The Predictive Power of Macroeconomic Variables on the Indian Stock Market Utilizing an ANN Model Approach : An Empirical Investigation Based on BSE Sensex

Chhabra, Meghna

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DC Field	Value	Language
dc.contributor.author	Chhabra, Meghna	-
dc.date.accessioned	2024-09-06T07:18:40Z	-
dc.date.available	2024-09-06T07:18:40Z	-
dc.date.issued	2023	-
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/437	-
dc.description	Stock market prediction has long been of great interest to several investors, professionals, and researchers. Due to an inherently noisy and highly volatile environment, an accurate prediction of stocks is a difficult procedure (Ticknor, 2013). Various factors, including market news, political developments, and crises like COVID-19, influence the stock market’s movement. Several factors that affect the stock market have been discussed in previous literature, including the foreign exchange rate, the “index of industrial production,” the “consumer price index,” the “long-term interest rates,” the price of gold, Crude oil prices, the Indian Volatility Index, Morgan Stanley Capital International (MSCI) World Index, and the MSCI Emerging Market Index is added as a new variable to add novelty to the research.	en_US
dc.description.abstract	The paper focuses on the use of Artificial Neural Networks (ANNs) for forecasting time series data of the stock market since ANNs are dynamic and are more capable of handling complex data sets in comparison to conventional forecasting techniques such as regression, Logistic regression, and have massive potential for the prediction of stock market prices.	en_US
dc.language.iso	en	en_US
dc.publisher	Folia Oeconomica Stetinensia	en_US
dc.subject	Foreign Exchange	en_US
dc.subject	BSE Sensex	en_US
dc.title	The Predictive Power of Macroeconomic Variables on the Indian Stock Market Utilizing an ANN Model Approach : An Empirical Investigation Based on BSE Sensex	en_US
dc.type	Article	en_US
Appears in Collections:	PGDM

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