Multi Factor Risk Assessment in Emerging Markets: A Comparative Analysis of Neural Network Architectures

Abstract

Financial markets that are developing are highly volatile, structurally inefficient, and intricate in the interaction of various risk factors, and proper risk assessment is a difficult task. Such environments can also have non-linear relationships and dynamic time trends that are not easily predicted using traditional statistical and econometric models. In this paper, a multi-factor risk assessment framework is formulated based on the state of the art neural network architecture, Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), hybrid CNN-LSTM networks, Autoencoders, and Transformer-based attention models are presented. The suggested solution incorporates a range of different data sets like market indicators and the macroeconomic variables and sentiment analysis to improve predictive power. The performance of the hybrid and attention-based architectures is proved to be much better than the conventional models as in experimental results, they are more accurate, have better F1-scores and small prediction errors. The results point to the usefulness of deep learning models in modeling intricate financial trends and enhancing decision-making in emerging economies.