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Journal article
Associating Fundamental Features with Technical Indicators for Analyzing Quarterly Stock Market Trends Using Machine Learning Algorithms
BOHR International Journal of Computer Science, Vol.1(1), pp.88-103
2021
Abstract
The stock market is the primary entity driving every major economy across the globe, with each investment designed to capitalize profit while decreasing its associated risks. As a result of the stock market’s importance, there have been enumerable studies conducted with the goal of predicting the stock market through data analysis techniques including machine learning, neural networks, and time series analysis. This paper uses machine learning algorithms to perform stock market indices classification using fundamental data, while classifying the indices using technical indicators, with data derived from Yahoo Finance on the top 100 indices in the NASDAQ stock market from January 2000 to December 2020.
Details
- Title
- Associating Fundamental Features with Technical Indicators for Analyzing Quarterly Stock Market Trends Using Machine Learning Algorithms
- Publication Details
- BOHR International Journal of Computer Science, Vol.1(1), pp.88-103
- Resource Type
- Journal article
- Publisher
- BOHR Publishers
- Identifiers
- 99380482395306600
- Academic Unit
- Computer Science; Hal Marcus College of Science and Engineering
- Language
- English