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.
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
What’s in the box? Bad year reveals alt premia’s gaps
Average fund is down almost 5%, but gap between best and worst performers is 14%
Lesson from alt premia’s horrible year: be patient
Investment approach’s diversification benefits can’t be relied on in the short term
Factor funds ‘do right things for wrong reasons’ – Intech
Firm says conventional investing wisdom is missing out on alpha
Do or die – asset managers take up data science
Firms are scanning an ocean of text and images, as well as big number sets, to grab an edge
Banks discreetly seek personnel to mine alt data riches
Citi, Credit Suisse, HSBC and Morgan Stanley are hiring data scientists for a plethora of new initiatives
From AI to cheese: funds seek fixes for trend following
Firms turn to machine learning, hybrid products and new markets to boost returns
Financial risks don’t go on holiday
Better mapping of financial system would help avoid seasonal surprises, argues Andrew Lo
Bridgewater co-CIO on risk parity, correlations and contagion
All Weather fund's approach remains poorly understood, says Prince
Banks look to spin money from their own data
Big banks are tiptoeing forward with datasets for sale despite a host of internal obstacles
AllianceBernstein digs into its own data, looking for alpha
Firm combs through information about its portfolio managers for signs of bias and bad habits
UBS AM joins buy-siders building central data science teams
Data science unit will serve firm’s non-quant investment staff
Winton’s David Harding on turning away from trend following
Founder explains decision to scale back weighting of strategy that made firm’s name
Lo’s ‘dynamic alpha’ gives quants new tool to fine-tune strategies
Time-sensitive measure could help manage systemic risk too
A fool’s gold (or data) mine
Quants are building statistical toolkits to avoid the pitfalls of data mining