Machine learning
Podcast: Brigo on derivatives, AI, machine learning and more
Genuine artificial intelligence remains "very, very far away", says Imperial College's Brigo
Investors warm to quant tools to gauge political risk
Many funds have lost confidence in traditional ways of measuring political risk
Review of 2017: All sorts of volatility, bar one
Markets were oddly calm this year, while everything else was in motion
Market Technology Awards 2018: New problems, new solutions
Vendors embrace cloud, alternative languages and agile approaches
Machine learning is not just for the buy side
Sell-side quants develop machine learning technique to optimise margin costs
Evolutionary algos for optimising MVA
Alexei Kondratyev and George Giorgidze apply two evolutionary algos to MVA optimisation
Degree of influence, 2017: Quants dissect initial margin
Initial margin, optimal execution and applications of machine learning were the hottest topics of 2017
Quant hedge fund of the year: Man AHL
Risk Awards 2018: New roles for machine learning, frontier markets and OTC data give fund an edge
JP Morgan’s CRO on the bank’s six buckets of risk
Risk30: From loan losses to electromagnetic pulses, JPMorgan Chase has a place for it
Quant funds look past the obvious for uses of alternative data
Many systematic investors are sceptical but a few are finding ways to make new data work
Nex’s Spencer on tech, Brexit and the UK’s identity crisis
Risk30: Icap founder fears return of exchange controls under a Labour government
Banks apply machine learning to CCAR models
ML models benchmarked against traditional iterations to avoid ‘black box’ perception
ANZ’s Whelan on China, data science and ROE
Risk30: markets business at ANZ is picking new targets
The future of risk in 10 interviews: volatility, liquidity and tech
Fed’s Powell, JP Morgan CRO, Bridgewater co-CEO all feature in upcoming profiles
AQR’s Cliff Asness: ‘machine learning worries me’
Leading quant cautious on machine learning’s use with limited data
Top 10 energy risks and challenges
Regulation and changing market dynamics among threats to energy in year ahead
Machine learning could solve optimal execution problem
Reinforcement learning can be used to optimally execute order flows
Machine learning for trading
Gordon Ritter applies reinforcement learning to dynamic trading strategies with market impact
Banks tout machine learning amid regulatory concerns
Machine learning being used to build challenger models for model validation
Firms race to apply machine learning to liquidity risk models
As key regulatory deadline looms, US mutual funds are waiting to see if machine learning can enhance liquidity risk models
This tangled web: banks seek to contain systemic model risk
Network studies are being used to identify model dependencies and concentrations
Banks warned off machine learning for model risk
Banks acknowledge they “cannot hide behind a complex tool” to assess interconnectedness
Bloomberg testing use of image recognition in volatility trading
Computers could be used to spot kinks in volatility surfaces
BlackRock to use machine learning to gauge liquidity risk
Firm close to rolling out new models for redemption risk and market liquidity