FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Behavioral Finance
FinTech
Milo Bianchi
Toulouse School of Economics
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
FinTech
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
FinTech
Recent advantages in information technology open vast possibilities
to o¤er …nancial services in innovative ways
Fintech: “technologically enabled …nancial innovation that could
result in new business models, applications, processes or products
with an associated material e¤ect on …nancial markets and
institutions and the provision of …nancial services.” (Financial
Stability Board 2017)
Enthusiasts talk about a revolution that will disrupt and reshape the
…nancial service industry
Partly due to regulatory arbitrage (Buchak et al. 2018)
Signi…cant decrease in operational costs
Incorporate alternative sources of information
Entry of new players and spill over and improve the e¢ ciency of
existing institutions
Distinctive aspect of …ntechs: exploit economies of scope not only
between …nancial services but also with other business activities
(Barba Navaretti et al. 2018)
Example: platforms o¤ering e-commerce and …nancial services
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Fintech and Financial Inclusion
Developing and developed countries aim at expanding access to
…nancial services and promoting their use in a way that improves
peoples’lives
Can new …nancial technologies can be used to promote …nancial
inclusion?
Decrease of transaction costs (Goldfarb and Tucker 2017)
Supply side (lower …xed costs allow serving poorer customers)
Demand side (easier to access e.g. via mobile phones)
Increased accountability of service providers (e.g. …nancial advice)
Veri…able procedures may improve investors’trust and participation
(Philippon 2017)
Novel sources of information and information processing methods.
Services tailored to the preferences and the needs of the individual
client
Novel sources of information may be more accessible, reliable and
instructive than traditional ones
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Risks of Fintech
Some household may lack an appropriate level of digital literacy
Buchak et al. (2018): …ntech expansion in the US has been more
rapid in counties with higher levels of education
Trust of new technologies: most people believe that new technologies
make their lives easier, but a minority trust an automated procedure
for …nancial services (HSBC 2018)
Further increase the need for improved …nancial education and
consumer protection (Campbell et al. 2011)
Simply facilitating access can make consumers worse o¤ (e.g. Barber
Odean 2001 for online trading)
Automated provision bypasses human intermediaries
Expand the range of products that households can have access to
(e.g. Chinese P2P lending crisis in summer 2018: mix of investors’
poor risk assessment, platforms’hazardous practices, unclear
regulatory framework).
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
FinTech and Behavioral Finance
Behavioral …nance has accumulated evidence on "biases" and
motivated interventions (e.g. "Save More Tomorrow")
Fintech can be used to shed new light on these (and other) biases
Fintech can be used to make investors less exposed to these biases
Behavioral …nance can suggest which types of AI can be most useful
for investors
We will explore these themes in the context of …nancial advice
and later provide other examples in the case of limited attention and
credit scoring
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Financial Advice
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Financial Advice
Households need to take complex decisions in various …nancial
domains, and not all households have the necessary skills.
Natural response: delegate to professional experts
Von Gaudecker (2015): poor …nancial literacy is associated to poor
portfolio performance, but the e¤ect fades away for those who rely
on professional advice
But delegation has its limits
Delegating is costly, only those with enough wealth may a¤ord it or
…nd it pro…table.
Advisors need themselves to have the skills to o¤er services that are
suited to the clients’requirements.
Advisors should have the incentives to act in the client’s best interest.
Evidence shows this need not be the case
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Misguided Beliefs
Foerster et al. (2017): Does One Size Fit All?
Trading and portfolio information on 10,000 …nancial advisors and
800,000 clients in four Canadian …nancial institutions
Clients’observable characteristics (risk tolerance, age, income,
wealth, occupation, …nancial knowledge) jointly explain only 12% of
the cross-sectional variation in risky share
On their own, advisor e¤ects explain 22% of the variation in risky
share, and when added to the model with investor characteristics the
adjusted R 2 goes from 12% to 30%
When clients switch advisor, their portfolios shift away from the
allocation common to the old advisor’s clients and toward the
allocation held by the new advisor’s clients (against explanation
based on matching between advisor and client characteristics)
Linnainmaa et al. (2018): Misguided Beliefs
Advisors invest very similarly to their clients (active management,
return chasing, underdiversi…cation)
True even when advisors leave the …rm (expensive funds are not
simply used to convince clients)
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Con‡icted advice
Mullainathan et al. 2012: trained auditors, posing as customers,
randomly assigned to four di¤erent treatments representing di¤erent
investment strategies and biases (“chasing fund returns”, “employer
stocks”, “cash” and wants to increase returns, "no bias" low fee well
diversi…ed).
signi…cant bias towards active management, initial support for
client’s requests, …nal reccomendations orthogonal to that
advice fails to de-bias clients and if anything may exaggerate existing
biases or even make clients worse o¤
Foà et al. 2018: choice between …xed rate and adjustable rate
mortgages (FRM vs ARM)
Not only relative prices matter, but also time varying bank
characteristics that a¤ect the bank’s cost of o¤ering FRM vs ARM
(e.g. bank’s cost of …nancing with …xed vs variable rate bonds)
Beshears et al. 2018 provide a review
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Theoretical Framework
Inderst and Ottaviani (2012)
Consumer has two options, A and B.
