Stock Prediction Using Machine Learning Algorithms with Special Reference to Technical Indicators
5th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021
; 248:319-327, 2022.
Article
in English
| Scopus | ID: covidwho-1593664
ABSTRACT
Machine learning algorithms have increasingly become chosen tools for stock price prediction. Using a variety of financial news as an input to compare various algorithms for accuracy level has been extensively studied. However, taking some of the prominent technical indicators as an input to test the algorithms’ prediction accuracy for a stock price has remained less explored. This study focuses on using chosen seventeen technical indicators to compare selected algorithms to test the prediction accuracy for six Indian stocks as a sample. This study covers the critical time period of the outbreak of the Covid-19 pandemic and attempts to capture the impact on accuracy levels of algorithms. Three algorithms are tested, and among them random forest algorithm has demonstrated superior results. Based on these results, this study proposes a framework to create a platform for further application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Machine, learning, algorithms; Stock, prediction; Technical, indicators; Decision, trees; Electronic, trading; Forecasting; Learning, algorithms; Machine, learning; Accuracy, level; Critical, time; Financial, news; Prediction, accuracy; Stock, predictions; Stock, price; Stock, price, prediction; Technical, indicator; Time-periods; Financial, markets
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
5th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS