Nifty Index Research Articles

A Study on Stock Analysis of Automobile Industries

This study investigates the stock movement of automobile companies listed on the NIFTY50 index, focusing on five major firms: TVS Motor, MRF, Ashok Leyland, Maruti Suzuki, and Tata Motors. Using quarterly and annual data from January 2021 to December 2021, the analysis identifies key trends and volatility factors impacting stock prices in the automotive sector. Key findings suggest that fluctuations in stock prices are influenced by quarterly performance, industry trends, and macroeconomic conditions. Additionally, the report highlights the impact of the COVID-19 pandemic on the sector, with companies facing challenges such as reduced demand, increased costs, and supply chain disruptions. The study concludes that stock movements in this industry are volatile, urging investors to monitor these fluctuations closely before making investment decisions.

Predicting Bank Nifty Movements Based on ICICI, HDFC, and Axis Bank Returns

This study investigates the link between the returns of ICICI Bank, HDFC Bank, and Axis Bank and the overall performance of the Bank Nifty index, which is a crucial measure of the Indian banking sector's health on the National Stock Exchange. Using five years of historical data, regression analysis and descriptive statistics evaluate trends, correlations, and prediction accuracy in anticipating Bank Nifty returns based on the performance of these large banks. The study finds that while Bank Nifty displays stability as it aggregates multiple bank stocks, individual banks like Axis Bank and HDFC exhibit distinct volatility patterns, with Axis Bank showing significant fluctuations due to firm-specific and external factors.Regression results show a little association between individual bank returns and the Bank Nifty, indicating that the index has limited predictive value for single equities. Nonetheless, knowing these patterns may help investors manage risk, anticipate market changes, and develop portfolio diversification methods. The findings emphasize the relevance of sector-specific patterns, implying that although Bank Nifty captures broad market movements, other elements have a distinct influence on each bank's performance, providing insights for more sophisticated investment and risk management methods.

Asymmetric Effects and Volatility Clustering in NSE NIFTY 50: A Comparative Analysis of GARCH Models

This study aims to analyze the volatility patterns, Clustering, and asymmetric effects in the NSE NIFTY 50 index. It involves using daily returns data from the NSE NIFTY 50 from 01 Jan 2010 to 31 Dec 2023. Daily closing prices are obtained from the official NSE website, and returns are calculated based on these prices. EGARCH (1, 1), TARCH (1, 1), GARCH (1, 1), GARCH-M (1,1), and models are utilized to predict volatility, capturing volatility clustering and leverage effects. Using both the Akaike and Schwarz criteria, EGARCH (1,1) was demonstrated to be the best model. The findings reveal that Fluctuations in the Indian stock market, particularly shown in the NIFTY index of the NSE were the highest in the year 2020 due to the COVID-19 pandemic. Past NIFTY returns demonstrate a GARCH effect on today’s NIFTY returns. The results of the research suggest that the impact of negative and positive shocks on the stock returns series varies. Volatility is largely caused by negative news compared to positive news. It is suggested that the model exhibits a leverage effect and that different types of information produce various types of shock to the volatility of the Nifty index return. The findings show that investors of NSE, particularly those involved with the NIFTY50, face heightened volatility and increased risk, especially during periods of significant events.

The Impact of the COVID-19 Pandemic on metal, mining, and allied sectors in the Indian Stock Market

The paper investigates the effect of the first and second waves of COVID-19 on metal, mining, and its associated sectors stocks in India. Furthermore, the paper explores how the share price of the Indian metal, mining, and allied sectors is impacted and how it differs from the rest of the market. We also compare the effect of the pandemic waves on share prices related to metal, mining, and associated sectors in India during the study period, which ranges from January 2020 to December 2021. The share prices are collected from CMIE prowess for the sample firms. We use event study methodology to explore the impact of the different waves on the above-mentioned sectors. The finding exhibits that the first wave of the pandemic was more extreme in terms of the adverse price reaction than the second wave of the pandemic in the short term in the first five days. In the first wave, a more negative impact was on the Nifty50 index than the metal index. The firms in the metal and allied sectors experienced more negative price reactions than the metal index on the announcement of the lockdown event in the first wave. In the second wave, the impact was less than experienced by the firms in the first wave. We present the comparative empirical findings in daily and period-wise frequency to understand the effect of the 1st and 2nd waves of the pandemic on metal, mining, and its associated sectors in India. Our paper will contribute to the literature on extreme events and assist different stakeholders.

