Neural networks
How XVA quants learned to trust the machine
Initial scepticism about using neural networks for derivatives pricing is giving way to enthusiasm
How algos are helping inflation-wary investors
Buy-siders look to machine learning for clues on the effect of rising prices on portfolios
Deep XVAs and the promise of super-fast pricing
Intelligent robots can value complex derivatives in minutes rather than hours
Banks fear Fed crackdown on AI models
Dealers say agencies’ request for info could prompt new rules that stifle model innovation
Generating financial markets with signatures
Signatures can provide the synthetic data to train deep hedging strategies
In fake data, quants see a fix for backtesting
Traditionally quants have learnt to pick data apart. Soon they might spend more time making it up
Nowcasting networks
The authors devise a neural network-based compression/completion methodology for financial nowcasting.
Neural network middle-term probabilistic forecasting of daily power consumption
The authors propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature.
Technology innovation of the year: Scotiabank
Risk Awards 2021: New risk engine can run nearly a billion XVA calculations per second
Setting boundaries for neural networks
Quants unveil new technique for controlling extrapolation by neural networks
Deep asymptotics
Introducing a new technique to control the behaviour of neural networks
Danske quants discover speedier way to crunch XVAs
Differential machine learning produces results “thousands of times faster and with similar accuracy”
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.
Three adjustments in calibrating models with neural networks
New research addresses fundamental issues with ANN approximation of pricing models
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
Podcast: Horvath and Lee on market generator models
Quants explain the application of the latest techniques
At Numerai, real-world figures need not apply
AI hedge fund CEO sees the light in black-box technology
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…
Podcast: Kondratyev and Schwarz on generating data
Market generator models may aid areas of finance where data is limited or sensitive
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
Rising star in quant finance: Blanka Horvath, Aitor Muguruza and Mehdi Tomas
Risk Awards 2020: New machine learning techniques bring ‘rough volatility’ models to life
Goldman improves execution ‘by 50%’ with new algos
Bank uses neural networks and other AI tools to cut slippage in stock trading