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Artykuły w czasopismach na temat „BSE SENSEX”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 31 lipca 2025

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1

Surana, Lotica. "VAR and VECM models were used to investigate the factors that influence of Indian securities market performance, including the period of Covid 19's financial crises." Journal of Decision Analytics and Intelligent Computing 3, no. 1 (2023): 197–220. http://dx.doi.org/10.31181/10025112023s.

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Using a dummy variable, we evaluate commodities in addition to macroeconomic considerations of the Indian securities market from 2010 to 2021, which includes the era of the Covid 19 crises. We used the Bombay Stock Exchange (BSE) (Sensex) for securities market performance and developed a Vector Auto regressive models that combines the short- and long-run model of economics. On stock price indexes, we discovered that the Indian securities market reflects both macroeconomic indicators and prices of commodity. Growth in the economy, inflation, interest, rates, currency rates, crude oil prices, an

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2

Ajmera, Mukeshkumar C. "ANALYSIS ON RELATIONSHIP AND EFFECT OF GOLD PRICE ON BSE SENSEX INDEX IN INDIA." Journal of Commerce, Economics & Computer Science 11, no. 02 (2025): 153–57. https://doi.org/10.62823/jcecs/11.02.7680.

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An attempt has been made by researcher to study the relation between gold price and its effect on Sensex of BSE. The researcher has selected secondary data. The time for the data is 2015 to 2025. The researcher has used statistical tools like correlation, regression and descriptive statistics. The correlation between gold price and BSE Sensex has been positive and significant. Whereas Regression shows R value indicates very strong and positive relation between gold price and Sensex value. R Square 94.4% shows fluctuation caused by gold price. It is concluded that gold price influences BSE Sens

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3

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 (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 co

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4

Sathya. M. "Impact of Macroeconomic Variables on the BSE Sensex: An Empirical Analysis." Journal of Information Systems Engineering and Management 10, no. 49s (2025): 124–31. https://doi.org/10.52783/jisem.v10i49s.9817.

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Purpose – This study aims to conduct an empirical analysis of the influence of key macroeconomic variables on the BSE Sensex, a benchmark index of the Indian stock market. While existing literature has explored various external factors affecting stock prices, this research specifically focuses on how macroeconomic indicators such as WPI, inflation, IIP, Gold Price, Crude oil, and Exchange rates growth shape the behaviour and volatility of the BSE Sensex. The findings of this study seek to provide valuable insights for investors, policymakers, and researchers, enhancing their understanding of t

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5

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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6

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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7

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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8

Sharma, Nitika, Kshitij Bhargava, and Dixit Sunail. "Risk and Return Relationship: A Study of BSE Sensex Stocks in Indian Stock Market." Journal of Technology Management for Growing Economies 15, no. 1 (2024): 41–61. https://doi.org/10.15415/jtmge/2024.151005.

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Background: The present study is based on risk and return analysis of BSE Sensex stocks, which is the index of the Bombay Stock Exchange, and is based on the secondary data from the past 5 years. The Sensex index is the Bombay Stock Exchange of India’s benchmark that is broadly based on the stock market index of the Indian equity market. Purpose: The main objective of the study was to investigate the risk and return of the stocks listed in Sensex and create a portfolio that reduces the unsystematic risk through diversification. Methods: The study used secondary data from the past 5 years to an

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9

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 (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 f

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10

Ruchi and Rakesh Kumar. "Impact of Investor Sentiment on Stock Market Returns: Evidence from India." Asian Journal of Economics, Business and Accounting 25, no. 5 (2025): 35–42. https://doi.org/10.9734/ajeba/2025/v25i51782.

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Aims: Behavioral finance assumes that investors choose investments as on their varied opinions and sentiments about the market. This study tries to determine the impact of investor sentiment on stock market returns by constructing an Investor sentiment index for the Indian market. Methodology: The present study employs BSE Sensex- financial year returns of ten years, and the sample period ranges from 1 April 2014 to 31 March 2024. The Principal Component Technique is applied to form an investor sentiment index using seven separate market proxies. Results: The regression analysis's beta value o

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11

Bansilal Shah, Dr Milankumar. "Performance and Influence of Banking Sector Companies in the BSE Sensex 30: A Research Study." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45011.

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This study explores the role, performance, and impact of banking sector companies listed on the BSE Sensex 30. The paper examines the market capitalization, shareholder returns, and volatility characteristics of these companies and their contribution to the overall index movement. Statistical tools, including ANOVA, are used to analyze performance variations within the banking sector. Keywords: BSE Sensex, banking sector, shareholder wealth, performance analysis, Indian banks

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12

Allimuthu, Dr S. "A Study on the Interrelationship between the Stock Markets and the Foreign Exchange Market in India." International Journal of Innovative Research in Engineering and Management 10, no. 5 (2023): 83–87. http://dx.doi.org/10.55524/ijirem.2023.10.5.14.

