Статті в журналах: "NIFTY BANK"

1

Vijay, Dr S., and Dr V. Prabakaran. "A study on Bank and IT nifty influence on Nifty 50." Journal of University of Shanghai for Science and Technology 23, no. 12 (December 18, 2021): 316–22. http://dx.doi.org/10.51201/jusst/21/121030.

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NIFTY 50 is one of the benchmark indices which is been used in India. The index is built upon by the companies which are represented from various sectors. Each sector is given separate weightage depending upon the representations from each sector. This paper focused on to identify Pairwise Granger Causality between NIFTY 50 Index, NIFTY Bank Index and NIFTY IT Index. The researcher also focused on to identify the influence of Bank NIFTY and IT NIFTY and NIFTY 50 Index. The research was carried out with a total of 532 observations (closing value of each index) of spanned across January 2018 – February 2020. The study revealed that Bank NIFTY Granger Cause IT NIFTY and not vice-versa. NIFTY 50 Index is highly influenced by Bank NIFTY IT NIFTY.

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2

Narayan, Parab, and Y. V. Reddy. "Exploring the Causal Relationship Between Stock Returns, Volume, and Turnover across Sectoral Indices in Indian Stock Market." Metamorphosis: A Journal of Management Research 16, no. 2 (November 12, 2017): 122–40. http://dx.doi.org/10.1177/0972622517730140.

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The traditional saying “Market Discounts Everything” is applicable to stock returns, trading volume, and turnover as well. The present study is an analytical attempt to examine the causal relationship between stock returns, trading volume, and turnover across 10 sectoral indices of National Stock Exchange (NSE) for the period 2006–2016. To critically examine this relation, the study uses various statistical techniques such as descriptive statistics, correlation analysis, regression analysis, and econometric tests such as Granger causality test and augmented Dickey–Fuller test. The required analyses have been performed using statistical software E-views, SPSS, and Microsoft Excel. The study noticed a weak positive relationship between stock returns and turnover for Nifty Auto Index, Nifty Bank Index, Nifty Financial Services Index, Nifty Media Index, Nifty Metal Index, and Nifty Private Bank Index. The study also found a significant impact of turnover on stock returns in the case of Nifty Auto Index, Nifty Bank Index, Nifty FMCG Index, Nifty Metal Index, and Nifty Pharma Index and a significant impact of volume on stock returns in the case of Nifty Bank Index, Nifty FMCG Index, and Nifty Pharma Index. Augmented Dickey–Fuller test suggests that there exists no unit root in the data ( p < 1) and the data are stationary. It is evident from the study that the causal relationship between stock returns, turnover, and volume varies across the sectoral indices.

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3

Gadasandula, Krishna. "Effect of Macroeconomic Determinants on Indian Stock Market." Asian Journal of Managerial Science 8, no. 2 (May 5, 2019): 22–27. http://dx.doi.org/10.51983/ajms-2019.8.2.1556.

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Stock market is one of the important forms of investment. The prices of stock markets are affected by much macro-economic factors. The study investigates the relationships between the Indian stock market index (NSE Nifty) and four macroeconomic variables, namely, GDP, Inflation, Exchange Rate and Bank Rate. The data is collected on a quarterly basis for the time period March 2000 to December 2017. The study employs the Johansen’s co-integration approach to the long-run equilibrium relationship between stock market index and macroeconomic variables. For causality analysis, the study carried out Granger and Geweke causality tests. From this paper it is observed that the Granger causality test results do not demonstrate the presence of any bidirectional causality. The results show the unidirectional causal associations running from GDP to Inflation, Bank Rate to GDP, Exchange Rate to GDP, NIFTY Index to GDP, Exchange Rate to Inflation, NIFTY Index to Inflation, and Bank Rate to NIFTY Index. Apart from that, the results also show no causal association between Inflation and Bank Rate, Bank Rate and Exchange Rate, and Exchange Rate and NIFTY Index. However, the bidirectional causal associations appear. When we look into the results of Geweke causality analysis shows that bidirectional causal associations exist between Inflation and Bank Rate, and Exchange Rate and Nifty Index.

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4

D., Bhuvaneshwari. "Impact of Covid-19 on the Financial Sector Indices." International Research Journal of Business Studies 14, no. 2 (November 15, 2021): 137–45. http://dx.doi.org/10.21632/irjbs.14.2.137-145.

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This study is an attempt to assess the impact of Covid-19 and the lockdown pronounced thereof on the Nifty sectoral indices with specific reference to the financial sector indices owing to their significance in the economy. The OLS regression, Granger Causality and Impulse Response Function were estimated to measure the changes in the future responses of Nifty 50 to the changes in the select sectoral indices, namely, Nifty Bank, Nifty Financial Services and Nifty Private Banks and Nifty PSU Banks for the period consisting two sub-periods, i.e., the first sub-period from April 2019 to March 2020 are assumed as the preCovid-19 period and the second sub-period from April 2020 to March 2021 is assumed as the period during Covid-19. The results indicated that the shock of the Covid-19 had an impact on the financial sector indices in India during the Covid-19 period.

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5

Subha, M. V., and A. Musthaffa. "Testing Pricing Relationship between Bank Nifty futures & Bank Nifty Indices – Evidences from India." Asian Journal of Research in Social Sciences and Humanities 6, no. 7 (2016): 1397. http://dx.doi.org/10.5958/2249-7315.2016.00520.7.

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6

Singh, Gurmeet. "Estimating Optimal Hedge Ratio and Hedging Effectiveness in the NSE Index Futures." Jindal Journal of Business Research 6, no. 2 (September 4, 2017): 108–31. http://dx.doi.org/10.1177/2278682117715358.

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This study attempts to study and suggest an optimal hedge ratio to Indian investors and traders by examining the three main indices of National Stock Exchange of India (NSE), namely, NIFTY, Bank NIFTY, and IT NIFTY, over the sample period from January 2011 to December 2015. The present study estimated the hedge ratio through six econometric models, namely, OLS, GARCH, EGARCH, TARCH, VAR, and VECM, in the minimum variance hedge ratio framework as suggested by Ederington (1979). The findings of the present study confirm the theoretical properties of Indian cash and futures market and suggest that the optimal hedge ratio estimated through EGARCH model was lowest for the NIFTY and Bank NIFTY, and that for IT NIFTY, the OLS model shows the lowest optimal hedge ratio as compared to that estimated through other models.