Analysis and Prediction of Stock Price using HMM and Facebook’s Prophet Computational Models

In recent times, the utilization of Statistical and Machine Learning techniques has gained prominence in the realm of financial data analysis. These methods are applied to various types of financial data, encompassing textual information, numerical data, and graphical representations. This study aims to compare the performance of two prominent forecasting methods, Hidden Markov Models and Facebook’s Prophet in the context of stock price prediction. Assessing the predictive accuracy, interpretability, and adaptability of both approaches through empirical experiments and case studies sheds light on their respective advantages and limitations. These experiments demonstrate that the predicted stock prices are in closer proximity to the actual price when compared to using a single data source. Furthermore, the achieved MAPE are 0.01, 0.025 and respectively, outperforming conventional methodologies. Our validation of effectiveness extends to real-world datasets encompassing the NIFTY50 Index. These findings offer valuable insights for researchers and practitioners seeking effective strategies for stock price prediction.

Indirect Tax Reform And Market Efficiency: An Empirical Study of Indian Stock Market

The biggest tax reform of the Indian Government in this present decade is the Goods and Services Tax (GST), which was passed in parliament on 8th September 2016 and was enforced from 1st July 2017. Eight years of data from the Bombay Stock Exchange Sensex and the National Stock Exchange Nifty50 index is studied. The data is divided into two sub-periods before GST was passed from parliament period - I and after GST was passed from the parliament of India period - II. The study intends to test the market efficiency in its weak form. To check the time series data for normality, stationarity, random walk, and autocorrelation function, Kolmogorov-Smirnov (KS) test, Augmented Dickey-Fuller (ADF) test, Phillips Perron (PP) test is used for the analyse of the time-series data. The study found that returns are not normally distributed and the market is inefficient in a weak form for both periods under study.

Unveiling the interlinkage between Ethereum and Nifty indices: impact of cryptocurrency on Indian equity markets post Covid-19

Unveiling the interlinkage between Ethereum and Nifty indices: impact of cryptocurrency on Indian equity markets post Covid-19

Investigation of the Herding Behaviour in the Indian Stock Market during the Russia and Ukraine Crisis: Evidence from the Nifty-50 Index

Investigation of the Herding Behaviour in the Indian Stock Market during the Russia and Ukraine Crisis: Evidence from the Nifty-50 Index

An empirical investigation of return spillover and volatility dynamics of Indian sectoral indices

ABSTRACT This research delves into the interconnectedness and volatility dynamics within sectoral indices amid the COVID-19 pandemic, to determine the most appropriate model for elucidating these dynamics. It addresses the burgeoning interest among investors in comprehending sectoral dynamics within India’s stock markets. Leveraging five years of daily closing price data (April 2017 to December 2021) sourced from the National Stock Exchange’s official website for 16 sectoral indices and the Nifty50 index, the study employs the Diebold-Yilmaz Spillover 2012 framework alongside four GARCH models (E-GARCH, T-GARCH, FIE-GARCH, and FE-GARCH). Empirical analysis underscores that the mid-small financial sector is the riskiest sector among others, advocating for the inclusion of the pharmaceutical sector to mitigate portfolio risks. Furthermore, the research underscores the persistence of volatility across sectors and identifies the TGARCH model as the appropriate model to capture the volatility for most sectoral indices.