Product providers n = A, B have per-unit cost of production cn and
charge prices pn (to the consumer)
A is better for some consumers, B is better for other consumers
Prior that A is better is q0 , for B it is 1 q0
Consumers get utility vh when consuming the appropriate product
and vl when consuming the inappropriate product, vl < vh
Adviser can assess which product is better for a particular consumer
q = Pr (θ = A ) is the posterior probability that A is the appropriate
product, G (q ) is the cdf of q
Adviser receives a commision fn (from the provider) when the
consumer buys product n
The adviser also incurs some utility cost ρ when the consumer buys
the inappropriate product (public information)
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Biased Advice
Suppose consumers follow the reccomendation
Advising A gives fA
(1
q )ρ, advising B gives fB
qρ
Indi¤erent if q = q ,
q =
1
2
fA
fB
ρ
.
Unbiased if vA (q ) = vB (q ), that is
qvh + (1 q )vl = (1 q )vh + qvl at q = q , that is
q = 1/2.
Advice is biased unless fA = fB or ρ ! ∞
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Interventions with rational consumers
Consumers are rational and form a belief about q , call it q̂
When A is reccomended they expect
E ( vA j q
q̂ ) =
Z 1
q̂
vA ( q )
g (q )
dq.
1 G (q̂ )
Mandatory discolure of commisions gives q̂ = q
If fA is raised, consumer anticipates that A is advised too often and
buys it only if pA is decreased
E¤ect is not there when commisions are not disclosed (consumer
rationality gives q̂ = q at equilibrium, but q̂ is unchanged after
deviations)
Commisions are higher when not disclosed
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Interventions with rational consumers
If cA = cB , fA = fB and q = 1/2
Disclusure a¤ects the level of commisions, but advice is unbiased
anyway
Suppose cA < cB and fA > fB , so q < 1/2
when commisions are not disclosed, more e¢ cient …rms have
relatively more incentive to increase commisions
when commisions are disclosed, the e¤ect is dampened since " fn
gives # pn and this is more costly for more e¢ cient …rms as they
have larger market share (and cannot price discriminate)
disclosure reduces the di¤erence fA fB and when ρ is su¢ ciently
high it can decrease e¢ ciency (A has too little market share)
disclosure is always desirable when ρ is low (mandatory disclosure
policies are substitute to policies that increase adviser liability)
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Naive consumers
Consumers are not always aware of con‡icts of interest (Chater et al.
2010. Anagol et al. 2012)
Suppose that q̂ = 1/2 (consumers believe that advises are
unbiased) and fA > fB (suppose e.g. that B=no purchase)
larger pA is associated to larger fA (from the …rm’s optimality
condition), so consumers are worse o¤ both as they are too likely to
buy A and because they pay larger pA
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Free advice
Suppose that in addition the advisor can charge a …xed F to the
customer, unconditional on purchase
F and pA are chosen so that the consumer is indi¤erent between
getting the advice and not buying at all
joint pro…ts for the adviser and the …rm are maximized with F = 0 :
if F > 0, can decrease F and increase pA and still make the
consumer indi¤erent. At the same time, " pA gives " fA and this
increases sales since consumers keep q̂ = 1/2
Possibility to exploit naive consumers gives rise to "free advice"
rival …rms will not gain market shares having a more transparent
strategy with F > 0 (Gabaix Liabson 2006)
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Interventions with naive consumers
If mandatory disclosure acts as "eye opener" so that q̂ = q that is
good for consumers (and we are back to the rational case)
If information is not su¢ ciently salient, commisions could be capped.
But this would bene…t vertically integrated providers (if adviser is
employee of the provider, incentives can come from promotion and
not from commisions)
Disclosing commisions can also prevent informationally overloaded
consumers to pay attention to other dimensions (Lacko and
Pappalardo 2007)
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Robo Advising
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Robo Advising
Robo-advising: delivery and execution of …nancial advice through
automated algorithms on digital platforms
Can delegation can be designed to reduce transaction costs, agency
con‡icts, and be able to provide customized advice in time of need?