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7

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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8

Ashri, Dhananjay, Bibhu Prasad Sahoo, Ankita Gulati, and Irfan UL Haq. "Repercussions of COVID-19 on the Indian stock market." Linguistics and Culture Review 5, S1 (November 17, 2021): 1495–509. http://dx.doi.org/10.21744/lingcure.v5ns1.1792.

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The present paper determines the repercussions of the coronavirus on the Indian financial markets by taking the eight sectoral indices into account. By taking the sectoral indices into account, the study deduces the impact of virus outbreak on the various sectoral indices of the Indian stock market. Employing Welch's t-test and Non-parametric Mann-Whitney U test, we empirically analysed the daily returns of eight sectoral indices: Nifty Auto, Nifty FMCG, Nifty IT, Nifty Media, Nifty Metal, Nifty Oil and Gas, Nifty Pharma, and Nifty Bank. The results unveiled that pandemic had a negative impact on the automobile, FMCG, pharmaceuticals, and oil and gas sectors in the short run. In the long run, automobile, oil and gas, metals, and the banking sector have suffered enormously. The results further unveiled that no selected indices underperformed the domestic average, except NIFTY Auto.

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9

Sehgal, Meru, and Shruti Gupta. "Stock Markets in Changing Times." International Journal of Business Analytics 8, no. 3 (July 2021): 14–25. http://dx.doi.org/10.4018/ijban.2021070102.

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The impact of COVID-19 on the stock markets of US, UK, and India has been analyzed. Daily market returns of the stock indices (Dow Jones Industrial Average, FTSE-100, Nifty 50 Index, and Nifty Bank Index) have been examined using paired t-test for 40 days before and after the reporting of the first case. Index performance has also been investigated for the quarter ending June 2020 along with comparative performance analysis of the indices with Nifty Bank Index. The results showed that markets have borne substantially negative returns, but they are not statistically significant. This indicates the resilience of these markets to restore to previous index levels after taking a short-term hit. This paper adds value to the literature by acting as a resource for academia as well as industry by spelling out changes in markets during this pandemic and supporting evidence from Indian banks that are catalysts of growth for businesses in uncertain times.

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10

Garg, Deeksha. "Does the Nature of Index and Liquidity Influence the Mispricing in Future Contracts in India?" Applied Finance Letters 9, SI (November 18, 2020): 15–22. http://dx.doi.org/10.24135/afl.v9i2.244.

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In this study, we investigate the variations in the mispricing of futures in Nifty (benchmark index), Bank Nifty and Nifty IT. Using a regression model on 1230 observations for the period of 1 January 2014 to 31 December 2018, we find no significant mispricing exists in the last week to the expiry of the contract in all three indices. This finding supports the existing literature that as the contract moves towards the maturity date, its value converges the market value. However, the main highlight of the paper is to reveal the difference in the life of mispricing in different indices. This difference in mispricing can be allocated to the liquidity in that indices. We report that being the most liquid, Bank Nifty is having mispricing only in 1 week (first week) of the contract, after that no significant mispricing exists in mispricing, Nifty shows significant mispricing for the first two weeks and Nifty IT shows mispricing for all weeks except last week. This is the pioneering work which considers the sectoral differences while evaluating futures mispricing. The findings of this study will provide a useful insight to the regulator and investors.

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11

M. N., Nikhil, Suman Chakraborty, Lithin B. M., Sanket Ledwani, and Satyakam. "Modeling Indian Bank Nifty volatility using univariate GARCH models." Banks and Bank Systems 18, no. 1 (March 17, 2023): 127–38. http://dx.doi.org/10.21511/bbs.18(1).2023.11.

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The crumble of financial markets due to the recent crises has wobbled precariousness in the stock market and intensified the returns vulnerability of banking indices. Against this backdrop, this study intends to model the volatility of the Indian Bank Nifty returns using a battery of GARCH specifications. The finding of the present research contributes to the literature in three ways. First, volatility during the sample period, which corresponds to a time of stress (a bear market), is more persistent, with an estimated coefficient of 0.995695. Moreover, when volatility rises, it persists for a long time before returning to the mean in an average of 16 days. Second, for a positive γ, the results insinuate the possibility of an “anti-leverage effect” with a coefficient of 0.139638. Thus, the volatility of the Bank Nifty returns tends to rise in response to positive shocks relative to negative shocks of equal magnitude in India. Finally, the findings demonstrate that EGARCH with Student’s t-distribution offers lower forecast errors in modeling conditional volatility.

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12

Paul, Parmod Kumar, Om Prakash Mahela та Baseem Khan. "Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)". Complexity 2022 (15 січня 2022): 1–8. http://dx.doi.org/10.1155/2022/8196436.

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For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index ( λ ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020.

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13

Dr.C.Swarnalatha, Dr C. Swarnalatha, and K. S. Karthik Babu. "A Study on Impact of Dividend on Nifty Bank Stocks." Paripex - Indian Journal Of Research 3, no. 4 (January 15, 2012): 1–3. http://dx.doi.org/10.15373/22501991/apr2014/90.

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14

Rane, Nitish, and Pooja Gupta. "Impact of Financial Ratios on Stock Price: Evidence from Indian Listed Banks on NSE." Revista Gestão Inovação e Tecnologias 11, no. 4 (September 16, 2021): 5132–44. http://dx.doi.org/10.47059/revistageintec.v11i4.2553.

