Risk Assessment and Management in Stock Trading using Artificial Intelligence

Making determinations in the ever-changing world of stock trading has frequently involved a complicated interaction between information and experience. With so many companies to choose from, investors frequently find it difficult to identify profitable options. The transformational potential of artificial intelligence (AI) to improve and streamline stock trading decisions is explored in this study. Support Vector Machines (SVM), Principal Component Analysis (PCA), and Neural Networks (NN) are three different approaches that are combined in this study to create a strong stock recommendation system that is compatible with investor strategies and risk tolerance. To improve computational efficiency, traders with similar tastes were grouped together using hierarchical clustering. The results showed promise. The NN model demonstrated a training accuracy of 94.8%, whilst the SVM model displayed an accuracy of 92.3%. The fact that PCA was able to capture all of the dataset's variation with just five main components demonstrated its superiority. Moreover, an evaluation of Value-at-Risk and Expected Returns for the suggested portfolios revealed the intrinsic trade-off between possible benefits and related hazards. This study essentially highlights the enormous potential of AI to transform stock trading by providing accurate recommendations that maximize profitability while blending in with investor profiles.