cap companies, belonging to nine different sectors, to investigate the sector-wise impact of market volatility on Small-cap businesses in India.

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38

Narang, Monika, Mahesh Chandra Joshi, Kiran Bisht, and Arun Pal. "Stock portfolio selection using a new decision-making approach based on the integration of fuzzy CoCoSo with Heronian mean operator." Decision Making: Applications in Management and Engineering 5, no. 1 (March 15, 2022): 90–112. http://dx.doi.org/10.31181/dmame0310022022n.

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The main objective of stock portfolio selection is to distribute capital to selected stocks to get the most profitable returns at a lower risk. The performance of a stock depends on a number of criteria based on the risk-return measures. Therefore, the selection of shares is subject to fulfilling a number of criteria. In this paper, we have adopted an integrated approach based on the two-stage framework. First, the heronian mean operator (improved generalized weighted heronian mean and improved generalized geometric weighted heronian mean) is combined with the traditional Combined compromise solution (CoCoSo) method to present a new decision-making model for dealing with stock selection problem. Second, Base-criterion method is used to calculate the relative optimal weights of the specified decision criteria. Despite the uncertainties, the advanced CoCoSo-H model eliminates the efficacy of anomalous data and make complex-decisions more flexible. A case study of stock selection for portfolio under National stock exchange (NSE) is discussed to validate the applicability of the proposed model. Different portfolio () have been constructed using Particle swarm optimization (PSO). The outcome shows the prominence and stability of the proposed model when compare to previous studies.

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39

Peswani, Shilpa Girish. "Returns to Low Risk Investment Strategy." Applied Finance Letters 6, no. 01 (December 6, 2017): 2–15. http://dx.doi.org/10.24135/afl.v6i01.65.

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The paper studies the low risk anomaly in the Indian market using entire National Stock Exchange (NSE) as sample from January 2001 to June 2016. It provides evidence that low risk portfolio sorted for total risk, systematic risk as well as unsystematic risk individually for the large cap, mid cap, small cap and the entire NSE universe give higher returns to the investor as compared to high risk portfolio. The difference of returns from low risk portfolio versus high risk portfolio is positive as well as economically and statistically significant for all the risk measures. The results also prove that low risk portfolio investing strategy returns outperform the benchmark portfolio. Using either total volatility, idiosyncratic volatility or beta as a risk measure in stocks, the low risk portfolio gives higher returns even after controlling for the well-known size, value and momentum factors. The excess returns are the highest for low risk portfolio sorted for volatility of large cap stocks. Most of the low risk portfolios consists of growth and winner stocks. In conclusion, the low risk portfolio investment strategy is independent of size and gives positive excess returns as compared to high risk portfolio in the Indian stock market.

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40

Majumder, Sayantan Bandhu, and Ranjanendra Narayan Nag. "Shock and Volatility Spillovers Among Equity Sectors of the National Stock Exchange in India." Global Business Review 19, no. 1 (September 28, 2017): 227–40. http://dx.doi.org/10.1177/0972150917713290.

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The basic thrust of this article is to examine how shocks and volatility are transmitted across sector indices. This article employs the autoregressive asymmetric BEKK-GARCH model. The study is based on daily data from the National Stock Exchange (NSE) of India from January 2004 to January 2014. Volatility spillover was found to be bidirectional among the two pro-cyclical sectors: Finance and IT. But, there was a unidirectional shock and volatility spillover from the non-cyclical FMCG sector to both the pro-cyclical sectors. The FMCG sector has remained almost unaffected by the spillover from the other sectors. Moreover, the evidence of asymmetric spillover has been found to be present in most of the case. Second, correlations between the sectors were found to be higher during the period of global financial crisis. But no such evidence was found in the context of the Euro zone debt crisis. Understanding the dynamics of shocks and volatility transmission is necessary for risk management in general and for optimal portfolio allocation and hedging strategy in particular. To the best of our knowledge, this is the first study on Indian stock market which has analysed the dynamics of shock and volatility transmission across sector indices.

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Agarwal, Samiksha, and Lekha Chakraborty. "Business Taxation in an Emerging Economy: Analysing Corporate Tax Incidence." Review of European Studies 11, no. 2 (March 24, 2019): 8. http://dx.doi.org/10.5539/res.v11n2p8.

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This paper estimates the incidence of corporate taxes in an emerging economy –India- using the data from 5,666 business firms listed in the Bombay Stock Exchange (BSE) and the National Stock Exchange of India (NSE) for the period 2000-15. Using the dynamic panel models, we find that capital bear the burden of corporate taxation relatively more than the labour. Our findings highlight that the effective tax rate is higher for the small corporate firms than the gigantic firms. The tax policy implications for strengthening the wage bargaining frameworks is insignificant as we found the wage determination in India is mostly outside the purview of fiscal policy practices. Further research is required to understand whether less incidence of corporate taxation on wages in India is due to base erosion and profit shifting.

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Saji, T. G., and S. Harikumar. "Earnings Growth and Value Premium: The Indian Experience." Vikalpa: The Journal for Decision Makers 40, no. 4 (December 2015): 444–54. http://dx.doi.org/10.1177/0256090915608542.

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Executive Summary This article addresses two main questions: Does a value premium exist in emerging market like India? If so, how pervasive is it in different market conditions? Value premium is assumed to be the difference in stock returns of undervalued and overvalued firms with a unique industry profile. The sample for the study consisted of 32 companies from the Information Technology (IT) sector, the stocks of which had traded continuously in the National Stock Exchange (NSE) during the period 2000–2010. Prowess and Capitaline constitute the sources for the firm-level financial data, and NSE web sources provide data related to share prices and market capitalization. The study involved a two-step empirical procedure: an exploratory factor analysis and a regression modelling under Ordinary Least Square (OLS) method. Exploratory factor analysis identified earnings growth and Earnings Price (E/P) rate as the prime determinants of stock returns. Expected earnings growth significantly explained E/P rate under OLS regression framework. The study then estimated normal E/P rate for the individual stock and compared the same with the actual E/P. If the actual E/P for a particular stock was greater than its estimated E/P, it was inferred that the stock was undervalued, the reverse being the case for overvaluation. The findings of this research provide empirical validity of use of E/P rate in identifying mispriced stocks in the Indian context. Undervalued stocks can produce better returns compared to overvalued stocks, and their success has been both persistent and impressive. E/P rate or P/E ratio is a valuable analytic device when properly interpreted. The publicly available E/P rate seems to possess information content and warrants an investor’s attention at the time of his portfolio formation or revision. The search process involving E/P rate suggests that the best buy would be the stock whose reported earnings per share is expected to grow most rapidly.

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Dixit, Jitendra Kumar, and Vivek Agrawal. "Foresight for stock market volatility – a study in the Indian perspective." foresight 22, no. 1 (November 13, 2019): 1–13. http://dx.doi.org/10.1108/fs-05-2019-0040.

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Purpose Volatility is a permanent behavior of the stock market around the globe. The presence of the volatility in the stock price makes it possible to earn abnormal profits by risk seeking investors and creates hesitancy among risk averse investors as high volatility means high return with high risk. Investors always consider market volatility before making any investment decisions. Random fluctuations are termed as volatility of stock market. Volatility in financial markets is reflected because of uncertainty in the price and return, unexpected events and non-constant variance that can be measured through the generalized autoregressive conditional heteroscedasticity family models and that will give an insight for investment decision-making. Design/methodology/approach Daily data of the closing value of Bombay Stock Exchange (BSE) (Sensex) and National Stock Exchange (NSE) (Nifty) from April 1, 2011 to March 31, 2017 is collected through the web-portal of BSE (www.bseindia.com) and NSE (www.nseindia.com) for the analysis purpose. Findings The outcome of the study suggested that P-GARCH model is most suitable to predict and forecast the stock market volatility for both the markets. Research limitations/implications Future research can be extended to other stock market segments and sectoral indices to explore and forecast the volatility to establish a trade-off between risk and return. Originality/value The results of previous studies available are not conducive to this research, and very limited scholarly work is available in the Indian context, so required to be re-explored to identify the appropriate model to predict market volatility.

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Osuala, A. E., U. A. Onoh, and G. U. Nwansi. "Presidential Election Results and Stock Market Performance: Evidence From Nigeria." Applied Economics and Finance 5, no. 2 (February 9, 2018): 117. http://dx.doi.org/10.11114/aef.v5i2.3016.

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The study investigates the effect of Presidential election results on the performance of an emerging stock market using the case of the 2011 and 2015 Presidential elections in Nigeria. Adopting Event Study methodology to analyse the secondary data obtained from the Nigerian Stock Exchange (NSE) and some national dailies, the results of the study suggest that the 2011 presidential election result had negative significant impact on the performance of the stock market. On the other hand, the 2015 Presidential election result had positive but insignificant impact on the stock market as evidenced by the average and cumulative abnormal returns on the event date and one day post-event date- an indication that the result of the 2015 Presidential election was a welcomed development as leadership changed from PDP to All Progressives Congress (APC).

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45

Pathak, Rajesh, Thanos Verousis, and Yogesh Chauhan. "Information Content of Implicit Spot Prices Embedded in Single Stock Future Prices: Evidence from Indian Market." Journal of Emerging Market Finance 16, no. 2 (July 10, 2017): 169–87. http://dx.doi.org/10.1177/0972652717712373.

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This study examines the information content of pricing error, measured by the difference between the implied price computed using the cost of carry model and the spot price of Single Stock Futures (SSFs), traded on National Stock Exchange (NSE), India. The returns of portfolios, based on ranking of such pricing errors, are investigated. The consistency of results is verified by controlling for established risk factors, that is, market, size, value and momentum premium, and idiosyncratic factors such as firm’s liquidity and size. Our study reveals that the pricing error is a priced risk factor that contains incremental information about stock returns of day t, and not beyond. We conclude that implied spot prices from stock futures market are useful for traders to profit in the spot market. JEL Classification: G120, G130

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46

Ali, Farman, Pradeep Suri, Tarunpreet Kaur, and Deepa Bisht. "Cointegration and causality relationship of Indian stock market with selected world markets." F1000Research 11 (November 1, 2022): 1241. http://dx.doi.org/10.12688/f1000research.123849.1.

