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Personalized Wealth Management through Case-based Recommender Systems

AI*IA 2014 - Special Track on AI and Economy

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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
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) 
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
(from Matrix) 
decision-making 
is actually challenging 
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
paradox of choice 
(Barry Schwartz, TED talk “Why more is less”) 
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
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Personalized Wealth Management through Case-based Recommender Systems

  • 1. 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
  • 2. 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
  • 3. 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
  • 4. 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) 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
  • 5. (from Matrix) decision-making is actually challenging 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
  • 6. paradox of choice (Barry Schwartz, TED talk “Why more is less”) 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
  • 7. (financial) overload 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
  • 8. solution: personalization 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
  • 9. Insight: to adapt asset portfolios on the ground of personal user profile and needs 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
  • 10. Solution Recommender Systems 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
  • 11. Recommender Systems Relevant items (movies, news, books, etc.) are suggested to the user according to her preferences. 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
  • 12. 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) 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
  • 13. 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) 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
  • 14. 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) 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
  • 15. Recommender Systems success stories “People who bought…” on Amazon “Discover” on Spotify 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
  • 16. Recommender Systems (unexpected) success stories 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
  • 17. recommending financial products is a complex task 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
  • 18. flocking 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
  • 19. flocking Too many users could be moved towards the same suggestions 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
  • 20. flocking consequence: price manipulation (as in trader forums) 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
  • 21. poor knowledge 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
  • 22. poor knowledge Features describing both assets classes and private investors are poorly meaningful 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
  • 23. Solution Case-based Recommender Systems 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
  • 24. 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 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
  • 25. ....but what do we actually mean with ‘case’ ? 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
  • 26. 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 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
  • 27. case-base example user solution evaluation 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
  • 28. case-base example user solution evaluation User Features Risk Profile: Low Financial Experience: High Financial Situation: Very High Investment Goals: Medium Temporal Goals: Medium 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
  • 29. case-base example user solution evaluation User Features Risk Profile: Low Financial Experience: High Financial Situation: Very High Investment Goals: Medium Temporal Goals: Medium Obbligazionario Euro Bot 30% Obbligazionario High Yield 10% Obbligazionario Globale 22% Azionario Europa 23% Azionario Paesi Emergenti 7% Flessibili Bassa Volatilità 8% 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
  • 30. case-base example user solution evaluation User Features Risk Profile: Low Financial Experience: High Financial Situation: Very High Investment Goals: Medium Temporal Goals: Medium Obbligazionario Euro Bot 30% Obbligazionario High Yield 10% Obbligazionario Globale 22% Azionario Europa 23% Azionario Paesi Emergenti 7% Flessibili Bassa Volatilità 8% monthly rate (e.g.) +0.22% 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
  • 31. case-based RSs solving cycle 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
  • 32. case-based reasoning for personalized wealth management 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
  • 33. scenario “Scrooge McDuck wants to get richer. He decided to invest some of his savings and he asked for help to a financial advisor” 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
  • 34. step 1 user modeling 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
  • 35. scenario Which features may describe Scrooge McDuck? step 1 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
  • 36. scenario User Features Risk Profile: Low step 1 Financial Experience: High Financial Situation: Very High Investment Goals: Medium Temporal Goals: Medium 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
  • 37. scenario User Features Risk Profile: Low Financial Experience: High Financial Situation: Very High Investment Goals: Medium Temporal Goals: Medium MiFID-based step 1 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
  • 38. in a classical pipeline, the target user would have received a “model” portfolio tailored on her profile 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
  • 39. in a pipeline fostered by a recommender system, the financial advisor can analyze the portfolios proposed to similar users to tailor the proposal 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
  • 40. step 2 retrieval of similar users 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
  • 41. given a case base, it is necessary to define a similarity measure to compute how similar two cases are 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
  • 42. given a case base, it is necessary to define a similarity measure to compute how similar two cases are vector space representation 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
  • 43. cosine similarity to get the most similar users 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
