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EMNLP 
2014ㄞ䜏఍ 
Using 
structure 
events 
to 
predict 
stock 
price 
movements: 
an 
empirical 
inves>ga>on 
ᆤᆏṇᚿ 
m.tsubosaka@gmail.com
⤂௓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฼⏝
⫼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
᪤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
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
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
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
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 
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
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
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
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
฼⏝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
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
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
᭱⤊ⓗ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
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

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EMNLP2014_reading

  • 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