1.
EMNLP
2014ㄞ䜏఍
Using
structure
events
to
predict
stock
price
movements:
an
empirical
inves>ga>on
ᆤᆏṇᚿ
m.tsubosaka@gmail.com

2.
⤂௓ㄽᩥ
• Using
structured
events
to
predict
stock
price
movement:
an
empirical
invesgaon
– 䝙䝳䞊䝇グ஦䛛䜙ᑗ᮶䛾௻ᴗ䛾ᰴ౯䛾ື䛝䛜䛹
䛖䛺䜛䛛䜢ண 䛧䛯䛔
– ᚑ᮶◊✲䛿グ஦䛾BOW≉ᚩ㔞䜢୺䛻฼⏝
– ᮏ◊✲䛿᪤Ꮡ◊✲䛻ᑐ䛧䛶஧䛴䛾ᕤኵ䜢䛩䜛䛣
䛸䛻䜘䛳䛶㧗䛔⢭ᗘ䜢㐩ᡂ
• グ஦䛛䜙ᵓ㐀໬䛥䜜䛯䜲䝧䞁䝖䜢ᢳฟ䛧䛶䛭䜜䜢≉ᚩ
㔞䛻฼⏝
• ᰴ౯䛾ື䛝䛾Ꮫ⩦䛻Deep
Neural
Network䜢฼⏝

3.
⫼ᬒ
• ᰴ౯䛾ື䛝䜢ண 䛧䛯䛔
– ᕷሙ䛾ື䛝䛜ண ྍ⬟䛛䛹䛖䛛䛻䛴䛔䛶䛿ከ䛟䛾㆟ㄽ䛜䛒䜛
– ౛䛘䜀䝣䜯䜲䝘䞁䝇䛷౑䜟䜜䜛Random
walk⌮ㄽ䛿౯᱁䛿䝷䞁
䝎䝮䛻Ỵᐃ䛥䜜䜛䛸䛔䛖௬ᐃ䛻ᇶ䛵䛔䛶䛔䜛
• ㏆ᖺ䛷䛿web䛾䝙䝳䞊䝇グ஦䜢⮬↛ゝㄒฎ⌮ᢏ⾡䛷ฎ⌮
䛧䛶౯᱁䜢ண 䛩䜛䛸䛔䛖◊✲䛜䛔䛟䛴䛛䛺䛥䜜䛶䛔䜛
– Das
and
Chen
2008,
Tetlock+
2007
2008,Si+
2013,
Xie+
2013

4.
᪤Ꮡ◊✲䛷䛾ㄢ㢟
• 䝙䝳䞊䝇グ஦䛛䜙౯᱁䜢ண 䛧䜘䛖䛸䛩䜛䛸䛝
᪤Ꮡ◊✲䛿BOW䜔ᅛ᭷⾲⌧䛺䛹༢⣧䛺≉ᚩ
㔞䛧䛛⏝䛔䛶䛣䛺䛛䛳䛯
– “Apple
has
sued
Samsung
for
copying…”䛾䜘䛖䛺
ᩥ❶䛛䜙{“Apple”
,
“sued”
,
“Samsung”
,
“copying”}䛾䜘䛖䛺≉ᚩ㔞䜢సᡂ
– 䛣䛾≉ᚩ㔞䛛䜙Apple䜔Samsung䛾ᰴ౯䛜䛹䛖䛺
䜛䛛䜢ண 䛩䜛䛾䛿ᅔ㞴

5.
Structured
Event
repsentaon
• ᩥ❶䛛䜙O1(Actor)䛜O2(Object)䛻P(Acon)䛧
䛯䛸䛔䛖᝟ሗ䜢ྲྀ䜚ฟ䛩
– Microso]
agrees
to
buy
Nokia’s
mobile
phone
business䛾䜘䛖䛺ᩥ❶䛛䜙
– E
=
(Actor=Microso],
Object
=
Nokia’s
mobile
phone
business,
Acon=buy)
– E=
(O1,P,O2,T)䛸䛔䛖䝍䝥䝹䛷⾲⌧䛩䜛
• 䛣䛣䛷T䛿䜲䝧䞁䝖䛾᫬้䛷䛒䜛䛜䚸䛣䜜䛿䝙䝳䞊䝇グ
஦䛷䛒䜜䜀䝕䞊䝍䛛䜙ྲྀ䜚ฟ䛩䛣䛸䛜䛷䛝䜛

