NeuroTrader: Redefining Portfolio Strategies through Advanced Machine Learning
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Abstract
The stock market is cooperative trading network involving company shares and their derivatives. This market provides dominant amount of contribution on contemporary economies as it’s a place where companies raise huge amount of money to speed up start-ups, to extend existing business, consolidate actions and pay off debt. Stock price prophecy is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent years. In this paper, an innovative approach to enhance stock market prediction, addressing key limitations in existing forecasting models is introduced. This research explores how advanced technologies like Artificial Intelligence and Machine Learning can improve stock market predictions and sentiment analysis from web-scraped news articles. By using unstructured data for models finBERT, Vader, and LSTM, the work aims to build a model that aids to make financial analysis more accurate and accessible to everyone.
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