Quant investing
Investing is often seen as a blend of art and science, but the past decade has seen rapid growth in strategies that try to exclude the former entirely, instead seeking sources of return that are a provable feature of a market. These scientific investors are generally poorly served by mainstream finance publications.
Our aim is to cover, in plain English, the ideas and trends that matter. That means articles on research – into new sources of premia and shifting market behaviours, for example – as well as products and strategy. We also report on the related topics of data science, and trends in the use of artificial intelligence and machine learning.
Technical content can be found in our ‘Cutting Edge’ section, where we publish practitioner-focused, peer-reviewed papers.
Quants warn on credit risk in stocks
Conventional models may be missing explosion in novel exposure
To handle Covid-19 we need better data
Winton’s David Harding says unconscious bias in test data means epidemic is poorly understood
Covid transparency would soothe markets – Harvey
“Why aren’t our policy-makers sharing their models?” asks Duke University economist
Lighting up the black box: a must for investors?
Many contend you must be able to interpret machine learning in order to use it
At Numerai, real-world figures need not apply
AI hedge fund CEO sees the light in black-box technology
Factor strategies seesaw in coronavirus-hit markets
Quants struggle to second-guess ongoing effect of virus on investments
‘Quantamental’ approach convinces Morgan Creek CEO
Proponent of big-picture investing sees growing role for machines, but with caveats
Prising open the black box of AI
Shapley values, Lime and other tools can help decipher machine learning’s output. It’s a start…
Fuzzy data stalls ESG alpha hunt
Quants searching for ESG signals have reached very different conclusions. Mostly they blame the data
How diversifying too far weakened alt risk premia’s rebound
Strategies that hurt ARP funds in 2018 did better but some cancelled out last year
The age of ethical investing, but can quants cope?
Systematic managers grapple with ESG demands of clients
Study finds ‘significant’ crowding in hedge funds
New research links herd investing to bigger drawdowns in stressed markets
ESG like a new factor, alt managers say
Shift of capital to sustainable investing predicted to disrupt established strategies
Wells Fargo uses machine learning for performance attribution
Clustering algo delivers speedier and more accurate explanations of portfolio returns
Man and machine need each other – Systematica CEO
“The errors made by humans and robots are different,” says Leda Braga
Don’t invest in bad ESG companies, hedge funds told
Managers have seen a “sea change” in attitudes to sustainability
Ripping up the old asset class labels
Outmoded classifications of securities may be concealing market risk. AI has a better idea
Neuberger Berman gets its Sherlock on
Asset manager deploys quant-cum-sleuth to sniff out portfolio risk
Quants – pick your alt data wisely, says Goldman MD
Investors must balance risk of false signals versus missing out on alpha, says Matthew Rothman
Ex-Credit Suisse quants embrace machine learning
Founders of XAI Asset Management grapple with unsupervised learning and the problems of explainability
Allocation models that know their unknowns
Quants say probabilistic programming beats machine learning in balancing strategies
In factor timing, ‘where?’ matters as much as ‘when?’
Goldman quants’ thought experiment shows timing works best for low-Sharpe strategies
Quant funds look to AI to master correlations
Machine learning shows promise in grouping assets better, predicting regime shifts
Intercept: AQR’s risk-catching machine
CRO Mike Patchen has helped build a system to identify risks before they grow or spread