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Journal articles on the topic 'BSE SENSEX'

Author: Grafiati

Published: 11 September 2023

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1

Sudhamathi, R. K. "Forecasting bse sensex movement using arima modelling." Asian Journal of Research in Business Economics and Management 11, no. 7 (2021): 11–17. http://dx.doi.org/10.5958/2249-7307.2021.00007.4.

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2

Mukherjee, I., Soumya Chatterjee, A. Giri, and P. Barat. "Understanding the pattern of the BSE Sensex." Physica A: Statistical Mechanics and its Applications 482 (September 2017): 262–75. http://dx.doi.org/10.1016/j.physa.2017.04.026.

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3

Chandra, N. Rama, M. Ramesh, M. Bhupathi Naidu, and M. Venkataramanaiah. "Forecasting of BSE sensex using neural networks." ACADEMICIA: An International Multidisciplinary Research Journal 12, no. 11 (2022): 249–59. http://dx.doi.org/10.5958/2249-7137.2022.00881.3.

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4

Singh, Amit Kumar, Rajat Agarwal, and Rohit Kumar Shrivastav. "Returns and Volatility Spillover Between BSE SENSEX and BSE SME Stock Exchange of India." SEDME (Small Enterprises Development, Management & Extension Journal): A worldwide window on MSME Studies 48, no. 3 (September 2021): 257–71. http://dx.doi.org/10.1177/09708464211070054.

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Investigating the impact of volatility spillover among various markets has been the subject matter of numerous research. This study investigates the dynamic relationship between the Bombay Stock Exchange index (SENSEX) and the small and medium enterprises (SME) stock index (BSE SME) in India. The study uses univariate autoregressive conditional heteroskedasticity (ARCH)/generalised autoregressive conditional heteroskedasticity (GARCH) models to model the time-varying volatility of the BSE SME market and multivariate BEKK-GARCH analysis to model the volatility of the SENSEX and BSE SME Index considering the existence of some linkages between them. The study is based on the daily stock indices data ranging from 16 August 2012 to 31 March 2021. Furthermore, the study reveals statistically significant internal volatility spillovers in the SME stock market and the cross-volatility transmission between the two indices. It affirms statistically significant volatility and return spillover between the main market index, SENSEX and SME index, BSE SME. The findings of this research have important implications for the diversification process. It provides crucial signals to investors, portfolio managers and policymakers, especially when there has been much impetus and promotion from the Indian government and emerging foreign investments in India’s SMEs in recent years.

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5

P. Sakthivel, S. Rajaswaminathan, R. Renuka, and N. R.Vembu. "Dynamic Relationship between Crude Oil and Stock Prices in India: Before And After the Subprime Financial Crisis 2008." GIS Business 14, no. 6 (November 26, 2019): 96–104. http://dx.doi.org/10.26643/gis.v14i6.11683.

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This paper empirically discovered the inter-linkages between stock and crude oil prices before and after the subprime financial crisis 2008 by using Johansan co-integration and Granger causality techniques to explore both long and short- run relationships. The whole data set of Nifty index, Nifty energy index, BSE Sensex, BSE energy index and oil prices are divided into two periods; before crisis (from February 15, 2005 to December31, 2007) and after crisis (from January 1, 2008 to December 31, 2018) are collected and analyzed. The results discovered that there is one-way causal relationship from crude oil prices to Nifty index, Nifty energy index, BSE Sensex and BSE energy index but not other way around in both periods. However, a bidirectional causality relationship between BSE Energy index and crude oil prices during post subprime financial crisis 2008. The co-integration results suggested that the absence of long run relationship between crude oil prices and market indices of BSE Sensex, BSE energy index, Nifty index and Nifty energy index before and after subprime financial crisis 2008.

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6

Singh, Ravi. "BSE Sensex: Case study of political events as a major factor which impacts Sensex." International Journal of Research in Finance and Management 1, no. 1 (January 1, 2018): 09–12. http://dx.doi.org/10.33545/26175754.2018.v1.i1a.3.

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7

Seal, Jayanta Kumar, and Jasbir Singh Matharu. "Long-Term Performance of Buybacks in India." Global Business Review 19, no. 6 (September 3, 2018): 1554–66. http://dx.doi.org/10.1177/0972150918794737.