Stock market prediction employing ensemble methods: the Nifty50 index

<span lang="EN-US">Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions.</span>

A study on the behaviour of the Capital Market with special reference to the NIFTY Index

A study on the behaviour of the Capital Market with special reference to the NIFTY Index

An Innovative Study on Stock Price Prediction for Investment Decision Through ARIMA and LSTM with Recurrent Neural Network

The securities market is extremely volatile and difficult to prognosticate. Stock prices are depending upon numerous factors. To reduce the risk of volatility, it is very important to apply an accurate mechanism to forecast stock prices. The importance of share price prediction forecasting in finance and economics has sparked researchers’ interest in creating more reliable forecasting models over time. In this research paper, the researchers try to explore two different applications based on linear and nonlinear (RNN) functions. The criteria for the stock price predictions are evolved using Auto Regressive Integrated Moving Average (ARIMA) which takes the linearity function from the past share prices. The ARIMA model assumes the future prices usually be similar to past. Sudden changes may not reflect in this model. The nonlinearity Recurrent Neural Network (RNN) is going to be applied for share price prediction so that it can be taken into account the quick changes that are occurring in the market environment. To test the RNN, the study used the Long Short Term Memory (LSTM) model which takes the support of Artificial Intelligence. Taking the sample of share prices of banks listed in the NIFTY index, the ARIMA and LSTM have been performed and analyzed. Stock price predictions for banks listed in the NIFTY bank index are found better with the ARIMA model than with the LSTM model.

Markov Chain Model for NIFTY50 Index

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 the Markov model in this study. The daily fluctuations in the NIFTY 50 Index, which includes the top 50 firms of Indian stocks are tracked and analyzed. The TPM and IPV, the anticipated number of transitions, and the expected number of return times are established.

Factors Influencing Pricing Multiples: Evidence from India

This study attempts to enhance insights into the valuation process by exploring factors influencing pricing multiples, considering their crucial role in corporate decisions and investment strategies. The reference securities are adopted from the Nifty-50 index. The findings show that almost all the dependent and majority of independent variables are right skewed and confirm the leptokurtic nature in the distribution. The Jarque-Bera test for normality is significant for all the variables, except for LTG. Majority of the fundamentals have positive and weak association with the multiples. ROCE is found to be significant in three models – PE, PB, and PS. STG, NPM and Leverages are important for certain specific multiples. The results of the diagnostic tests confirm that there is no collinearity issue, and the residuals are not normally distributed, not auto correlated, and heteroscedasticity is noticed for PE and PS models.

Stock Price Prediction and Stock Behaviour using GA and LR with NIFTY50 index Based on Daily Charts

The price of a company's stock, which can increase in lockstep with the price of a single share, is the one of the indicator to measure its performance. Clients or stockholding companies find it challenging to make long-term projections regarding the value of specific stocks due to the unpredictable nature of stock prices. Consequently, there is no business-related subject more talked about than stock market predictions. It is crucial to resolve this issue in a way that benefits buyers and investors because they frequently experience investment losses. Machine learning is useful in developing model for stock value predictions. We are utilizing Python and Linear Regression, one of the Machine Learning statistical techniques for predictive analysis, to create a stock price prediction website in order to address this issue. Our study primarily focuses on the NIFTY50 index's performance in distributed lag with the purpose of predicting stock prices in the Indian stock market. Several useful characteristics of the NIFTY50 lag index were extracted by means of a genetic algorithm. After that, we uncovered hidden correlations between the stock index and a given stock's trend by using the linear regression classifier. For the purpose of testing our approach, we used it to forecast the future of three distinct equities. In comparison to state-of-the-art forecasting methodologies, our experimental results demonstrated an accuracy of 82.55%. For the purpose of predicting daily changes in stock prices, the NIFTY50 stock index proved to be useful.

