Financial	
  Methods	
  in	
  Online	
  
Adver3sing	
  
Dr.	
  Jun	
  Wang	
  
UCL	
  
ACM	
  SIGIR	
  IATP13	
  
Acknowledgements	
  
Bowei	
  Chen,	
  Mathema3cal	
  Finance	
   Shuai	
  Yuan,	
  Computer	
  Science	
  
When	
  online	
  adver3sing	
  goes	
  wrong	
  
h9p://mashable.com/2008/06/19/contextual-­‐adverNsing/	
  
Web	
  users	...
What	
  is	
  the	
  (fundamental)	
  problem?	
  
•  AutomaNcally	
  place	
  the	
  right	
  ad	
  for	
  the	
  right	
...
Why	
  financial	
  methods?	
  
•  Our	
  observaNons	
  3+	
  years	
  ago:	
  
– although	
  mulNple	
  parNcipants	
  f...
An	
  analogy	
  with	
  Financial	
  markets	
  
•  Ads	
  (display	
  opportuniNes)	
  are	
  “traded”	
  
based	
  on	
...
Ads	
  prices	
  are	
  vola3le	
  	
  
(a) (b)The	
  price	
  movement	
  of	
  a	
  display	
  opportunity	
  from	
  Ya...
Research	
  opportuni3es	
  
•  Need	
  Ad’s	
  Futures	
  Contract	
  and	
  Risk-­‐reduc4on	
  Capabili4es	
  
–  Automa...
Current	
  trends	
  in	
  online	
  adver3sing	
  
•  Spot	
  markets:	
  Real-­‐Time	
  Bidding	
  (RTB)	
  
allows	
  s...
Summary	
  	
  
•  Spot	
  market:	
  Real-­‐Nme	
  Bidding	
  
Shuai	
  Yuan,	
  Jun	
  Wang,	
  Real-­‐Nme	
  Bidding	
 ...
Summary	
  	
  
•  Spot	
  market:	
  Real-­‐Nme	
  Bidding	
  
Shuai	
  Yuan,	
  Jun	
  Wang,	
  Real-­‐Nme	
  Bidding	
 ...
Real-­‐3me	
  bidding	
  
Real-­‐3me	
  bidding	
  
“This	
  is	
  Lawrence	
  from	
  India.	
  I	
  was	
  searching	
  Recommender	
  
model	
  i...
The	
  winning	
  bids	
  and	
  hourly	
  average	
  
Figure 7: The time series snippet of winning bids and its hourly
av...
Daily	
  Pacing	
  
ons but more
Wilk test [26]
the number
tisers spend
iser submits
from spend-
) and as fast
may lead to...
The	
  frequency	
  factor	
  	
  
•  How	
  many	
  Nmes	
  ads	
  (a.k.a.	
  creaNves)	
  would	
  
be	
  displayed	
  t...
The	
  Recency	
  Factor	
  	
  
•  helps	
  to	
  decide	
  to	
  bid	
  or	
  not	
  based	
  on	
  how	
  
recently	
  ...
Summary	
  	
  
•  Spot	
  market:	
  Real-­‐Nme	
  Bidding	
  
Shuai	
  Yuan,	
  Jun	
  Wang,	
  Real-­‐Nme	
  Bidding	
 ...
Why	
  Ad	
  Futures	
  (1)	
  
•  Suppose	
  there	
  is	
  a	
  travel	
  insurance	
  company	
  
whose	
  major	
  cus...
Why	
  Ad	
  Futures	
  (2)	
  
•  In	
  March	
  the	
  company	
  plans	
  an	
  adverNsement	
  
campaign	
  in	
  thre...
Why	
  Ad	
  Futures	
  (3)	
  
•  If	
  the	
  company	
  worries	
  that	
  the	
  future	
  price	
  of	
  
the	
  impr...
Why	
  Ad	
  Futures	
  (4)	
  
