Technical paper/Neural networks
A credit card fraud detection model based on a stacked temporospatial graph attention residual network
The authors put forward a model based on a stacked temporospatial graph attention residual network dedicated to credit card fraud detection.
Green risk identification and risk measurement in fintech: a particle swarm optimization fuzzy analytic hierarchy process and sparrow search algorithm quantile regression neural network approach
Robust financial calibration: a Bayesian approach for neural stochastic differential equations
AI as pricing law
Probabilistic classification with discriminative and generative models: credit-scoring application
The author investigates how probabilistic classification can be used to enhance credit-scoring accuracy, offering a robust means for assessing model performance under various reliability criteria
Deep learning alpha signals from limit order books
An analysis on network architectures applied to limit order book data is presented
Machine learning and a Hamilton–Jacobi–Bellman equation for optimal decumulation: a comparison study
This paper ascertains a decumulation strategy for the holder of a defined contribution pension plan with an approach based on neural network optimization.
On deep portfolio optimization with stocks, bonds and options
The authors put forward a neural-network machine learning algorithm for time-inconsistent portfolio optimization.
An explicit scheme for pathwise cross valuation adjustment computations
The authors put forward a simulation/regression scheme for a class of anticipated backward stochastic differential equations, where the coefficient entails a conditional expected shortfall of the martingale part of the solution.
The power of neural networks in stochastic volatility modeling
The authors apply stochastic volatility models to real-world data and demonstrate how effectively the models calibrate a range of options.
Research on the multifractal volatility of Chinese banks based on the synthetic minority oversampling technique, edited nearest neighbors and long short-term memory
The authors propose the SMOTEENN-LSTM method to predict risk warnings for Chinese banks, demonstrating the improved performance of their model relative to commonly used methods.
Quantifying credit portfolio sensitivity to asset correlations with interpretable generative neural networks
This study introduces a method for assessing the impact of asset correlations on credit portfolio value-at-risk using variational autoencoders (VAEs), offering a more interpretable approach than previous methods and improving model interpretability.
Neural joint S&P 500/VIX smile calibration
A one-factor stochastic local volatility model can solve the joint calibration problem
Overfitting in portfolio optimization
The authors measure the performance of sample-based rolling-window neural network (NN) portfolio optimization strategies and demonstrate that correctly set up NN-based strategies can outperform the 1/N strategy.
Neural stochastic differential equations for conditional time series generation using the Signature-Wasserstein-1 metric
Using conditional neural stochastic differential equations, the authors propose a means to improve the efficiency of generative adversarial networks and test their model against other classical approaches.
An optimal control strategy for execution of large stock orders using long short-term memory networks
Using a general power law in the Almgren and Chriss model and real data, the authors simulate the execution of a large stock order with an appropriately trained LSTM network.
Robust pricing and hedging via neural stochastic differential equations
The authors propose a model called neural SDE and demonstrate how this model can make it possible to find robust bounds for the prices of derivatives and the corresponding hedging strategies.
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
Deep learning for efficient frontier calculation in finance
The author puts forward a means to calculate the efficient frontier in the Mean-Variance and Mean-CVaR portfolio optimization problems using deep neural network algorithms.
Technical indicator selection and trading signal forecasting: varying input window length and forecast horizon for the Pakistan Stock Exchange
This paper investigates how input window length and forecast horizon affect the predictive performance of a trading signal prediction system.
Pricing barrier options with deep backward stochastic differential equation methods
This paper presents a novel and direct approach to solving boundary- and final-value problems, corresponding to barrier options, using forward pathwise deep learning and forward–backward stochastic differential equations.
Predicting financial distress of Chinese listed companies using a novel hybrid model framework with an imbalanced-data perspective
In this paper a novel hybrid model framework is constructed to solve the problem of predicting the financial distress of Chinese listed companies using imbalanced data.
Customer churn prediction for commercial banks using customer-value-weighted machine learning models
In this paper the authors propose a framework to address the issue of customer churn prediction, and they quantify customer values with the use of an improved customer value model.
Covariance estimation for risk-based portfolio optimization: an integrated approach
This paper presents a stochastic optimization framework for integrating time-varying factor covariance models in a risk-based portfolio optimization setting.