経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)

Χόʔઌۜߦͷू߹஌ʹΑΔ֎ࠃҝସϨʔτ༧ଌPrediction of Foreign Exchange Rates by Collective Intelligenceof Counter Party Banksླ໦ ৎ༟ 1 ∗Takehiro SUZUKI1

ླ໦ ஐ໵ 1,2
Tomoya SUZUKI1,211

ҵ৓େֶେֶӃཧ޻ֶ‫ڀݚ‬ՊGraduate School of Science and Engineering, Ibaraki University2ίϥϘ΢Οζ‫ࣜג‬ձࣾ
2
CollabWiz, Inc.

Abstract: In foreign-exchange (FX) dealing, FX brokers basically cancel out the orders fromtheir customers to prevent the price fluctuation risk by cover transactions with global megabanks
called Counter Party (CP). Each CP has huge amount of money to play a role of market reader,and might have proprietary know-how to foresee future price movements. From this viewpoint,
we try to extract their knowledge by a machine learning approach, and therefore we apply thestacking method that aggregates some predictors to extract the ensemble knowledge. If CP’s pricequotations are decided by foreseeing future price possibilities, their quotations can be considered
as predictors. From this concept, we apply the stacking method to their quotations and obtain theensemble knowledge from them. Through some simulations using real price data, we could confirm that the given ensemble knowledge improves the prediction accuracy of FX price movements