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This study tries to find out the post-announcement performance of the buyback firms over the long term in India using data from the Bombay Stock Exchange (BSE). We have chosen the event study methodology and compared the performance for a one-year, a three-year, and a five-year period. The benchmarks chosen are the Sensex, the BSE 500, the size matched firms and the size and industry matched firms. The findings of the study show overperformance by buyback firms when compared to the Sensex and the BSE 500 but no conclusion can be drawn when compared to the size matched firms and size and industry matched firms.

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8

Sudhamathi, R. K., and M. Ganeswari. "Relationship between FDI and BSE Sensex – An Empirical Study." Asian Journal of Research in Social Sciences and Humanities 9, no. 4 (2019): 1. http://dx.doi.org/10.5958/2249-7315.2019.00007.8.

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9

T., Lakshmanasamy. "Relationship Between Exchange Rate and Stock Market Volatilities in India." International Journal of Finance Research 2, no. 4 (November 8, 2021): 244–59. http://dx.doi.org/10.47747/ijfr.v2i4.443.

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With increasing globalisation and integration of national stock exchanges, for the global investor, the portfolio risk increases not only from the local stock market volatility but also in the exchange rate risk. This paper examines the exchange rate volatility effect on volatility in stock market return from India’s perspective for the period January 2010 to December 2015, applying ARCH and GARCH estimation. The daily data of the BSE SENSEX returns, exchange rates of US dollar/rupee, British pound/rupee, Euros/rupee are used. It is estimated that the Euro/rupee exchange rate volatility has a significant positive effect on the BSE SENSEX return volatility, while the effect of the US dollar/rupee and British pound/rupee exchange rate the volatilities are insignificantly negative. The larger GARCH parameter over the ARCH term indicates that the own lagged values of the stock return cause more volatility in stock returns than the innovations. There exists a highly persistent effect of shocks to the BSE SENSEX return and the volatility effect wanes only slowly

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10

Ramachandra, N., M. Bhupathi Naidu, Sk Nafeez Umar, and K. Murali. "Arima Model with Box-Cox Transformed Univariate Variable in BSE Sensex." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1010–16. http://dx.doi.org/10.22214/ijraset.2022.47509.

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Abstract: Fluctuation of the stock market’s impact on investments of stocks. Sensex prediction plays an important role in the investment of markets. Predicting the stock market is difficult in market scenarios. The present study attempted to predict the stock market due to its complicated features and also compared different Auto-Regressive Integrated Moving Average (ARIMA) models to get the appropriate stock forecasting model using various Box-Cox transformations by using BSE Sensex past daily closing data. The ARIMA Model6 (0 1 0) showed accurate results in calculating the Mean Absolute Percentage Error (MAPE) and Bayesian Information Criterion (BIC) values, which indicates the potential of using the ARIMA model for accurate stock forecasting

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11

Dr.M.Anbukarasi, Dr M. Anbukarasi. "A Methodological Analysis On Impact Of Institutional Investments On Bse Sensex Return." Indian Journal of Applied Research 3, no. 11 (October 1, 2011): 67–69. http://dx.doi.org/10.15373/2249555x/nov2013/21.

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12

Pandey, Dr Viplaw Kishore. "The Correlation analysis of COVID-19 result and Stock Market : Study of BSE-Sensex." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 5669–72. http://dx.doi.org/10.17762/turcomat.v12i3.2241.

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It has been observed that from the month of Nov, 2020 BSE Sensex is back in its mood and is going has made an all time high. Now the market is in a jubilant mood. During the COVID-19 period the market has behaved like a roller costa and every good and bad news about the Pandemic has equivalently affected the Sensex too, if not in absolute terms at least in terms of its direction. The initial days of the Covid-19 pandemic has created a huge losses to the investors in the financial market. Almost all the economic activity was stopped which has resulted in a huge loss to the business. After the removal of the lockdown in phased wise manner the market particularly the stock market has a frequent swing and in last couple of month it has been able to reach all time high mark of 50 K. The present article aims to analyse the journey of the BSE Sensex during the COVID-19 period from January second week of 2020 till date

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13

Jain, Aayush. "Ascertaining Price Determinants and Forecasting Model for BSE Sensex Stocks." Asian Journal of Research in Banking and Finance 7, no. 5 (2017): 60. http://dx.doi.org/10.5958/2249-7323.2017.00029.3.

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14

Subbarayan, Baranidharan, and S. Vanitha. "Cointegration and Causality of Macroeconomic Variables towards BSE Sensex Returns." Asian Journal of Research in Business Economics and Management 4, no. 12 (2014): 41. http://dx.doi.org/10.5958/2249-7307.2014.01004.4.

