=Paper=
{{Paper
|id=Vol-1349/paper6
|storemode=property
|title=Case-based Recommender Systems for Personalized Finance Advisory
|pdfUrl=https://ceur-ws.org/Vol-1349/paper06.pdf
|volume=Vol-1349
|dblpUrl=https://dblp.org/rec/conf/finrec/MustoS15
}}
==Case-based Recommender Systems for Personalized Finance Advisory==
https://ceur-ws.org/Vol-1349/paper06.pdf
Case-based Recommender Systems
for Personalized Finance Advisory
Cataldo Musto1 and Giovanni Semeraro1
1 Abstract and diversify the investments over time. Similarly, CF algorithms
can hardly be adopted because of the well-known sparsity problem,
Wealth Management is a business model operated by banks and bro- which makes very difficult to identify the neighbors of the target user.
kers, that offers a broad range of investment services to individual These dynamics suggest to focus on different recommendation
clients to help them reach their investment objectives. Wealth man- paradigms. Given that financial advisors have to analyze and sift
agement services include investment advisory, subscription of man- through several investment portfolios4 before providing the user with
dates, sales of financial products, collection of investment orders by a solution able to meet her investment goals, the insight behind
clients. Due to the complexity of the tasks, which largely require our recommendation framework is to exploit Case-Based Reasoning
a deep knowledge of the financial domain, a trend in the area is the (CBR) to tailor investment proposals on the ground of a case base of
exploitation of recommendation technologies to support financial ad- previously proposed investments.
visors and to improve the effectiveness of the process.
The talk presents a framework to support financial advisors in the
task of providing clients with personalized investment strategies. The 3 Methodology
methodology is based on the exploitation of case-based reasoning Our recommendation process is based on the typical CBR workflow
and the introduction of a diversification technique. A prototype of described in [2] and sketcted in Figure 3. Our pipeline is structured
the framework has been used to generate personalized portfolios, and in three different steps:
its performance, evaluated against 1,172 real users, shows that the
yield obtained by recommended portfolios overcomes that of portfo-
lios proposed by human advisors in most experimental settings.
2 Introduction
Wealth management services have become a priority for most finan-
cial services companies. As investors are pressing wealth managers
to justify their value proposition, turbulences in financial markets re-
inforce the need to improve the advisory offering with more cus-
tomized and sophisticated services. As a consequence, a recent trend
in wealth management is to improve the advisory process by exploit-
ing recommendation technologies. However, some peculiarities of
the financial domain make hard to put into practice the most common
recommendation approaches, as the Content-Based (CB) or the Col-
laborative Filtering (CF). As regards CB recommenders, the avail-
able content, which is necessary to feed a CB recommendation algo-
rithm, is very inadequate and not meaningful, since each user can be
just modeled through her risk profile2 along with some demographi- Figure 1. Case-Based Reasoning for Personalized Wealth Management
cal features. Similarly, financial products are described through a rat-
ing3 provided by credit rating agencies, an average yield on different
time intervals and the category it belongs to. In this recommenda- (1) Retrieve and Reuse: retrieval of similar portfolios is performed
tion setting a pure CB strategy is likely to fail, since the overlap be- by representing each user through a feature vector: risk profile, in-
tween features is very poor. Moreover, the over-specialization prob- ferred through the standard MiFiD questionnaire5 , investment goals,
lem [1], typical of CB recommenders, may collide with the fact that temporal goals, financial experience, and financial situation have
turbulence and fluctuations in financial markets suggest to change been chosen as features. Each feature is represented on a five-point
1 Dipartimento di Informatica, Universita degli Studi di Bari ”Aldo Moro”, ordinal scale, from very low to very high. Next, cosine similarity is
Bari, Italy, email:{cataldo.musto, giovanni.semeraro}@uniba.it adopted to retrieve the most similar users (along with the portfolios
2 The Risk Profile is defined as ”an evaluation of an individual or organiza-
they agreed) from the case base.
tion’s willingness to take risks”. Typically, this value is obtained by con-
ducting the above mentioned standard MiFiD questionnaire. 4 http://en.m.wikipedia.org/wiki/Portfolio (finance)
3 http://en.wikipedia.org/wiki/Credit rating 5 http://en.wikipedia.org/wiki/Markets in Financial Instruments Directive
(2) Revise: candidate solutions retrieved at step 1 are typically too
many to be consulted by a human advisor. Thus, the Revise step fur-
ther filters this set to obtain the final solutions. To revise the candidate
solutions, four techniques are compared:
(a) Basic Ranking: portfolios are ranked in descending cosine
similarity order, according to the scores returned by the R ETRIEVE
step. The first k portfolios are returned to the advisor as final solu-
tions.
