Stock price prediction

Keywords: Stock, price

Background

Stock price prediction is a critical task in financial markets, aiming to forecast future price movements based on historical data. Artificial Neural Networks (ANNs) have shown promising results in modeling complex patterns and relationships in stock market data, making them a suitable tool for stock price prediction. Some of the major factors that affect the stock market prices are that they are volatile and they maintain non-linear relationships among the previous trends. Because of these two features, the prediction process makes the ANN undergo critical challenges as they don’t have fixed patterns and they are not at all stable. However, the author has tried to predict the stock prices based on the previous historical data using the ANN model. Overall after a thorough analysis of the dataset, the author has built a good ANN model to predict the outcome of the stock prices. Finally, after training the model on the historical data, the author has achieved an accuracy score of around 99%.

Aim & Objectives

Aim
To Predict the Stock Price using DL Techniques
Objective

Develop an ANN model to predict stock prices, enhancing investment decision-making and portfolio management.
Create an ANN system that analyzes historical market data to forecast future stock price trends.
Evaluate the ANN's prediction accuracy, aiming to provide valuable insights for traders and investors.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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