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15

Polisetty, Aruna, Dr D. Prasanna Kumar, and Mrs Jikku Susan Kurian. "Influence of Exchange Rate on BSE Sensex & NSE Nifty." IOSR Journal of Business and Management 18, no. 09 (September 2016): 10–15. http://dx.doi.org/10.9790/487x-1809021015.

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16

Ramesh, M., C. Mani, B. Hari Mallikarjuna Reddy, and M. Venkataramanaiah. "Forecasting of bse sensex using simple exponential smoothing (SES) method." ACADEMICIA: An International Multidisciplinary Research Journal 11, no. 3 (2021): 656–65. http://dx.doi.org/10.5958/2249-7137.2021.00630.3.

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17

Kavita. "Volatility of Indian Stock Market-A Study of BSE Sensex." MERI-Journal of Management & IT 11, no. 1 (October 1, 2017): 67. http://dx.doi.org/10.25089/meri/2017/v11/i1/164013.

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18

Chatterjee, Soumya, Indranil Mukherjee, and P. Barat. "Analysis of the behaviour of the detrended BSE sensex data." Chaos, Solitons & Fractals 113 (August 2018): 186–96. http://dx.doi.org/10.1016/j.chaos.2018.06.005.

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19

Yadav, Anita, and Pankaj Kumar. "The Role of FDI in the Development of the Indian Stock Market." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1187–92. http://dx.doi.org/10.22214/ijraset.2022.40738.

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Abstract: With the extreme advancement in the Foreign Direct Speculation (FDI) system during past years, Indian capital market has had the option to pull in unfamiliar speculators impressively. There has been critical upsurge in the unfamiliar direct interest in India. With this, India has arisen as quite possibly the most preferred objective for interest on the planet. Since the turn of events and the unpredictability of the Indian financial exchange has been generously affected by the inflow of FDI. Consequently, the current paper Endeavor to dissect the effect of FDI inflows on the development of BSE (Bombay Stock Trade) SENSEX and NSE (National stock trade) CNX Nifty during the time frame under investigation. The examination is basically founded on ten long time optional information for the time frame from April 2006 to March 2016. The Simple Linear Regression (stepwise strategy), Karl Pearson's coefficient of relationship, Analysis of Variance, Normal P- P plot, disperse plot, Histogram, spellbinding measurements (Mean and Standard Deviation), Compounded Annual Growth Rate, Trend Percentage and so on are the devices for the examination of information utilizing the factual bundle for sociologies (SPSS). FDI was found altogether connected with both the business sectors with the coefficient of relationship being 0.666 what's more, 0.682 separately. It was additionally discovered that FDI has influenced BSE SENSEX up to 44 percent and CNX Nifty up to 47 percent. The study inferred that progression of FDI in India has critical effect on BSE SENSEX and NSE Nifty developments. Keeping in view the discoveries of the examination, it has been proposed that the public authority of India alongside its executing bodies should attempt to pull in additional furthermore, more FDI for the smooth and fast advancement of the stock market and the economy in general. Keywords: Regression, Correlation, Indian Stock Market, Stock market development, BSE Sensex, NSE-Nifty, FDI

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20

Saldanha, Avil, and Rajendra Desai. "A Study of Calendar Effect on Stocks in the BSE Sensex." International Journal of Management Studies VI, no. 1(7) (January 30, 2019): 111. http://dx.doi.org/10.18843/ijms/v6i1(7)/14.

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21

Bhullar, Pritpal Singh, Pradeep Gupta, and Dyal Bhatnagar. "Impact of Covid-19 on Volatility of BSE Sensex Stock Index." International Journal of Electronic Finance 11, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijef.2022.10044813.

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22

Bhullar, Pritpal Singh, Pradeep Kumar Gupta, and Dyal Bhatnagar. "Impact of COVID-19 on volatility of BSE Sensex stock index." International Journal of Electronic Finance 11, no. 2 (2022): 175. http://dx.doi.org/10.1504/ijef.2022.122185.

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23

Mishra, Shraddha, and Raj Kumar. "Investigation of overvalued and undervalued stocks: the case of BSE Sensex." International Journal of Business Excellence 10, no. 2 (2016): 177. http://dx.doi.org/10.1504/ijbex.2016.077993.