compared to the machine learning using a single CP’s price quotation.

1͸͡Ίʹ֎ࠃҝସূ‫ۚڌ‬औҾ (Foreign Exchange: FX) ʹ͓͍ͯɼళ಄֎ࠃҝସূ‫ۚڌ‬औҾऔѻ‫( ऀۀ‬ҎԼɼFX ‫ۀ‬ऀ) ͸‫ސ‬٬ͱ֤ࠃͷ௨՟ؒͷചങΛߦ͏ࡍʹɼՁ֨มಈʹ൐͏ଛࣦͷϦεΫΛ‫ݮ‬Β͢ΧόʔऔҾΛۜߦͱߦ͏ɽ͜ΕΒͷΧόʔઌۜߦΛΧ΢ϯλʔύʔςΟ(CounterParty: CP) ͱ‫ͼݺ‬ɼFX ‫ऀۀ‬͸ CP ͔Βૹ৴͞ΕΔ֤௨՟ؒͷ Bid(ച஋) ͱ Ask(ങ஋) ͷҝସϨʔτ‫ݐ‬஋ (ҝସ૬৔Ͱۜߦ͕ެද͢ΔऔҾՁ֨) ʹ‫ސ͍ͯͮج‬٬ʹఏࣔ͢ΔࣗࣾͷϨʔτΛੜ੒͍ͯ͠Δɽ͜ͷͱ͖ CP ͔Βૹ৴͞ΕΔҝସϨʔτ‫ݐ‬஋Λ CP Ϩʔτɼ‫ސ‬٬ʹఏࣔ͢ΔࣗࣾͷϨʔτΛϋ΢εϨʔτͱ‫Ϳݺ‬ɽ͜͜Ͱ CP͔ΒϨʔτΛड৴͢Δߏ଄্ɼϋ΢εϨʔτੜ੒ʹ௿஗Ԇ͕ൃੜ͢Δɽ͜ͷΑ͏ͳड৴ʹΑΔ஗Ԇ΍γεςϜো֐ʹΑΔ஗ԆʹΑΓଞͷ FX ‫ͱऀۀ‬େ͖͘ҟͳΔϨʔτΛఏࣔͯ͠͠·ͬͨ৔߹ɼ‫ࡋؒऀۀ‬ఆऔҾऀ (֤‫ؒऀۀ‬ͷϋ΢εϨʔτͷՁ֨ࠩΛར༻ͯ͠ऩӹΛ্͛Δ‫ސ‬٬) ͷλʔήοτʹ͞ΕΔාΕ͕͋Δɽ͜͜Ͱ FX‫ޙؒ࣌୹ۃ͕ऀۀ‬ͷ CP ϨʔτΛ༧ଌ͠ɼ͜ͷ஗ԆΛ∗ ࿈བྷઌɿҵ৓େֶେֶӃཧ޻ֶ‫ڀݚ‬Պ஌ೳγεςϜ޻ֶઐ߈ɹɹɹɹɹɹ˟ 316–8511 ҵ৓‫ݝ‬೔ཱࢢத੒୔ொ 4–12–1
ɹɹɹɹɹɹ E-mail: 17nm926l@vc.ibaraki.ac.jp௿‫͖Ͱݮ‬Ε͹ɼऩӹͷѱԽΛ཈੍Ͱ͖Δɽ͞ΒʹϨʔτߋ৽ͷૣ͞͸ɼࢢ‫گ‬৘ใʹର͢Δਖ਼֬ੑ͔Β‫ސ‬٬Λଟ֫͘ಘͰ͖ɼଞͷ FX ‫ͱऀۀ‬ͷ‫ڝ‬૪ʹ͓͍ͯ༗རʹͳΔϝϦοτ΋͋Δɽ͜ΕΒͷ‫͔఺؍‬ΒɼCP ϨʔτͷকདྷՁ֨Λ༧ଌ͢Δ͜ͱ͸ FX ‫ऀۀ‬ͷऩӹ֫ಘʹ‫͕ܨ‬ΔՄೳੑ͕͋Δɽ͜͜ͰকདྷՁ֨ͷ༧ଌʹؔͯ͠ɼΧόʔઌͰ͋Δ CPʹண‫͢؟‬Δɽ֤ CP ಉ࢜͸ऩӹ֫ಘ‫ڝ‬૪ͷͨΊ૬৔Λ͋Δఔ౓ઌಡΈͯ͠ CP ϨʔτΛੜ੒͓ͯ͠ΓɼCP ͝ͱʹ༷ʑͳϊ΢ϋ΢Λ༗͍ͯ͠ΔՄೳੑ͕͋Δɽͦ͜Ͱຊ‫Ͱڀݚ‬͸ FX ‫ऀۀ‬ͷཱ৔ʹͳΓ [1]ɼ֤ CP ͷ‫ݐ‬஋৘ใ [2] ΛΞϯαϯϒϧֶशͷҰछͰ͋ΔελοΩϯ
ά [3] ͷ֓೦Λ༻͍ͯू߹஌ [4–7] Λநग़͢Δɽ͜ͷू߹஌Λ༻͍Δ͜ͱͰޮ཰తࢢ৔Ծઆ [8] ʹΑͬͯϥϯμϜ΢ΥʔΫͷΑ͏ʹ༧ଌෆՄೳͱ͞ΕΔҝସมಈʹରͯ͠ɼͲͷఔ౓༧ଌྗΛߴΊΒΕΔ͔‫͢ূݕ‬Δɽ

2ελοΩϯάʹΑΔू߹஌ͷ֫ಘ·ͣҰൠతͳελοΩϯάΛਤ 1 ʹࣔ͢ɽ‫ڞ‬௨ͷΦϦδφϧσʔλΛ༻͍ͯෳ਺ͷҟͳΔ༧ଌϞσϧΛֶश͠ɼͦΕΒ͕ग़ྗͨ͠༧ଌ஋ʹରͯ͠৽ͨʹผͷ༧

経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)

ଌϞσϧͰϝλֶश͢Δ͜ͱͰɼ࠷ऴతͳ༧ଌ஋Λಘ
Δ [9]ɽ͜Εʹରͯ͠ຊ‫Ͱڀݚ‬͸ਤ 2 ʹࣔ͢Α͏ʹɼ‫ڞ‬௨ͷΦϦδφϧσʔλΛશੈքͷࢢ‫گ‬৘ใͱ‫ཱͯͯݟ‬ɼϊ΢ϋ΢Λ༗͢Δ֤ CP Λ༧ଌ‫͑ߟͱث‬Δɽ͜ΕΒΛχϡʔϥϧωοτϫʔΫͰϝλֶश͢Δ͜ͱͰɼ֤ CPͷϊ΢ϋ΢Λ౷߹ͨ͠ू߹஌Λ֫ಘ͢Δɽ͜ͷू߹஌͕࠷ऴతͳ༧ଌ஋ͱͯ͠‫ػ‬ೳ͢Δͷ͔֬ೝ͢ΔͨΊɼCPຖͷ‫ݐ‬஋৘ใͷΈΛ‫ػ‬ցֶश͢Δ༧ଌϞσϧ (‫ݸ‬ผ๏)ͱελοΩϯάͷ֓೦ΛԠ༻ͨ͠ఏҊ๏Λൺֱ͢Δɽू߹஌ͷޮՌΛࣔ͢Ұྫͱͯ͠ଟ༷ੑ༧ଌఆཧ [10]Λ঺հ͢Δɽ֤༧ଌϞσϧ͔ΒಘΒΕΔ༧ଌ஋ͷ෼ࢄ(ଟ༷ੑ) ͕େ͖͍΄Ͳू߹஌ͷ༧ଌ‫͕ࠩޡ‬ॖখ͢Δ͜ͱ͕஌ΒΕ͍ͯΔɽશ N ‫ݸ‬ͷ༧ଌϞσϧʹ͓͍ͯɼୈi ൪໨ͷϞσϧʹΑΔ‫ݸ‬ผͷ༧ଌ஋Λ ŷi ɼ͜ΕΒΛ౷!N߹ͨ͠ू߹஌ʹΑΔ༧ଌ஋Λ E[ŷ] = N1i=1 ŷi ɼਅ஋Λ y ͱ͢ΔͱɼҎԼͷ౳͕ࣜ੒ཱ͢Δɽ

(y − E[ŷ])2 =N
N
1 "
1 "
(y − ŷi )2 −(ŷi − E[ŷ])2 (1)N i=1N i=1͜͜Ͱࠨล͸ू߹஌ͷ༧ଌ‫ࠩޡ‬ɼӈลୈ 1 ߲͸֤༧ଌϞσϧͷฏ‫ۉ‬༧ଌ‫ࠩޡ‬ɼӈลୈ 2 ߲͸༧ଌϞσϧͷ෼ࢄ஋ʹ૬౰͢Δɽͭ·Γଟ༷ͳ CP ͷ‫ݐ‬஋৘ใΛ౷߹͢Δ͜ͱͰ‫͍ޓ‬ͷෆਖ਼֬ͳ༧ଌΛଧͪফ͠߹͏͜ͱͰू߹஌Λ֫ಘ͠ɼӈลୈ 2 ߲ͷΑ͏ͳ‫ࠩޡ‬௿‫ޮݮ‬ՌΛ‫ظ‬଴Ͱ͖Δɽิ଍ͱͯ͠ɼઆ໌ม਺ʹ༻͍Δ CP ͓Αͼ௨՟ϖΞΛ૿΍͢΄Ͳ‫ػ‬ցֶशʹ൐͏ೖྗ࣍‫૿͕਺ݩ‬Ճ͢Δͨ
ΊɼDeep Neural Network [11] ͕ޮՌతͰ͋Ζ͏ɽ͔͠͠ຊߘͷઃఆ (࣍ষ) Ͱ͸ൺֱత௿࣍‫͋Ͱݩ‬ΔͨΊɼ௨ৗͷ 3 ૚χϡʔϥϧωοτϫʔΫΛ༻͍Δɽ

3

ਤ 1: ҰൠతͳελοΩϯάͷྫ໰୊ઃఆຊ‫Ͱڀݚ‬͸ɼ2017 ೥ 9 ݄ 4 ೔͔Β 9 ݄ 30 ೔·Ͱͷ
FX ‫ૹʹऀۀ‬৴͞Εͨ‫ ܭ‬12 ࣾͷ CP Ϩʔτ͔ΒɼʮυϧԁʯɼʮϢʔϩԁʯɼʮϢʔϩυϧʯͷ 3 ͭͷ௨՟ϖΞΛ࢖༻͢Δɽͳ͓ྲྀಈੑ͕ߴ͘ɼϚʔέοτΠϯύΫτ
͕૬ରతʹখ͍͞ (=ࢢ৔ࢀՃऀ͕ଟ͍) ౰೔‫ޕ‬લ 8 ͔࣌Βཌ೔‫ޕ‬લ 5 ࣌·ͰΛ 1 ೔෼ͷࢢ৔σʔλͱͯ͠ѻ͏ɽͦͯ͠ɼ͋Δ 1 ೔Λֶश‫ؒظ‬ɼ࣍ͷ 1 ೔Λςετ‫͢ͱؒظ‬Δɽྫ͑͹ɼ
ʮ9 ݄ 4 ೔‫ޕ‬લ 8 ͔࣌Β 9 ݄ 5 ೔‫ޕ‬લ 5 ࣌ͷσʔλͰֶशˠ 9 ݄ 5 ೔‫ޕ‬લ 8 ͔࣌Β 9 ݄6 ೔‫ޕ‬લ 5 ࣌ͷσʔλͰςετʯͷΑ͏ʹߦͬͨɽ֎
ࠃҝସऔҾ͸౔೔͕‫ٳ‬ΈͷͨΊɼ1 िؒʹߦ͏γϛϡϨʔγϣϯ͸ 4 ‫ͳͱؒظ‬Γɼ4 िؒ෼ͷσʔλΛ༻͍ͨͨΊ߹‫ ܭ‬16 ‫ͳͱؒظ‬Δɽ·ͨɼૹ৴͞ΕΔϨʔτ͸
Bid(ച஋) ͱ Ask(ങ஋) ͷ 2 ஋͕ଘࡏ͢ΔͨΊɼ16 ‫ظ‬ؒ × 2 ஋ = ‫ ܭ‬32 ύλʔϯͷγϛϡϨʔγϣϯΛ࣮ࢪ͢Δɽ ༧ଌର৅ͷ໨తม਺͸࣌ࠁ t + 1 (100[ms] ‫ )ޙ‬ͷυ

ਤ 2: CP ϨʔτΛ༻͍ͨελοΩϯά (ఏҊ๏)ϧԁͷϕετϨʔτ (શ CP ʹ͓͚Δ࠷΋ߴ͍ Bid(ച஋) ͓Αͼ࠷΋௿͍ Ask(ങ஋)) ͱ͢Δɽυϧԁ͸௨՟ϖΞͷதͰ΋ྲྀಈੑ͕ߴ͘ɼऔҾճ਺΋ଟ͍ར఺͕͋Δɽ༧ଌʹ༻͍Δઆ໌ม਺͸ɼա‫ڈ‬ͷυϧԁϨʔτ͓Αͼؔ࿈͢ΔϢʔϩԁ͓ΑͼϢʔϩυϧͷ 3 छྨΛ༻͍ɼ࣌ؒཤྺΛֶश͢ΔΑ͏ʹ࣌ࠁΛ t (‫)ࠁ࣌ࡏݱ‬ɼt − 1(100[ms] ա‫)ڈ‬ɼt − 2 (200[ms] ա‫ )ڈ‬ͷΑ͏ʹϥάΛऔͬͯχϡʔϥϧωοτϫʔΫʹೖྗͨ͠ɽ·ͨɼ֤௨՟ϖΞʹ͓͍ͯҝସϨʔτ x(t) ͷՁ֨ଳ͕ҟͳΔͨΊɼऩӹ཰ r(t) ʹม‫ͨ͠׵‬ɽr(t) =x(t) − x(t − 1)x(t − 1)(2)ྫͱͯ͠ਤ 3 ʹɼม‫׵‬લͱม‫ޙ׵‬ͷྫΛࣔ͢ɽχϡʔϥϧωοτϫʔΫͷߏ੒ͱͯ͠ɼग़ྗ૚ͷχϡʔϩϯ਺͸ 1 ͭͰ͋Γɼதؒ૚ͷχϡʔϩϯ਺͸ೖྗ૚ͷ 1.1
ഒ (όΠΞε߲‫ؚ‬Ή) ʹઃఆͨ͠ɽೖྗ૚ͷχϡʔϩϯ

経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)

਺ʹ͍ͭͯ͸ɼ• ‫ݸ‬ผ๏ͷ৔߹ɿCP ਺ (= 1)× ௨՟ϖΞ਺ (= 3)× ࣌ؒཤྺ਺ (= 3)+ όΠΞε߲ (= 1)=10 ‫ݸ‬• ఏҊ๏ͷ৔߹ɿCP ਺ (= 12)× ௨՟ϖΞ਺ (= 3)× ࣌ؒཤྺ਺ (= 3)+ όΠΞε߲ (= 1)=109 ‫ݸ‬ͱͳΔɽͳֶ͓श܎਺͸શͯ 0.001 ʹઃఆͨ͠ɽ4༧ଌγϛϡϨʔγϣϯਤ 3: (্ਤ) ࣮ࡍͷҝସϨʔτ x(t)ɼ(Լਤ) ม‫ޙ׵‬ͷ

