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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	
  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	
  	
  
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	
  
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	
  	
  
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	
  	
  
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)
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.	
  	
  	
  
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	
  
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	
  
	
  
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	
  	
  
	
  
 Financial methods in online advertising
Real-­‐3me	
  bidding	
  
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	
  
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
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).	
  
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-
The	
  Recency	
  Factor	
  	
  
•  helps	
  to	
  decide	
  to	
  bid	
  or	
  not	
  based	
  on	
  how	
  
recently	
  the	
  ad	
  was	
  displayed	
  to	
  the	
  same	
  
user.	
  	
  
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	
  
	
  
Why	
  Ad	
  Futures	
  (1)	
  
•  Suppose	
  there	
  is	
  a	
  travel	
  insurance	
  company	
  
whose	
  major	
  customers	
  are	
  found	
  through	
  
online	
  adverNsing	
  
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	
  
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).	
  	
  
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	
  
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	
  	
  	
  	
  
C.	
  There	
  are	
  many	
  pricing	
  schemes	
  
Based	
  on	
  cost	
  per	
  click,	
  cost	
  per	
  impression	
  
etc.	
  
However,	
  online	
  adver3sing	
  is	
  different	
  	
  
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	
  	
  
	
  
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.	
  
•  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
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	
  
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
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
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
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
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	
  
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
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	
  
Ad	
  op3on	
  pricing	
  for	
  the	
  keywords	
  `canon	
  cameras',	
  `nikon	
  camera‘	
  and	
  `yahoo	
  web	
  hos3ng‘.	
  
Experimental Results:
Monte Carlo Simulation
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.	
  
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,	
  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
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’.
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	
  
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	
  
Thanks	
  for	
  your	
  aRen3on	
  

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Financial methods in online advertising

  • 1. Financial  Methods  in  Online   Adver3sing   Dr.  Jun  Wang   UCL   ACM  SIGIR  IATP13  
  • 2. Acknowledgements   Bowei  Chen,  Mathema3cal  Finance   Shuai  Yuan,  Computer  Science  
  • 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. 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. 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. 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. 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. 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. 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. 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. 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      
  • 14. 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  
  • 15. 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
  • 16. 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).  
  • 17. 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-
  • 18. The  Recency  Factor     •  helps  to  decide  to  bid  or  not  based  on  how   recently  the  ad  was  displayed  to  the  same   user.    
  • 19. 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    
  • 20. Why  Ad  Futures  (1)   •  Suppose  there  is  a  travel  insurance  company   whose  major  customers  are  found  through   online  adverNsing  
  • 21. 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  
  • 22. 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).    
  • 23. 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  
  • 24. 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        
  • 25. C.  There  are  many  pricing  schemes   Based  on  cost  per  click,  cost  per  impression   etc.   However,  online  adver3sing  is  different    
  • 26. 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      
  • 27. 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.  
  • 28. •  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
  • 29. Multi-Keyword Multi-Click Advertisement Option Contract for Sponsored Search [B.Chen  et  al.  2013]  
  • 30. 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
  • 31. 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
  • 32. 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
  • 33. 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
  • 34. 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  
  • 35. 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
  • 36. 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  
  • 37. Ad  op3on  pricing  for  the  keywords  `canon  cameras',  `nikon  camera‘  and  `yahoo  web  hos3ng‘.   Experimental Results: Monte Carlo Simulation
  • 38. 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.  
  • 39. 3939 About 15.73% of keywords that can be effectively priced into an option contract under the GBM.
  • 40. The test of Arbitrage
  • 41. 4141
  • 42. 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
  • 43. 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’.
  • 44. 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  
  • 45. 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  
  • 46. Thanks  for  your  aRen3on