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Mishra, Shraddha, and Raj Kumar. "Investigation of overvalued and undervalued stocks: the case of BSE Sensex." International Journal of Business Excellence 10, no. 2 (2016): 177. http://dx.doi.org/10.1504/ijbex.2016.10000039.

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25

Sharma, Aviral, Vishal Bhatnagar, and Abhay Bansal. "SENSEX Price Fluctuation Forecasting Comparison Between Global Indices and Companies Making It." Journal of Global Information Management 26, no. 3 (July 2018): 90–104. http://dx.doi.org/10.4018/jgim.2018070107.

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This article describes how the stock markets form the pivot point in any economy and the health of the economy is depicted by the major indices of that market. These indices tell the overall working of the markets. SENSEX of the sensitivity index of Bombay Stock Exchange (BSE) and is one of the major stock indices traded in India which is impacted by a large number of global and domestic factors. A fall in the stock market of the United States of America or any other global market triggers a change in SENSEX as well. Thus, showcasing the high-end correlation between global markets. In this article, the authors are analyze and forecast the impact of major world indices on the SENSEX using ANN's.

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26

Shaik, Muneer, and Maheswaran S. "Evidence of excess volatility based on a new robust volatility ratio." Journal of Economic Studies 45, no. 4 (September 10, 2018): 855–75. http://dx.doi.org/10.1108/jes-06-2017-0150.

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Purpose The purpose of this paper is twofold: first, to propose a new robust volatility ratio (RVR) that compares the intraday high–low volatility with that of the intraday open–close volatility estimator; and second, to empirically test the proposed RVR on the cross-sectional (CS) average of the constituent stocks of India’s BSE Sensex and US’s Dow Jones Industrial Average index to find the evidence of “excess volatility.” Design/methodology/approach The authors model the proposed RVR by assuming the logarithm of the price process to follow the Brownian motion. The authors have theoretically shown that the RVR is unbiased in the case of zero drift parameter. Moreover, the RVR is found to be an even function of the non-zero drift parameter. Findings The empirical results show that the analysis based on the RVR supports the existence of “excess volatility” in the CS average of the constituent stocks of India’s BSE Sensex and US’s Dow Jones index. In particular, the authors have observed that the CS average of individual constituent stocks of BSE Sensex is found to be more excessively volatile than the US’s Dow Jones index during the period of the study from January 2008 to September 2016, based on multiple k-day time window analysis. Practical implications The study has implications for the policy makers and practitioners who would like to understand the volatility behavior in the asset returns based on the RVR of this study. In general, the proposed model can be used as a specification tool to find whether the stock prices follow the random walk behavior or excessively volatile. Originality/value The authors contribute to the existing volatility literature in finance by proposing a new RVR based on extreme values of asset prices and absolute returns. The authors implement the bootstrap technique on RVR to find the estimates of mean and standard error for multiple k-day time windows. The RVR can capture the excess volatility by comparing two independent volatility estimators. This is possibly the first study to find the CS average of all the constituent stocks of BSE Sensex based on the RVR.

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27

Das, Santu, Jamini Kanta Pattanayak, and Pramod Pathak. "Effect of quarterly earnings announcement under different market conditions." Journal of Indian Business Research 6, no. 2 (June 10, 2014): 128–54. http://dx.doi.org/10.1108/jibr-09-2013-0087.

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Purpose – The main purpose of this research study is to investigate the impact of quarterly earnings announcements on stock price movement of the firms constituting the SENSEX under two different market conditions – booming followed by recessionary. Analysis of price effect of quarterly earnings announcements during the five-year period prior to trading suspension, which is also characterized by a booming market condition have been made. Similar analysis during the five-year period following the trading suspension and marked by recessionary market condition has also been carried out side by side. Design/methodology/approach – Event study methodology using daily returns and market model has been used for the purpose of analyzing the quarterly earnings announcement effects on the security prices of the firms. A sign test has also been used along with the event study. Findings – The study reveals that quarterly earnings announcement does not have statistically significant effect on stock returns during the booming as well as the recessionary market conditions. The impact of quarterly earnings announcements on stock price movement of firms constituting the SENSEX has been similar for both periods undertaken in the study. Research limitations/implications – The study has been undertaken using the firms listed in BSE SENSEX. The effect of the quarterly earnings announcement with reference to firms listed in other indices, if covered, may provide different sets of results. Originality/value – The paper identifies the informational value of quarterly earnings announcement of BSE-SENSEX.

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28

Upadhyay, Deepika, and Swetha Wenona Suvarna. "Impact of Demonetization on the Indian Stock Market." Paradigm 22, no. 2 (August 7, 2018): 175–84. http://dx.doi.org/10.1177/0971890718788226.

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Demonetization is the act of eradicating a currency unit from circulation. Indian economy witnessed this on 8 November 2017 when Prime Minister Narendra Modi announced that the two highest denomination currency notes, that is, ₹500 and ₹1,000 ceased to be legal tender. As most of the transactions in the country are based on cash only, the announcement resulted into huge hue and cry nationwide. It was estimated that approximately 86 per cent of cash was washed off from circulation. The currency notes that were rendered invalid were replaced by the new currency notes of ₹500 and ₹2,000 later. The article intends to investigate the impact of demonetization on the Bombay Stock Exchange (BSE). An event study methodology has been used to analyse the impact of the announcement on its most important index—S&P (Standard & Poor’s) BSE SENSEX index and the 30 top trading stocks which comprise S&P BSE SENSEX. The study period is divided into pre- and post-demonetization announcement. The empirical results indicate that there was no striking impact of the demonetization announcement on the stock returns during the period of the study.

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29

P., Savitha. "Foreign Capital Inflows (FII and FDI) and its Impact on BSE Sensex." International Journal of Management Studies VI, no. 1(8) (January 30, 2019): 70. http://dx.doi.org/10.18843/ijms/v6i1(8)/11.

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30

Raychaudhuri, Debaditya. "BSE Sensex Closing Index Data Analysis and Forecasting using the ARIMA Model." International Journal of Computer Sciences and Engineering 7, no. 6 (June 30, 2019): 379–89. http://dx.doi.org/10.26438/ijcse/v7i6.379389.

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31

Ramesh, M., C. Mani, B. Hari Mallikarjuna Reddy, and M. Venkataramanaiah. "Forecasting of bse sensex using auto regressive integrated moving average (arima) method." ACADEMICIA: AN INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL 11, no. 2 (2021): 203–13. http://dx.doi.org/10.5958/2249-7137.2021.00341.4.

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32

Arulraj, Malarvizhi, Meghana Pvs, and R. Karthika. "Global Portfolio Optimization for BSE Sensex using the Enhanced Black-Litterman Model." Procedia Engineering 38 (2012): 2987–97. http://dx.doi.org/10.1016/j.proeng.2012.06.349.

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33

Broca, Dilbagh S. "Monitoring Volatility Changes on the Bombay Bourse: A Control Chart Approach." Vikalpa: The Journal for Decision Makers 20, no. 3 (July 1995): 43–52. http://dx.doi.org/10.1177/0256090919950304.

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In this study by Dilbagh S Broca, a control chart approach is developed for dynamically monitoring changes in the volatility of aggregate share prices on the Bombay Stock Exchange (BSE) as measured by the BSE Sensitive Index of Equity Prices. This approach is tested on daily Sensex movements from 1990 through 1992 and the results show that several volatility changes may have taken place many of which either signal the onset⁄decline of speculative manias witnessed in recent years or broadly correlate with release of extraordinary information.

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34

Mukhopadhyay, Debabrata. "Indian Stock Market and Macroeconomic Variables: An Empirical Investigation Based on BSE SENSEX." MUDRA : Journal of Finance and Accounting 6, no. 2 (December 1, 2019): 13. http://dx.doi.org/10.17492/mudra.v6i2.188930.

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35

Bora, Bedanta, and Anindita Adhikary. "Risk and Return Relationship -An Empirical Study of BSE Sensex Companies in India." Universal Journal of Accounting and Finance 3, no. 2 (April 2015): 45–51. http://dx.doi.org/10.13189/ujaf.2015.030203.

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36

Singh, Rajdeep, Kanwaljeet Singh, and Prabhjot Kaur. "DYNAMICS OF FOREIGN INSTITUTIONAL INVESTMENTS AND EQUITY RETURNS IN INDIA." International Journal of Research -GRANTHAALAYAH 4, no. 6 (June 30, 2016): 1–7. http://dx.doi.org/10.29121/granthaalayah.v4.i6.2016.2630.

