Neural networks
Why AI will never predict financial markets
Laws that govern swings in asset prices are beyond statistical grasp of machine learning technology, argues academic Daniel Bloch
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.
Can machine learning help predict recessions? Not really
Artificial intelligence models stumble on noisy data and lack of interpretability
Neural joint S&P 500/VIX smile calibration
A one-factor stochastic local volatility model can solve the joint calibration problem
JP Morgan pulls plug on deep learning model for FX algos
US bank turns to less complex models that are easier to explain to clients
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.
Rising star in quantitative finance: Sigurd Emil Rømer
Risk Awards 2023: Doctoral dissertation outlines more efficient way to simulate rough volatility models
Podcast: Piterbarg and Antonov on alternatives to neural networks
Two novel approximation techniques can overcome the curse of dimensionality
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
Quant house of the year: Crédit Agricole CIB
Asia Risk Awards 2022
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.