4.1

ਖ਼౴཰ͱ F ஋ʹΑΔධՁऩӹ཰ r(t)χϡʔϥϧωοτϫʔΫͷग़ྗ஋Ͱ͋Δऩӹ཰ r̂(t+1)͕ਖ਼ͳΒ͹࣍࣌ࠁͷҝସϨʔτ x̂(t + 1) ͸্ঢɼෛͳ
Β͹Լམɼ0 ͳΒ͹ෆมͱ͠ɼϨʔτมಈํ޲ʹؔ͢Δ
ਖ਼౴཰ A ΛධՁ͢Δɽ͔͠͠༧ଌ஋ r̂(t + 1) ͕ஸ౓ 0ʹͳΔ͜ͱ͸ແ͍ͨΊɼֶशର৅ͷ໨తม਺ʹ͍ͭͯඪ४ภࠩ σ Λ‫ٻ‬Ίɼ͜ͷ ±σ ͷൣғ಺ͷ r̂(t + 1) ΛෆมͱΈͳͨ͠ɽਖ਼౴཰ A ͷࢉग़ʹ͸ɼ• ্ঢͱ༧ଌ͠ɼ࣮ࡍʹ্ঢͨ͠ճ਺ɿUwin• ্ঢͱ༧ଌ͕ͨ͠ɼ൓ͯ͠Լམͨ͠ճ਺ɿUlose• Լམͱ༧ଌ͠ɼ࣮ࡍʹԼམͨ͠ճ਺ɿDwin• Լམͱ༧ଌ͕ͨ͠ɼ൓্ͯ͠ঢͨ͠ճ਺ɿDloseΛ༻͍ɼ࣍ࣜʹΑͬͯ‫͢ࢉܭ‬Δɽ

Uwin + Dwin
A=Uwin + Ulose + Dwin + Dloser̂(t)ɼ(Լਤ) ςετ࣌ (Test) ͷਅ஋ r(t) ͱ༧ଌ஋ r̂(t)(3)͜ͷΑ͏ʹෆมͱ༧ଌͨ͠৔߹Λআ͘͜ͱͰɼ‫ݩ‬ʑͷσʔλʹෆมͷׂ߹͕ଟ͘ਖ਼౴཰͕ 100[%] ʹۙ͘ͳΔ
͜ͱΛ๷͙ɽ͞Βʹɼ্ঢ·ͨ͸Լམʹؔ͢Δೋ୒໰
୊ͱͳΔͨΊɼ50[%] Λ‫ج‬४ʹ༗༻ੑΛධՁͰ͖Δɽ͞Βʹɼਖ਼౴཰Λࢉग़͢Δࡍʹσʔλ਺ͷภΓΛߟྀͯ͠ɼF ஋ (F–measure) ʹΑΔධՁ΋ߦ͏ɽ͜Ε͸ҎԼͷద߹཰ P recision ͱ࠶‫ ཰ݱ‬Recall ͔Βࢉग़͞ΕΔɽ

Recall =Uwin
Uwin + Ulose

Uwin
Uwin + Dlose

(4)
(5)

•Ask(ങ஋) ϨʔτΛ༧ଌର৅ʹ͢Δ৔߹
P recision =DwinDwin + Dlose

Recall =(6)Dwin
Dwin + Ulose(7)͜ΕΒͷ F ஋͸ҎԼͷΑ͏ʹࢉग़͞ΕΔɽ

F =4.2•Bid(ച஋) ϨʔτΛ༧ଌର৅ʹ͢Δ৔߹
P recision =ਤ 4: (্ਤ) ֶश࣌ (Learning) ͷਅ஋ r(t) ͱ༧ଌ஋2P recision × Recall
P recision + Recall(8)݁Ռ͓Αͼߟ࡯

ਤ 4 ͸͋Δֶश‫ؒظ‬ɾςετ‫ؒظ‬ͷऩӹ཰ (Returnrate) ͷਅ஋ͱ༧ଌ஋Λ͍ࣔͯ͠ΔɽTrue Output ͸ਅ஋ɼPredicted Output ͸༧ଌ஋Ͱ͋Δɽ͜ͷΑ͏ͳ݁Ռʹ‫͍ͯͮج‬ɼఏҊ๏ͱ‫ݸ‬ผ๏Λൺֱͨ͠ਖ਼౴཰ͷ݁ՌΛද 1ɼF ஋ͷ݁ՌΛද 2 ʹࣔ͢ɽྫͱͯ͠ʮ9݄ 5 ೔ʯ͕ࣔ͢ͷ͸ɼ9 ݄ 5 ೔‫ޕ‬લ 8 ͔࣌Β 9 ݄ 6 ೔‫ޕ‬લ 5 ࣌Ͱ͋Δɽ֤දʹ͓͍ͯɼ‫ݸ‬ผ๏ʹΑΔ݁ՌΛʮCP(A),CP(B),ʜ,CP(L)ʯͷ֤߲໨ʹࣔ͢ɽ·ͨɼ‫ݸ‬

経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)

ผ๏ʹΑΔ݁Ռͷฏ‫ۉ‬஋Λ M ean{CP}ͱද͢ɽදதͷ֤߲໨ʹ͓͍ͯɼBid(ച஋) ͱ Ask(ങ஋) ΛͦΕͧΕ༧ଌͨ݁͠ՌΛಉ࣌ʹ͍ࣔͯ͠Δɽ͜ΕΒΑΓɼఏҊ๏͸ඞͣ͠΋ৗʹ 1 ҐͰ͸ͳ͍͕ɼ҆ఆ্ͯ͠ҐΛอ͍ͬͯΔɽରͯ͠‫ݸ‬ผ๏͸ྑ͘ͳΔ৔߹΋ѱ͘ͳΔ৔߹΋͋ΓɼॱҐ͕҆ఆ͠ͳ͍ɽ͜ͷ݁Ռ͔Βɼू߹஌ͷޮՌ͕ൃ‫͞ش‬Ε͍ͯΔͱߟ͑ΒΕΔɽ·ͨɼ9 ݄ 15 ೔ͷΑ͏ʹఏҊ๏ͷ஋͕ஶ͘͠௿͍৔߹΋֬ೝͰ͖Δɽ͜Ε͸ֶश‫ͱظ‬ςετ‫Ͱظ‬૬৔͕େ͖͘ҟͳΓɼֶशͨ͠ࢢ৔ͷಈ͖͕ςετ‫ʹظ‬ద༻͍ͯ͠ͳ͍ͨΊͱߟ͑ΒΕΔɽ(2017 ೥ 9 ݄ 15 ೔͸๺ே઱͕஄ಓϛαΠϧΛൃࣹ͠ɼ๺ւಓ্ۭΛ௨աͨ͠‫ޙ‬ଠฏ༸্ʹམԼ͠ɼඈߦ‫͕཭ڑ‬ա‫࠷ڈ‬௕ͷ໿ 3700km ʹୡͨ͠ͱ͞Εͨχϡʔε͕͋Γɼ͜Ε͕ҝସมಈʹӨ‫ڹ‬Λ‫ͨ͠΅ٴ‬Մೳੑ͕͋Δɽ) ·ͨ F ஋ʹ͓͍ͯ‫ݸ‬ผ๏ͷ݁Ռʹେ͖ͳ͕ࠩ֬ೝͰ͖ΔͨΊɼ+σ Λ্ճΓ্ঢͱ൑ผ͞Εͨσʔλ਺ͱɼ−σ ΛԼճΓԼམͱ൑ผ͞Εͨσʔλ਺ʹภΓ͕͋Δͱߟ͑ΒΕΔɽ͞Βʹਤ 5 ͓Αͼਤ 6 ʹࣔ͢Α͏ʹɼਖ਼౴཰ͱ F ஋ʹ͍ͭͯɼఏҊ๏ͱ‫ݸ‬ผ๏ (ฏ‫ۉ‬஋) ͷൺֱΛߦͬͨɽਤ 5 ͸ਖ਼౴཰ɼਤ 6 ͸ F ஋Λࣔ͢ɽͦΕͧΕ Bid(ച஋) ͱ Ask(ങ஋) ͷ༧ଌ݁ՌΛ͍ࣔͯ͠Δɽ͜ΕΒΑΓɼਖ਼౴཰͸ʮ9/14 ͷ Askʯͱʮ9/15ʯͷ 2 ‫ؒظ‬Λআ͘શͯͷ‫ؒظ‬ɼF ஋͸શͯͷ‫͍͓ͯʹؒظ‬ɼఏҊ๏͕‫ݸ‬ผ๏ (ฏ‫ۉ‬஋) Λ্ճΔ݁Ռͱͳͬͨɽ͜ΕΒ͸֤ CPʹΑͬͯҟͳΔকདྷՁ֨ͷ‫ݟ‬௨͠ΛूஂֶशʹΑͬͯ౷߹͢Δ͜ͱͰɼΑΓઌ‫ྗݟ‬ͷ͋Δू߹஌Λ֫ಘͰ͖ͨ͜ͱΛ͓ࣔͯ͠Γɼ͜ͷ݁Ռ͔Β΋ू߹஌ͷޮՌ͕ൃ‫͞ش‬Ε͍ͯΔͱߟ͑ΒΕΔɽ·ͨɼ΋͠ࢢ৔͕ϥϯμϜ΢ΥʔΫͳΒ͹ਖ਼౴཰͸্ঢ͔Լམͷೋ୒໰୊ʹΑΓ 50[%] ʹͳΔͱߟ͑ΒΕΔ͕ɼఏҊ๏Ͱ͸΄΅શͯͷ‫ Ͱؒظ‬70[%] ͔Β 80[%] Λ௒͑Δ஋Λ͓ࣔͯ͠Γɼ‫ݸ‬ผ๏ (ฏ‫ۉ‬஋) ʹ͓͍ͯ΋΄ͱΜͲͷ‫ Ͱؒظ‬50[%] Λ௒͍͑ͯΔɽ͕֤ͨͬͯ͠ CP ͸ࢢ৔Λ༧ଌ͢Δϊ΢ϋ΢Λ͓࣋ͬͯΓɼ͞ΒʹελοΩϯάʹΑͬͯΑΓ༏Εͨू߹஌Λ֫ಘͰ͖ͨͱߟ͑ΒΕΔɽ