Even if so, which investors would be willing to delegate? And stick
to the advice even when tempted to do otherwise?
Dietvorst et al. (2019) "Overcoming algorithm aversion: People will
use imperfect algorithms if they can (even slightly) modify them."
Understanding whether investors’rationally choose to delegate and
to follow the advices is key for designing interventions aimed at
improving their welfare.
Inderst and Ottaviani (2012): the e¤ects of standard interventions
(say, regulating advisors’compensation schemes, or mandating
disclosure of possible con‡icts of interest) can go either way
depending on investors’degree of sophistication
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
The promises and pitfalls of robo-advising
D’Acunto et al. 2019: study a portfolio optimizer introduced by a
brokerage house in India in 2015
Compute the optimal (mean variance) weights in investment account
Depicts the e¢ cient frontier for the investor, shows the optimized
position and investor’s current position
Study e¤ects on diversi…cation, performance, attention, other
behavioral biases
Single-di¤erence results (control for unobserved time-invariant
investor characteristics)
Exploit quasi-random variation as human advisers called their clients
to promote the portfolio optimizer
Treated clients: reached by human advisers and used the optimizer
that day
Control clients: human advisers tried to contact on the same day but
did not answer the phone
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Data
Large brokerage house in India introduced robo advising for its
individual clients in July 2015
Portfolio Optimizer dataset: instances in which a client of the
brokerage house used the portfolio optimizer (Jul 2015 - Feb 2017)
Transactions dataset: full trading history of each client of the
brokerage house from (Apr 2015 - Jan 2017)
Holdings dataset: monthly asset holdings for each client (Jan 2016 Jan 2017)
Logins dataset: date and time at which the account was accessed
(Apr 2015 - Jan 2017)
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Portfolio Optimizer
The tool uses the classical Markowitz (1952) mean-variance
optimization
Inputs are the vector of mean returns and the variance-covariance
matrix, output is a set of portfolios that maximize Sharpe ratio
Adapted to consider time variation in the investment opportunity set,
investor’s horizon, returns not normally distributed, short-selling
constraints, ...
Education: investors can visualize the portfolio choices (current and
target portfolios) in a mean–variance space
Flexibility: investors are free to deviate from the robo-advisor
Simplicity: click a button to execute in one batch all the trades to
get to their target portfolio
All investors were already receiving human advice
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Users vs Non-Users
Users are similar to non-users in terms of demographic
characteristics, but less prone to behavioral biases, have larger
portfolios, higher trading activity and superior risk-adjusted
performance.
E¤ects of robo taking:
Investors with less than 10 stocks increased the number of stocks
and experience sharp declines in portfolio volatility
Investors with 10 or more stocks, decrease the number of stocks
(optimizer recommended closing positions in stocks that would be
shorted), portfolio volatility also decreased
Market-adjusted investment performance improved for less diversi…ed
investors, not for diversi…ed investors
Reduced – but not eliminated – pervasive behavioral biases
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Behavioral Finance
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Milo Bianchi
FinTech
Financial Advice
Behavioral Finance
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Milo Bianchi
FinTech
Financial Advice
Behavioral Finance
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Behavioral Biases
Disposition e¤ect: tendency to realize gains more often than losses
PGR >PLR, where PGR =Realized Gains/(Realized Gains + Paper
Gains) and PLR =Realized Losses/(Realized Losses + Paper Losses)
Trend chasing: tendency to purchase stocks after a set of subsequent
increases in price
For each purchased stock, Trend Chasing = Days Price
Increase/(Days Price Increase + Days Price Decrease) in the 5 days
before the purchase date
Rank e¤ect: tendency to sell the best- and the worst-performing
stocks in the portfolios, ignoring stocks with intermediate
performance
Signi…cant di¤erence between Best-Middle and Worst-Middle
Best = Best Sold/(Best Sold + Best not Sold), similarly for Worst
and Middle
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Behavioral Finance
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Human-Robot Interactions
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Human-robo interactions
Is the robo intended to replace or promote investors’judgment and
actions?
Optimal degree of automation is not obvious
Human-robo interactions can reduce algo aversion (Bianchi Brière
2021)
Can these interactions promote reliance on the robot? Even in
response to shocks?