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This study aims to examine the impact of financial ratios on the stock prices of companies listed on NIFTY Bank. Nifty Bank is a sub-index of NIFTY 50 and has various listed banks included based on the criteria given by NSE. This study data has been taken from the period 2010-2019 and taken from the company annual reports. The analysis is done using panel data regression and other tests to verify the best model for the dataset. The results obtained from this study show that the capital adequacy ratio and the dividend payout ratio do not impact the stock price. In contrast, earnings per share, net NPA ratio, and basic earnings per share, net profit margin, and net interest margin exhibited a relationship with the stock price. In the Indian context, there is less research available on this topic, and the idea chosen for the study is original. Along with this, the data collected for the study and the code used for analysis is original work. New investors can use the results of this study in the Indian stock market to analyze a stock and take proper investment decisions. Another practical usage of this study is that banking sector companies can improve their ratios to attract new investors.

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15

N., Rane,, and Gupta, P. "Impact of Financial Ratios on Stock Price: Evidence from Indian Listed Banks on NSE." CARDIOMETRY, no. 24 (November 30, 2022): 449–55. http://dx.doi.org/10.18137/cardiometry.2022.24.449455.

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This study aims to examine the impact of financial ratios on the stock prices of companies listed on NIFTY Bank. Nifty Bank is a sub-index of NIFTY 50 and has various listed banks included based on the criteria given by NSE. This study data has been taken from the period 2010-2019 and taken from the company annual reports. The analysis is done using panel data regression and other tests to verify the best model for the dataset. The results obtained from this study show that the capital adequacy ratio and the dividend payout ratio do not impact the stock price. In contrast, earnings per share, net NPA ratio, and basic earnings per share, net profit margin, and net interest margin exhibited a relationship with the stock price. In the Indian context, there is less research available on this topic, and the idea chosen for the study is original. Along with this, the data collected for the study and the code used for analysis is original work. New investors can use the results of this study in the Indian stock market to analyze a stock and take proper investment decisions. Another practical usage of this study is that banking sector companies can improve their ratios to attract new investors.

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16

Prem, Cyano, Dr M. Babu, C. Hariharan, and R. Muneeswaran. "Volatility Transmission in Indian Banking Sector." Restaurant Business 118, no. 7 (July 16, 2019): 161–65. http://dx.doi.org/10.26643/rb.v118i7.8012.

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Any new information about the economy is transmitted fast and it may influence the financial markets, positively or negatively. The present study used GARCH (1, 1) and EGARCH models, to investigate the volatility of Indian banking sectors indices, namely, Nifty PSU Index and Nifty Private Bank Index of NSE India Ltd. The result of the study confirmed that the high volatility was found in both the bank indices. At the same time, negative information about Indian economics did affect the PSU and Private Bank Sector indices during the study period. Finally, the study concluded that bad news travels fast and it increased volatility more than good. Hence the Government should give more information and awareness programme to the people before the implementation of any economic policy.

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17

P, Jebah Shanthi. "The Regression Analysis of Daily Stock Returns of Nifty PSU Bank." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1948–53. http://dx.doi.org/10.5373/jardcs/v12sp7/20202309.

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18

DR.S.RAJAMOHAN, DR S. RAJAMOHAN, and M. MUTHUKAMU M.MUTHUKAMU. "Bank Nifty Index and Other Sectoral Indices of NSe- A Comparitive Study." Paripex - Indian Journal Of Research 3, no. 4 (January 15, 2012): 147–49. http://dx.doi.org/10.15373/22501991/apr2014/47.

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19

Kumar Digal, Sabat, Yashmin Khatun, and Braja Sundar Seet. "COVID-19 Impact on Nifty Banks: An Event Study Methodology." Journal of International Business and Economy 22, no. 1 (December 1, 2020): 83–108. http://dx.doi.org/10.51240/jibe.2021.1.4.

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The financial sector, because of its catalytic role in the economy, has always been in the eye of the storm in economic difficulties. Due to the pandemic, the stock market had lost about 27 percent by April 2020 and bank nifty has had a lion’s share in pushing the index down to this level. Uncertainty arose as the containment of the disease and the availability of vaccines remain uncertain; this contributed to the plunge in investor confidence. Because of the central role of banks in the development initiatives of the governments, COVID-19 has become a significant threat to the sustainability of the banks globally, especially in developing economies. However, we believe every downfall brings in new opportunities for the investors. Therefore, the present study attempted to study both the gloom and boon and observed that there were short-term abnormal returns to the investors of nifty banks in two different periods - the detection of the first case of COVID-19 in India and the lockdown periods in India. The impacts of both the events are calculated by applying Market and Risk Adjusted model, Market Adjusted Return model and Mean Adjusted Return model. The paper concludes that the impacts were insignificant during the first period and was quite significant in the subsequent period. Nifty banks have earned negative abnormal returns during the pre-lockdown period and positive abnormal returns during post lockdown period which indicates that the markets reacted positively as India implemented the first lockdown.

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20

P, Triveni, and Anupama Kumari. "Impact of Demonetisation on Bank Stock." Ushus - Journal of Business Management 17, no. 3 (July 1, 2018): 27–38. http://dx.doi.org/10.12725/ujbm.44.4.

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This paper empirically examines the impact of demonetisation on Indian bank stock. On 8th November 2016, the Indian Prime Minister, Narendra Modi declared demonetisation in an unscheduled live broadcast. Currency notes of Rs 500 and Rs 1000 were demonetised from the midnight of 8th November 2016. This was issued to curb black money and corruption. During demonetisation, the index values of the stock market SENSEX, NIFTY and BANKEX went down. This research mainly focusses on how demonetisation affected the stock price of the top Indian banks. With the help of CAAR, change in return and change in volatility, the effect of demonetisation on the stock market was calculated. Furthermore, an understanding of the impact of demonetisation on bank stocks was attempted. The evaluation helped in concluding that there has been no impact on the bank stocks post demonetisation.

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21

Et.al, Dr A. Anis Akthar Sulthana Banu. "“A Study On The Sustainable Investment Funds With Sepcial Reference To State Bank Of India Esg Mutual Fund Shcemes”." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 261–66. http://dx.doi.org/10.17762/turcomat.v12i6.1363.

