Machine learning
Quantum computing experts voice explainability fears
Risk Live: big speed-ups for quantum-powered models could prompt bigger questions from regulators
Generating financial markets with signatures
Signatures can provide the synthetic data to train deep hedging strategies
Acadian builds ‘green screen’ to auto-filter ESG phoneys
$110 billion quant investor creates automated system to spot greenwashers
Fake data can help backtesters, up to a point
Synthetic data made with machine learning will struggle to capture the caprice of financial markets
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
A general framework for the identification and categorization of risks: an application to the context of financial markets
This paper is, to the best of the authors' knowledge, the first to develop an algorithm-based and generally applicable framework that generates an extensive and integrated identification and categorization scheme of certain risks by using text mining and…
BNP Paribas AM turns to machine learning for carbon emissions
AI may help fund manager count emissions that companies fail to report
Rough volatility’s steampunk vision of future finance
Some of the trickiest puzzles in finance could be solved by blending old and new technologies
Using payments data to nowcast macroeconomic variables during the onset of Covid-19
Economic prediction during a crisis is challenging because of the unprecedented economic impact of such an event, which increases the unreliability of traditionally used linear models that employ lagged data. The authors help to address this challenge by…
The volatility paradigm that’s stirring up options pricing
‘Rough volatility’ models promise better pricing and hedging of options. But will they catch on?
Gradient boosting for quantitative finance
In this paper, the authors discuss how tree-based machine learning techniques can be used in the context of derivatives pricing.
Factor woes prove need for better timing – QuantZ’s Sharma
Investors should switch between factors as alphas change, says quant
Deep hedging strays when volatility gets rough – study
In the most realistic simulations, data-driven approach fared 30% worse than conventional hedging
From use cases to a big data benchmarking framework in clearing houses and exchanges
In this paper, we propose a conceptual framework that links the technical and business benchmarks in the domain of clearing houses and securities exchanges.
As machines disrupt investing, people still have a role to play
Despite AI’s growth, investing still needs human adaptability and judgement, writes Schroders’ Lim
Top 10 op risks 2021: conduct risk
Remote working vastly complicates the job of conduct risk supervisors
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.
Forecasting Bitcoin returns: is there a role for the US–China trade war?
In this paper, the authors extend the related literature by examining whether the information on the US–China trade war can be used to forecast the future path of Bitcoin returns, controlling for various explanatory variables.
Technology innovation of the year: Scotiabank
Risk Awards 2021: New risk engine can run nearly a billion XVA calculations per second
Solving final value problems with deep learning
Pricing vanilla and exotic options with a deep learning approach for PDEs
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
Degree of influence: volatility shakes markets and quant finance
Volatility and machine learning were among the top research areas for quants this year
Machine learning will create new sales-bots – UBS’s Nuti
Technologists working to automate indications of interest from trading desks