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Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

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47

Ali, Farman, Pradeep Suri, Tarunpreet Kaur, and Deepa Bisht. "Cointegration and causality relationship of Indian stock market with selected world markets." F1000Research 11 (August 10, 2023): 1241. http://dx.doi.org/10.12688/f1000research.123849.2.

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Анотація:

Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

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48

Camilleri, Silvio John. "Do call auctions curtail price volatility? Evidence from the National Stock Exchange of India." Managerial Finance 41, no. 1 (January 12, 2015): 67–79. http://dx.doi.org/10.1108/mf-10-2013-0292.

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Purpose – The purpose of this paper is to empirically investigate whether call auctions which batch orders for simultaneous execution, may restrain stock market volatility. Design/methodology/approach – The authors use high-frequency data to investigate volatility changes following the suspension of opening and closing call auctions on the National Stock Exchange (NSE) of India in 1999. The authors evaluate this issue by considering both modelled and realised volatility. Using a GARCH approach the authors model intra-day volatility for the trading days preceding and succeeding the auction suspension. The authors also scrutinise return distributions to look for volatility changes during different parts of the day. Findings – When interpreted collectively, the empirical results suggest that the auction suspension was followed by reduced volatility particularly in the middle of the trading day and at the closing. Practical implications – Given that auctions are often incorporated in trading systems with the aim of curtailing volatility, the main conclusion, that the auction suspension was followed by lower volatility, has important practical inferences. Auctions cannot be automatically relied on to reduce volatility. The intricacies of the auction protocol and their interaction with ancillary market microstructure features may impact on auction efficacy. Originality/value – The paper adopts a novel approach towards assessing the effectiveness of auctions by considering an unusual occurrence of an auction suspension. The empirical setting enables a clear comparison of the respective regimes since these periods do not materially differ in other subsidiary aspects. This is a noteworthy factor, since the empirical contexts considered in prior studies, often feature several simultaneous changes.

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Debata, Byomakesh, and Jitendra Mahakud. "Interdependence between Monetary Policy and Stock Liquidity: A Panel VAR Approach." Margin: The Journal of Applied Economic Research 12, no. 4 (September 4, 2018): 387–413. http://dx.doi.org/10.1177/0973801018786270.

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This article examines the relationship between monetary policy and individual stock liquidity in an order-driven emerging stock market like India. This study considers stocks listed in National Stock Exchange of India (NSE) and continuously traded from April 2002 to March 2015. Considering the multiple dimensions of liquidity, this study uses five different liquidity proxies to capture the various facets of liquidity such as trading activity, price impact and transaction cost. An array of macroeconomic and firm-specific control variables are used while analysing the liquidity and monetary policy relationship. Econometric methods like panel vector autoregressive (VAR)–Granger causality test, impulse response functions and variance decomposition analysis have been employed to carry out this analysis. The empirical findings suggest that monetary policy significantly Granger-causes stock liquidity, and the expansionary monetary policy characterised by low interest rate and higher money supply is positively associated with individual stock liquidity in India. The impact of monetary policy on liquidity of individual stocks is more prominent during financial crisis. The findings of the present study have certain theoretical as well as practical implications. The market participants in equity market can improve the forecasting of liquidity of their investment portfolio by employing monetary policy along with individual asset’s characteristics. Regulators and policymakers may consider the cross-sectional relationship between stock liquidity and monetary policy as an important source of information for policy formulation and implementation. JEL Classification: E44, E51, G12

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Mangla, Divya, and Bharti Parkar. "A STUDY ON CALCULATING, RISK, RETURN AND PROPORTION OF EACH SECURITY IN THE PORTFOLIO DIVERSIFICATION." International Journal of Social Sciences & Economic Environment 6, no. 1 (June 30, 2021): 08–14. http://dx.doi.org/10.53882/ijssee.2021.0601002.

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Objectives: Applying Sharpe's single index model, the current study seeks to create an optimum capital structure. This is the simplest and most generally utilised model for putting together an ideal portfolio. Design Methodology: The Nifty 50 index data was gathered from the NSE website during a five-year timeframe. The study employs a descriptive research design. The study relies on secondary data. Secondary data is gathered from sources such as the National Stock Exchange (NSE), the Reserve Bank of India (RBI), Money Control, and Yahoo Finance. Findings: Precisely eight equities were identified to be representative of the efficient portfolio out of 50 assessed for the analysis. The portfolio construction yield was 36.54 percent, greater than the normal Nifty return, and the portfolio beta is well connected with the stock market. Research Implications: This research will help traders build the best portfolio possible using any selection of equities they choose. Scope for future work / Research limitations: Several other generic and macroeconomic factors influence the behavior of a stock, therefore investors should take these into account when choosing stocks for their ideal portfolio. Other indices and different combinations of equities can be used to do further research. Originality/value: Through using Sharpe's Single Index Model, this study seeks to design an efficient portfolio by evaluating 50 stocks that make up the Nifty 50 index. Keywords: Portfolio Risk-Return, Beta value, Optimal Portfolio Paper Type: Research Paper

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