Technical paper/Deep learning
Neural networks unleashed: joint SPX/VIX calibration has never been faster
SPX and VIX options can be jointly calibrated in real time with deep neural networks
A three-stage fusion model for predicting financial distress considering semantic and sentiment information
The authors apply sentiment analysis to management discussion and analysis texts to aid the prediction of financial distress with an innovative three-phase fusion model.
Incorporating financial reports and deep learning for financial distress prediction: empirical evidence from Chinese listed companies
The authors investigate the use of text information processing methods for financial distress prediction and how this method can be combined with traditional means to improve prediction accuracy.
Pricing high-dimensional Bermudan options using deep learning and higher-order weak approximation
The authors propose a deep-learning-based algorithm for high-dimensional Bermudan option pricing with the novel feature of discretizing the interval between early-exercise dates using a higher-order weak approximation of stochastic differential equations.
Neural variance reduction for stochastic differential equations
This paper proposes the use of neural stochastic differential equations as a means to learn approximately optimal control variates, reducing variance as trajectories of the SDEs are simulated.
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.
Momentum transformer: an interpretable deep learning trading model
An attention-based deep learning model for trading is presented
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
Deep calibration of rough volatility models
Rough vol models are calibrated and fitted to SPX and Vix smiles
An end-to-end deep learning approach to credit scoring using CNN + XGBoost on transaction data
The authors find that machine learning methods can generate satisfactorily performing credit score models based on data from the 90-days prior to the score date, where traditional models can perform poorly.
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.
Deep hedging: learning to remove the drift
Removing arbitrage opportunities from simulated data used for training makes deep hedging more robust
Multi-horizon forecasting for limit order books
A multi-step path is forecast using deep learning and parallel computing
Deep learning profit and loss
The P&L distribution of a complex derivatives portfolio is computed via deep learning
Deep learning for discrete-time hedging in incomplete markets
This paper presents several algorithms based on machine learning to solve hedging problems in incomplete markets.
NLP and transformer models for credit risk
News feeds are factored into models to predict credit events
Generating financial markets with signatures
Signatures can provide the synthetic data to train deep hedging strategies
Solving final value problems with deep learning
Pricing vanilla and exotic options with a deep learning approach for PDEs
Machine learning hedge strategy with deep Gaussian process regression
An optimal hedging strategy for options in discrete time using a reinforcement learning technique
Detecting changes in asset co-movement using autoencoders
ARR aims to anticipate volatility patterns to provide signals for risk management and trading