Long-term e¤ects of robo-advice
Promote learning and …nancial capability
Having humans-in-the-loop may be harmful to performance
Ge et al. (2021) on peer-to-peer lending, Green and Chen (2019) on
judges
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Bianchi Briere (2023)
Robo-advisor introduced by a French asset manager on a large set of
Employee Savings Plans
Robo builds the investor’s pro…le, suggests a portfolio allocation, and
sends alerts over time in case of deviations from the target
Investors are the ultimate decision makers (as opposed to
robo-managed accounts)
Focus on human-robo interaction: "intelligence augmentation" (IA)
rather than AI, often about substitution
Sample includes investors with small portfolios, little experience and
typically no access to …nancial advising
Large debate on …nancial inclusion and …nancial inequalities
Exploit knowledge of the robo rules and di¤erent sources of variation
Allows addressing self-selection issues
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Main Results
Increased attention after take up (complementarity)
Increased trading and rebalancing activities
Reduced distance from target allocations (role of alerts)
Increased portfolio returns
Mostly driven by improved rebalancing
Automatic rebalancing would improve only marginally
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Related Literature
Robo advising and …nancial decisions (D’Acunto and Rossi 2021;
Bianchi and Brière 2021)
Focus on human-robo interactions and investors’choices over time,
long term investment
Automation and …nancial inclusion (Reher and Sokolinski 2021)
Small investors need not lose controls over their portfolios (key to
promote learning and …nancial capability?)
Asset returns and wealth inequality (Lusardi, Michaud and Mitchell
2017; Bach, Calvet and Sodini 2020, Fagereng, Guiso, Malacrino
and Pistaferri 2020)
Automated advice may partly limit these patterns
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Data
Employee Savings Plans
Each year, employees receive a sum of money that they allocate
between a menu of funds proposed by their employer
Investment is locked in either for 5 years or until retirement,
employees can increase their investment and rebalance their portfolio
over time as they wish
Robo treatment
Elicits information (risk-aversion, …nancial knowledge, horizon)
Proposes an allocation within the current menu, and if accepted
implements it
Sends email alerts if current allocation is too far from proposed
allocation
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Behavioral Finance
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Milo Bianchi
FinTech
Financial Advice
Behavioral Finance
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
Other Applications
AI and Financial Services
Data
Robo is proposed to employers from August 2017
Monthly data from Sept 2016 to June 2021
Sample: all takers as of November 2018 (14,576 - out of 1.2M
exposed) and a random sample of 20,000 non takers, 20,000 non
exposed, 20,000 curious
Account level data (portfolio choices, returns, risk) + digital
footprints (connections) + robo data (pro…le, proposed allocation)
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Basic Speci…cation
OLS:
0
yi ,t = αi + βTi ,t + Xi ,t γ + µt + εi ,t ,
(1)
αi and µt are contract and time …xed e¤ects
Ti ,t = 1 if the robo is adopted in contract i in period t
Xi ,t individual and portfolio characteristics (past risky share, past
returns, account value, ...)
standard errors are clustered by individual and time
control group are robo-curious individuals (unless speci…ed otherwise)
similarly, for regressions at the individual level
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
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Attention and Trading
Table: Investors’Attention and Trading
(1)
Dep. Variable
Robo treated*after
(2)
(3)
Number of Connections
0.283***
(0.074)
Sample
Individual Fixed E¤ects
Contract Fixed E¤ects
Time Fixed E¤ects
Observations
R-squared (within)
866,366
0.01
(4)
Trades
(5)
Robo( >t)
(6)
Individual
0.094***
(0.014)
0.044***
(0.004)
0.003
(0.002)
0.270***
(0.079)
0.138***
(0.039)
No rem
No Sub
x
x
x
x
x
x
x
x
x
x
663,343
0.01
852,208
0.01
3,589,424
0.01
3,589,424
0.01
3,589,424
0.01
Note: This table reports the results of OLS regressions. In columns 1-3, the dependent variable is the number
of connections per month. In column 2, we exclude the month before and the month at which the individual
has received the variable remuneration. In column 3, the sample excludes the two months around the robosubscription. In column 4, the dependent variable is the number of allocation changes per month; in columns
5-6, the dependent variable is the number of allocation changes suggested by the robot and directly chosen by the
individual, respectively. Columns 1-3 include individual and time …xed e¤ects, columns 4-6 include contract and
time …xed e¤ects. Controls include the average equity share and the average returns over the past 12 months,
the account value in the previous month, the value of the yearly variable remuneration. Standard errors, doubleclustered by individual and time, are in parenthesis. ,
and
denotes signi…cance at 10%, 5% and 1% level,
respectively.
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Rebalancing
Robo service sends alerts to investors when current allocation is far
from the target (de…ned at the time of subscription or latest
pro…ling).
