STOCK MARKET PREDICTION: AN ENSEMBLE APPROACH

STOCK MARKET PREDICTION: AN ENSEMBLE APPROACH

Cecil Ignatius Hermina¹, Sreelakshmi Varma M², Dr. N Kanthimathi³, Saranya N⁴

¹ Department of Electronics and Communication, Bannari Amman Institute of Technology

² Department of Electronics and Communication, Bannari Amman Institute of Technology

³ Department of Electronics and Communication, Bannari Amman Institute of Technology

⁴ Department of Electronics and Communication, Bannari Amman Institute of Technology

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Abstract - AI stock market prediction requires processing a lot of data and making predictions based on that analysis, which is a challenging task. By analysing data from a variety of sources, including news stories, earnings reports, and social media, AI techniques like machine learning algorithms and natural language processing can help discover trends and forecast changes in the stock market. It is crucial to keep in mind, though, that numerous unknown events have an impact on the stock market. For investors, forecasting is highly challenging to do profitably because of the noisy and static data. It is advised to employ AI predictions as one of several inputs in a well-diversified investment plan to attain accurate stock market predictions. The most recent stock market-related forecast methodology, despite considerable effort, contains major flaws. It makes sense to assume that this study's stock prediction is the result of an integrated process.

Key Words:  Stock prediction, ML, AI, LSTM, Linear regression, SVC.