A Study on Risk and Return Analysis and Data Envelopment Analysis of Public and Private Sectors Banks: A Five -Year Study (2018-2022)

Risk and returns are like two faces of the same coin, if an investor wants to gain higher returns, he must also accept the fact that this would also increase the amount of risk involved. Most of the times, individual decision process is influenced by risk and return analysis. The study is focused on analysing the performance of those nationalized banks listed in the NSE with respect to return, risk and beta for the period 1st January 2017 to 31st December 2017. The term risk is a situation in which the possibility of the consequence can be predicted, but uncertainty is the situation where the possibility cannot be predicted. Risk and uncertainty are part and parcel of investment The possibility of risk is a loss for the investors. The methodology adopted includes analysis of the performance of banking sector considering Bank Nifty Index as benchmark. Risk and return of banking stocks as well as Data Envelopment analysis method is used for analysing the efficiency of banks. The entire study is based on secondary data collected from the NSE. The data collected was based on the monthly prices of the bank stocks listed in Bank Nifty. The reason for selecting monthly prices is to measure the shortterm variations in the banking stocks due to various other internal and external factors. The findings of the study revealed that if the investors are ready to take high risk for more returns, the investors are suggested to invest in stocks like Bank of India and Punjab National Bank in which risk and return are high. The investors who prefer low risk and return are suggested to invest in Axis Bank stock.

Impact of Macroeconomic Variables on the Performance of the Indian stock market

This paper analyses the macroeconomic factors influencing performance of Indian stock market index NIFTY 50. The study is unique in nature as it employ both, primary and secondary to derive the conclusion. Initially, nine years monthly data of macroeconomic variables are regressed against monthly performance of NIFTY 50 index using logistic regression. In second stage the outcome of regression analysis is ratified with primary data collected through face to face interview of the stock market expert. The secondary data analysis confirm Dow jones index and exchange rate movement are the main determinants of NIFTY 50 index. However, expert opined that other factors like political stability, economic situation in developed and bilateral relations of India with other countries are also important to forecast the NIFTY 50 index movement. This is the first study in Indian context, to combine domestic and international factors to forecast NIFTY 50 index movement.

Effectiveness of deterministic option pricing models: new evidence from Nifty and Bank Nifty Index options

AbstractThis research delves into the empirical performance of deterministic option pricing models in the dynamic financial landscape of India. The primary focus is on uncovering pricing discrepancies and discerning whether these disparities arise from inherent limitations in the theoretical foundations of the models or are influenced by the trading behaviors of market participants. The investigation centers on the analysis of call and put option contracts for the Nifty Index and Bank Nifty Index, both extensively traded on the National Stock Exchange (NSE) of India. The study’s findings highlight that models developed to address the theoretical constraints of the benchmark Black–Scholes model demonstrate noteworthy performance. However, the complexity of these models does not consistently translate into enhanced pricing efficiency. Notably, the Black–Scholes and Practitioner Black–Scholes models exhibit superior performance across various moneyness-maturity categories. Furthermore, the research underscores the substantial impact of option contract liquidity on the efficiency of the pricing models. Specifically, highly traded at-the-money and out-of-the-money option contracts exhibit a higher level of pricing accuracy.

Trade-Off vs. Pecking Order Theory: Panel Data Approach

In this study, an attempt has been made to investigate any significant changes in variables influencing the capital structure decisions among selected companies from Nifty-50 Index. The financing behaviour of the companies are explained by using Panel data analysis – fixed and random effects. As per the analysis, it is observed that Business Risk, Growth Rate, Profitability, Size (log assets) and Size (log sales) are the significant variables influencing the capital structure over the study period. Finally, the study concludes that neither the pecking order theory nor the trade-off theory fully explains the determinants of capital structure for the selected companies.

Measuring Asymmetric Volatility of Bank Nifty Index using Egarch Model

The paper examines the significance of volatility models in forecasting future volatility for effective portfolio allocation and risk reduction. It compares the performance of symmetric and asymmetric models in estimating conditional variance, and linear versus non-linear GARCH models. Using secondary data from the National Stock Exchange's Nifty Bank index, the study applies Exponential GARCH (1,1) to measure asymmetric volatility and conducts various tests to confirm the suitability of the data for analysis. The results indicate clustering of volatility in Nifty Bank returns over a four-year period, with the presence of asymmetrical effects and leverage constants. The study concludes that negative information has a greater impact on volatility than positive surprises, and that market fluctuations are inversely related to stock market performance. This research provides valuable insights for portfolio selection, risk management, and asset pricing in the context of increasing volatility across various markets and industries.