How do investors respond to those alerts?
Are alerts e¤ective to get investors stay closer to the target? (typical
problem for less sophisticated investors, Bianchi 2018)
Indirect evidence on whether trust in the robo persists after having
experienced the service, and after relatively large shocks
Behavioral Finance
Milo Bianchi
FinTech
Financial Advice
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Rebalancing
Table: Alerts and Rebalancing
(1)
Connections
(2)
Rebalancer
(3)
(4)
(5)
Change in Distance Actual - Target Equity
Robo treated*Alert
0.308***
(0.051)
0.295***
(0.048)
-0.039***
(0.0031)
Alert
0.144
(0.117)
0.104***
(0.015)
0.033***
(0.004)
Dep. Variable
-0.006***
(0.001)
Alert MIF
0.001*
(0.001)
Sample
Individual Fixed E¤ects
Contract Fixed E¤ects
Time Fixed E¤ects
Observations
R-squared (within)
Behavioral Finance
Robo takers+curious
Robo takers
x
x
x
x
x
x
x
x
x
x
208,705
0.01
1,434,041
0.15
1,283,997
0.01
679,577
0.01
614,292
0.01
Note: This table reports the results of OLS regressions. In column 1, the dependent variable is the number
of connections per month. In column 2, the dependent variable is a dummy equal to one if the investor
rebalances the portfolio in month t. In columns 3-5, the dependent variable is the change in the distance
between the actual and the target equity share between t+1 and t-1. In columns 1-3, the sample is restricted
to robo-takers and robo-curious. Alert is a dummy equal to one if the distance between the actual and the
target allocation is above the alert threshold, and to zero otherwise. For robo-takers, the target allocation
is the one proposed by the robot; for robo-curious, it is the one held at the time of the completion of the
robo-survey. In columns 4-5, the sample is restricted to robo-takers. Alert MIF is a dummy equal to one if
the investor receives an alert as they have not completed the pro…ling survey requested by the regulator. All
regressions include time and contract …xed e¤ects.
Milo Bianchi
FinTech
Financial Advice
Robo Advising
Human-Robot Interactions
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AI and Financial Services
Alerts
Alerts increase investors’attention
Alerts increase the probability of rebalancing (29% vs. 10%)
Alerts induce robo takers to decrease their distance from the target
by 4% more than robo curious
Conditionally on being alerted, the average distance is 11%
E¤ect of the MIF alert is very small (placebo)
Behavioral Finance
Milo Bianchi
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Robo Advising
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Returns
Realized Returns
Expected Returns (as in Reher Sokolinski 2021)
5-factors model: 3 equity factors (Fama-French’s market, size,
value), 2 …xed-income factors (Barclays’U.S. and Global Bond
Index, taken from Bloomberg)
Risk-free rate: one-month Treasury yield (also used for money
market funds)
βf computed on the longest possible time-series, from 1990, when
our …xed-income factor become available, to 2021, the end of our
sample
Time-varying expected returns: Rt (x ) = ∑f βf (x )Rtf
Behavioral Finance
Milo Bianchi
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Robo Advising
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Returns
Table: Equity Exposure and Returns
(1)
Equity Share
Robo treated*after
0.030***
(0.003)
(2)
(3)
Realized Returns
0.024***
(0.005)
Equity Share
Contract Fixed E¤ects
Time Fixed E¤ects
Observations
R-squared
0.021***
(0.004)
(4)
(5)
Expected Returns
0.025***
(0.003)
0.093***
(0.007)
0.020***
(0.003)
0.135***
(0.006)
x
x
x
x
x
x
x
x
x
x
2782081
0.01
3174911
0.01
3174652
0.01
3173599
0.01
3173326
0.05
Note: This table reports the results of OLS regressions. In column 1, the dependent variable is the
equity share. In columns 2-4, the dependent variable is the annual returns at the contract level. In
columns 3-5, the dependent variable is the expected annual returns at the contract level. Controls
include the account value in the previous month, the value of the yearly variable remuneration, a
dummy if the variable remuneration was received in the current month and a dummy if the variable
remuneration was received in the past month. Standard errors, double-clustered by individual and
time (i.e., year-month), are in parenthesis. ,
and
denotes signi…cance at 10%, 5% and 1%
level, respectively.
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Returns
Estimated e¤ects are large
For an average investor (34k euros in the plan, 17 years horizon),
increase in yearly returns by 2.4% = increase in …nal wealth by 17k
euros
For robo-takers, the fee is on average equal to 0.04% of the
portfolio.
