Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Stock market investment strategies are intricate and require analyzing vast amounts of data. In recent years, researchers have increasingly explored machine learning techniques to determine their effectiveness in improving market forecasting compared to traditional methods. This study aims to identify future research directions in stock market prediction using machine learning by reviewing existing literature. A systematic literature review methodology is employed to analyze peer-reviewed journal articles from the past two decades, grouping studies based on similar methodologies and contexts. The key categories identified include LSTM, RNN, Random Forest combined with other techniques, and hybrid or alternative artificial intelligence approaches. Each category is examined to highlight common findings, unique insights, limitations, and areas requiring further research. The study concludes with an overview of findings and recommendations for future investigations.
"Stock Price Prediction Using Multiple ML Algorithms.", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a287-a291, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504042.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator