Machine learning
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
Earnings call analysis 2.0 goes beyond good and bad words
Quants develop new ways to extract signals from media-savvy chief executives and their financial statements
Strengthening risk frameworks
Interest rates might be a challenge, but they are also an opportunity for insurers, Gus Ortega, head of operational risk management at Voya Financial, tells Risk.net
Shaping the future of risk and finance with analytics and integrated technology
This webinar explores how to enhance business planning activities, while accelerating regulatory demands with limited resources amid a need to derive greater value from the analytic lifecycle
Vulnerabilities arise in financial services as AI and machine learning use balloons
The scale at which financial services firms are adopting artificial intelligence and machine learning continues to grow, bringing with it new dimensions of risk and vulnerability. In a recent Risk.net webinar sponsored by TCS, experts discussed…
Goldman exec: rogue algos could spark ‘systemic’ crashes
Device proliferation and digital assets also altering risk environment, says Europe op risk head
Machine learning models: the new standard in capital markets
Zoi Fletcher speaks to Alexander Sokol, founder and executive chairman at CompatibL, about why he believes machine learning technology will be used to calculate risk measures across the industry going forward
Banks strive for machine learning at quantum speed
Embryonic work on quantum neural networks raises hope of faster, more accurate models
Quant of the year: Hans Buehler
Risk Awards 2022: Architect of deep hedging aims to supplant orthodox models with method based purely on data
Equity derivatives house of the year: JP Morgan
Risk Awards 2022: US dealer filled flow gaps with a little help from some robots
Derivatives house of the year: JP Morgan
Risk Awards 2022: Big bet on AI is delivering results
Podcast: UBS’s Gordon Lee on conditional expectations and XVAs
Top quant explains why XVA desks need a neighbour and a reverend
Dynamically controlled kernel estimation
An accurate data-driven and model-agnostic method to compute conditional expectations is presented