Quant
How AI could tear up risk modelling canon
BlackRock, MSCI, La Française among firms looking to replace traditional, linear risk models
A tech-driven transformation
A panel of experts explores how greater collaboration between risk and finance teams can garner significant benefits and add value, how technological innovation is making the regulatory landscape more complicated to navigate and produce transformative…
Model risk management transformation
Financial institutions have been maturing their approaches to MRM and – as models become more complex and pervasive, and regulatory expectations continue to increase – leading financial institutions seek faster and further movement. Ashutosh Nawani, head…
Model risk managers: banking’s future VIPs
Risk Live: Machine learning models are changing the risk profile of banks, says UBS CRO
One size does not fit all – Adapting to meet investment goals
Guillaume Arnaud, global head of quantitative investment strategies (QIS), and Sandrine Ungari, head of cross-asset quantitative research at Societe Generale, explore the benefits of QIS for investors, why flexibility is crucial for investors to meet…
Cleaning noisy data ‘almost 70%’ of machine learning labour
Quants flag signal-to-noise ratio as key to reducing overfitting risk
Machine learning governance
The ability of machine learning models to read great quantities of unstructured data, spot patterns and translate it into actionable information is driving a significant uptake in the technology. David Asermely, SAS MRM global lead, highlights the need…
Alternative Liquidity Measures
Is book depth a sufficiently representative measure of market liquidity? A look at trade matching performance under different market volatility environments
Danske Bank hires Alexandre Antonov
Risk’s 2016 quant of the year to join Danish firm from Standard Chartered
Stick to core skills or risk data overload, says Goldman quant
Data-as-a-service chief says asset managers risk being swamped by new types of information
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
Making technology count in a C/ETRM world
As businesses grow, so does their need for modern, agile and cost-effective commodity/energy trading risk management (C/ETRM) solutions. Pioneer Solutions explores how its next-generation, highly configurable C/ETRM systems take advantage of the latest…
Baselines for applying machine learning to investing
Techniques that worked in the natural sciences may not translate well to financial markets
The future of operational risk management
As the efficiency of operational risk management remains a top priority and pressure to maximise value increases, emerging technology could prove crucial. Nitish Idnani, leader of oprisk management services at Deloitte, explores how the oprisk management…
Funds use artificial intelligence to weigh ethical investing
Quants explore links between ESG investment and outperformance
Stability heightens flash crash risks – research
Liquidity breaks down when latent orders are revealed too slowly, quant firm says
CSOP to tap Chinese interest in quant with momentum index
Hong Kong asset manager plans to spin off products from recently launched cross-asset index
Quants clash: machine learning or linear models?
Some studies say the algorithms beat the common models; other studies say the opposite