•  Equally,	
  search	
  engines	
  and	
  large	
  publishers	
  
could	
  agree	
  to	
 ...
However,	
  online	
  adver3sing	
  is	
  different	
  	
  
•  A.	
  Non-­‐storability:	
  unlike	
  stocks	
  or	
  other	...
C.	
  There	
  are	
  many	
  pricing	
  schemes	
  
Based	
  on	
  cost	
  per	
  click,	
  cost	
  per	
  impression	
  ...
Ad	
  Op3ons	
  Pricing	
  is	
  the	
  Core	
  
•  Due	
  to	
  the	
  differences	
  menNoned,	
  financial	
  models	
  a...
GSP-Based Keyword Auction
Search	
  engine	
  
Online	
  adver3sers	
  
1st	
  ad	
  slot	
  
£	
  0.2	
  
£	
  0.32	
  
£...
•  VolaNlity	
  in	
  revenue.	
  
•  Uncertainty	
  in	
  the	
  bidding	
  and	
  charged	
  prices	
  for	
  
adverNser...
Multi-Keyword Multi-Click
Advertisement Option Contract
for Sponsored Search
[B.Chen	
  et	
  al.	
  2013]	
  
An	
  opNon	
  is	
  a	
  contract	
  in	
  which	
  the	
  opNon	
  seller	
  grants	
  the	
  opNon	
  buyer	
  the	
  
...
online	
  adver3ser	
   search	
  engine	
  
sell	
  a	
  list	
  of	
  ad	
  keywords	
  via	
  a	
  mulN-­‐keyword	
  mu...
3232
online	
  adver3ser	
   search	
  engine	
  
t	
  =	
  T	
  
Timeline	
  
exercise	
  100	
  clicks	
  of	
  ‘MSc	
  ...
3333
online	
  adver3ser	
   search	
  engine	
  
t	
  =	
  T	
  
Timeline	
  
if	
  the	
  adverNser	
  thinks	
  the	
  ...
Benefits of Ad Option
Adver3ser	
   Search	
  Engine	
  
§  secure	
  ad	
  service	
  delivery	
  
§  reduce	
  uncerta...
3535
§  No-­‐arbitrage	
  [F.Black	
  and	
  M.Scholes1973;	
  H.Varian1994]	
  	
  
§  StochasNc	
  underlying	
  keywo...
Ad Option Pricing: Formula
§  n=1,	
  Black-­‐Scholes-­‐Merton	
  European	
  call	
  
§  n=2,	
  Peter	
  Zhang	
  dual...
Ad	
  op3on	
  pricing	
  for	
  the	
  keywords	
  `canon	
  cameras',	
  `nikon	
  camera‘	
  and	
  `yahoo	
  web	
  ho...
Fig.	
  empirical	
  example	
  for	
  the	
  keyword	
  ‘lawyer’,	
  where	
  the	
  Shapiro-­‐Wilk	
  test	
  is	
  with...
3939
About 15.73% of keywords that can be effectively
priced into an option contract under the GBM.
The test of Arbitrage
4141
4242
§  n=1,	
  analyNcal	
  proof	
  +	
  numerical	
  valuaNon	
  
§  n>=2,	
  numerical	
  valuaNon	
  
Bowei	
  Chen...
4343
An	
  empirical	
  example	
  of	
  search	
  engine's	
  revenue	
  for	
  the	
  keyword	
  `equity	
  loans'.	
  
...
Concluding	
  remarks:	
  “Recipe	
  for	
  Disaster”-­‐	
  Model	
  That	
  
“Killed”	
  Wall	
  Street?	
  
	
  •  Wall	...
For	
  more	
  informa3on,	
  please	
  refer	
  to	
  
Shuai	
  Yuan,	
  Jun	
  Wang,	
  Real-­‐Nme	
  Bidding	
  for	
  ...
Thanks	
  for	
  your	
  aRen3on	
  
 Financial methods in online advertising
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Financial methods in online advertising

