Personalized Wealth Management through Case-based Recommender Systems

AI*IA 2014 - XIII AI*IA Symposium on Artificial Intelligence
Special Track on AI for Society and Economy
Pisa (Italy) - 12.12.2014
Giovanni Semeraro, Cataldo Musto
Personalized Wealth Management
through Case-based
Recommender Systems

one minute
on the Web
G.Semeraro, C.Musto - Personalized Wealth Management through Case-based Recommender Systems
AI*IA 2014 - Special Track on AI for Society and Economy - Pisa (Italy) - 12.12.14

we can handle 126 bits of information
we deal with 393 bits of information
ratio: more than 3x
(Source: Adrian C.Ott, The 24-hour customer)

(from Matrix)
decision-making
is actually challenging

paradox of choice
(Barry Schwartz, TED talk “Why more is less”)

(financial) overload

solution: personalization

Insight:
to adapt asset
portfolios
on the ground of personal
user profile and needs

Solution
Recommender Systems

Recommender Systems
Relevant items (movies, news, books, etc.) are suggested to
the user according to her preferences.

definition
Recommender Systems have the goal of guiding the
users in a personalized way to interesting
or useful objects in a large space of possible
options.
Burke, 2002 (*)
(*) Robin D. Burke: Hybrid Recommender
Systems: Survey and Experiments. UMUAI,
volume 12, issue 4, 331-370 (2002)

does it fit our scenario?
“we are leaving the age of information, we are entering the age of recommendation”
(C.Anderson, The Long Tail. Wired. October 2004)

Recommender Systems
“[...] The technology is used by shopping websites such as Amazon,
which receives about 35 percent of its revenue via product
recommendations. It is also used by coupon sites like Groupon; by
travel sites to suggest flights, hotels, and rental cars; by social-networking
sites such as LinkedIn; by video sites like Netflix to
recommend movies and TV shows, and by music, news, and food
sites to suggest songs, news stories, and restaurants, respectively.
Even financial- services firms recently began using
recommender systems to provide alerts for investors about
key market events in which they might be interested”
(N.Leavitt, “A technology that comes highly recommended” - http://tinyurl.com/d5y5hyl)

Recommender Systems
success stories
“People who bought…”
on Amazon
“Discover”
on Spotify

Recommender Systems
(unexpected) success stories

recommending financial products
is a complex task

flocking

flocking
Too many users could be moved
towards the same suggestions

flocking
consequence: price manipulation
(as in trader forums)

poor knowledge

poor knowledge
Features describing both assets
classes and private investors are
poorly meaningful

Solution
Case-based Recommender Systems

case-based RSs
• Inspired by case-based reasoning
• Similar problems solved in the past are
used as knowledge base
• Reasoning by analogy
• The recommendation process relies on
the retrieval and the adaptation of the
solutions adopted to solve similar cases

....but
what do we actually mean with ‘case’ ?

case base
• A case is a the formalization of a
previously solved problem
• In our setting
• Description of a user
• Description of a portfolio
• An evaluation of the proposed solution

case-base
example
user solution evaluation

case-base
example
User Features
Risk Profile: Low
Financial Experience: High
Financial Situation: Very High
Investment Goals: Medium
Temporal Goals: Medium

case-base
example
User Features
Risk Profile: Low
Obbligazionario Euro Bot 30%
Obbligazionario High Yield 10%
Obbligazionario Globale 22%
Azionario Europa 23%
Azionario Paesi Emergenti 7%
Flessibili Bassa Volatilità 8%

case-base
example
User Features
Risk Profile: Low
monthly rate (e.g.)
+0.22%

case-based RSs
solving cycle

case-based reasoning for
personalized wealth management

scenario
“Scrooge McDuck wants to
get richer. He decided to
invest some of his savings
and he asked for help to a
financial advisor”

step 1
user modeling

scenario
Which features
may describe
Scrooge McDuck?
step 1

scenario
User Features
Risk Profile: Low
step 1

scenario
User Features
Risk Profile: Low
MiFID-based
step 1

in a classical pipeline, the target user
would have received a “model” portfolio
tailored on her profile