  • 44. scenario step 2 case base 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
  • 45. scenario step 2 0.3 0.7 0.9 0.1 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
  • 46. scenario step 2 similarity score 0.3 0.7 0.9 0.1 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
  • 47. scenario step 2 0.3 0.7 0.9 0.1 neighborhood (helpful cases) 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
  • 48. scenario Obbligazionario Euro Bot 30% Obbligazionario High Yield 15% Obbligazionario Globale 15% Azionario Europa 20% Azionario Paesi Emergenti 12% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot 30% Obbligazionario High Yield 10% Obbligazionario Globale 22% Azionario Europa 23% Azionario Paesi Emergenti 7% Flessibili Bassa Volatilità 8% step 2 solutions proposed to the neighbors are labeled as candidate solutions 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
  • 49. step 3 revise of the solution 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
  • 50. in real-world scenarios, the case base contains many helpful cases 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
  • 51. in real-world scenarios, the case base contains many helpful cases it is necessary to introduce strategies to filter and rank the cases 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
  • 52. revise step 3 We defined two ranking strategies • Diversification • Financial Confidence Value (FCV) 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
  • 53. revise diversification insight: filtering out too similar solutions 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
  • 54. revise diversification identification of the best subset of similar cases which maximize the relative diversity 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
  • 55. revise Obbligazionario Euro Bot 30% Obbligazionario High Yield 15% Obbligazionario Globale 15% Azionario Europa 20% Azionario Paesi Emergenti 12% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot 30% Obbligazionario High Yield 10% Obbligazionario Globale 22% Azionario Europa 23% Azionario Paesi Emergenti 7% Flessibili Bassa Volatilità 8% diversification Obbligazionario Euro Bot 15% Obbligazionario High Yield 25% Obbligazionario Globale 10% Azionario Europa 40% Azionario Paesi Emergenti 2% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot 20% Obbligazionario High Yield 20% Obbligazionario Globale 12% Azionario Europa 35% Azionario Paesi Emergenti 5% Flessibili Bassa Volatilità 8% 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
  • 56. revise Azionario Paesi Emergenti 5% Flessibili Bassa Volatilità 8% X X diversification Obbligazionario Euro Bot 30% Obbligazionario High Yield 15% Obbligazionario Globale 15% Azionario Europa 20% Azionario Paesi Emergenti 12% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot 30% Obbligazionario High Yield 10% Obbligazionario Globale 22% Azionario Europa 23% Azionario Paesi Emergenti 7% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot 15% Obbligazionario High Yield 25% Obbligazionario Globale 10% Azionario Europa 40% Azionario Paesi Emergenti 2% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot 20% Obbligazionario High Yield 20% Obbligazionario Globale 12% Azionario Europa 35% 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
  • 57. 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) 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
  • 58. 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 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
  • 59. Obbligazionario Euro Bot --- 30% Obbligazionario High Yield 15% Obbligazionario Globale 15% Azionario Europa +++ 20% Azionario Paesi Emergenti 12% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot --- 30% Obbligazionario High Yield 10% Obbligazionario Globale 22% Azionario Europa +++ 23% Azionario Paesi Emergenti 7% Flessibili Bassa Volatilità 8% revise Obbligazionario Euro Bot --- 15% Obbligazionario High Yield 25% Obbligazionario Globale 10% Azionario Europa +++ 40% Azionario Paesi Emergenti 2% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot --- 20% Obbligazionario High Yield 20% Obbligazionario Globale 12% Azionario Europa +++ 35% Azionario Paesi Emergenti 5% Flessibili Bassa Volatilità 8% FCV 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
  • 60. Obbligazionario Euro Bot --- 30% Obbligazionario High Yield 15% Obbligazionario Globale 15% Azionario Europa +++ 20% Azionario Paesi Emergenti 12% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot --- 30% Obbligazionario High Yield 10% Obbligazionario Globale 22% Azionario Europa +++ 23% Azionario Paesi Emergenti 7% Flessibili Bassa Volatilità 8% revise Obbligazionario Euro Bot --- 15% Obbligazionario High Yield 25% Obbligazionario Globale 10% Azionario Europa +++ 40% Azionario Paesi Emergenti 2% Flessibili Bassa Volatilità 8% Obbligazionario Euro Bot --- 20% Obbligazionario High Yield 20% Obbligazionario Globale 12% Azionario Europa +++ 35% Azionario Paesi Emergenti 5% Flessibili Bassa Volatilità 8% FCV 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
  • 61. step 4 review 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
  • 62. financial advisor and private investor can further discuss the portfolio 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
  • 63. 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% 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
  • 64. step 5 retain 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
  • 65. 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 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
  • 66. case base 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
  • 67. (new) case base 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
  • 68. evaluation 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
  • 69. evaluation what is the average yield of recommended portfolios? 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
  • 70. evaluation what is the average yield of recommended portfolios? can recommender systems suggest better investment portfolios than human advisors? 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
  • 71. 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 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
  • 72. 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 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
  • 73. 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 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
  • 74. 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! 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
  • 75. questions? Giovanni Semeraro giovanni.semeraro@uniba.it Cataldo Musto cataldo.musto@uniba.it