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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM34729
Volume:
8 |
5 |
May 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Forecasting stock market returns is challenging due to market dynamics. This study employs Artificial Neural Network and Random Forest algorithms to predict closing prices for five diverse companies. Utilizing financial data, new variables were created as model inputs. Evaluation using RMSE and MAPE metrics shows promising performance, highlighting the efficacy of these models in predicting stock prices .
KEYWORDS
Publication Date
5/27/2024
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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