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This study aims at examining the short-run and long-run dynamic linkages among exchange rates and stock market index in India through a structured cointegration and Granger causality tests. Daily exchange rates of USD, EUR, JPY, and GBP to INR along with the daily movement of NSE NIFTY and BSE SENSEX for a period spanning 20 years from 1 January 2003 to 23 November 2022 were used for the analysis. The results reveal that there is no evidence for a stable long-run relationship between stock market index and the exchange rates under study. However, the VAR-based Granger causality test shows that

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13

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 (2018): 09–12. http://dx.doi.org/10.33545/26175754.2018.v1.i1a.3.

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14

Seal, Jayanta Kumar, and Jasbir Singh Matharu. "Long-Term Performance of Buybacks in India." Global Business Review 19, no. 6 (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 indust

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15

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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16

T., Lakshmanasamy. "Relationship Between Exchange Rate and Stock Market Volatilities in India." International Journal of Finance Research 2, no. 4 (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

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17

Mishra, Rohit, Sarvesh Mohania, and Vishesh Dahiya. "COVID-19, a Past Perspective for the Future: With Reference to BSE SENSEX." Asian Journal of Economics, Business and Accounting 25, no. 4 (2025): 423–30. https://doi.org/10.9734/ajeba/2025/v25i41760.

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This Research paper studies the impact of Covid-19 on Bombay Stock Exchange (BSE SENSEX) in the year 2020 (January to March 2020) and how the V shape recovery is seen in the economy. Covid-19 affected not only Indian Capital Markets but had an impact all over the world. This has led to one of the greatest downfalls in the world stock market. S&P BSE SENSEX was trading more than 42000 index in January 2020 but at the end of January it closed at its lowest level in 2020 at 40,723 points. Expectations from the Union Budget-2020-21 along with the December quarter earning had kept investors jit

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18

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 (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 Perc

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19

Kalimuthu, K., and S. Shaik. "Sustainability Indices and the Calendar Effect." Finance: Theory and Practice 28, no. 1 (2024): 43–51. http://dx.doi.org/10.26794/2587-5671-2024-28-1-43-51.

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The investing landscape has undergone a significant shift. Investors are interested in stocks that not only increase shareholder wealth but also give high priority to environmental, social, and governance issues. The purpose of the study is to examine the presence of a calendar effect on the BSE sustainability indices. The daily closing prices of the BSE CARBONEX, BSE GREENEX, BSE 100, BSE Sensex, and Nifty have been collected. The study is using various methods like descriptive statistics, the unit root test, the day of the week return, the ordinary least squares method (OLS), and the GARCH (

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20

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 (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 r

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21

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 (2011): 67–69. http://dx.doi.org/10.15373/2249555x/nov2013/21.

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22

R., Nalini, and Ashwini S. "IMPACT OF FOREIGN DIRECT INVESTMENT (FDIs) ON INDIAN STOCK MARKET." Shanlax International Journal of Arts, Science and Humanities 6, S2 (2019): 208–17. https://doi.org/10.5281/zenodo.2566263.

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<em>The international investment for Indian capital market is predominantly sourced through Foreign Direct Investment (FDI) for primary market and Foreign Institutional Investment (FII) for secondary market. The benefits of stability, productivity and control make FDI a preferable investment avenue for any developing economy. This paper attempts to study the trends of FDIs and Indian capital market as revealed by the BSE SENSEX AND NSE NIFTY. The study also examines whether there exists significant relationship between fund flow FDIs and Indian stock Market (NSE-CNX NIFTY and BSE SENSEX). The

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23

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 (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 developm

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24

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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25

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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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 (2016): 10–15. http://dx.doi.org/10.9790/487x-1809021015.

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

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29

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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30

Dutta, Deep. "ARIMA-Based Forecasting of S&P BSE SENSEX Returns." Management Journal for Advanced Research 3, no. 6 (2023): 1–8. http://dx.doi.org/10.54741/mjar.3.6.1.

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Investment in the stock market requires a delicate balance between profitability and risk management, with risk aversion playing a vital role. This study explores the ARIMA forecasting method to predict S&P BSE SENSEX returns, providing valuable insights for investors and financial experts. Using a 3-year dataset, the ARIMA (3,1,1) model was identified as the optimal choice. Diagnostic checks confirmed its reliability, ensuring unbiased and accurate forecasts. In static forecasting, the model exhibited high-quality performance with low error rates. Dynamic forecasting further revealed prec

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31

Pathak, Harsh Raj, and Satish Kumar. "Do Crude Oil Price Fluctuations Affect the Sectoral Stock Returns: Evidence from India." Journal of Commerce and Accounting Research 13, no. 3 (2024): 31–43. http://dx.doi.org/10.21863/jcar/2024.13.3.004.

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Crude oil is a vital energy source for industrialised and developing countries. Both investors who trade crude oil derivatives and firms that use oil as raw material for production monitor oil supply and demand. Disruption in the flow of oil in the commodity markets, therefore, leads to oil price volatility that affects major economies worldwide. Often, geopolitical and natural conditions could adversely impact the oil price and the financial markets react to such fluctuations in oil prices. This paper analyses the effects of oil price changes on sectorial stock returns in India. Using daily r

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32

Prashanth Kumar G. and Nagendra Marisetty. "Dynamic Interdependence: Analysing BSE SENSEX and its Relationship with Global Stock Indices from 2018 to 2022." Journal of Economics, Management and Trade 29, no. 10 (2023): 202–14. http://dx.doi.org/10.9734/jemt/2023/v29i101155.

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This comprehensive analysis delves into the intricate relationship between the Bombay Stock Exchange SENSEX (BSE SENSEX), India's leading stock market index, and popular international stock indices during three distinct research periods from 2018 to 2022. The study employs a combination of correlation analyses and multiple regression models to explore how SENSEX interacts with global counterparts, shedding light on the evolving dynamics of the Indian stock market. The five-year research period from 2018 to 2022 reveals that SENSEX displays a robust positive correlation with various internation

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33

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) (2019): 111. http://dx.doi.org/10.18843/ijms/v6i1(7)/14.

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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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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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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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Yadav, Monika, and Pushpender Kadian. "Day-of-the-Week Effect during COVID-19 Pandemic: An Empirical Study of Indian Stock Market." Journal of Commerce and Accounting Research 14, no. 1 (2025): 97–103. https://doi.org/10.21863/jcar/2025.14.1.008.

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In the context of an efficient market, it is conventionally anticipated that uniform returns would manifest across weekdays. This study empirically scrutinised the manifestation of the day-of-the-week (DOW) effect within the Indian stock market amid the COVID-19 pandemic. Utilising daily closing price data spanning the COVID-19 period from March 11, 2020, to September 30, 2021, for three key indices (BSE Sensex, Nifty-50, and SX 40) listed on the Bombay Stock Exchange (BSE), National Stock Exchange (NSE), and Metropolitan Stock Exchange of India (MSEI), respectively, dummy variable regression

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Goel, Himanshu, Monika Agarwal, Meghna Chhabra, and Bhupender Kumar Som. "The Predictive Power of Macroeconomic Variables on the Indian Stock Market Utilizing an Ann Model Approach: An Empirical Investigation Based on BSE Sensex." Folia Oeconomica Stetinensia 23, no. 2 (2023): 116–31. http://dx.doi.org/10.2478/foli-2023-0022.

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Abstract Research background The paper focuses on the use of Artificial Neural Networks (ANNs) for forecasting time series data of the stock market since ANNs are dynamic and are more capable of handling complex data sets in comparison to conventional forecasting techniques such as regression, Logistic regression, and have massive potential for the prediction of stock market prices. Purpose Artificial neural networks are an effective method for forecasting time series. Therefore, this study aims to forecast the closing price of the BSE Sensex using artificial neural networks (ANNs). Research m

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40

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 (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 art

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41

Shaik, Muneer, and Maheswaran S. "Evidence of excess volatility based on a new robust volatility ratio." Journal of Economic Studies 45, no. 4 (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

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42

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 (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 si

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Upadhyay, Deepika, and Swetha Wenona Suvarna. "Impact of Demonetization on the Indian Stock Market." Paradigm 22, no. 2 (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. Th

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

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

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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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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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Pradeep, K. V., and K. Simmy. "Unlocking the Secrets of Sensex Returns: The Crucial Role of Valuation in Different Time Horizons." International Journal of Accounting and Business Finance 10, no. 1 (2024): 49–63. http://dx.doi.org/10.4038/ijabf.v10i1.151.

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The study attempts to analyse the return generated by BSE Sensex for the past 24 years and the risk associated with investments in different periods. The study also explores the relationship between valuation ratios and Sensex returns. For this purpose, Sensex data (Index value, Price-earnings ratio, Price-to-book value) for 24 years (1998 to 2022) is taken. The data is analysed using Compounded Annual Growth Rate (CAGR) for different time periods along with standard deviation to assess the return and risk. The association between valuation ratios and index return is analysed using correlation

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Broca, Dilbagh S. "Monitoring Volatility Changes on the Bombay Bourse: A Control Chart Approach." Vikalpa: The Journal for Decision Makers 20, no. 3 (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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Enow, Samuel Tabot. "Phase distribution and phase correlation: Evidence in international financial markets." International Journal of Business Ecosystem & Strategy (2687-2293) 7, no. 2 (2025): 244–49. https://doi.org/10.36096/ijbes.v7i2.795.

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The aim of this study was to investigate phase distribution and phase correlation dynamics across international financial markets to uncover cyclical patterns, synchronization, and contagion effects. The sample financial markets were the S&P 500, DAX, Nikkei 225, FTSE 100, Shanghai Composite, BSE Sensex with daily closing prices ranging from 2018–2023. Using the Hilbert-Huang Transform complemented by Phase Concentration Index, Kuiper tests, and Granger causality, the results reveal distinct phase clustering in the Shanghai and BSE Sensex. Developed markets exhibit lower volatility cluster

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