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Socially Responsible Investment (SRI) refers to the allocation of funds in certain practises that have a high social impact. It includes assessing businesses on the Environmental , Social and Governance (ESG) screens. A socially conscious investor may either invest directly in financial markets or through investment instruments such as mutual funds via ESG fund schemes. Very few of the numerous mutual fund organizations have implemented ESG Fund schemes to appeal to SRI investors. The SBI Mutual Fund is the first AMC to follow this and has been benchmarked against the Nifty 100 ESG indices. A correlation analysis is made among the results of the SBI Mutual Fund and the NIFTY to compare the four different types of SBI ESG funds and their sector wise participation in different industries. This research paper is methodological in nature as it interprets the published secondary data sources of the SBI Mutual Fund and the NIFTY indices. The goal of this paper is to assess the efficacy of the ESG Equity Fund in the investment portfolio of mutual fund investors and to enable small and medium-sized investors to contribute their money to ESG-driven mutual fund schemes.

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22

Dr.S.RAJAMOHAN, Dr S. RAJAMOHAN, and M. MUTHUKAMU M.MUTHUKAMU. "Impact of Selective Corporate Events on Price Movements of Stocks of Bank Nifty Index." Indian Journal of Applied Research 4, no. 4 (October 1, 2011): 317–20. http://dx.doi.org/10.15373/2249555x/apr2014/98.

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23

Matha, Rajeev, Geetha E., Satish Kumar, and Raghavendra. "Dynamic relationship between equity, bond, commodity, forex and foreign institutional investments: Evidence from India." Investment Management and Financial Innovations 19, no. 4 (October 20, 2022): 65–82. http://dx.doi.org/10.21511/imfi.19(4).2022.06.

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The interrelationship between equity, bond, commodity and forex movements can provide investors with abundant trading opportunities regardless of whether one market is trending upward or downward. Hence, to understand the interlinkage between markets, this study examines the long-run and causal linkage between forex, G-sec bonds, oil prices, gold rates, foreign institutional investment (FII) flows, and equity market and sectoral index returns. Daily time-series data from August 2012 to August 2021 were considered for empirical analysis. Johansen’s cointegration test revealed that foreign exchanges like USD, Euro, GBP and Yen, oil and gold rates, G-bond returns and FII flows were significantly cointegrated with the stock market and sectoral indices in the long run. Further, Granger causality found a uni-directional relationship between forex rates (i.e., USD, Euro, Yen) and the market, as well as sectoral indices, except Nifty 50 and Nifty IT indices. Oil price movements were found to effectively predict future price changes of Nifty consumer durables, auto, IT indices. Gold prices are useful to predict Nifty-Auto, Bank, Financial Services, Oil &amp; Gas and PSU. The study also found a bi-directional relationship from FII inflows to the stock market and sectoral indices. The findings suggest that forex rates, oil prices and FII flows significantly affect India’s stock market and sectoral performance. The study contributes to the existing literature by comprehensively examining the interlinkage between commodities such as oil and gold, foreign exchanges like USD, Euro, GBP and Yen, G-bond, FII flows and the stock market, and fourteen sectoral indices in the Indian context.

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24

R, Sharmila, Kavitha R, and Ananthi S. "Technical analysis on select stocks of banking sector." Journal of Management and Science 6, no. 3 (December 31, 2016): 231–42. http://dx.doi.org/10.26524/jms.2016.21.

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Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is “likely” to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. This study is based on the analysis of four Nifty Bank Index stocks namely Axis Bank, Bank of Baroda, State Bank of India and ICICI bank listed in National Stock Exchange. Technical indicators such as Relative strength index (RSI), Rate of change (ROC) and Moving Average (MA) are used in the study. This paper aims at carrying out Technical Analysis of the securities of the selectedbanking stocks and to assist investment decisions in this Indian Market.

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25

SHAIK, MUNEER, and Aditya Sejpal. "The Comparison of GARCH and ANN Model for Forecasting Volatility: Evidence based on Indian Stock Markets." Journal of Prediction Markets 14, no. 2 (December 11, 2020): 103–21. http://dx.doi.org/10.5750/jpm.v14i2.1843.

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In this paper, we study the performance of the Artificial Neural Networks (ANNs) and GARCH modelsto predict the volatility of the Indian stock market indices namely, NIFTY 50, NIFTY Bank and NIFTYFMCG. We have used the GARCH (1,1) and Recurrent Neural Network, a type of neural network whichis widely used for predicting time series data. The purpose of the study is to investigate if the ArtificialNeural Networks perform better than the traditional GARCH (1,1) model. An out of sample testingmethodology is applied to the most recent 20 percent of the observations for all the three indices. Wehave used Root Means Squared Error (RMSE) and Mean Absolute Error (MAE) as metrics to evaluatethe volatility predicting performances of the models. The results show no clear evidence of ANN modelperforming better than GARCH model for any of the three indices. ANNs may prove to be betterindicators in periods with low volatility while its performance deteriorated in periods with highvolatility.

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26

Mamilla, Rajesh, Chinnadurai Kathiravan, Aidin Salamzadeh, Léo-Paul Dana, and Mohamed Elheddad. "COVID-19 Pandemic and Indices Volatility: Evidence from GARCH Models." Journal of Risk and Financial Management 16, no. 10 (October 17, 2023): 447. http://dx.doi.org/10.3390/jrfm16100447.

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This study examines the impact of volatility on the returns of nine National Stock Exchange (NSE) indices before, during, and after the COVID-19 pandemic. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) modelling to analyse investor risk and the impact of volatility on returns. The study makes several contributions to the existing literature. First, it uses advanced volatility forecasting models, such as ARCH and GARCH, to improve volatility estimates and anticipate future volatility. Second, it enhances the analysis of index return volatility. The study found that the COVID-19 period outperformed the pre-COVID-19 and overall periods. Since the Nifty Realty Index is the most volatile, Nifty Bank, Metal, and Information Technology (IT) investors reaped greater returns during COVID-19 than before. The study provides a comprehensive review of the volatility and risk of nine NSE indices. Volatility forecasting techniques can help investors to understand index volatility and mitigate risk while navigating these dynamic indices.

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27

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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A.S., SURESH. "Study on Comparison of Risk-Return Analysis of Public and Private Sector Banks listed on Bank Nifty." Journal of Business Management and Economic Research 2, no. 1 (March 30, 2018): 1–8. http://dx.doi.org/10.29226/tr1001.2018.5.

