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Littérature scientifique sur le sujet « Nifty50 »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Nifty50"

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Jain, Abhimanyu, Himanshu Goel, Sakshi Jain, and Yukta Sharma. "Nexus between Foreign Institutional Investors and NSE during Covid." MUDRA: Journal of Finance and Accounting 9, no. 2 (2022): 60–71. http://dx.doi.org/10.17492/jpi.mudra.v9i2.922204.

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This paper aims to analyze the relation between FII and Nifty50. An empirical investigation has been conducted to determine the cointegration and causality between FII and Nifty50 using Johansen’s cointegration and Granger causality techniques. Monthly data points spanning the year 2021 have been used for empirical investigation. Empirical results of the cointegration technique reveal no cointegrating equations between the FII and Nifty50. The results of Granger causality reveals that there is no significant relationship between FII and Nifty50. The results of this paper can be useful for reta

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Kumar, Gaurav, and Arun Kumar Misra. "Long run commonality in Indian stocks: empirical evidence from national stock exchange of India." Journal of Indian Business Research 12, no. 4 (2020): 441–58. http://dx.doi.org/10.1108/jibr-09-2016-0091.

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Purpose The purpose of this paper is to investigate long-run commonality in liquidity using multiple proxies computed from limited order book data of NIFTY50 stocks. The findings indicate the existence of systematic liquidity or commonality on NIFTY50 market and comprising industries. Design/methodology/approach The sample comprises all intraday transactions corresponding to NIFTY 50 stocks for April 2015. The study runs firm by firm time series regressions to test the concept of long-run commonality, while controlling other effects. Findings Strong evidence is found in support of long-run com

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Choudhary, Neha, Aditya Nandedkar, Vivek Ahire, and Prof N. H. Deshpande. "Decoding Nifty50’s Price Enigma: Machine Learning Powered Approach." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4946–50. http://dx.doi.org/10.22214/ijraset.2024.59843.

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Abstract: This study uses machine learning and Long Short-Term Memory (LSTM) networks to decipher the price riddle of Nifty50, a well-known stock market index in India. Using Long Short-Term Memory (LSTM) recurrent neural networks, which are renowned for their capacity to recognise sequential patterns, we aim to forecast and comprehend the intricate dynamics influencing Nifty50's price fluctuations.

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Sinha, Dr Rupesh Kumar. "Cross-Market Linkages: Analyzing NIFTY50’s Relationship with Major Asian Indices." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41654.

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The international financial markets are increasingly interconnected, and each stock index affects others based on economic linkages, sentiment, and capital flows. The current study explores the spillover relationship between NIFTY50 and six major global stock indices: KOSPI (South Korea), HK50 (Hong Kong), FTSE (United Kingdom), NIKKEI225 (Japan), SSEC (China), and TSEC50 (Taiwan). These relationships are important for investors, policymakers, and financial economists to understand market integration and risk diversification approaches. Through statistical methods, the study establishes the si

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Kumar, Sunil, and Vipin Kumar Meena. "Credit Risk and financial performance banks: a panel data analysis." International Journal of Accounting, Business and Finance 1, no. 2 (2022): 36–47. http://dx.doi.org/10.55429/ijabf.v1i2.70.

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The NIFTY50 index is the premier benchmark for the Indian stock market, comprising 50 companies from 13 diverse sectors. As five of the country's largest commercial banks are part of this index, their performance indicates the overall market performance. This study aimed to investigate the correlation between credit risk and the financial performance of these NIFTY50-indexed banks. Utilizing secondary data collected from the banks' annual reports over the past decade (2012-2021), the study employed panel data regression analysis to examine the relationship between return on equity and return o

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Seema, Balan, and Balaji Srinivasan B. "A Study on Optimum Portfolio Using Abnormal Return With Special Reference to Nifty." Shanlax International Journal of Management 6, S1 (2019): 37–43. https://doi.org/10.5281/zenodo.2567681.

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The Indian stock market had witnessed an incredible growth in the last two years.Almost all the stocks in the nifty extremely outperformed and so is the nifty. Even the recent economic reforms like demonetisation have little impact on the stock market. Investors who were waited with patience all these times got fructified from their investments. The study had taken into consideration the 48 stocks in Nifty50 for two years from April 2015 to March 2017. This study was made an attempt to construct an optimum portfolio using the concepts of abnormal return based on CAPM. The portfolio with all sc

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V. Parimyndhan. "Markov Chain Model for NIFTY50 Index." Journal of Electrical Systems 20, no. 7s (2024): 2294–301. http://dx.doi.org/10.52783/jes.3964.

