AI-Driven Stock Portfolio Optimization with Real-Time Predictive Analytics
DOI:
https://doi.org/10.15662/IJARCST.2022.0503003Keywords:
Investment Strategy, Artificial Intelligence, Statistical forecasts, Real-time data, Machine learning, Capital marketsAbstract
Introducing Artificial Intelligence (AI) with real-time predictive analytics in stock portfolio management – innovation in behaviour. In portfolios that were managed using the traditional models, their ability to change their angle of operation when market conditions change is minimal, and therefore, they offer substandard performance. Modern AI strategies employ form ML to process vast financial data, detect patterns, and forecast stock performance levels with high precision. This paper presents an elaborate proof of how an AI-driven model for portfolio management emerged and how realistic simulations applied to the model showed that it would work. Important issues, including overfitting, data quality, and demands for increased computational capabilities, are analysed, as well as ideas on how to address them in order to increase model reliability and expand its potential for future scaling.
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