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Stock Price Prediction based on Gradient Descent using a Back Propagation Neutral Network

Dharmaraja Selvamuthu*

1Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.

*Corresponding Author:
Dharmaraja Selvamuthu
Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
+91 7486953214
[email protected]

Received date: 05-01-2023, Manuscript No. jibc-23-89990; Editor assigned date: 07-01-2023, Pre QC No. jibc-23-89990 (PQ); Reviewed date: 24-01-2023, QC No. jibc-23-89990; Revision date: 31-01-2023, Manuscript No: jibc-23-89990 (R); Published date: 07-02-2023

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As an AI language model, I can give you some information and general pointers about stock price prediction, but this is a difficult endeavour and there is no sure-fire way to anticipate future stock prices.

Here are some general steps that you can follow to predict stock prices

Gather data: This data should include information on the company's financial performance, industry trends, and macroeconomic factors [1].

Choose a model: Select a suitable predictive model that can accurately forecast future stock prices. Commonly used models include time series analysis, regression analysis, and machine learning algorithms such as random forests, neural networks, and support vector machines.

Apply the model: It is important to remember that the accuracy on various factors such as market volatility, unexpected events, and changes in the business environment [2].

Here's a general outline of the process

Data: Collect the historical data of the stock prices and pre-process them. Divide the data into training and testing sets. Normalization or scaling of the data is also important before feeding the data to the neural network [3].

Model creation: Create a backpropagation neural network model using any popular machine learning library such as Keras, Tensor Flow, or PyTorch. The model should have an input layer, one or more hidden layers, and an output layer. The number of neurons in the input layer should correspond to the number of features in the input data [4].

Training: Train the model using the training set. The training process involves passing the input data through the model, computing the prediction, comparing it with the actual output, and adjusting the weights using gradient descent. The process is repeated for a fixed number of epochs or until convergence.

Testing: Test the model using the testing set. Compute the prediction using the trained model and compare it with the actual output to evaluate the accuracy of the model.

Prediction: Once the model is trained and tested, it can be used to predict the stock prices for future periods. It's worth noting that stock price prediction is a challenging task, and there are many factors that can influence the price of a stock, such as market sentiment, news events, and economic indicators. Therefore, it's important to carefully select the features and ensure that the data is representative of the market conditions [5].


In conclusion, predicting stock prices is a challenging task due to the complex and volatile nature of financial markets. While various techniques and models exist for stock price prediction, no method can guarantee 100% accuracy. It is important to understand that stock prices are influenced by a wide range of factors, including economic conditions, company performance, global events, and investor sentiment, among others. It is also worth noting that past performance is not always a reliable indicator of future results, and relying solely on historical data may not always be an accurate predictor of future stock prices. As such, it is essential to use multiple sources of data and a variety of analytical tools to improve the accuracy of stock price predictions.