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Static vs. Dynamic E¤ects on Returns
Total change in returns results from a static e¤ect occurring at
subscription (due to the allocation change) and a dynamic e¤ect
occurring after subscription (due to di¤erent portfolio rebalancing)
Both e¤ects can be important, but di¢ cult to estimate
Exploit the knowledge of the robo algorithm
Identify curious-close investors as
Being robo-curious, and
Having a portfolio allocation hold when completing the survey close
to the one that the robot would have implemented (5% distance in
equity share)
Had they taken the robot, the changes would have been essentially
those associated with rebalancing behaviors after subscription
Comparing their returns to those of robo-takers, we can estimate the
dynamic e¤ect
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Table: Equity Exposure and Returns: Comparing to Curious
Close
(1)
Equity Share
Robo treated*after
0.010**
(0.004)
(2)
(3)
Realized Returns
0.010***
(0.003)
Equity Share
Contract Fixed E¤ects
Time Fixed E¤ects
Observations
R-squared
0.009***
(0.00320)
(4)
(5)
Expected Returns
0.011***
(0.002)
0.049***
(0.005)
0.010***
(0.002)
0.072***
(0.005)
x
x
x
x
x
x
x
x
x
x
1127745
0.01
1275225
0.01
1275225
0.01
1273690
0.01
1273690
0.03
Note: This table reports the results of OLS regressions. The control group is restricted to robocurious for whom the di¤erence between the equity share held at the time of the completion of the
robo-survey and the one proposed by the robot was less than 5%. In column 1, the dependent variable
is the equity share. In columns 2-4, the dependent variable is the annual returns at the contract level.
In columns 3-5, the dependent variable is the expected annual returns at the contract level. Controls
include the account value in the previous month, the value of the yearly variable remuneration, a
dummy if the variable remuneration was received in the current month and a dummy if the variable
remuneration was received in the past month. Standard errors, double-clustered by individual and
time (i.e., year-month), are in parenthesis. ,
and
denotes signi…cance at 10%, 5% and 1%
level, respectively.
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Automatic rebalancing
Dynamic e¤ect accounts for about 40-50% of total e¤ect
Not too far from rebalancing premia observed in the mutual fund
literature (Brandt and Denison 2014, Berk and Van Binsbergen
2015, Maeso and Martellini 2020)
What if the robot could implement automatic rebalancing?
Evidence in other domains that humans-in-the-loop may be harmful
for performance (Ge et al. 2021, Green and Chen 2019)
Compute counterfactual returns in case of immediate rebalancing
upon reception of the alert and exactly as suggested by the robot
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Figure: Cumulative Returns: Automatic, Robo-Takers and Robo-Curious
Note: This …gure displays the cumulative returns experienced by various groups of investors over time. The value of the portfolio at the
introduction of the robot (September 2017) is normalized to 100 for all groups of investors. On the horizontal axis, time is expressed
in months; on the vertical axis, cumulative returns are computed as the average returns experienced by a given group of investor. The
dark-grey dotted line correspond to robo-curious for whom the di¤erence between the equity share held at the time of the completion of the
robo-survey and the one proposed by the robot was less than 5%. The middle-grey dashed line corresponds to robo-takers. The light-grey
solid line corresponds to (…ctitious) investors who would automatically rebalance their portfolio immediately upon reception of the alert and
exactly as suggested by the robot.
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Automatic rebalancing
On average, the cost of retaining control is not large
In annual terms, counterfactual returns are 0.025% larger than actual
returns.
Di¤erence between automatic and takers is signi…cantly smaller than
the one between takers and curious close.
By the end of the sample, the average cumulative returns for
automatic rebalancers are 12.85%, for robo-takers 12.61%, for
curious close 7.03%.
Important heterogeneity across investors
top 1% of the cost distribution, annual counterfactual returns are
6.9% larger than actual returns
bottom 1% of the cost distribution, annual counterfactual returns are
7.66% smaller than actual returns.
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Selection
Previous analysis: address potential endogeneity of the take-up
decision by exploiting discontinuities in the robo algorithm or by
controlling for time-invariant individual-speci…c characteristics (while
comparing takers to curious)
What if we vary the control group?
Compare takers to non-takers: conditional on exposure, e¤ect of
taking up the service
Compare takers to not-exposed (exposure depends on a decision of
the employer, not of the individual investor)
What if we look for shocks to take-up?