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Bhatia, Parul, and Priya Gupta. "Sub-prime Crisis or COVID-19: A Comparative Analysis of Volatility in Indian Banking Sectoral Indices." FIIB Business Review 9, no. 4 (December 2020): 286–99. http://dx.doi.org/10.1177/2319714520972210.

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Stock market volatility may be a function of company, industry, or world over information made public. The present study has investigated the volatility of Indian banking sectoral indices with the general banking index for two shocking events: the Sub-prime crisis and COVID-19. A comparative analysis of both the shocks leading to these indices’ volatility has been conducted using symmetric and asymmetric models. This study’s findings show that these indices’ volatile behaviour has been strong enough to persist in the market with the leverage effect present during the sub-prime crisis. This effect disappeared for Nifty Bank Indices and Private Sector Bank Indices as compared to Public Sector Undertaking Bank Indices during COVID-19 (probably because the pandemic is not over yet). With GARCH and EGARCH models, the study suggests that the investors may use the diversification approach, in the long run, to safeguard their portfolio values to survive from global shocks.

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Dogra, Varun, Aman Singh, Sahil Verma, Abdullah Alharbi, and Wael Alosaimi. "Event Study: Advanced Machine Learning and Statistical Technique for Analyzing Sustainability in Banking Stocks." Mathematics 9, no. 24 (December 20, 2021): 3319. http://dx.doi.org/10.3390/math9243319.

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Machine learning has grown in popularity in recent years as a method for evaluating financial text data, with promising results in stock price projection from financial news. Various research has looked at the relationship between news events and stock prices, but there is little evidence on how different sentiments (negative, neutral, and positive) of such events impact the performance of stocks or indices in comparison to benchmark indices. The goal of this paper is to analyze how a specific banking news event (such as a fraud or a bank merger) and other co-related news events (such as government policies or national elections), as well as the framing of both the news event and news-event sentiment, impair the formation of the respective bank’s stock and the banking index, i.e., Bank Nifty, in Indian stock markets over time. The task is achieved through three phases. In the first phase, we extract the banking and other co-related news events from the pool of financial news. The news events are further categorized into negative, positive, and neutral sentiments in the second phase. This study covers the third phase of our research work, where we analyze the impact of news events concerning sentiments or linguistics in the price movement of the respective bank’s stock, identified or recognized from these news events, against benchmark index Bank Nifty and the banking index against benchmark index Nifty50 for the short to long term. For the short term, we analyzed the movement of banking stock or index to benchmark index in terms of CARs (cumulative abnormal returns) surrounding the publication day (termed as D) of the news event in the event windows of (−1,D), (D,1), (−1,1), (D,5), (−5,−1), and (−5,5). For the long term, we analyzed the movement of banking stock or index to benchmark index in the event windows of (D,30), (−30,−1), (−30,30), (D,60), (−60,−1), and (−60,60). We explore the deep learning model, bidirectional encoder representations from transformers, and statistical method CAPM for this research.

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Sengupta, Indrani, and Dhaval Maheta. "Stock Price Volatility of NSE Thematic Consumption Index: An Econometric Analysis." Management Insight - The Journal of Incisive Analysers 16, no. 02 (December 25, 2020): 17–22. http://dx.doi.org/10.21844/mijia.16.2.3.

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Had the Indian economy were a person, its income in 2020-21 and 2021-22 would be much less than what it was in 2019-20. This is what the recent World Bank predictions says. There is vast, perhaps unparalleled, economic pain ahead. The World Bank released its Global Economic Prospects report in the second week of June, expecting India’s gross domestic product (GDP) to contract by 3.2% in 2020-21. A moderate recovery growth is expected from 3.1% in 2021-22. India is not the only country which will face this quandary. As per the statistics, generally March and April each contributes to the sales turnover of 12% every year, but March 2020 has witnessed a downfall of 55% year on year amidst the corona- induced lockdown. Undoubtedly, the pandemic has a tremendous impact on these, but the industry certainly needs to cope us with the current situation and some key transitions should be made in their approach to sales, logistics, marketing to customer service. So, as an investor we need to know how the consumption market was just before the Covid-19 hit the Indian premise. The consumption industry is further segregates into durable, non-durable goods and services industry. This paper compares the price volatility of the stock prices of three firms who are into consumer goods with its related NSE Nifty consumption index. Data has been taken from NSE website and the time period of the study is 2015-2019. The data has further been treated with time series analysis using multiple regression which tries to test whether there is any connect between the trends of the stock prices of firms vis-à-vis the Nifty index of the sector. The study also attempts to identify patterns between the regressor and the regressands.

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Kumar, S. S. Pavan. "An Empirical Study on Effect of Demonetization on NSE Bank Nifty Stocks using Event Study Methodology." Asian Journal of Management 9, no. 3 (2018): 1055. http://dx.doi.org/10.5958/2321-5763.2018.00165.8.

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Khera, Aastha, and Neelam Dhanda. "Empirical Relationship between Macroeconomic Variables and Stock Prices of Indian Banking Sector: A Vector Error Correction Model Approach." Review of Finance and Banking 12, no. 2 (December 31, 2020): 189–98. http://dx.doi.org/10.24818/rfb.20.12.02.06.

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This existing study aims to investigate the relationship between Indian Bankingstock market prices and macroeconomic variables. The proxy for the Indian Banking stockmarket is Nifty Bank while Foreign Reserve, Exchange Rate (Indian vs US Dollar), Interestrate, and CPI are proxies of macroeconomic variables. Johansen Cointegration and VectorError Correction Model (VECM) on monthly data from January 2013 to July 2020 have beenapplied. Considering the results of cointegration, it is found that there is a long-run asso-ciation between the Indian Banking stock market and constituent macroeconomic variables.Next, the employment of VECM is done for inspecting long run and short-run causality.The result reveals long-run equilibrium in Indian commercial bankís stock prices comingfrom macroeconomic variables. This study has considerable imputations that investors candiversify their portfolio according to the ináuencing power of constituent selected macro-economic variables in the short run and the long run. Exchange rate and foreign reservesdrive the banking stock market in the short run whereas CPI and Interest rate do not createany signiÖcant impact.

