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.
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
Arnott, Harvey: machine learning dangerous when data thin
Experts warn ML should be used “for its correct purpose”
Factor timing: scant upside, big downside
Stock selection trounces “tempting” factor timing in study
Banks bet on data to rescue research
Barclays, Morgan Stanley, UBS among those using data science to pep up their research offerings
Teach history to avoid mistakes of yesterday’s quants
Quant grads should be taught follies of LTCM, Gaussian copula and London Whale, writes UBS’s Gordon Lee
Learning algos that learn how to learn
Knowing what to remember and what to forget could help machines beat quant and discretionary investors
How quants at Value Partners pick Macau’s casino winners
Hotel data on ‘high rollers’ helps group make casino investment calls, as quant influence grows
When bonds struggle, so does alt premia – research
Ties between alternative risk premia and fixed income closer than appreciated
Buy-side quant of the year: Gordon Ritter
Risk Awards 2019: Quant uses new tech to tackle old problem of optimal execution
From trend follower to trailblazer
New fund targets commodities others are “scared” to trade – from asphalt to glass panels
The machine shines in Hong Kong A-share fund
Strategy run by ChinaAMC (HK) combines machine learning with human judgement to outdo rivals