in a pipeline fostered by a recommender system, the financial
advisor can analyze the portfolios proposed to similar users
to tailor the proposal

step 2
retrieval of similar users

given a case base, it is necessary to
define a similarity measure to
compute how similar two cases are

given a case base, it is necessary to
define a similarity measure to
compute how similar two cases are
vector space representation

cosine similarity to get the most similar users

scenario
step 2
case base

scenario
step 2
0.3
0.7
0.9
0.1

scenario
step 2
similarity score
0.3
0.7
0.9
0.1

scenario
step 2
0.3
0.7
0.9
0.1
neighborhood
(helpful cases)

scenario
step 2
solutions proposed to the neighbors are labeled as
candidate solutions

step 3
revise of the solution

in real-world scenarios, the case base
contains many helpful cases

in real-world scenarios, the case base
contains many helpful cases
it is necessary to introduce strategies
to filter and rank the cases

revise
step 3
We defined two ranking strategies
• Diversification
• Financial Confidence Value (FCV)

revise
diversification
insight: filtering out too similar solutions

revise
diversification
identification of the best subset of similar cases
which maximize the relative diversity

revise
diversification

revise
Flessibili Bassa Volatilità 8% X X diversification

revise
FCV
• Simple insight
• We know the historical yield for each of
the assets class in the portfolio
• FCV ranks first the solutions composed
by a combination of asset classes close
to the optimal one (according to
previous yield)

revise
FCV
Total yield is the (Generated yield) (Drift Factor)
product of the
yield generated
by each asset
class with the its
percentage in the
portfolio
Ratio between
the yield
generated by the
asset classes in
the portfolio and
its complement

Obbligazionario Euro Bot --- 30%
Azionario Europa +++ 20%
revise
FCV

step 4
review

financial advisor and private investor
can further discuss the portfolio

review
interactive personalization
Original Discussed Gap
Euro Bot 30% 30%
Obbligazionario
High Yield 12.5% 10% -2.5%
Obbligazionario
Globale 18.5% 20% +1.5%
Obbligazionario
Azionario Europa 21.5% 24% +2.5%
Azionario Paesi
Emergenti 9.5% 8% -1.5%
Flessibili Bassa
Volatilità 8% 8%

step 5
retain

retain
an evaluation score is finally assigned to the proposed
solution
yield, e.g.
good solutions are stored in the case base and exploited
for future recommendations

case base

(new) case base

evaluation

evaluation
what is the average yield of
recommended portfolios?

evaluation
what is the average yield of
recommended portfolios?
can recommender systems suggest
better investment portfolios than
human advisors?

experiment 1
revise strategies (leave-one-out evaluation)
Yield
0,28
0,224
0,168
0,112
0,056
0
Basic Diversification FCV FCV + Div
0,28 0,27
0,24 0,25
0,22
0,22
0,2
0,15
0,16
0,18 0,18
1 5 10
neighbors
0,13
best performing configuration provides 0,28% monthly yield

experiment 2
comparison to baselines (leave-one-out evaluation)
Human Collaborative FCV
1 5 10
recsys better than humans!
Yield
0,28
0,224
0,168
0,112
0,056
0
neighbors
0,28 0,27
0,22
0,2 0,2 0,2
0,17 0,17 0,17

experiment 3
ex-post evaluation (6 months, with real data)
0,16
0,128
0,096
0,064
0,032
Basic FCV FCV + Div Collaborative Human
0,06 0,06 0,06
0,04 0,04
1 5 10
FCV and Diversification is the best one
Yield
0
neighbors
0,05
0,11
0,12
0,16
0,09
0,1
0,16
0,06
0,08
0,15

recap
•Personalized Wealth Management
• Application of case-based reasoning
• Geometrical similarity measure to identify the most
similar previously solved cases
• Introduction of diversification and re-ranking
techniques
• More than 3% yield for year
• Experiments shows that recommended portfolios
overcome the real ones for almost all the users
• Working Demo!

questions?
Giovanni Semeraro
giovanni.semeraro@uniba.it
Cataldo Musto
cataldo.musto@uniba.it

Personalized Wealth Management through Case-based Recommender Systems

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