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S. M., Shanavas. "A Comparative Study on the Share Price Movement of Public and Private Banking Sector Companies with Reference to Nifty Bank." International Journal of Management Studies V, Special Issue 5 (August 31, 2018): 108. http://dx.doi.org/10.18843/ijms/v5is5/14.

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Karthikeyan, P. "Correlation Analysis of Public Sector Banks and Nifty Banks with Nifty in NSE." Asian Journal of Research in Business Economics and Management 7, no. 5 (2017): 271. http://dx.doi.org/10.5958/2249-7307.2017.00055.x.

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36

Panigrahi, Ashok Kumar, Kushal Vachhani, and Suman Kalyan Chaudhury. "Trend identification with the relative strength index (RSI) technical indicator –A conceptual study." Journal of Management Research and Analysis 8, no. 4 (December 15, 2021): 159–69. http://dx.doi.org/10.18231/j.jmra.2021.033.

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We all must agree that the word "trend" is now the buzzword of the stock market. As a part of investment strategy and analysis, it is always suggested that the investors should keep an eye on medium-term and short-term changes in addition to longer-term (secular) patterns. Traders and investors use the RSI as a momentum indicator. Overbought and oversold situations are indicated by RSI values between 70 and 30. Over the past two decades, several techniques have been developed to analyze NIFTY 50 data for investment purposes. In this paper, we have estimated the returns by looking at the two trends i.e., 50-50 and 60-40. In addition to this, how to trade and back-test our strategy is also explained. Applying these two RSI strategies to the NIFTY 50 chart revealed that 50-50 offers a higher long-term return, while 60-40 provides a superior short-term return. Finally, the strategies' returns F-statistics and P-values were calculated and analyzed to determine their significance level and acceptability.

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37

Reddy, Y. V., and A. Sebastin. "Interaction Between Forex and Stock Markets in India: An Entropy Approach." Vikalpa: The Journal for Decision Makers 33, no. 4 (October 2008): 27–46. http://dx.doi.org/10.1177/0256090920080403.

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Interactions between the foreign exchange market and the stock market of a country are considered to be an important internal force of the markets in a financially liberalized environment. If causal relationship from a market to the other is not detected, then informational efficiency exists in the other whereas existence of causality implies that hedging of exposure to one market by taking position in the other market will be effective. The temporal relationship between the forex market and the stock market of developing and developed countries has been studied, especially after the East Asian financial crisis of 1997–98, using various methods like cross-correlation, cross-spectrum, and error correction model, but these methods identify only linear relations. A statistically rigorous approach to the detection of interdependence, including non-linear dynamic relationships, between time series is provided by tools defined using the information theoretic concept of entropy. Entropy is the amount of disorder in the system and also is the amount of information needed to predict the next measurement with a certain precision. The mutual information between two random variables X and Y with a joint probability mass function p(x,y) and marginal mass functions p(x) and p(y), is defined as the relative entropy between the joint distribution p(x,y) and the product distribution p(x)*p(y). Mutual information is the reduction in the uncertainty of X due to the knowledge of Y and vice versa. Since mutual information measures the deviation from independence of the variables, it has been proposed as a tool to measure the relationship between financial market segments. However, mutual information is a symmetric measure and does not contain either dynamic information or directional sense. Even time delayed mutual information does not distinguish information actually exchanged from shared information due to a common input signal or history and therefore does not quantify the actual overlap of the information content of two variables. Another information theoretic measure called transfer entropy has been introduced by Thomas Schreiber (2000) to study the relationship between dynamic systems; the concept has also been applied by some authors to study the causal structure between financial time series. In this paper, an attempt has been made to study the interaction between the stock and the forex markets in India by computing transfer entropy between daily data series of the 50 stock index of the National Stock Exchange of India Limited, viz., Nifty and the exchange rate of Indian Rupee vis- à- vis US Dollar, viz., Reserve Bank of India reference rate. The entire period–November 1995 to March 2007–selected for the study, has been divided into three sub-periods for the purpose of analysis, considering the developments that took place during these sub-periods. The results obtained reveal that: there exist only low level interactions between the stock and the forex markets of India at a time scale of a day or less, although theory suggests interactive relationship between the two markets the flow from the stock market to the forex market is more pronounced than the flow in the reverse direction.

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38

Vijay, Vivek, and Parmod Kumar Paul. "An Optimal Band for Prediction of Buy and Sell Signals and Forecasting of States." International Journal of Applied Management Sciences and Engineering 2, no. 2 (July 2015): 33–53. http://dx.doi.org/10.4018/ijamse.2015070103.

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A trading band, based on historical movements of a security price, suggests buy or sell pattern. Bollinger band is one of the most famous bands based on moving average and volatility of the security. The authors define a new trading band, namely Optimal Band, to forecast the buy or sell signals. This optimal band uses a linear function of local and absolute extrema of a given financial time series. The parameters of this linear function are then estimated by simple linear optimization technique. The authors then define different states using various upper and lower values of Bollinger band and the optimal band. The approach of Markov and Hidden Markov Models are used to forecast the future states of given time series. The authors apply all the techniques on the closing price of Bombay stock exchange and intra-day price series of crude oil and Nifty stock exchange.

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39

Ghosh, Bikramaditya, and Emira Kozarević. "Identifying explosive behavioral trace in the CNX Nifty Index: a quantum finance approach." Investment Management and Financial Innovations 15, no. 1 (March 3, 2018): 208–23. http://dx.doi.org/10.21511/imfi.15(1).2018.18.

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The financial markets are found to be finite Hilbert space, inside which the stocks are displaying their wave-particle duality. The Reynolds number, an age old fluid mechanics theory, has been redefined in investment finance domain to identify possible explosive moments in the stock exchange. CNX Nifty Index, a known index on the National Stock Exchange of India Ltd., has been put to the test under this situation. The Reynolds number (its financial version) has been predicted, as well as connected with plausible behavioral rationale. While predicting, both econometric and machine-learning approaches have been put into use. The primary objective of this paper is to set up an efficient econophysics’ proxy for stock exchange explosion. The secondary objective of the paper is to predict the Reynolds number for the future. Last but not least, this paper aims to trace back the behavioral links as well.