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For the last two decades, financial market forecasting has been one of the sensational topics in research, but the market prediction is not easy because of its uncertainty. Though the trend is uncertain by its nature, some researcher is attempted to trace the movement with more accuracy. This study focuses on creating and analyzing a stochastic model that employs the Markov Chains method to anticipate stock prices on the stock exchange. By supposing that prices fluctuate have the feature of Markov dependence with their transition probabilities, study, four states are determined to construct th

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Meher, Bharat Kumar, Iqbal Thonse Hawaldar, Santosh Kumar, and Abhishek Kumar Gupta. "Modelling Market Indices, Commodity Market Prices and Stock Prices of Energy Sector using VAR with Variance Decomposition Model." International Journal of Energy Economics and Policy 12, no. 4 (2022): 122–30. http://dx.doi.org/10.32479/ijeep.13161.

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The study aims to examine the existence of a correlation between the stock prices of the energy sector, commodities prices of the energy sector, and market indices. The study uses an empirical approach to develop various VAR (Vector Autoregression) with Variance Decomposition Models for each company under the energy sector indexed in NIFTY50 by considering daily prices for 3 years. For a comparative study, the data have been divided into two parts. The first part is considered pre-COVID era, i.e., from July 1, 2018, to December 31, 2019, and the second part is considered post-COVID era, i.e.,

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Ms. A.Amali Vinupriyadharshini, Dr Vijayakumari Joseph,. "MEASURING VOLATILITY OF NIFTY50 AND SENSEX UNDER DIFFERENT ERROR DISTRIBUTION METHODS OF E-GARCH FOR THE PERIOD BETWEEN 2011 TO 2016." Psychology and Education Journal 58, no. 2 (2021): 6586–92. http://dx.doi.org/10.17762/pae.v58i2.3192.

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Volatility has always been a part and parcel of stock market. Understanding the volatility is very difficult though measuring it is not impossible. Choosing the right method to meausre the volatility is very crucial and important to get the reliable and accurate results. This study aims at measuring volatility of Nifty50 and Sensex under different error distribution methods of E-GARCH model. E-GARCH is one of the reliable ARCH models that measures persistent volatility and asymmetric effects. This paper bring out the best suited model for Nifty50 and Sensex in measuring the volatility under di

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Daliwala, Noel. "A STUDY OF THE DAY OF THE WEEK EFFECT IN BANK NIFTY AND NIFTY50." International Journal of Management, Economics and Commerce 1, no. 2 (2024): 89–94. http://dx.doi.org/10.62737/2n82nb22.

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Seasonality and seasonal variations are in any commodity market and India is also privy to it. Seasonality is an anomaly. Day of the week effect is an anomaly, which this paper tries to unravel. Many researchers have investigated the effects of seasonal fluctuations such as, , the day-of-the-week, day-of-the-month, and month-of-the-year effects. Historical evidence suggests that these anomalies are prevalent in the market and if properly studied investors can take advantage of the market and try to beat the Efficient Market Hypothesis (EMH). This paper attempts to understand the effect of day-

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Thèses sur le sujet "Nifty50"

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Yutzy, Evan. "Nifty Shades of Beige: The Exploration of Color Lexicology Related to Sexual Identity." Kent State University Honors College / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ksuhonors1430744655.

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DEKAA, BARNAA LOHITAA. "STUDY OF IMPACT OF DEMONETIZATION ON INDIAN STOCK MARKET." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16990.

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On 8th November 2016, the then Prime Minister of India, Mr. Narendra Modi announced that the Indian Currency Notes of value 500 and 1000 will no more be a legal tender. This news came as storm for the Indian which was received with mixed feelings. Prior to Demonetization, cash in circulation was nearly 220 Billion dollars of which approximately 87% i.e. nearly 190 Billion Dollar was in INR 500 & INR 1000 currency notes. This meant its impact was bound to be felt by all sections of the society. In this present paper, we wanted to analyse the impact of this event of demonetization particu

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Anderson, BR. "Structure, alteration and mineralisation of the Nifty Copper Deposit, Western Australia: Implications for ore genesis." Thesis, 1999. https://eprints.utas.edu.au/12960/1/Front_Anderson_B_PhD1999.pdf.