6

5[8] Eugene F. Fama, “Eﬃcient Capital Markets: A
Review of Theory and Empirical Work,” Journalof Finance, vol.25, no.2, pp.383–417, 1970.

·ͱΊຊ‫Ͱڀݚ‬͸ɼ֎ࠃҝସऔҾΛ஥հ͢Δ FX ‫ऀۀ‬ͷཱ৔ʹͳΓɼ֤ CP ͔Β഑৴͞ΕΔ‫ݐ‬஋Λ֤༧ଌ‫͔ث‬Βͷग़ྗ஋ͱΈͳ͠ɼ͜ΕΒΛελοΩϯάʹΑͬͯूஂֶश͢ΔՁ֨༧ଌϞσϧΛఏҊͨ͠ɽ࣮ࡍͷ‫ݐ‬஋σʔλΛ༻͍ͨ༧ଌγϛϡϨʔγϣϯΛ௨ͯ͡ɼ‫ݸ‬ผͷ CPຖͰ༧ଌϞσϧΛߏங͢ΔΑΓ΋ελοΩϯάʹΑͬͯશ CP Λ౷߹͢Δ͜ͱͰ༧ଌਫ਼౓Λ޲্Ͱ͖Δ͜ͱΛ֬ೝͨ͠ɽࠓ‫ޙ‬ͷ՝୊ࣗൃతͳऩӹ֫ಘͷͨΊɼຊఏҊख๏ʹΑΔՁ֨༧ଌΛࣗ‫ݾ‬ചങͷ൑அࡐྉʹར༻Ͱ͖ΔՄೳੑ͕͋Δɽ͜Εʹ͍ͭͯ౤ࢿγϛϡϨʔγϣϯΛ௨ͯ͡‫ݕ‬౼͢Δɽ·ͨຊߘʹ͓͍ͯ͸ 100[ms] ઌΛ༧ଌର৅ͱ͍ͯ͠Δ͕ɼࠓ‫ޙ‬͸ 200[ms] ઌ΍ 300[ms] ઌͷΑ͏ʹ༧ଌ‫ؒظ‬ΛԆ௕ͭͭ͠ɼ༧ଌྗΛอ࣋Ͱ͖Δ͔‫͢ূݕ‬Δɽຊ‫ڀݚ‬ͷҰ෦͸ɼจՊলՊ‫ݚ‬අ‫ج‬൫‫( ڀݚ‬C)(No.16K00320)
ͷॿ੒ʹΑΓߦΘΕ·ͨ͠.