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Agarwal, Saurabh, and Megha Agarwal. "Back to Basics: Does Benjamin Graham Filters help identify Value Stocks on Nifty 500?" Effulgence-A Management Journal 18, no. 2 (July 1, 2020): 1. http://dx.doi.org/10.33601/effulgence.rdias/v18/i2/2020/01-12.

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41

Amudha, R., K. S. Kanna, A. Dhanalakshmi, and Easwaramoorthy Rangaswamy. "The dynamic causality model on foreign institutional investments and nifty indices." Multidisciplinary Science Journal 5 (August 29, 2023): 2023ss0319. http://dx.doi.org/10.31893/multiscience.2023ss0319.

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Post LPG, the Foreign Institutional Investors (FIIs) Investments, has created a massive impression on the Indian Equity market as well as the Indian economy, contributing toward several financial reforms of the Indian Government, RBI and SEBI, to a major extent, dancing to the tune of, the various Indian and other global economic crisis, to name a few, the US subprime crisis, demonetization in India and the Covid ’19 pandemic lockdown. The recent Covid-19 pandemic triggered fears of global recession due to which, the foreign investors started sculling back to pull out from the equity markets in India, a total net outflow of Rs. 1,12,189 crore (Rs.59,377.1 crore syphoned out from equities and Rs.52,811 crore from the debt segment) the highest withdrawal ever, in March 2020 by the FPIs, had a worst impact on the Indian Equity Market to the extent of a enormous drop of 1203 points of BSE Sensex and the broader NSE Nifty Index tanked 344 points in March 2020. This negative economic effect has prompted to analyze the impact of the FIIs Investments not only on the India’s major Index, i.e., Nifty but also its selected sectoral indices. The impact of FII investments has been specifically analyzed on both bidirectional and uni-directional relationship applying Granger causality model, considered more appropriate and have chosen the VAR framework for suitable outcome of the results of this study.

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Balasubramanian, Niranjana. "An MFDFA Study to find Herd Behaviour and Information Asymmetry during Demonetization." Ushus Journal of Business Management 19, no. 3 (August 26, 2020): 41–59. http://dx.doi.org/10.12725/ujbm.52.3.

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The paper attempts to identify how the Indian stock market reacts to an unusual event like demonetization through the observation of herd behavior. The data set considered is the NIFTY 50 index collected on 9th November 2016. This method could become a failure if giant investors are well aware of the massive proceeding as stock markets are prone to information asymmetry. Thus, the existence of the same is checked using Hill estimator. The sectoral herding behavior is also captured for three selective sectors namely PSU banks, Energy sector, and Automobile sector as each sector may pose a different response towards the event. The volatility index is examined for a time period of 10 years from 2008 to 2018.

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43

P. Kumar and H. N. Archana. "A comparative study of Bollinger Bands and rate of change with reference to NIFTY." Prayukti – Journal of Management Applications 02, no. 02 (2022): 113–20. http://dx.doi.org/10.52814/pjma.2022.2205.

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The purpose of this study is to compare Bollinger Bands (BB) and Rate of Change (ROC) indicators on Nifty50 for the financial year starting from 01/04/2021 to 31/03/2022 to find out which one of the indicator or the combination of the indicator can generate better returns with the use of Streak software. Streak is an algo trading platform which facilitates traders to back test trading strategies by giving some entry and exit conditions and deploy the same for automatic trading. A good technical indicator should give fewer signals with high accuracy as there will be more brokerage costs involved if there are more signals. In this study it is found that BB can give fewer signals with greater accuracy. ROC gives more signals with poor accuracy. Superior returns can be obtained if these indicators are combined. Therefore, it is always recommended to investors not to use any indicator in isolation as it is prone to more risk

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44

Singh, Kamred Udham, Sun-Yuan Hsieh, Chetan Swarup, and Teekam Singh. "Authentication of NIfTI Neuroimages Using Lifting Wavelet Transform, Arnold Cat Map, Z-Transform, and Hessenberg Decomposition." Traitement du Signal 39, no. 1 (February 28, 2022): 265–74. http://dx.doi.org/10.18280/ts.390127.

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The technological progress in digital medical imaging has enabled the diagnosis of various ailments, and thus upgraded the global healthcare system. In the era of coronavirus 2019 (COVID-19), telemedicine plays the crucial role of supporting remote medical consultation in rural locations. During the remote consultation, numerous medical images are sent to each radiologist via the Internet. There has been a surge in the number of attacks on digital medical images worldwide, which severely threatens authenticity and ownership. To mitigate the threat, this paper proposes a robust and secure watermarking approach for NIfTI images. Our approach painstakingly incorporates a watermark into the chosen NIfTI image slice, aiming to accurately fit the watermark, while preserving the medical information contained in the slice. Specifically, the original image was converted through the lifting wavelet transform (LWT), realizing excellent modification during insertion. Next, Z-transform was applied over the low-low (LL) band, and the Hessenberg decomposition (HD) was performed on the transformed band, which contains the maximum energy of the image. Afterwards, Arnold Cat map was employed to scramble the watermark, before inserting it into the slice. Simulation results show that our approach strikes a perfect balance between security, imperceptibility, and robustness against various attacks, as suggested by metrics like peak signal-to-noise ratio (PSNR), normalized correlation (NC), structural similarity index measure (SSIM), and universal image quality index Q.

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45

Muschelli, John, Elizabeth Sweeney, and Ciprian M. Crainiceanu. "freesurfer: Connecting the Freesurfer software with R." F1000Research 7 (May 16, 2018): 599. http://dx.doi.org/10.12688/f1000research.14361.1.

