BlackRock Robo‐Advisor 4.0: When artificial intelligence replaces human discretion

• DOI: http://doi.org/10.1002/jsc.2201
• Published date: 01 July 2018
• Date: 01 July 2018
• Author: Damir Tokic

RESEARCH ARTICLE

DOI: 10.1002/jsc.2201

Strategic Change. 2018;27(4):285–290. wileyonlinelibrary.com/journal/jsc © 2018 John Wiley & Sons, Ltd. 285

Abstract

lach!och, the lar]est &S inestment mana]ement Crm, has started to rerlace human stochŊ

richers with the =ull automated inestment rro]ram, based on sel=Ŋlearnin] arঞCcial intelli]ence

al]orithms. $his is the Crst hi]hŊrroCle case in the Cnancial industry where arঞCcial intelli]ence

rerlaces human discreঞon. $he adanced ersion o= lach!och !oboŊdisor can also be

customied to rer=orm within the strate]ic rlannin] =rameworh. uman discreঞonary decisionŊ

mahin] jobs are at rish o= bein] rerlaced by machines.

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lach!och Inc. is the lar]est &S inestment mana]ement comrany

with Ū5.1 trillion in assets under mana]ement (&) and 1ƒ,000

=ullŊঞme emrloyees (see $able 1). lach!och deries a substanঞal rorŊ

ঞon o= its reenue =rom inestment adisory and administraঞon =ees,

which are based on rredetermined rercenta]es o= the marhet alue o=

assets under management.

lach!och has been able to charge relaঞely higher =ees =or serŊ

ices related to management o= acঞe etuity =unds, when comrared

to the =ees related to other tyres o= assets under management. s $able 2

shows, in 201ѵ these acঞe etuity =unds accounted =or only ѵѷ o=

&, but rroduced 1ѵѷ o= reenues, mahing this the lach!ochĽs

most rroCtable business segment. lach!och had been emrloying

strictly human stochŊrichers to manage these acঞely traded rortŊ

=olios, with erectaঞons to rroduce a relaঞely beer rer=ormance

comrared to the rassie inde =unds or iŊshares.

oweer in 201ѵ, as $able 1 shows, these acঞe etuity =unds

ererienced oer Ū20 billion in oulows, as inestors rotated their

=unds into the rassie iŊshare inde =unds. ore imrortantly, accordŊ

ing to orningstar, only 11ѷ o= lach!och acঞe etuity =unds hae

beaten their benchmarks (Thomas, 2017).

ccording to lack!ock O Laurence ink, the likely reason =or

the relaঞe underrer=ormance o= acঞe etuity =unds, and the resultŊ

ing oulows, is the limitaঞon o= a human discreঞon in acঞe rorolio

management and stockŊricking: ľThe democraঞaঞon o= in=ormaঞon

has made it much harder =or acঞe management. We hae to change

the ecosystemŌthat means relying more on big data, arঞCcial intelŊ

ligence, =actors and models within tuant and tradiঞonal inestment

strategies.Ŀ (Thomas, 2017) lack!ock eecuঞe ark Wiseman

added: ľThe old way o= reorle siমng in a room ricking stocks, thinkŊ

ing they are smarter than the net guyŌthat does not eist anymore.Ŀ

(Thomas, 2017)

In arch o= 2017, lack!ock announced the rlan to allocate

around 11ѷ o= acঞe etuity =unds to ľtuanঞtaঞe strategies that

adort a more ruleŊbased arrroach to inesঞng.Ŀ s rart o= restrucŊ

turing, 7 out o= 5ƒ stock rickers will leae the =unds, along with ƒѵ

sta@ members connected with the =unds (Thomas, 2017). ased

on these announcements, lack!ock intends to gradually rerlace

human direcঞon with arঞCcial intelligence, to imrroe the rer=orŊ

mance o= acঞely traded etuity =unds, and to stor the oulows

=rom itsĽ most rroCtable diision. ssenঞally, lack!ock has started

to rerlace human stockŊrickers with !oboŊdisor 4.0, which is a

=ull automated inestment rrogram, based on sel=Ŋlearning arঞCcial

intelligence algorithms.

Deloie (201ѵ) deCnes a !oboŊdisor as ľan online rorolio

management soluঞon that aims to inest client assets by automaঞng

client adisory.Ŀ ľ!oboĿ stands =or ľan automated rrocess without the

inYuence o= human being, using mathemaঞcal algorithms to surrort

inestment decisions.Ŀ ľdisorĿ stands =or ľwealth management serŊ

ice.Ŀ The objecঞe o= !oboŊdisory is to ľrromise easy inestment

success een when there is limited Cnancial background knowledge,

ঞme, or intenঞon to deal with selecঞon inestment ehicles.Ŀ urther,

ľclients trans=er the resronsibility o= asset allocaঞon to an algorithm.Ŀ

Ѵach!och !oboŊA7bsor ƓĺƏĹ )_;m arঞCcbaѴ bmt;ѴѴb];mc;

r;rѴac;s _lam 7bscr;ঞomŖ

albr $ohbc

Internaঞonal &niersity o= onaco/IS ,

aris, onaco, rance

orr;srom7;mc;

Damir Tokic, Internaঞonal &niersity o=

onaco, 2, enue lbert II, onaco 98000,

rance.

mail: dtokicŠinseec.com

* JL classiCcaঞon codes: 24, 11, ѵ14, 15

International University of Monaco/INSEEC,

Paris, Monaco, France

Strategic Change. 2018;27(4):285–290. wileyonlinelibrary.com/journal/jsc © 2018 John Wiley & Sons, Ltd. 285