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Computational Advertising has recently emerged as a new scientific sub-discipline, bridging the gap among the areas such as information retrieval, data mining, machine learning, economics, and game theory. In this tutorial, I shall present a number of challenging issues by analogy with financial markets. The key vision is that display opportunities are regarded as raw material “commodities” similar to petroleum and natural gas – for a particular ad campaign, the effectiveness (quality) of a display opportunity shouldn’t rely on where it is brought and whom it belongs, but it should depend on how good it will benefit the campaign (e.g., the underlying web users’ satisfactions or respond rates). With this vision in mind, I will go through the recently emerged real-time advertising, aka Real-Time Bidding (RTB), and provide the first empirical study of RTB on an operational ad exchange. We show that RTB, though suffering its own issue, has the potential of facilitating a unified and interconnected ad marketplace, making it one step closer to the properties in financial markets. At the latter part of this talk, I will talk about Programmatic Premium, i.e., a counterpart to RTB to make display opportunities in future time accessible. For that, I will present a new type of ad contracts, ad options, which have the right, but no obligation to purchase ads. With the option contracts, advertisers have increased certainty about their campaign costs, while publishers could raise the advertisers’ loyalty. I show that our proposed pricing model for the ad option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keywords) and is also multi-exercisable (multi-clicks). Experimental results on real advertising data verify our pricing model and demonstrate that advertising options can benefit both advertisers and search engines.

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Financial methods in online advertising

