Neural networks
The machines are coming for your pricing models
Deep learning is opening up new frontiers in financial engineering and risk management
The rise of the robot quant
The latest big idea in machine learning is to automate the drudge work in model-building for quants
Deep hedging and the end of the Black-Scholes era
Quants are embracing the idea of ‘model free’ pricing and hedging
Fishing for collateral with neural nets
SocGen quant uses deep learning technique to optimise collateral substitution
Optimal posting of collateral with recurrent neural networks
Pierre Henry-Labordère applies neural networks to a control problem approach for managing collateral
How AI could tear up risk modelling canon
BlackRock, MSCI, LFIS among firms looking to replace traditional, linear risk models
Systematic manager puts up guardrails for AI
Boston-based Acadian aims to limit risks from complex, machine learning algorithms
Fund houses get picky over where to use machine learning
Buy-siders limit usage of deep learning techniques due to haziness over their inner workings
Smart weaponry aids bank fight against money laundering
Advanced algos and machine learning gain credence as regulators encourage innovation
Ensemble models in forecasting financial markets
In this paper, the authors study an evolutionary framework for the optimization of various types of neural network structures and parameters.
Could machine learning improve CVA and IM calculations?
Banks have built ways to calculate CVA more quickly, but neural networks could offer more accurate method
CVA and IM: welcome to the machine
Henry-Labordere proposes a neural networks-based technique to price counterparty risk and initial margin
Honesty is key to machine learning’s future – Roberts
Oxford-Man Institute director on why tomorrow’s models will gracefully admit defeat
Quants use AI to cut through murk of ‘sustainability’
Separating the wheat from the chaff is fundamental to ESG investing. Machine learning can do that
AI moves into middle office at energy firms
Energy firms explore how artificial intelligence can boost returns
Learning algos that learn how to learn
Knowing what to remember and what to forget could help machines beat quant and discretionary investors
BlackRock shelves unexplainable AI liquidity models
Risk USA: Neural nets beat other models in tests, but results could not be explained
Machine learning hits explainability barrier
Banks hire AI industry experts in face of growing regulatory scrutiny
Dilated convolutional neural networks for time series forecasting
In this paper, the authors present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture.
An empirical study on credit risk management: the case of nonbanking financial companies
The aim of this paper is to predict future default behaviors of nonbank financial company customers using credit scores.
UBS’s CRO on the hunt for hidden risks
Swiss bank has rung the changes in its attempt to catch hard-to-measure risks, but “you are never safe”, warns Christian Bluhm
Chaotic behavior in financial market volatility
In this paper, the authors present a robust method for the detection of chaos based on the Lyapunov exponent, which is consistent even for noisy and finite scalar time series.
How machine learning could aid interest rate modelling
Standard Chartered quant proposes machine-learning technique to better capture rate dynamics
Podcast: Quantum computing to boom in next three to five years
Quant speaks of collaboration with Nasa and machine-learning algos for yield curves