000			01706nam a22001577a 4500
003			OSt
005			20240528160120.0
008			240528b |||||||| |||| 00| 0 eng d
100			_aRakesh Kumar, Kajol Verma _938964
245			_aInvestigating the Efficacy of ARIMA Models for Predicting Dow Jones Industrial Average Stock Prices International Journal of Financial Management
300			_a34-39p
520			_aThe prediction of stock price volatility holds significant importance in the realms of economics and finance, offering substantial benefits to both investors and economists. This paper employs the Autoregressive Integrated Moving Average (ARIMA) model to forecast the stock prices of the Dow Jones Industrial Average (DJI), a key index in the financial market. The study utliises daily data of the Dow Jones Industrial Average (DJI) spanning from 1 April 2021 to 31 March 2023. Empirical evidence strongly suggests the effectiveness of ARIMA models in predicting DJI stock prices. Furthermore, the study’s findings reveal that the ARIMA model excels particularly in short-term forecasting, demonstrating favourable performance when compared to existing techniques for stock price prediction. Through the utilisation of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) criteria, the study identifies ARIMA (1, 1, 0) as the optimal model for accurately forecasting the share price of DJI within the specified timeframe.
653			_a ARIMA Model, Dow Jones Industrial Average (DJI), USA, Stationarity
773	0		_054838 _983606 _dNew Delhi Publishing India Group 2023 _o55514215 _tInternational Journal of Financial Management _x2229-5682
942			_2ddc _cJA-ARTICLE
999			_c54979 _d54979