  1. 1. Financial  Methods  in  Online   Adver3sing   Dr.  Jun  Wang   UCL   ACM  SIGIR  IATP13  
  2. 2. Acknowledgements   Bowei  Chen,  Mathema3cal  Finance   Shuai  Yuan,  Computer  Science  
  3. 3. When  online  adver3sing  goes  wrong   h9p://mashable.com/2008/06/19/contextual-­‐adverNsing/   Web  users  were  unlikely  to  click  a  shoes  ad  that  appeared  along  side  an  arNcle  about   the  rather  gruesome  story  about  severed  feet  washing  up  on  shore    
  4. 4. What  is  the  (fundamental)  problem?   •  AutomaNcally  place  the  right  ad  for  the  right   web  page  at  the  right  -me  for  the  right  user   •  The  match  is  scienNfically  difficult     – because  ulNmately  ads  shouldn’t  be  matched  to   the  web  pages  where  they  are  to  be  allocated,     – but  the  underlying  web  users,  whose  informaNon   needs  and  reacNons  are,  however,  not  known  a   priori  
  5. 5. Why  financial  methods?   •  Our  observaNons  3+  years  ago:   – although  mulNple  parNcipants  from  ad  providers,   adver-sers,  and  web  users,  the  industry  and   research  seem  unbalanced   •  It  is  unclear  who  should  do  that.  “search  engines”  as   publishers,  ad  networks,  doing  the  keyword  matching   etc   •  li9le  research  has  been  found  from  the  bidders  (the   adverNsers)’s  perspecNve  to  help  them  manage  their   campaigns    
  6. 6. An  analogy  with  Financial  markets   •  Ads  (display  opportuniNes)  are  “traded”   based  on  the  dual  force  of  supply  (publishers)   and  demand  (adverNsers)   Display   OpportuniNes   =?   “raw  materials”  like   petroleum  and   natural  gas    
  7. 7. Ads  prices  are  vola3le     (a) (b)The  price  movement  of  a  display  opportunity  from  Yahoo!  ads  data   Under  GSP  (generalized  second  price  aucNon)     (a) (c)
  8. 8. Research  opportuni3es   •  Need  Ad’s  Futures  Contract  and  Risk-­‐reduc4on  Capabili4es   –  AutomaNon  is  constrained  mainly  to  “spots”  markets,  i.e.,  any   transacNon  where  delivery  takes  place  right  away   –  No  principled  technologies  to  support  efficient  forward  pricing   &risk  management  mechanisms   •  If    we  got  Futures  Market,    adverNsers  could  lock  in  the  campaign  cost  and   Publishers  could  lock  in  a  profit   •  Need  to  engineer  a  Unified  Ad  Exchange     –  The  ad  market  is  in  the  hands  of  a  few  key  players.  Each  individual   player  defines  its  own  ad  system   –  Arbitrage  opportuniNes  exist.     Two  display  opportuniNes  with  similar  targeted  audiences  and  visit   frequency  may  sell  for  quite  different  prices  on  two  different   markets.      
  9. 9. Current  trends  in  online  adver3sing   •  Spot  markets:  Real-­‐Time  Bidding  (RTB)   allows  selling  and  buying  online  display  adverNsing   in  real-­‐Nme  one  ad  impression  at  a  Nme   •   Future  markets:  ProgrammaNc  Premium   to  use  automated  procedures  to  reach  agreement   of  sales  between  buyers  (adverNsers)  and  sellers   (publishers)   •  With  the  two  trends,  a  step  closer  to  the  financial   markets  where  unificaNon  and  interconnecNon   are  strongly  promoted  
  10. 10. Summary     •  Spot  market:  Real-­‐Nme  Bidding   Shuai  Yuan,  Jun  Wang,  Real-­‐Nme  Bidding  for  Online   AdverNsing:  Measurement  and   Analysis,  AdKDD’13   h9p://arxiv-­‐web3.library.cornell.edu/abs/1306.6542     •  Ad  opNons   Bowei  Chen,  Jun  Wang,    Ingemar  Cox,  and  Mohan   Kankanhalli,  MulN-­‐Keyword  MulN-­‐Click  OpNon  Contracts   for  Sponsored  Search  AdverNsing,  under  submission,   2013     h9p://arxiv.org/abs/1307.4980    
  11. 11. Summary     •  Spot  market:  Real-­‐Nme  Bidding   Shuai  Yuan,  Jun  Wang,  Real-­‐Nme  Bidding  for  Online   AdverNsing:  Measurement  and   Analysis,  AdKDD’13   h9p://arxiv-­‐web3.library.cornell.edu/abs/1306.6542     •  Ad  opNons   Bowei  Chen,  Jun  Wang,    Ingemar  Cox,  and  Mohan   Kankanhalli,  MulN-­‐Keyword  MulN-­‐Click  OpNon  Contracts   for  Sponsored  Search  AdverNsing,  under  submission,   2013      
  12. 12. Real-­‐3me  bidding  
  13. 13. Real-­‐3me  bidding   “This  is  Lawrence  from  India.  I  was  searching  Recommender   model  in  web  and  found  your  webpage  in  search  engine.   Then,  I  visited  your  webpage  searching  relevant  contents   and  saw  unrelevant  Google  add  in  "Research  Team"  page   (aRached  screenshot).  This  add  might  vary  from  country  to   country.  But  I  feel  it  will  mislead  and  give  wrong  opinion  to   users  who  visit  your  webpage.”                                                                                                                -­‐  Lawrence  from  India  
  14. 14. The  winning  bids  and  hourly  average   Figure 7: The time series snippet of winning bids and its hourly average of a single placement. The hourly average series peaks around 6-8am every day when there are less impressions but more Figure 8: The d against hour-of
  15. 15. Daily  Pacing   ons but more Wilk test [26] the number tisers spend iser submits from spend- ) and as fast may lead to depletes too r in the day, tance of pre- The uniform f high qual- , the pacing m; if there is the pacing get (usually d daily pac- Ps. er of bidders ised respec- series reach e number of against hour-of-day. Their correlation plot shows a clear lag of when they reach the maximum in a day. This lag indicates the unbalance of supply and demand of the market in certain hours. Besides, the fact that there are more bidders in the morning may be due to the mixture of hour-of-day targeting and no daily pacing setup. The plots used 3 months worth of data sampled from a single placement. Note for some placements the lag was not very clear. Figure 9: An interesting instance we found in the dataset: an advertiser switched from no pacing to even daily pacing. He was bidding at a flat CPM. With ad exchanges, the large amount of choose  from  spending  it  evenly  throughout  a  day  (uniform  pacing)  and  as  fast  as   possible  (no  pacing).  
  16. 16. The  frequency  factor     •  How  many  Nmes  ads  (a.k.a.  creaNves)  would   be  displayed  to  a  single  user.     llo- ent n a nta- red g nd- ery ors re-
  17. 17. The  Recency  Factor     •  helps  to  decide  to  bid  or  not  based  on  how   recently  the  ad  was  displayed  to  the  same   user.    
  18. 18. Summary     •  Spot  market:  Real-­‐Nme  Bidding   Shuai  Yuan,  Jun  Wang,  Real-­‐Nme  Bidding  for  Online   AdverNsing:  Measurement  and   Analysis,  AdKDD’13     •  Ad  opNons   Bowei  Chen,  Jun  Wang,    Ingemar  Cox,  and  Mohan   Kankanhalli,  MulN-­‐Keyword  MulN-­‐Click  OpNon   Contracts  for  Sponsored  Search  AdverNsing,  under   submission,  2013     h9p://arxiv.org/abs/1307.4980    
  19. 19. Why  Ad  Futures  (1)   •  Suppose  there  is  a  travel  insurance  company   whose  major  customers  are  found  through   online  adverNsing  
  20. 20. Why  Ad  Futures  (2)   •  In  March  the  company  plans  an  adverNsement   campaign  in  three  months  Nme  as  they  think   there  will  be  more  opportuniNes  to  sell  their   travel  insurance  products  in  the  summer  
  21. 21. Why  Ad  Futures  (3)   •  If  the  company  worries  that  the  future  price  of   the  impressions  will  go  up,  they  could  hedge   the  risk  (lock  in  the  campaign  cost)  by   agreeing  to  buy  (taking  a  long  posiNon)  the   display  impressions  in  3  months  Nme  for  an   agreed  price  (taking  a  long  posiNon  in  a  3-­‐ month  forwarding  market).    
  22. 22. Why  Ad  Futures  (4)   •  Equally,  search  engines  and  large  publishers   could  agree  to  sell  (taking  a  short  posiNon)   the  display  impressions  in  the  future  if  worry   the  price  will  go  down  (lock  in  a  profit).     •  IntuiNvely,  also  useful  for  inventory   management  
