Abstract- This report extensively examines diverse methods for predicting stock market movements. It delves into three primary approaches: Fundamental Analysis, Technical Analysis, and the integration of Machine Learning techniques. Our findings align with the weak form of the Efficient Market Hypothesis, suggesting that historical prices alone might not hold significant predictive value, yet out-of-sample data could be indicative. We highlight the impact of relevant news on the fluctuations of stocks within listed companies, demonstrating its influence on market movements. Furthermore, our analysis underscores the potential utility of Fundamental Analysis and Machine Learning in assisting investors' decision-making processes.
Keywords- Stock market prediction, Machine Learning, Efficient Market Hypothesis, Fundamental Analysis, Technical Analysis
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DOI:
10.17148/IJARCCE.2024.13449
[1] Jay Kadam, Jayesh Kasbe, Nachiket Nalawade, Abhinav Readdy, Prof. Trupti Sonkusare, "Stock Market Prediction Using Machine Learning," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2024.13449