6.
Event
extracon
• Fader+
2011䛾ᡭἲ䜢฼⏝
– Idenfying
relaons
for
open
informaon
extracon,
EMNLP
2011
• ௨ୗ䛾஧䛴䛾ไ⣙䜢䜏䛯䛩䜘䛖䛺P䜢᥈䛩
– Syntacc
constraint:
ືモ䛷ጞ䜎䜚๓⨨モ䛷⤊஢䛩䜛
– Lexical
constraint:
P䛿」ᩘ䛾䝁䞊䝟䝇䛻ฟ⌧䛩䜛
• P䜢᥈䛧䛶䛝䛯䜙୍␒㏆䛔ᕥ䛻䛒䜛ྡモྃO1䜢
᥈䛧䛶䛟䜛
• ྠᵝ䛻୍␒㏆䛔ྑ䛻䛒䜛ྡモྃO2䜢᥈䛧䛶䛟䜛

7.
Event
generalizaon
• “Microso]
swallows
Nokia’s
phone
business”
• “Microso]
purchases
Nokia’s
phone
business”
• 䛾஧䛴䛿ྠ䛨䜲䝧䞁䝖
• WordNet,
VerbNet䛾䜘䛖䛺䜸䞁䝖䝻䝆䞊᝟ሗ
䜢౑䛳䛶୍⯡໬䛩䜛
• ౛䛘䜀”buy”䛸䛔䛖༢ㄒ䜢”get_class”䛻ኚ᥮
䛩䜛

8.
ண 䝰䝕䝹
• ᪤Ꮡ◊✲㻌⩣᪥/⩣㐌/⩣᭶䛾ᰴ౯䛜ୖ䛜䜛䛛ୗ
䛜䜛䛛䛷SVM䜢౑䛳䛶஧್ศ㢮
• ᮏ◊✲
Deep
neural
network䝰䝕䝹䛻䜘䜚ண

9.
Feature
representaon
• (O1,P,O2,T)䛸䛔䛖䝍䝥䝹䛜䛒䛳䛯ሙྜ䚸
(O1,P,O2,O1+P,P+O2,O1+P+O2)䛸ኚ᥮䛧䛯≉
ᚩ㔞䜢฼⏝䛩䜛

10.
ᐇ㦂䝕䞊䝍
• 2006ᖺ䛛䜙2013ᖺ䜎䛷䛾Reuters,Bloomberg䛾䝕䞊䝍
䜢฼⏝
• ๓᪥䛾䝙䝳䞊䝇グ஦䛾ෆᐜ㞟ྜ䛛䜙⩣᪥/⩣㐌/⩣᭶
䛾SP
stock䛜ୖ䛜䜛䛛ୗ䛜䜛䛛䜢ண
– ᫬㛫䛻䛴䛔䛶䛿䜒䛳䛸▷ᮇ㛫䛾ண 䜢⪃䛘䜙䜜䜛䛜䛭䛾
⢏ᗘ䛷䛾ᰴ౯䛚䜘䜃䝙䝳䞊䝇䛾䝕䞊䝍䛜䛺䛔䛯䜑௒ᅇ䛿
ᐇ㦂䛧䛺䛛䛳䛯