  23. 23. However,  online  adver3sing  is  different     •  A.  Non-­‐storability:  unlike  stocks  or  other   common  communiNes  (petroleum  and  natural  gas  ),   we  cannot  buy  and  keep  an  impression  (thus  ad   display  slot)  for  a  period  of  Nme  and  sell  it  later   in  order  to  gain  the  profit  by  its  price  moment   –  This  is  in  fact  more  similar  to  electricity/energy   markets   •  B.  Not  just  about  the  price  movements:  the  risk   also  lies  in  the  uncertainty  of  the  number  of   impressions  (number  of  visits)  and  click-­‐through   rate  in  the  future        
  24. 24. C.  There  are  many  pricing  schemes   Based  on  cost  per  click,  cost  per  impression   etc.   However,  online  adver3sing  is  different    
  25. 25. Ad  Op3ons  Pricing  is  the  Core   •  Due  to  the  differences  menNoned,  financial  models  and   theories  cannot  be  directly  employed     –   need  to  rethink  what  is  the  “fair”  price  for  ads   •  We  have  developed  a  novel  ad  opNon  pricing  model  to   understand  a  “fair”  price  in  various  senngs,  e.g.,     sponsored  search,  contextual  adverNsing,  and  banner   ads      
  26. 26. GSP-Based Keyword Auction Search  engine   Online  adver3sers   1st  ad  slot   £  0.2   £  0.32   £  0.15   £  0.22   2nd  ad  slot   …   £  0.2     £  0.22     Choose  the  adverNser  with  the  highest  bid,  but  normally   charge  him  based  on  the  second  highest  bid  price,  called   Generalised  Second  Price  (GSP)  auc3on  model.  
  27. 27. •  VolaNlity  in  revenue.   •  Uncertainty  in  the  bidding  and  charged  prices  for   adverNsers'  keywords.   •  Weak  brand  loyalty  between  the  adverNser  and  the  search   engine.   Problems of Auction Mechanism
  28. 28. Multi-Keyword Multi-Click Advertisement Option Contract for Sponsored Search [B.Chen  et  al.  2013]  
  29. 29. An  opNon  is  a  contract  in  which  the  opNon  seller  grants  the  opNon  buyer  the   right  but  not  the  obligaNon  to  enter  into  a  transacNon  with  the  seller  to   either  buy  or  sell  an  underlying  asset  at  a  specified  price  on  or  before  a   specified  date.       The  specified  price  is  called  strike  price  and  the  specified  date  is  called   expiraNon  date.  The  opNon  seller  grants  this  right  in  exchange  for  a  certain   amount  of  money  at  the  current  Nme  is  called  opNon  price.   Option Contract
  30. 30. online  adver3ser   search  engine   sell  a  list  of  ad  keywords  via  a  mulN-­‐keyword  mulN-­‐click   opNon   mul3-­‐keyword  mul3-­‐click  op3on  (3  month  term)   upfront  fee     (m  =  100)   keywords  list   fixed  CPCs   £5   ‘MSc  Web  Science’   £1.80   ‘MSc  Big  Data  AnalyNcs’   £6.25   ‘Data  Mining’   £8.67   t  =  T   Timeline   submit  a  request  of  guaranteed   ad  delivery  for  the  keywords  ‘MSc   Web  Science’,  ‘MSc  Big  Data   AnalyNcs’  and  ‘Data  Mining’  for   the  future  3  month  term  [0,  T],   where  T  =  0.25.   t  =  0   pay  £5  upfront  opNon  price  to   obtain  the  opNon.   Selling & Buying An Option
  31. 31. 3232 online  adver3ser   search  engine   t  =  T   Timeline   exercise  100  clicks  of  ‘MSc  Web   Science’  via  opNon.   t  =  0   pay  £1.80  to  the  search  engine  for   each  click  unNl  the    requested  100   clicks  are  fully  clicked  by  Internet   users.   t  =  t1   reserve  an  ad  slot  of  the  keyword  ‘MSc  Web  Science’  for   the  adverNser  for  100  clicks  unNl  all  the  100  clicks  are   fully  clicked  by  Internet  users..   t  =  t1c                                       Exercising the Option
  32. 32. 3333 online  adver3ser   search  engine   t  =  T   Timeline   if  the  adverNser  thinks  the  fixed   CPC  £8.67  of  the  keyword  ‘Data   Mining’  is  expensive,  he/she  can   a9end  keyword  aucNons  to  bid  for   the  keyword  as  other  bidders,  say   £8.   t  =  0   pay  the  GSP-­‐based  CPC  for  each   click  if  winning  the  bid.   t  =  …   select  the  winning  bidder  for  the  keyword  ‘Data  Mining’   according  to  the  GSP-­‐based  aucNon  model.   Not Exercising the Option
