Learning Risk Dynamics in Financial Time Series via Deep Neural Networks

Abstract

Indicators derived from financial time series data may yield different values under different accounting standards, and their selection is often influenced by human factors. To address the diversification and complexity of feature distributions in financial time series, this paper proposes an anomaly detection method tailored to generalized financial time series data. The proposed approach captures intrinsic representation patterns of financial sequences and treats the outputs obtained from a recurrent neural network as learned knowledge. A standard classifier is then employed to adapt the marginal distribution in the feature space, followed by further prediction using a latent variable regression model to enhance predictive accuracy. Subsequently, a latent variable regression framework is constructed for financial risk forecasting, identifying potential financial risks by modeling the feature distributions embedded in financial time series data. Experimental results demonstrate the feasibility and effectiveness of the proposed model.