(b) Greedy Diversification: this strategy implements the diver-
sification algorithm described in [3]. The algorithm tries to diver-
sify the final solutions by iteratively picking from the original set of
Figure 3. Ex-post evaluation
candidate solutions the ones with the best compromise between co-
sine similarity and intra-list diversity with respect to the previously
picked solutions. At each step of the strategy, the solution with the
best compromise is removed from the set of candidate solutions and The performance of the framework has been evaluated in an ex-
is stored in the set of final solutions. perimental session against 1,172 real users. Results show that the
(c) FCV: Financial Confidence Value (FCV) calculates how close yield obtained by recommended portfolios overcomes that of port-
to the optimal one is the distribution of the asset classes in a portofo- folios proposed by human advisors in many experimental settings.
lio, according to the average historical yield obtained by each class. As shown in Figure 2, FCV significantly outperforms human recom-
Given a set of asset classes A, for each portfolio p the set P , of the mendations (the average monthly yield increases from 0.18 to almost
asset classes in it, and its complement P are computed. Next, FCV 0.30) for all the neighboorhood (put on the X axis) taken into account.
is formally defined as: The experimental results were further confirmed by an ex-post eval-
uation performed on real financial data from January to April 2014.
As shown in Figure 3, this experiment provided very interesting re-
F CV (p) = Y (p)log(λ)+1 (1) sults: beyond confirming the goodness of FCV-based ranking and
the statistically significance of the gap with respect to both collab-
|P | P|P | orative and human baselines, the most interesting outcome was that
X yai
Y (p) = pai ∗ yai λ = P i=1 (2) the combination of the diversification technique and FCV can further
|P |
i=1 y
k=1 ak
improve the performance of the proposed portfolios. This result sug-
where pai and yai are the percentage and the average yield of the gests that the integration of the approaches can make the framework
i-th asset class in the portfolio, respectively. Y (p) is the total yield even more effective. This is due to the fact that a combined strategy
obtained by the portfolio, and λ is a drift factor which calculates can merge the advantages of a ranking based on past performance,
the ratio in terms of average yield between the asset classes in the as FCV, with an algorithm that may lead to more diverse recommen-
portfolio and those which are not in. For values of λ ≥ 1, it acts as dations. This makes the investment strategy better, since the human
a boosting factor (for λ 1, it acts as a dumping factor). Through advisor does not base her investment proposal on a set of very similar
this strategy, all the candidate solutions are ranked according to the portfolios, but rather on a set of diversified solutions which is more
FCV score and thetop-k solutions are returned to the advisor. stable and effective, especially when market fluctuations have to be
(d) FCV + Greedy: this combined strategy first uses the greedy tackled.
algorithm to diversify the solutions, then exploits the FCV to rank
the portfolios and obtain the final solutions. 4 Deployment of the framework
(3) Review and Retain: in the Review step the user and the human
advisor can further discuss and modify the portfolio, before generat- A demo version of the platform is available online6 .
ing the final solution for the user. If the monthly yield obtained by the Given that the platform is supposed to be of aid for financial ad-
newly recommended portfolio is acceptable, the solution is stored in visors, it lets the advisor to select the current user as well as the
the case base and can be used in the future as input to resolve similar recommendation technique to be adopted. Next, the ”Recommenda-
cases. tion” button shows the most promising portfolios for the target users
along with the distribution of the asset classes. The distribution can
be further discussed by user and advisor before coming to the final
proposal which is stored in the case base.
REFERENCES
[1] P. Lops, M. de Gemmis, and G. Semeraro, ‘Content-based recommender
systems: State of the art and trends’, in Recommender Systems Hand-
book, pp. 73–105. Springer, (2011).
[2] F. Lorenzi and F. Ricci, ‘Case-based recommender systems: a unify-
ing view’, in Intelligent Techniques for Web Personalization, 89–113,
Springer, (2005).
[3] B. Smyth and P. McClave, ‘Similarity vs. diversity’, in Case-Based Rea-
Figure 2. In vitro evaluation soning Research and Development, 347–361, Springer, (2001).
6 http://193.204.187.192:8080/OBWFinance/ - Login: 2 - Password: 12345