  33. 33. Benefits of Ad Option Adver3ser   Search  Engine   §  secure  ad  service  delivery   §  reduce  uncertainty  in  aucNons   §  caps  ad  cost.     §  selling  the  inventory  in  advance;   §  having  a  more  stable  and  predictable             revenue  over  a  long-­‐term  period;     §  Increasing  adverNsers’  loyalty  
  34. 34. 3535 §  No-­‐arbitrage  [F.Black  and  M.Scholes1973;  H.Varian1994]     §  StochasNc  underlying  keyword  CPC  [P.  Samuelson1965]     §  Terminal  value  formulaNon   Bowei  Chen,  Jun  Wang,    Ingemar  Cox,  and  Mohan  Kankanhalli,   MulN-­‐Keyword  MulN-­‐Click  OpNon  Contracts  for  Sponsored  Search   AdverNsing,  under  submission,  2013     h9p://arxiv.org/abs/1307.4980   Ad Option Pricing: Building Blocks
  35. 35. Ad Option Pricing: Formula §  n=1,  Black-­‐Scholes-­‐Merton  European  call   §  n=2,  Peter  Zhang  dual  strike  European  call   §  n>=3,  Monte  Carlo  method   Bowei  Chen,  Jun  Wang,    Ingemar  Cox,  and  Mohan  Kankanhalli,   MulN-­‐Keyword  MulN-­‐Click  OpNon  Contracts  for  Sponsored  Search   AdverNsing,  under  submission,  2013     h9p://arxiv.org/abs/1307.4980  
  36. 36. Ad  op3on  pricing  for  the  keywords  `canon  cameras',  `nikon  camera‘  and  `yahoo  web  hos3ng‘.   Experimental Results: Monte Carlo Simulation
  37. 37. Fig.  empirical  example  for  the  keyword  ‘lawyer’,  where  the  Shapiro-­‐Wilk  test  is  with  p-­‐value   0.1603  and  the  Ljung-­‐Box  test  is  with  p-­‐value  0.6370.  
  38. 38. 3939 About 15.73% of keywords that can be effectively priced into an option contract under the GBM.
  39. 39. The test of Arbitrage
  40. 40. 4141
  41. 41. 4242 §  n=1,  analyNcal  proof  +  numerical  valuaNon   §  n>=2,  numerical  valuaNon   Bowei  Chen,  Jun  Wang,    Ingemar  Cox,  and  Mohan  Kankanhalli,   MulN-­‐Keyword  MulN-­‐Click  OpNon  Contracts  for  Sponsored  Search   AdverNsing,  under  submission,  2013     h9p://arxiv.org/abs/1307.4980   Effects of Ad Options on Search Engine’s Revenue
  42. 42. 4343 An  empirical  example  of  search  engine's  revenue  for  the  keyword  `equity  loans'.   4 4.5 5 0 0.1 0.2 0.3 0.4 0.5 0.6 Fixed CPC Revenueofsearchengine (a) GBM Reve difference Option price Exp spot CPC 4 4.5 5 0 0.1 0.2 0.3 0.4 0.5 0.6 Fixed CPC Revenueofsearchengine (b) CEV Reve difference Option price Exp spot CPC 4 4.5 5 0 0.1 0.2 0.3 0.4 0.5 0.6 Fixed CPC Revenueofsearchengine (c) MRD Reve difference Option price Exp spot CPC 4.5 5 Fixed CPC (c) MRD Reve difference Option price Exp spot CPC 4 4.5 5 0 0.1 0.2 0.3 0.4 0.5 0.6 Fixed CPC Revenueofsearchengine (d) CIR Reve difference Option price Exp spot CPC 4 4.5 5 0 0.1 0.2 0.3 0.4 0.5 0.6 Fixed CPC Revenueofsearchengine (e) HWV Reve difference Option price Exp spot CPC Fig. 8. Empirical example of search engine’s revenue for the keyword ‘equity loans’.
  43. 43. Concluding  remarks:  “Recipe  for  Disaster”-­‐  Model  That   “Killed”  Wall  Street?    •  Wall  Street’s  math  models  for  minNng   money  worked  brilliantly...  unNl  one  of   them  devastated  the  global  economy   •  David  X.  Li's  Gaussian  copula  funcNon  as   first  published  in  2000.  Investors   exploited  it  as  a  quick  way  to  assess  risk   (the  chance  a  company  is  likely  to  default)   •  As  Li  himself  said  of  his  own  model:  "The   most  dangerous  part  is  when  people   believe  everything  coming  out  of  it.”   44  h9p://www.wired.com/techbiz/it/magazine/17-­‐03/wp_quant?currentPage=all  
  44. 44. For  more  informa3on,  please  refer  to   Shuai  Yuan,  Jun  Wang,  Real-­‐Nme  Bidding  for  Online   AdverNsing:  Measurement  and   Analysis,  AdKDD’13   h9p://arxiv-­‐web3.library.cornell.edu/abs/1306.6542     Bowei  Chen,  Jun  Wang,    Ingemar  Cox,  and  Mohan   Kankanhalli,  MulN-­‐Keyword  MulN-­‐Click  OpNon  Contracts   for  Sponsored  Search  AdverNsing,  under  submission,   2013     h9p://arxiv.org/abs/1307.4980  
  45. 45. Thanks  for  your  aRen3on  

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