Machine Learning-Based Market Forecasting Models for AI-Assisted Financial Prediction

Abstract

More accurate data-driven projections of market behavior are now available to financial forecasters thanks to artificial intelligence (AI). The non-linear patterns, volatility, and complexity of financial markets are so great that traditional methods of forecasting often fall behind. On the flip side, machine learning models can scour real-time and historical data sets for patterns, anomalies, and correlations to enhance their prediction capabilities. Decision trees, regression algorithms, support vector machines (SVMs), and deep learning structures like neural networks and recurrent models are the most common machine learning models used in artificial intelligence (AI) financial forecasting. how these models use a plethora of data, such as past prices, economic indicators, and sentiment studies extracted from social media and news, to forecast market trends, financial risks, and stock values. Concerns about data quality, overfitting, the interpretability of models, and market unpredictability are some of the downsides of using AI for forecasting. Accurate predictions are possible with the application of tactics for risk management, model optimization, and careful feature selection.