Quantitative analysis
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…
A helping hand – Addressing industry concerns
The Basel Committee on Banking Supervision’s final revisions to the FRTB guidelines aim to address industry concerns around complexity and capital implications. A forum of industry leaders discusses whether the changes have been effective and how banks…
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
Podcast: Princeton’s Carmona on the future of quant education
Course director discusses machine learning explainability and reclaiming game theory from economists
Functional programming reaches for stardom in finance
Fans highlight more reliable code, and suitability for complex tasks and distributed ledgers
Honesty is key to machine learning’s future – Roberts
Oxford-Man Institute director on why tomorrow’s models will gracefully admit defeat