Predicting Stock Market Trends: Machine Learning Approaches of a Possible Uptrend or Downtrend

Title

Predicting Stock Market Trends: Machine Learning Approaches of a Possible Uptrend or Downtrend

Creator

Joseph R.; Goswami M.

Description

This paper delves into a statistical analysis of the stock market, emphasizing the significance of accuracy in stock predictions. Large data sets can be handled by machine learning algorithms, which can also forecast outcomes based on past data and spot intricate patterns in financial data. They assist control risks, automate decision-making procedures, and adjust to changing circumstances. Multi-source data can be combined by ML models to provide a comprehensive picture of market circumstances. They can manage intricate, nonlinear interactions, provide impartial analysis, and lessen human bias. Models are able to adjust to shifting market conditions through ongoing learning and retraining. They must, however, exercise caution when deploying models in real-world situations and ensure that they are validated. Although machine learning has advantages for stock market analysis, it must be carefully evaluated for dangers and validated before being used in practical situations. The traditional machine learning model, Logistic Regression has been used in order to predict stock prices. It focuses on binary classification based on the trend of the stock. Through the model training and evaluation and additional analysis done on the results, this research contributes towards obtaining predictions and studying reasons of a possible uptrend or downtrend to further assist companies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Source

Communications in Computer and Information Science, Vol-2184 CCIS, pp. 376-387.

Date

2025-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Logistic Regression; Machine Learning; Stock Market

Coverage

Joseph R., Department of Computer Science and Engineering, Christ (Deemed to Be) University, Kengeri, Karnataka, Bangalore, 560074, India; Goswami M., Department of Computer Science and Engineering, Christ (Deemed to Be) University, Kengeri, Karnataka, Bangalore, 560074, India

Rights

Restricted Access

Relation

ISSN: 18650929; ISBN: 978-303171480-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-031-71481-8_29

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208243400&doi=10.1007%2f978-3-031-71481-8_29&partnerID=40&md5=257eb619090df60ae51ee8de027630ae