Instrument take-up by the fraction of takers in the same …rm
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Table: Control Groups
Dep. Variable
(1)
(2)
Connections
(3)
(4)
(5)
Trades
(6)
(7)
Equity
(8)
Returns
Treated*after
0.270**
(0.097)
0.299***
(0.090)
0.093***
(0.018)
0.090***
(0.016)
0.046***
(0.004)
0.051***
(0.004)
0.029***
(0.005)
0.042***
(0.008)
Control group
Exposed
Not-Exp
Exposed
Not-Exp
Exposed
Not-Exp
Exposed
Not-Exp
Individual FE
Contract FE
Time FE
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Observations
R-squared
832,283
0.01
809,014
0.01
3,676,837
0.01
3,835,642
0.01
2,781,104
0.01
2,851,673
0.01
3,202,133
0.01
3,300,663
0.01
Note: This table reports the results of OLS regressions. In columns 1 and 2, the dependent variable is the number of connections per
month; in columns 3 and 4, the dependent variable is the number of allocation changes per month; in columns 5 and 6, the dependent
variable is the equity share; in columns 7 and 8, the dependent variable is variable is the annual return. In columns 1,3,5 and 7, the
control group are exposed individuals who did not take the robot. In columns 2,4,6 and 8, the control group are individuals who have not
been o¤ered the robo-service. Controls include the account value in the previous month, the value of the yearly variable remuneration,
a dummy if the variable remuneration was received in the current month and a dummy if the variable remuneration was received in the
past month. Standard errors, double-clustered by individual and time (i.e., year-month), are in parenthesis. ,
and
denotes
signi…cance at 10%, 5% and 1% level, respectively.
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Table: IV Estimates
Dep. Variable
Robo treated*after
(1)
Connexions
(2)
Trading
(3)
Returns
(4)
Static
0.153*
(0.0840)
0.0666**
(0.0267)
0.0630***
(0.0150)
0.0369***
(0.00561)
First Stage: Robo Treated
Fraction of treated employees
12.992***
(2.0707)
3.225***
(0.3299)
3.084**
(0.3617)
3.084**
(0.3617)
F-Stat (…rst stage)
39.37
95.55
72.7
72.7
Observations
R-squared (within)
28,917
0.012
3,822,677
0.006
3,267,180
0.008
3,267,180
0.002
Note: This table reports the results of 2SLS regressions in which the probability to adopt the robo-service
is instrumented by the fraction of employees in the same …rm who have taken-up the robot. In column 1,
the dependent variable is the number of connections per month; in column 2, the dependent variable is
the number of allocation changes per month; in column 3, the dependent variable is the annual return; In
column 4, the dependent variable is the static e¤ect as de…ned in equation (4). In column 1, we include
individual and time …xed e¤ects; in columns 2-4, we include contract and time …xed e¤ects. Controls
include the account value in the previous month, the value of the yearly variable remuneration, a dummy if
the variable remuneration was received in the current month and a dummy if the variable remuneration was
received in the past month. Standard errors, double-clustered by individual and time, are in parenthesis.
,
and
denotes signi…cance at 10%, 5% and 1% level, respectively.
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Selection
When varying the control groups, results are similar to baseline
estimates
Our estimates are mainly driven by changes in behaviors within the
group of robo-takers
E¤ects depend on the adoption of the service, not just on the
observation of the robo recommendation
IV based on workplace adoption gives consistent results too
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Discussion
Robo can improve …nancial decisions
Importance of human/robo interactions (portfolio dynamics)
Possibly key for understanding long-term impacts
Open questions
E¤ects in good and bad times? (Bianchi Brière ...)
"Simple" algorithms in …nancial services
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Attention and Performance
Gargano, Antonio, and Alberto G. Rossi. "Does it Pay to Pay
Attention?." Review of Financial Studies (2016).
Detailed trading and performance data matched with measures of
attention:
Number of seconds spent on the brokerage account website over a
given time interval, the total number of pages visited by the investor,
the number of investor logins to the brokerage account website
Various sections of the website visited by investors (Balances,
Research, Trading, ..)
Tickers researched by investors
High-attention investors have superior performance
Purchase attention-grabbing stocks whose positive performance
persists for up to six months
Particularly pro…table when trading stocks with high uncertainty, but
for which a lot of public information is available.
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Credit Scoring
Berg, T., Burg, V., Gombović, A., & Puri, M. (2018). "On the Rise
of FinTechs–Credit Scoring using Digital Footprints," Review of
Financial Studies (2020)
Digital footprints predict consumer default?