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India has become a focus point and an attractive hub for foreign investor’s post 199. This international flow of capital was facilitated by increased globalization and the growth of information technology which has blurred national borders. Thus FII flows in India have continuously grown in importance post 1991. This paper examines the trend of FII flow in India from 2001 and 2015 and also examines the relationship between FII and the two important barometers of the Indian stock market, i.e., S&P BSE Sensex and CNX Nifty. The impacts of FII on the proxies for stock market, i.e., Sensex and nifty have been studied by employing simple regression analysis using E-views.

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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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38

Ramaiah Ramasamy, Rajamohan, and Sathish Pachiyappan. "Holding period for positive return from Indian mutual funds." Investment Management and Financial Innovations 16, no. 1 (April 2, 2019): 346–64. http://dx.doi.org/10.21511/imfi.16(1).2019.27.

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In India, households predominantly prefer to invest their surplus in financial securities, which provide stable return irrespective of whether they beat inflation or help in creating wealth. However, financial planners advise their clients to invest their surplus for long term in risky assets such as mutual funds to generate inflation beating returns. But when households ask for the meaning of long term in a definite number, it varies among the financial advisors. Hence, the study made an attempt to answer this question by calculating the minimum time duration required to generate a minimum positive return for two indices (NIFTY 50, S&amp;P BSE SENSEX) and 6 mutual fund schemes for a period of 23 years and the same two indices (NIFTY 50, S&amp;P BSE SENSEX) and 20 mutual fund schemes for a period of 12 years and found out that the time horizon or the long term to ensure minimum positive return ranges from 5 years to 9 years depending up on the type of fund or the level of risk associated with the mutual fund schemes.

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Bhatia, Aparna, and Subhash Chander. "Corporate Social Responsibility Disclosure by SENSEX Companies in India." Management and Labour Studies 39, no. 1 (February 2014): 1–17. http://dx.doi.org/10.1177/0258042x14535161.

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Economic and social performances are the two strong pillars of sustainable corporate growth. The companies in India are now showing a genuine interest in the upliftment of the stakeholders they serve. They have started giving Corporate Social Responsibility (CSR) a place in their overall strategies of growth. This paper studies the extent of CSR disclosure made by leading companies constituting BSE SENSEX in India. The disclosure practices of 25 of these companies have been studied for the year 2009–2010 by preparing a CSR Index. Content Analysis has been used. Company-wise score and item-wise score has been calculated. The results show that the CSR disclosure by the leading companies in India is low. The company-wise mean disclosure is just 31 per cent while the category-wise mean disclosure is 40.32 per cent. The category of ‘Others’ followed by ‘Environment’ and then ‘Community Involvement’ are the most well-disclosed areas.

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40

Ahluwalia, Hardeepika Singh, and Kulbir Kaur Bhatti. "Study of Relationship Between USD/INR Exchange Rate and BSE Sensex from 2008-2017." International Journal of Management Studies V, no. 3(1) (July 1, 2018): 106. http://dx.doi.org/10.18843/ijms/v5i3(1)/13.

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41

Sriram, M. "Determinants of RoE of S&P BSE Sensex Companies : A Panel Data Analysis." Indian Journal of Finance 12, no. 9 (September 11, 2018): 56. http://dx.doi.org/10.17010/ijf/2018/v12i9/131564.

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Bhatia, Aparna, and Poonam Mahajan. "Determinants of Corporate Social Disclosure: An Empirical Study of BSE-SENSEX Companies in India." IIMS Journal of Management Science 4, no. 2 (2013): 191. http://dx.doi.org/10.5958/j.0976-173x.4.2.015.

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V. Selvakumar, Dipak Kumar Satpathi, Abhinav Chhabra, and Arjita Nema. "DEEP LEARNING AND MACHINE LEARNING MODELS TO FORECAST BSE AND NIFTY SENSEX IT INDEX." Advances and Applications in Statistics 82 (October 22, 2022): 9–26. http://dx.doi.org/10.17654/0972361722077.

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The primary objective of this work is to build an appropriate mathematical model that helps predict the direction of stock market indices. The stock market is volatile and dynamic, and prediction of its movement will help investors make more optimal strategies and boost their profit. In this context, the data was collected from two major IT indices of India, the BSE IT Index and the NIFTY IT Index. In modeling the time series, autoregressive integrated moving average (ARIMA) was used initially, followed by various machine learning models, like artificial neural network (ANN), recurrent neural network (RNN), and convolutional neural network (CNN). The data analysis exhibited superior performance of ANN models compared to other models for performance criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). This exploratory analysis concluded that ANN models outperform other predicting models to greater accuracy, augmenting previous literature on stock market analysis. This machine learning approach would help investors design optimal strategies and boost their profits in the world of stock market.

