Foresti, Andrea2019-03-272019-03-272019-03https://hdl.handle.net/10986/31449This paper presents different deep neural network architectures designed to forecast the distribution of returns on a portfolio of U.S. Treasury securities. A long short-term memory model and a convolutional neural network are tested as the main building blocks of each architecture. The models are then augmented by cross-sectional data and the portfolio's empirical distribution. The paper also presents the fit and generalization potential of each approach.CC BY 3.0 IGOMACHINE LEARNINGNEURAL NETWORKSCONVOLUTIONLSTMMARKET RISKSECURITIES PORTFOLIOWorking PaperWorld Bank10.1596/1813-9450-8790