Machine learning
Imbalanced data issues in machine learning classifiers: a case study
The author outlines characteristics of machine learning classifiers, compares methods for dealing with imbalanced data issues, and proposes terms of best practice in model development, evaluation, and validation.
Machine learning and AI in model risk management: a quant perspective
Statistical risk models face issues of validity as unprecedented events and social phenomena occur. However, artificial intelligence (AI) and machine learning can assist models in maximising accuracy. By Tiziano Bellini, head of risk integration…
Explainable artificial intelligence for credit scoring in banking
The authors put forward an explainable machine learning model predicting credit default using a real-world data set provided by a Norwegian bank.
Citi quants’ AI model aims to hedge earnings surprises
Machine learning tool forecasts effect of shocks on implied volatility surfaces in minutes
Navigating the complex world of equity options data
In an exclusive Risk.net webinar, convened in collaboration with Cboe Global Markets, experts discussed the expanding world of equity options data, the rise of retail investment within it, and the technological challenges and opportunities associated…
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
Front-office reboot: how new technology, machine learning and data science are reshaping trading
With increasing regulatory scrutiny and market volatility, trading desks are seeking tools to help improve operational efficiency, streamline decision making and successfully manage risk. In this Risk.net webinar, viewers will learn about the front…
How to hone NLP’s detection skills: a cue from a super-sleuth
With a sharper focus, AI readers could help detect hidden exposures for investors
Fraud prevention solution of the year: Quantexa
Asia Risk Awards 2022
Best AI or machine learning innovation: Quantifi
Asia Risk Awards 2022
Machine learning models: the validation challenge
Machine learning models are seeing increasing demand across the capital markets spectrum. But how can firms improve their chances of gaining internal and regulatory approval for these type of models?
An effective credit rating method for corporate entities using machine learning
The authors propose a new method to design credit risk rating models for corporate entities using a meta-algorithm which exploits information embedded in expert-assigned credit ratings to rank customers.
Holistic credit risk assessment and the goal of safe unbiased modelling
In this Asia Risk webinar, experts in artificial intelligence (AI) and machine learning examined the growing applications being seen in the field and their merit in credit risk modelling
Geopolitical risk models not ‘rigorous’ enough, says quant
Joseph Simonian believes game theory and reinforcement learning could improve matters
Exploring the equity–bond relationship in a low-rate environment with unsupervised learning
The authors apply k-means clustering to low interest rate periods in order to analyze the equity hedging property of government bonds.
Amid macro storm clouds, a silver linings playbook for fintech
Banks and VCs believe inflation and rising interest rates will result in winners as well as losers
AI models point to recession, but quants won’t trade on them
Predicting the odds of a recession, and how markets will respond, is still a step too far for machines
JP Morgan quants are building deep hedging 2.0
New model uses Bellman technique to learn general derivatives hedging strategies
An end-to-end deep learning approach to credit scoring using CNN + XGBoost on transaction data
The authors find that machine learning methods can generate satisfactorily performing credit score models based on data from the 90-days prior to the score date, where traditional models can perform poorly.
‘Corrective’ algo tells quant firm when it’s wrong
QTS has built a machine to show whether a strategy is likely to succeed or flop
Semi-analytic conditional expectations
A data-driven approach to computing expectations for the pricing and hedging of exotics
The future is now: how data science is revolutionising risk management and finance
This webinar explores how your organisation can move beyond legacy technology, better meet investor demands and remain competitive by embracing the future of finance.
Why machine learning quants need ‘golden’ datasets
An absence of shared datasets is holding back the development of ML models in finance
JP Morgan’s deep hedging reaches cliquets
Euro Stoxx roll-out is live and S&P is next, despite exit of machine learning programme’s figurehead