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Banerjee, Kinjal, Chandradew Sharma, and N. Bittu. "Plunges in the Bombay stock exchange: Characteristics and indicators." International Journal of Modern Physics B 31, no. 22 (September 5, 2017): 1750160. http://dx.doi.org/10.1142/s0217979217501600.

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We study the various sectors of the Bombay Stock Exchange (BSE) for a period of eight years from January 2006–March 2014. Using the data of the daily returns of a period of eight years we investigate the financial cross-correlation co-efficients among the sectors of BSE and Price by Earning (PE) ratio of BSE Sensex. We show that the behavior of these quantities during normal periods and during crisis is very different. We show that the PE ratio shows a particular distinctive trend in the approach to a crash of the financial market and can therefore be used as an indicator of an impending catastrophe. We propose that a model of analysis of crashes in a financial market can be built using two parameters: (i) the PE ratio and (ii) the largest eigenvalue of the cross-correlation matrix.

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Sahu, Chinmoy. "Effectiveness of ‘Dogs of the Dow’ Investment Strategy in the Indian Context." Vikalpa: The Journal for Decision Makers 26, no. 1 (January 2001): 65–72. http://dx.doi.org/10.1177/0256090920010106.

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Investors have always yearned for ways to beat the market. In recent years, one popular strategy among American investors involves a portfolio comprising of the ten highest yielding stocks selected from among the 30 stocks in the Dow Jones Industrial Average (DJIA), one of the most popular stock indices of the US. Such ft portfolio based on the Dow Dividend Strategy (DDS) came to be known as the ‘Dogs of the Dow.’ Portfolio of ‘Dogs of the Dow’ has been found to outperform the Dow on numerous occasions. This paper studies the effectiveness of such a strategy in the Indian context by applying the same strategy to similar stocks in the 30- stock Sensitive Index (Sensex) of Stock Exchange, Mumbai (BSE) and evaluates the performance of ‘Dogs of the Sensex’ portfolio during the late 90s.

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Kumar, Dr Vishal, and Ritu Rani. "Performance Evaluation of Selected Banking Stocks Listed on Bombay Stock Exchange During Pre & Post Covid-19 Crisis." International Journal of Innovation and Economic Development 7, no. 3 (August 2021): 53–61. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.73.2005.

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Investing in the stock market has always been regarded as risky. Market sentiment is a factor that influences stock prices. The purpose of this study is to assess the performance of selected banking stocks based on risk and excess return generated by them during the study period. The study also determines the effect of certain financial variables on sample banking stocks during the time crisis of Covid’19. Economic variables such as the BSE Sensex, rate of exchange, variation in FII (Foreign Institutional Investors), and coupon rate of Government Sector (G-Sec) were analysed in conjunction with the analysis of banking stocks. The regression and correlation tests are used to determine the significance of variables using SPSS. Following the BSE’s performance provides insight into the future modifications throughout the price levels of bank shares. Following a sharp decline in the market, private sector bank stock prices are correct, but not public sector bank stock prices. Throughout the first part of the research, there is a direct relationship between the BSE, Sensex, and the selected stocks, but only a weak correlation with FII, G-Sec coupon rate, and the exchange rate. Along the second part of the research, the relationship between stock prices and economic variables varies widely between banks.

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Som, Bhupender Kumar, and Himanshu Goel. "Analyzing Dependence of Key Macroeconomic Variables on BSE Using Regression." International Journal of Applied Behavioral Economics 11, no. 1 (January 1, 2022): 1–12. http://dx.doi.org/10.4018/ijabe.308782.

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This paper aims to analyze the dependence of key macroeconomic variables on Bombay Stock Exchange (BSE) Sensex using regression modelling technique in R-studio. Monthly data points spanning a period of last years from 2012 to 2019 has been used for the empirical investigation. The results of the model indicate that Long Term Interest Rate (LTINT), Consumer Price Index (CPI) and Morgan Stanley Capital International (MSCI) are found to be significant while Index of Industrial Production (IIP) and Foreign Exchange (FX) are insignificant. Also, the value or r-square indicates that 93 percent of the variation in the dependent variable is explained by the selected Independent variables. Also, the dataset was checked for normality and linearity using appropriate graphs. The results of this paper will be of immense use for the investors in predicting the stock price movement.

