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Stock Market Prediction

This article discusses a neural network-based method for predicting stock market trends using historical data, achieving high accuracy in classifying stock price movements. The study emphasizes the advantages of machine learning over traditional statistical models in financial forecasting, particularly in addressing market volatility. The findings suggest that this approach can serve as a valuable tool for traders and analysts in the stock market.

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11 views2 pages

Stock Market Prediction

This article discusses a neural network-based method for predicting stock market trends using historical data, achieving high accuracy in classifying stock price movements. The study emphasizes the advantages of machine learning over traditional statistical models in financial forecasting, particularly in addressing market volatility. The findings suggest that this approach can serve as a valuable tool for traders and analysts in the stock market.

Uploaded by

stoxincart
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© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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SSRG International Journal of Computer Science and Engineering

ISSN: 2348-8387/ https://doi.org/10.14445/23488387/IJCSE-VXXXX

Original Article

Volume X Issue X, 1-4, Month 2023

© 2023 Seventh Sense Research Group®

Stock Market Prediction Using Neural Networks: A Machine Learning Approach

Muhsin Ali P 1, Akshay B2


1Stock Market & Finance Management, BTech EC-S3, Cochin University of Science & Technology,
Kerala, India.
2BTech Student, Department of ECE, Cochin University of Science & Technology, Kerala, India.
1muhsinali@gmail.com (Corresponding author mail id only allowed)

Abstract

This study presents a neural network-based approach to predict stock market trends using historical
stock price data. We employ a feedforward neural network with PyTorch to analyze features such as
open price, close price, and volume, achieving high accuracy in stock price movement classification.
The research highlights the potential of machine learning in financial forecasting, addressing the
global challenge of market volatility. Results demonstrate the model's ability to identify stock trends,
validated against real-world data. This work bridges data science and financial analytics, offering a
scalable tool for stock market traders and analysts.

Keywords

Artificial Intelligence, Stock Market Prediction, Machine Learning, Neural Network, Supervised
Learning

1. Introduction

Stock market prediction is a crucial area of research in financial analytics due to its significance in
investment and economic growth. Predicting stock trends using machine learning can provide
investors with an edge in decision-making. Traditional statistical models often struggle with the
SSRG International Journal of Computer Science and Engineering

ISSN: 2348-8387/ https://doi.org/10.14445/23488387/IJCSE-VXXXX

Original Article

Volume X Issue X, 1-4, Month 2023

© 2023 Seventh Sense Research Group®

complexity and non-linearity of stock market behavior. This study explores the use of neural
networks to enhance prediction accuracy, leveraging historical stock price data. By identifying
patterns in financial time-series data, machine learning models can provide valuable insights for
traders, portfolio managers, and financial institutions.

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