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The Nifty Cu deposit is located approximately 450km east of Port Hedland, Western Australia in subgreenschist grade rocks of the Proterozoic Paterson Orogen. The deposit consists of a secondary oxide resou~ce of 12.2 Mt@ 2.52% Cu and a primary sulphide ore body of94Mt @1.63% Cu (0.5% cut-off). Mineralisation is hosted in plunging syncline of carbonaceous and dolomitic shales of the upper Broadhurst Formation. The depositional age of the Broadhurst Formation is constrained to between 1132±21 and 816±6Ma. A reinterpretation of the local stratigraphy proposes that the mine sequence consist

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Dixit, Alok. "Pricing efficiency of S & P CNX Nifty index options: a study in Indian securities market." Thesis, 2009. http://localhost:8080/iit/handle/2074/4664.

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RANA, VINAY. "INVESTMENT IN BANKING AND FINANCIAL SERVICES SECTOR : PERFORMANCE EVALUATION OF ETF AND MUTUAL FUNDS." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18763.

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An exchange traded fund is an investment vehicle that reflects the performance of an underlying index by holding the assets like stocks, commodities or bonds to replicate the composition of the market index. Whereas a mutual fund is a type of financial instrument made up of a pool of money gathered from numerous investors to put in securities such as stocks, bonds, money market instruments, and other assets. Mutual funds are operated by fund managers, who attempt to produce capital gains or income for the fund's investors. This paper is an empirical study of the performance of excha

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Livres sur le sujet "Nifty50"

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Palmer, Carolann. Nifty ninepatches. That Patchwork Place., 1992.

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Rasmussen, Greta. Nifty fifty. Tin Man, 1988.

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Eldridge, Roy. The nifty cat. New World Records, 1986.

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Avery, Virginia. Virginia Avery's nifty neckwear. C&T Pub., 1994.

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Eric, Ladizinsky, and Yamamoto Neal, eds. 50 nifty science experiments. NTC Publishing, 1992.

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Eric, Ladizinsky, and Yamamoto Neal ill, eds. 50 nifty science experiments. Lowell House Juvenile, 1992.

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ill, Staunton James, and Yamamoto Neal ill, eds. 50 nifty origami crafts. Lowell House Juvenile, 1992.

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ill, Yamamoto Neal, ed. 50 nifty magic tricks. Lowell House Juvenile, 1992.

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Taylor, Kevin. 50 nifty travel games. Lowell House Juvenile, 1994.

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Hollow, Michele C. Nifty thrifty math crafts. Enslow Elementary, 2008.

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Chapitres de livres sur le sujet "Nifty50"

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Koushik, C., M. V. Pranav, R. K. Arjun, and S. Shridevi. "Hybrid Exponential Smoothing-LSTM-Based Univariate Stock Market Prediction for Financial Sectors in NIFTY50." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2980-9_28.

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Kotha, Dinesh Sai, Tribuvana Kartikeya Gundu, and U. M. Gopal Krishna. "Diving into Financial Forecasting: Analyzing Stock Price Prediction with LSTM, GRU, and GAN for Nifty50 NSE Index." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78940-3_25.

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Josephine, V. L. Helen, V. Harshitha Moulya, S. Jeevananda, and J. R. Prathuri. "Nifty index." In Deep Learning in Engineering, Energy and Finance. CRC Press, 2024. https://doi.org/10.1201/9781003564874-9.

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Dlugos, Jenn, and Charlie Hatton. "A Nifty Trip from Crypt to Tip." In You under the Microscope. Routledge, 2022. http://dx.doi.org/10.4324/9781003292111-30.

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Dasgupta, Rahul, Subhadip Mondal, and Nivedita Mandal. "How India Is Doing on ESG: A Study of Nifty100 Companies." In Interdisciplinary Research in Technology and Management. CRC Press, 2024. http://dx.doi.org/10.1201/9781003430469-79.

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Ashok Kumar, Balaji, and Lavanya Balaji. "Depiction of Nifty Midcap Index Efficiency Using ARIMA." In Proceedings of the 7th International Conference on Advance Computing and Intelligent Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-5015-7_11.

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Alexander, Rhoda, and Husam Aldin Al-Malkawi. "The Impact of Macroeconomic Factors on the Nifty Auto Index." In Lecture Notes in Civil Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27462-6_2.

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AbstractThe aim of the paper is to investigate the association between selected macroeconomic variables like crude price, exchange rate, index of industrial production, inflation, interest rate, repo rate, gold price and the auto index of the National Stock Exchange (NSE) of India during a time when the automotive sector in India witnessed the sharpest dip in sales. The study adopts Autoregressive Distributed Lag (ARDL) co-integration approach and performs suitable diagnostic tests. Results indicate that, exchange rate has a significant negative relationship with Nifty auto index in the long r

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Balaji, Lavanya, H. B. Anita, and Balaji Ashok Kumar. "Volatility Clustering in Nifty Energy Index Using GARCH Model." In Intelligent Communication Technologies and Virtual Mobile Networks. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1844-5_53.

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Swetha, B., and Kriti Arya. "Nifty 50 Prediction Using Machine Learning Algorithms and Technical Indicators." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1158-4_21.

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Kumar, Bhupendra, and Neha Yadav. "NIFTY-50 Index Forecasting Using CEEMDAN Decomposition and Deep Learning Models." In Proceedings on International Conference on Data Analytics and Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3432-4_27.

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Actes de conférences sur le sujet "Nifty50"

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Shivaram Patil, Pushpalata, Vinay Kukreja, Deepak Upadhyay, Manika Manwal, and Rishabh Sharma. "A Long Short-Term Memory Technique for NIFTY50 Stocks Price Prediction." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673459.

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Sanda, Preetham Sagar, Revanth Thota, Akhil Kodimala, Rishab Babu Gadari, Gagandeep Aurora, and Naga Padmaja. "Unlocking Stock Market Potential: Machine Learning Predictions for NIFTY50's Most Profitable Companies." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911573.

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Nandini, L., Manjunatha Naik, I. G. Srikanth, G. Srinivasa, and G. Sruthi. "Stock Price Prediction Using LSTM Model Of Machine Learning And Evaluation Of Its Prediction Accuracy With Reference To Nifty50 And Sensex." In 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2025. https://doi.org/10.1109/ickecs65700.2025.11034828.

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Hingane, Suraj, Manasi Bhate, Prakash Ukhalkar, Ravikant Zirmite, and Swati Hingane. "Exploring Investor Sentiment and Nifty 50 Volatility: A Behavioural Finance Perspective." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774954.

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Singh, Priya, and Manoj Jha. "Leveraging Deep Learning Ensembles for Stock Index Forecasting: A Nifty 50 approach." In 2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2025. https://doi.org/10.1109/sceecs64059.2025.10940898.

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Guptha, A. Sohan Avaneesh, Sri Surya Shobith Kamisetty, K. Rupesh Venkata Sai Krishna, Bharathi Mohan G, Gundala Pallavi, and Prasanna Kumar R. "Sector-Wise Portfolio Risk Assessment: Monte Carlo Simulation of NIFTY 50 Stocks." In 2025 International Conference on Advanced Computing Technologies (ICoACT). IEEE, 2025. https://doi.org/10.1109/icoact63339.2025.11005178.

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Roy, Sahajaunna, Abdul Hameed, Amrin Samar Sultana, and Rekha R. Nair. "Risk and Return Analysis of Prominent Sectoral Indices of NSE and NIFTY 50." In 2025 International Conference on Data Science and Business Systems (ICDSBS). IEEE, 2025. https://doi.org/10.1109/icdsbs63635.2025.11031781.

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Shivsharan, Nitin, Mandar Kulkarni, Prarthana Ghadigaonkar, Indragopal Bille, Amol Waingankar, and Duradundi Sawant Badkar. "A Stacked LSTM-Based Deep Learning Approach for Forecasting Nifty 50 Market Lows." In 2025 3rd International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2025. https://doi.org/10.1109/icici65870.2025.11069482.

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G, Kalaiarasan, Premjith B, and Nimal Madhu Manjiparambil. "Integrating Hybrid Decomposition Methods and LSTM for Enhanced NIFTY Index Prediction and Trading Strategies." In 2024 IEEE North Karnataka Subsection Flagship International Conference (NKCon). IEEE, 2024. https://doi.org/10.1109/nkcon62728.2024.10774789.

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Mohan, Bharathi, Abhivyakti Yadav, Jayant Toleti, D. Sai Sruthik Reddy, R. Kaif Ahmed, and Gottipati Koushitha. "Sentiment-Driven Predictive Models for Nifty 50 Stock Market Fluctuations: A Time Series Analysis Perspective." In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). IEEE, 2024. http://dx.doi.org/10.1109/iciteics61368.2024.10625537.

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