In this paper, the authors study an evolutionary framework for the optimization of various types of neural network structures and parameters.
Banks have built ways to calculate CVA more quickly, but neural networks could offer more accurate method
Henry-Labordere proposes a neural networks-based technique to price counterparty risk and initial margin
Oxford-Man Institute director on why tomorrow’s models will gracefully admit defeat
Separating the wheat from the chaff is fundamental to ESG investing. Machine learning can do that
Energy firms explore how artificial intelligence can boost returns
Knowing what to remember and what to forget could help machines beat quant and discretionary investors
Risk USA: Neural nets beat other models in tests, but results could not be explained
In this paper, the authors present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture.
The aim of this paper is to predict future default behaviors of nonbank financial company customers using credit scores.
Swiss bank has rung the changes in its attempt to catch hard-to-measure risks, but “you are never safe”, warns Christian Bluhm
In this paper, the authors present a robust method for the detection of chaos based on the Lyapunov exponent, which is consistent even for noisy and finite scalar time series.
Artificial neural networks can replace PCA for yield curves analysis
In this study, the authors address the fact that the ranking of classifiers varies for different criteria with measures under different circumstances, by proposing the simultaneous application of support vector machine and probabilistic neural network …
Relationships between order flow and price “are stable through time and across stocks and sectors”
Andres Hernandez presents a neural network approach to speed up model calibration