Technical paper/Neural networks
Deep asymptotics
Introducing a new technique to control the behaviour of neural networks
Differential machine learning: the shape of things to come
A derivative pricing approximation method using neural networks and AAD speeds up calculations
A k-means++-improved radial basis function neural network model for corporate financial crisis early warning: an empirical model validation for Chinese listed companies
This paper aims to simplify the early warning model for financial crises by collecting and analyzing the financial data of Chinese special treatment (ST) companies, normally listed companies and cancel special treatment (CST) companies.
Deep learning calibration of option pricing models: some pitfalls and solutions
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
Scoring models for roboadvisory platforms: a network approach
In this paper, the authors show how to exploit the available data to build portfolios that better fit the risk profiles of investors. This is made possible, on the one hand, by constructing groups of homogeneous risk profiles based on user responses to…
The market generator
A generative neural network is proposed to create synthetic datasets that mantain the statistical properties of the original dataset
Interpretability of neural networks: a credit card default model example
Recently developed techniques aimed at answering interpretability issues in neural networks are tested and applied to a retail banking case
Optimal posting of collateral with recurrent neural networks
Pierre Henry-Labordère applies neural networks to a control problem approach for managing collateral
Ensemble models in forecasting financial markets
In this paper, the authors study an evolutionary framework for the optimization of various types of neural network structures and parameters.
CVA and IM: welcome to the machine
Henry-Labordere proposes a neural networks-based technique to price counterparty risk and initial margin
Dilated convolutional neural networks for time series forecasting
In this paper, the authors present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture.
An empirical study on credit risk management: the case of nonbanking financial companies
The aim of this paper is to predict future default behaviors of nonbank financial company customers using credit scores.
Chaotic behavior in financial market volatility
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.
Curve dynamics with artificial neural networks
Artificial neural networks can replace PCA for yield curves analysis
Credit default prediction using a support vector machine and a probabilistic neural network
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 …
Model calibration with neural networks
Andres Hernandez presents a neural network approach to speed up model calibration