Neural networks
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
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