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Chethan, N., and R. Sangeetha. "Sentiment Analysis of Twitter Data to Examine the Movement of Exchange Rate and Sensex." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3323–27. http://dx.doi.org/10.1166/jctn.2020.9179.

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In this paper tweets available on social media about USD/INR exchange rate, BSE Sensex, NSE Nifty have been collected and Sentiment Analysis using R programming has been performed. A sentiment score has been obtained for each of the sentences and also word cloud plot have been obtained. In this paper twitter feeds are collected using the keywords: USD/INR, #USD/INR, #BSE, #Sensex, #NSE. For the purpose of obtaining the tweets, R programming is used. In this study to obtain the word cloud plot, the sentiment has been classified across 8 categories viz Anticipation, anger, trust, surprise, sadness, joy, fear and disgust. On a day to day basis, Sentiment Analysis gives the overall sentiment on a given day stating if the sentiment for a given day is either Positive or Negative or whether it is Neutral. It also breaks down the tweets into various categories which help in identifying the moods of the investors not only by the sentiment but also by the number of tweets. Further, the word cloud plot offers a simple and effective way of capturing the key events or news which was discussed on Twitter. Sentiment analysis can be used effectively by investors to make a prediction of what direction the stock price movements will happen based on the sentiment prevailing in the market. This study also shows how R programming can be used to perform sentiment analysis on the stock price movement based on twitter feeds. Word cloud can be used to visualize text data in which the size of each word cloud denotes its significance.

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Joshi, Bhagawati P., and Sanjay Kumar. "Fuzzy Time Series Model Based on Intuitionistic Fuzzy Sets for Empirical Research in Stock Market." International Journal of Applied Evolutionary Computation 3, no. 4 (October 2012): 71–84. http://dx.doi.org/10.4018/jaec.2012100105.

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Intuitionistic fuzzy sets introduced by Atanassov are generalization of fuzzy sets as they also handle the non-determinacy which is caused by degree of hesitation of decision maker. The present study proposes a computational method of forecasting for fuzzy time series. In the proposed method the notion of intuitionistic fuzzy set is used in fuzzy time series forecasting with simplified computational approach. The developed model has been tested on the movement of share market prices of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India. Further the method has been implemented for forecasting SENSEX of BSE. The suitability of the developed model has also been examined by comparing it with the other existing models to show its superiority.

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Parasuraman, N. R., P. Janaki Ramudu, and Nusrathuunisa . "Does Lintner model of dividend payout hold good? An Empirical evidence from BSE SENSEX firms." SDMIMD Journal of Management 3, no. 2 (September 1, 2012): 63. http://dx.doi.org/10.18311/sdmimd/2012/2743.

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This study primarily investigates into as to what influenced the dividends payment of BSE constituent companies for the years 2002 through as latest as 2011. The primary model used is that of Lintner (1956) with addition of relevant factors. The study tests three models including Lintner's basic model. While dividends paid is criterion variable in all the models, basic earnings and lagged dividends are predictor variables in the first model (Lintner model, 1956), cash earnings and lagged dividends in the second model and growth opportunities (depreciation and capital expenditure) in the third model are the predictor variables. The study tests the hypotheses if the dividends paid (criterion variable) depended on basic earnings, lagged dividends, cash earnings and capital expenditure. The multiple regression analysis has been performed using SPSS 15.0 version through ENTER method for every year and for all the years on an aggregate basis across the sample companies. Significance 'F' revealed that in all the three models dividends paid depended significantly (at 5% significance level) on all predictors variables. The value of multiple 'R' indicated that the models were very strong. Coefficient of determination (R<sup>2</sup>) also revealed that the explained portion of the relationship between criterion and predictor variables has been very high and significant enough to accept the model fit. However, standardized beta co-efficients (â) and 't' statistic revealed that basic earnings, cash earnings and lagged dividends exercised highest impact on dividends paid in most of the years during the study period. On the other hand, other predictor variables, depreciation and capital expenditure, did not have any significant impact on the dividends paid. The Durbin Watson coefficient indicated that multi co-linearity among predictor variables was strong enough to accept the validity of the model almost during the entire period of the study. Thus, the results and findings of the study support the prevalence and relevance of Lintner model of dividend policy. This means that the finance manager can't afford to ignore the variables like earnings capacity and lagged dividends while framing a dividend policy.

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