An economic analysis of Predictive Modelling of D-mart’s stock prices

Dr. Raghu C

An economic analysis of Predictive Modelling of D-mart’s stock prices

Authors: Dr. Raghu C

Unique Paper ID: 168657
Volume: 5
Issue: 11
PageNo: 767-771

Keywords: Forecasting Predictive Analysis Stock prices Volatality Trends.

Abstract:
Predictive modeling of stock prices is a sophisticated analytical technique that combines statistical methods, machine learning algorithms, and historical market data to forecast future price movements. This process plays a vital role in financial markets, as it enables investors, traders and analysts to make more informed decisions regarding their investment strategies. Given the complexity of stock price movements, predictive modeling helps in understanding underlying trends, minimizing risks, and maximizing returns. At the core of predictive modeling is the collection and analysis of historical stock price data. This data typically includes various metrics, such as opening, closing, high and low prices, along with trading volumes over a specified period. The analysis of this historical data allows for the identification of trends, seasonality and cyclic patterns that may influence future stock performance. Predictive modeling employs various statistical techniques, such as time series analysis, regression analysis, and volatility modeling, to forecast future prices. Time series analysis, for instance, helps in understanding temporal patterns and auto-correlations in stock prices, while regression analysis can identify relationships between stock prices and independent variables, such as economic indicators, interest rates and inflation rates. In recent years, machine learning algorithms have gained prominence in predictive modeling due to their ability to analyze large datasets and uncover complex patterns that traditional statistical methods may overlook. Techniques like decision trees, support vector machines, neural networks, and ensemble methods can be utilized to create predictive models that adapt and improve over time based on new data. These models can be trained on historical data to recognize patterns and relationships, enabling them to make predictions about future stock movements. This paper analyses a macro perspective of predictive modelling so as to create a more comprehensive model that can better reflect the complexities of the stock market and improve prediction accuracy.

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Cite This Article

ISSN: 2349-6002
Volume: 5
Issue: 11
PageNo: 767-771

An economic analysis of Predictive Modelling of D-mart’s stock prices

Available:https://ijirt.org/Article?manuscript=168657

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