| Autor: |
Yadav, Krishna, Singh, Ajay Pal, Sharma, Rohit, Chouhan, Rakesh |
| Předmět: |
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| Zdroj: |
Grenze International Journal of Engineering & Technology (GIJET); 2023, Vol. 9 Issue 2, p497-505, 9p |
| Abstrakt: |
Forecasting stock market trends is challenging due to the complexity and dynamics of the system. Time series analysis has become a popular method in recent years for predicting such trends. This research paper examines the use of time series analysis in stock market forecasting and evaluates the effectiveness of different statistical techniques in predicting future market trends. It examines multiple time series models, such as ARIMA and LSTM, to assess their performance in predicting the stock market. Additionally, the study explores the influence of economic indicators on stock prices and their incorporation into time series models to improve forecasting accuracy. The results suggest that the combination of time series analysis and economic indicators can be a useful approach to forecast stock market trends. However, the precision of the forecasts relies on the quality of historical data and specific market conditions. The study emphasizes the importance of acknowledging the uncertainty involved in stock market forecasts and the need for regular monitoring and adjustment of forecasting models. This research paper also provides significant insights into the practical application of time series analysis in forecasting stock market trends and underscores its potential in enhancing investment strategies. By leveraging historical data to identify significant patterns and trends, investors and analysts can gain deeper insights into future market performance, making well-informed investment decisions that lead to more successful outcomes. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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