Performance analysis of Indian stock market index using neural network time series model

Abstract The present study demonstrates modelling of conditional volatility of NIFTY 50 using GARCH (1,1) model. The daily returns data of the Indian stock market index NIFTY 50 is used for the period ranging from April 2010- March 2020. The data is analysed using R software. The study estimates and interprets the results arrived in the summary output of the R environment and demonstrates how to forecast the volatility of the returns based on the estimated parameters. Extracting the time series of conditional volatilities is also demonstrated in the study.

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Forecasting of Indian Stock Market Index S&P CNX Nifty 50 Using Artificial Intelligence

International Journal of Science and Research (IJSR) ◽

10.21275/v4i11.nov151255 ◽

2015 ◽

Vol 4 (11) ◽

pp. 2544-2550 ◽

Cited By ~ 3

Keyword(s):

Artificial Intelligence ◽

Stock Market ◽

Stock Market Index ◽

Market Index ◽

Indian Stock Market

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A NARX Neural Network Model To Predict One-Day Ahead Movement of The Stock Market Index of Malaysia

10.1109/aidas53897.2021.9574394 ◽

2021 ◽

Author(s):

Ali H. Dhafer ◽

Fauzias Mat Nor ◽

Wahidah Hashim ◽

Nuradli Ridzwan Shah ◽

Khairil Faizal Bin Khairi ◽

...

Keyword(s):

Neural Network ◽

Stock Market ◽

Network Model ◽

Neural Network Model ◽

Stock Market Index ◽

Market Index ◽

Narx Neural Network

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Asymmetric Volatility in the Nepalese Stock Market

Journal of Comparative International Management ◽

10.7202/1075637ar ◽

2021 ◽

Vol 22 (1) ◽

pp. 41-59

Author(s):

Dinesh Gajurel

Keyword(s):

Stock Market ◽

Spillover Effect ◽

Equity Markets ◽

Spillover Effects ◽

Stock Market Index ◽

International Equity Markets ◽

Asymmetric Volatility ◽

Market Index ◽

International Equity ◽

Indian Stock Market

This paper investigates the asymmetric volatility behavior of the Nepalese stock market including spillover effects from the US and Indian equity markets. I modeled asymmetric volatility within a generalized autoregressive conditional heteroskdasticy framework using comprehensive data for the Nepal stock market index. The results reveal a very different asymmetry compared to the results in other international equity markets: positive shocks increase volatility by more than negative shocks. The results further suggest that uninformed investors play a significant role in the Nepalese stock market. The spillover effect from the Indian stock market to the Nepalese stock market is negative. Overall, I conclude that a “fear of missing out” (FOMO) of noise traders as well as the deployment of pump and dump schemes are inherent features of the Nepalese stock market. The findings are very useful to policy makers and investors alike.

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A Study on Affiliation between Indian Stock Market and Macro Economic Variables

MUDRA Journal of Finance and Accounting ◽

10.17492/mudra.v5i01.13040 ◽

2018 ◽

Vol 5 (01) ◽

Author(s):

Pooja Chaturvedi Sharma

Keyword(s):

Stock Market ◽

Inflation Rate ◽

Stock Market Volatility ◽

Stock Market Index ◽

Complex Interplay ◽

Monthly Data ◽

Macroeconomic Variables ◽

Market Index ◽

Causality Tests ◽

Indian Stock Market

Stock market volatility is a result of complex interplay of a host of factors. Hence, it is difficult to make a correct assessment of its movement. Macroeconomic variables have are very much influential in context of the volatility of stock market. This study inspects the association amongst stock market index and selected macroeconomic variables. For the analysis unit root, co-integration, Granger causality tests and Johansen co-integration tests were performed. Outcomes of the study showed that all the variables namely money supply, exchange rate and inflation rate are positively correlated with the stock market index except gold prices. Co-integration existed between the stock market index and macroeconomic variables. The study uses monthly data of past ten years (i.e. from April 2008 to March 2018).

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