Even simple, easily accessible variables from the digital footprint can
equal or exceed the information content of credit bureau (FICO)
scores
Di¤erence in default rates between customers using iOS and Android
is equivalent to the di¤erence in default rates between a median
FICO score and the 80th percentile of the FICO score
Customers coming from a price comparison website are almost half
as likely to default as customers being directed to the website by
search engine ads
Their discriminatory power for unscorable customers is even better
than that for scorable customers.
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Robo Advices and AI
Most robo advising today build on rather simple procedures both in
terms of the information employed to pro…le the client and on how
this information is used to construct the optimal portfolio
Beketov, Lehmann and Wittke (2018): modern portfolio theory
remains dominant, forms of arti…cial intelligence are hardly employed
Why?
More AI is not feasible due to technological or knowledge constraints
Some scholars would not agree (Bartram, Branke and Motahari
(2020)
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Regulatory Challenges
More AI is not feasible due to regulatory constraints
Current U.S. discipline: as a registered investment advisor, a
robo-advisor has a …duciary duty to its clients (1940 Advisers Act,
adapted by the SEC in 2017)
Main requirements: elicit enough information on the client, use
properly tested and controlled algorithms, and fully disclose the
algorithms’possible limitations
Large legal debate on how much a robo advisor can and should be
subject to a …duciary duty
Cannot since programmed to serve a speci…c goal and to the client’s
broader interest (Fein 2017)
Cannot since they have limited knowledge of the client (Strzelczyk
2017)
Yes if recommendations are based on …nance theory and possible
con‡ict of interest are fully disclosed (Ji 2017, Clarke 2020)
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AI and its limits
More AI is not desirable
Black-box, hard to provide explanations, possibly very sensitive to
(hard to measure) individual characteristics
Exacerbate algorithmic biases
New risks: …nancial stability, cybersecurity (Patel and Lincoln (2019),
Board (2017))
Algorithm complexity is particularly problematic for those with lower
…nancial capabilities (Ryan, Trumbull and Tufano (2011) and Lerner
and Tufano (2011))
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Next Generation
New generations of robo use more automation, more data and more
complex models (Beketov et al. 2018)
Key challenges:
If increased AI means increased opacity, miss on key promises of
increased accountability and …nancial inclusion
Trust is key for technology adoption, even more so in the domain of
…nancial advice.
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An alternative path
Devise algorithms that can be easily interpreted and evaluated
Trust in (machine learning) technologies requires them to FEAS fair, explainable, accountable, and safe (Toreini, Aitken,
Coopamootoo, Elliott, Zelaya and van Moorsel 2020)
Jacovi, Marasovic, Miller and Goldberg (2020)
Extrinsic trust is based on the observation of the model’s behaviors,
intrinsic trust on the understanding of the underlying reasoning
process
Model’s behaviors needs to be predictable; rather than simply
accurate
User should gain the ability to assess the patterns that make the
model correct or incorrect
It is required to make explicit the contract against which
trustworthiness should be evaluated
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XAI
XAI (explainable arti…cial intelligence) - Biran and Cotton (2017) for
a recent survey
Possibility to explain a given prediction or recommendation, even if
based on a possibly very complicated model (say by evaluating the
sensitivity of the prediction when changing one of the inputs)
How much a given model can itself be explained
Explanations can help humans in performing a given task and at the
same time in evaluating a given model
Desiderata both in itself and to assess whether other desiderata, such
as fairness, privacy, reliability, robustness, causality, usability, are met
(Doshi-Velez and Kim 2017)
Not a new issue in AI (earlier approaches in expert systems in the
1970s and recommender systems, Biran and Cotton 2017).
Recent EU regulation: right to explanation, users can inquire about
the logic involved in an algorithmic decision a¤ecting them (say,
through pro…ling)
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XAI for robo advisors
Not easy for robo advisors
How to evaluate performance if AI gives fully personalized allocations
to be confronted against fully personalized benchmarks (Lo 2016).
How to build counterfactuals
How to explain/appreciated the underlying …nance model which
governs the algorithm, especially if one wishes to serve less
experienced investors
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AI revolution
The AI revolution has not happened yet (Jordan 2019)
Instead of better mimicking human interactions or most
sophisticated human thinking, the AI revolution will happen when
new forms of intelligence will be considered
Importing insights from social sciences seems crucial
Capture how humans actually think and behave (Lo (2019): include
forms of "arti…cial stupidity.")
Inform how explanations are and should be communicated (Miller
2019): explanations are selective, based on causal relations and
counterfactuals rather than on likely statistical relations, and involve
a social dimension in which explainers and "explainees" may interact
Help addressing causality, discussing counterfactuals, and new forms
of collective intelligence based on the understanding of how markets
functions and how they may fail (Jordan 2019).
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