11.
Overall
Results
• ≉ᚩ㔞䜢BOW䛛䜙䜲䝧䞁䝖䜈
• Ꮫ⩦ჾ䜢SVM䛛䜙DeepNN䛻ኚ᭦䛩䜛䛣䛸䛻
䜘䛳䛶ண ⢭ᗘ䛜ྥୖ䛧䛶䛔䜛

12.
㞃䜜ᒙ䛾ᩘ䛾ᙳ㡪
• 㞃䜜ᒙ䛾ᩘ䜢1䛛䜙2䛻䛧䛯䛸䛣䜝⢭ᗘ䛿ྥୖ
• ୍᪉䛷2䛛䜙5䜈䛸ቑ䜔䛧䛶䛳䛯䛸䛣䜝⢭ᗘ䛾
ྥୖ䛿䜋䜌ぢ䜙䜜䛺䛛䛳䛯

13.
฼⏝䛩䜛䝕䞊䝍㔞䛻䛴䛔䛶
• Reuter䛾䝍䜲䝖䝹䝕䞊䝍䛾䜏฼⏝䛧䛯᪉䛜䝁䞁䝔
䞁䝒䜒ྜ䜟䛫䛶฼⏝䛩䜛䜘䜚⢭ᗘ䛜㧗䛟䛺䛳䛯
• 䜎䛯bloomberg䛾䝍䜲䝖䝹䜒㏣ຍ䛧䛯ሙྜ䛿⢭ᗘ
䛜䜟䛪䛛䛺䛜䜙ୖ᪼䛧䛯
• 䝁䞁䝔䞁䝒䛾᝟ሗ䜢㏣ຍ䛧䛶䝕䞊䝍䜢ቑ䜔䛩䜘䜚
㉁䛜㧗䛔䝍䜲䝖䝹䛾䝕䞊䝍䛰䛡䜢౑䛳䛯᪉䛜䜘䛔
䛸䛔䛖⤖ᯝ䛻䛺䛳䛶䛔䜛

14.
ಶู䛾ᰴ౯䛾ண 䛻䛴䛔䛶
• ಶู䛾ᰴ౯䛾ண 䛻䛚䛔䛶䛿䛭䛾௻ᴗ䛻䛴䛔
䛶䛾グ஦䛾䜏฼⏝䛧䛯᪉䛜㧗䛔⢭ᗘ䛸䛺䛳䛶䛔
䜛
• 䜎䛯኱䛝䛔௻ᴗ䛾᪉䛜グ஦ᩘ䜒ከ䛔䛣䛸䛛䜙㧗
䛔⢭ᗘ䛷ண 䛜ฟ᮶䛶䛔䜛

15.
ᐇ㝿䛾NN䛾౛
• 䛒䜛䝙䝳䞊䝻䞁䛿
Google䛾㈙཰䛻㛵䛩
䜛䜲䝧䞁䝖䛸⤖䜃䛴䛡
䜙䜜䛶䛚䜚䚸ᰴ౯䛾䝥
䝷䝇䛻స⏝䛧䛶䛔䜛
• ୍᪉䛷䛒䜛䝙䝳䞊䝻䞁
䛿Google䛾ッゴ䛺䛹
䛾ᝏ䛔ホ౯䛸⤖䜃䛴
䛡䜙䜜䛶䛚䜚ᰴ౯䛻䝬
䜲䝘䝇䛻స⏝䛧䛶䛔䜛

16.
᭱⤊ⓗ䛺⤖ᯝ
• 䝔䝇䝖䝉䝑䝖䛻ᑐ䛧䛶䛣䜜䜎䛷䛷ᚓ䜙䜜䛯Ꮫ⩦
ჾ䜢㐺ᛂ䛧䛯䜙௨ୗ䛾䜘䛖䛺⤖ᯝ䛸䛺䛳䛯

17.
䜎䛸䜑
• Event-­‐based䛾ᩥ❶⾲⌧䜢౑䛖䛣䛸䛻䜘䛳䛶䚸
BOW䝧䞊䝇䛾ᩥ❶⾲⌧䜘䜚ᰴ౯䛾ண ⢭ᗘ䛜
㧗䛟䛺䛳䛯
• Deep
Neural
network䛾฼⏝䛻䜘䜚⥺ᙧ䝰䝕䝹䜢
౑䛖䜘䜚䜒ண ⢭ᗘ䛜㧗䛟䛺䛳䛯
• ᪤Ꮡ◊✲䛰䛸Senment
Analysis䜢฼⏝䛩䜛≀䛜
ከ䛔䛜䚸䛭䜜䛸䛾⤌䜏ྜ䜟䛫䜛䛣䛸䜒ྍ⬟䛷䛒
䜛
– Ex:
Bollen+,
Twiler
mood
predicts
the
stock
market,
2011