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We present the packagefreesurfer, a set of R functions that interface with Freesurfer, a commonly-used open-source software package for processing and analyzing structural neuroimaging data, specifically T1-weighted images. Thefreesurferpackage performs operations on nifti image objects in R using command-line functions from Freesurfer, and returns R objects back to the user. freesurferallows users to process neuroanatomical images and provides functionality to convert and read the output of the Freesurfer pipelines more easily, including brain images, brain surfaces, and Freesurfer output tables.

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46

Yap, Jennifer. "I as Collage: Playwright John Ng on the Modern Immigrant Experience." Canadian Theatre Review 110 (March 2002): 49–52. http://dx.doi.org/10.3138/ctr.110.014.

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“Asian American experience is a relatively new concept if you think about it. We only go back 20-30 years, so we have all of those issues to work through – chief of which is the immigrant experience. Writers need to get this out of their system. It’s their most immediate experience of all; growing up, family, generations, racism, gender. We have to get through that before we start getting into all the other iterations of American life. It takes a certain amount of maturity as a writer to hone in on the nitty gritty of relationships and look at other situations, and as we develop Asian American theatre that’ll happen.” -Ralph B. Peña (Eng 4l6)

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Chakravarty, S., P. K. Dash, V. Ravikumar Pandi, and B. K. Panigrahi. "An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting." International Journal of Applied Evolutionary Computation 2, no. 3 (July 2011): 39–58. http://dx.doi.org/10.4018/jaec.2011070104.

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This paper proposes a hybrid model, evolutionary functional link neural fuzzy model (EFLNF), to forecast financial time series where the parameters are optimized by two most efficient evolutionary algorithms: (a) genetic algorithm (GA) and (b) particle swarm optimization (PSO). When the periodicity is just one day, PSO produces a better result than that of GA. But the gap in the performance between them increases as periodicity increases. The convergence speed is also better in case of PSO for one week and one month a head prediction. To testify the superiority of the EFLNF, a number of comparative studies have been made. First, functional link artificial neural network (FLANN) and functional link neural fuzzy (FLNF) were combined with back propagation (BP) learning algorithm. The result shows that FLNF performs better than FLANN. Again, FLNF is compared with EFLNF where the latter outperforms the former irrespective of the periodicity or the learning algorithms with which it has been combined. All models are used to predict the most chaotic financial time series data; BSE Sensex and S&P CNX Nifty stock indices one day, one week and one month in advance.

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48

Shalini, Talwar, Shah Pranav, and Shah Utkarsh. "Picking Buy-Sell Signals: A Practitioner’s Perspective on Key Technical Indicators for Selected Indian Firms." Studies in Business and Economics 14, no. 3 (December 1, 2019): 205–19. http://dx.doi.org/10.2478/sbe-2019-0054.

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AbstractThe purpose of this study is to undertake technical analysis of selected companies included in the S&P CNX Nifty 50, a leading stock market index in India. We have used the stock price data of twenty leading listed firms in India for a period from January 1, 2012 through December 31, 2017. We have applied Guppy Multiple Moving Average (GMMA), Moving Average Convergence Divergence (MACD), Stochastic Relative Strength Index (Stoch RSI) and Average Directional Index (ADX) to Heikin Ashi charts to back test and provide entry and exit points for the players in the stock market. Analysis of the price information has revealed that the GMMA and ADX are effective indicators for most of the stocks under the study but they give late signals as compared to RSI and MACD. Further, the study has shown that though RSI and MACD give early signals, yet they are risky as the number of false signals generated by them is also found out to be quite high. The study is important as the findings can be used by investors, option traders and portfolio managers to get generate profitable trading signals and obtain good risk to reward ratios.

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Sharma, Ganesh, and Badri Aryal. "Household Economies of Chepang People in Chitwan." Economic Literature 13 (February 8, 2018): 39. http://dx.doi.org/10.3126/el.v13i0.19149.

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This study attempts to characterize a typical Chepang community in Chitwan district with reference to their economy at household level based on the study conducted in Lothar Village Development Committee. Chepang are considered to be one of the highly marginalized communities in Nepal having traditional subsistence based small economies. Their houses are small with mud floor, stone walls and straw roofs. One third of the Chepang households do not have toilets. They rear small number of mixed livestocks in a house eg. Cattle, buffaloes, poultry, goat and pig. They do not have household amenities like freeze, telephone, television, computer, motorcar and motorbike; but have mobile phones. More than ninty percent of Chepang go to jungle to collect one or the other types of edibles like githavyakur, wild fruits, and chiuri.Ninty five percent of Chepang people do not have bank account, thus rely on their friends and relatives for borrowing in household needs for money. Chi-square test reveals highly significant association between size of landholding and food sufficiency months, level of education and annual income, purpose of taking loan and sources of loan; as well as estimated annual income and account holding in bank. Economic Literature, Vol. XIII August 2016, page 39-45

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Dahunsi, Folasade, John Idogun, and Abayomi Olawumi. "Commercial Cloud Services for a Robust Mobile Application Backend Data Storage." Indonesian Journal of Computing, Engineering and Design (IJoCED) 3, no. 1 (March 10, 2021): 31–45. http://dx.doi.org/10.35806/ijoced.v3i1.139.

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Rapid advancements in the infrastructure of Information and Communication Technology (ICT) have led to radically new but ubiquitous technology; cloud computing. Cloud computing has gracefully emerged offering services that possess on-demand scalability, huge computing power, and a utility-like availability, all at a relatively low cost. It has unsurprisingly become a paradigm shift in ICT, gaining adoptions in all forms of application i.e., personal, academic, business, or government. Not only for its cost-effectiveness but also for its inherent ability to meet business goals and provide strategic ICT resources. More recently there have been advances in cloud computing leading to the evolution of newer commercial cloud services, one of which is the Mobile backend as a Service (MBaaS). The MBaaS is important and required for a robust mobile application back-end data storage and management. Its wide adoption and importance stem from its ability to simplify application development and deployment. Also, MBaaS is robust, with the ability to cope with errors by providing nifty tools and other features. These enable rapid scaffolding of mobile applications. This paper reviews Mobile backend as a Service (MBaaS) and provides required background knowledge on some cloud services and their providers to enable stakeholders to make informed decisions and appropriate choices.

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