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.
Stock-pickers take note: the quants are coming
Quant funds are turning their hand to fundamental investing
How does it look from space? Satellite surge to alter investing
Higher-frequency images set for use in entirely new ways and by more investors than before
Podcast: Venturelli and Kondratyev on quantum annealing
Authors show how quantum theory could aid portfolio construction
Quants blame crowding and concentration for bleak 2018
Funds are still struggling to explain why so many of them did so badly last year
Systematic manager puts up guardrails for AI
Boston-based Acadian aims to limit risks from complex, machine learning algorithms
China Minsheng and SocGen team up for quant index product
CMBC Macro 1 signal index attracts $580 million as investors adapt to products without performance guarantees
Funds hunt for the real McCoy in alternative data jungle
Uncorrelated information is a rare species but there are other ways to make funky new data work
Crowding can be good for quants (sometimes) – Goldman
Study finds timing dictates different results for convergent and divergent strategies in herd moves
Credit Suisse and Rayliant team up for China quant launch
Firms want to tap investors' zeal for systematic strategies in A-shares
Baselines for applying machine learning to investing
Techniques that worked in the natural sciences may not translate well to financial markets
Privacy risks dash funds’ alternative data dreams
Asset managers see value in alternative data in its rawer forms, but most won’t touch it
UBS Asset Management cools on alternative data
Buy-Side Risk USA: Firm checked several datasets and found little alpha
Some trend followers are less than ‘pure’ – study
Sick of waiting for a crisis, some commodity trading advisers move from ‘pure trend’ to ‘trend-plus’
A blueprint for alternative data in asset management
UBS Asset Management’s data chief describes how alternative data can aid the investment process
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
BlackRock’s psych team (yes) hunts for bias in trades
Portfolio managers asked to keep ‘trade diaries’ of the thinking that led up to poor investments
Quants clash: machine learning or linear models?
Some studies say the algorithms beat the common models; other studies say the opposite
Can nowcasting unlock factor timing?
Fulcrum Asset Management is running tests to see if fresher data can help improve factor allocations
Quants ‘running into walls’ with AI interpretability
Some firms “stumbling” with new technology, conference hears
Quants say big data is all buzz, no alpha
Efforts to extract alpha from alternative data have been “really unsuccessful”, says Domeyard’s Qi
Quants use AI to cut through murk of ‘sustainability’
Separating the wheat from the chaff is fundamental to ESG investing. Machine learning can do that
The common drivers behind alt risk premia’s difficult year
Statistical analysis shows four strategies caused most pain, but funds suffered differently
Dabbling in data science won’t cut it
Banks are seeking data-led boost for research arms – only a few will succeed