Honesty is key to machine learning’s future – Roberts
Oxford-Man Institute director on why tomorrow’s models will gracefully admit defeat
Stephen Roberts likens the habit among some investors of seeing order in data where none exists to ancient astrologers spotting lions, bears and deities among the stars.
As director of the Oxford-Man Institute of Quantitative Finance, Roberts’ research focuses on machine learning algorithms that discern patterns in markets. The trouble is that algorithms, he says, are even more capable than humans of finding “weird and wonderful things” in the numbers.
This tendency to overfit to the data has
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
More on Asset management
Can machine learning help predict recessions? Not really
Artificial intelligence models stumble on noisy data and lack of interpretability
How patchy liquidity is stymieing systematic credit
…and what investors like AllianceBernstein, Man Numeric and Acadian are doing about it
Asset managers offer tailored LDI to smaller pension schemes
Minimum AUM for customised hedging slashed from around £400m to £75m
How Bloomberg got liquidity seekers to trust its machine learning models
Recent liquidity squeezes have proved the worth of advanced models, argues the tech giant. Now the task is to explain their inner workings to machine learning sceptics
Asset-liability management: Special report 2023
There is nothing new about the dynamics behind the ALM banking crisis of earlier this year: maturity transformation, liquidity risk and interest rate risk are at the heart of the traditional banking business model. But these old threats have been given…
How small and medium-sized banks can enhance deposits modelling frameworks
Recent events have called into question the reliability of deposits as a primary source of funding for small and medium-sized banks. Stickiness of deposits that generations of bankers had counted on suddenly seem ephemeral
ALM banking after the crisis: stress-testing for more robust liquidity management practices
A panel of industry experts discusses a new age of depositor behaviour and the expected evolution of regulations in the wake of the ALM banking crisis. They share insights on achieving integrated approaches to ALM, as well as dynamic hedging strategies…
Fleeting volatility vexes trend followers
Jumpy markets give quant firms the jitters as tried-and-tested strategies struggle in 2023
