Neural networks
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
Curve dynamics with artificial neural networks
Artificial neural networks can replace PCA for yield curves analysis
Credit default prediction using a support vector machine and a probabilistic neural network
In this study, the authors address the fact that the ranking of classifiers varies for different criteria with measures under different circumstances, by proposing the simultaneous application of support vector machine and probabilistic neural network …
Neural network learns ‘universal model’ for stock-price moves
Relationships between order flow and price “are stable through time and across stocks and sectors”
Model calibration with neural networks
Andres Hernandez presents a neural network approach to speed up model calibration