ࢀߟจ‫ݙ‬[1] ຊ‫ڀݚ‬͸๭ళ಄֎ࠃҝସূ‫ۚڌ‬औҾऔѻ‫ͱऀۀ‬ͷ‫ڞ‬ಉ‫͋Ͱڀݚ‬Δɽ
[2] ๭ళ಄֎ࠃҝସূ‫ۚڌ‬औҾऔѻ‫͔ऀۀ‬Βೖखͨ͠ɽ
[3] Trevor Hastie, Robert Tibshirani and Jerome
Friedman, The Elements of Statistical Learning,
Springer, 2008.
[4] Scott E. Page, The Diﬀerence: How the Power ofDiversity Creates Better Groups, Firms, Schools,and Societies, Princeton University Press, 2008.
[5] James Surowiecki, The Wisdom of Crowds, Anchor, 2005.
[6] Tomoya Suzuki and Yuushi Ohkura, “Financial Technical Indicator Based on Chaotic Bagging Predictors for Adaptive Stock Selection inJapanese and American Markets,” Physica A,
vol.442, pp.50–66, 2016.
[7] Tomoya Suzuki, “Consensus Ratio and Two-steps
Selection to Detect Profitable Stocks,” International Federation of Technical Analysts (IFTA)
Journal, vol.18, pp.4–14, 2018.

[9] Zhi-Hua Zhou, Ensemble Methods Foundations
and Algorithms, Taylor & Francis Group, 2012.
[10] ੢֞௨ɼू߹஌ͱ͸Կ͔ɼதެ৽ॻɼ2013ɽ[11] Tomoya Onizawa, Takehiro Suzuki, Tomoya
Suzuki, “Predictability of Financial Market Indexes by Deep Neural Network,” Proceedingsof 2017 International Symposium on Nonlinear
Theory and its Applications, pp.166–169, 2017.

経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)

ද 1: ֤ςετ‫͚͓ʹؒظ‬Δਖ਼౴཰ [%]ɿBid(ࠨ)/Ask(ӈ)ɽ֤ྻʹ͓͍ͯਖ਼౴཰͕ߴ͍ 3 Ґ·ͰΛଠࣈͰࣔ͢ɽ
ςετ‫ؒظ‬

CP(A)
CP(B)
CP(C)
CP(D)
CP(E)
CP(F)
CP(G)
CP(H)
CP(I)
CP(J)
CP(K)
CP(L)
M ean{CP}ఏҊ๏ςετ‫ؒظ‬

CP(A)
CP(B)
CP(C)
CP(D)
CP(E)
CP(F)
CP(G)
CP(H)
CP(I)
CP(J)
CP(K)
CP(L)
M ean{CP}ఏҊ๏

9݄5೔
77.77/55.00
78.83/63.84
36.70/49.28
65.85/35.47
76.51/71.00
71.43/33.33
74.98/65.90
60.71/64.67
56.41/62.82
68.63/69.29
29.82/53.93
57.14/48.24
62.90/56.06
80.91/77.78

9݄6೔
59.24/62.27
72.91/70.32
68.19/51.34
63.07/68.42
69.34/72.14
61.89/71.44
75.98/77.03
68.94/69.99
52.06/57.54
75.48/70.38
75.90/70.05
63.36/67.55
67.20/67.37
78.67/77.29

9݄7೔
67.31/65.06
61.46/51.37
62.48/64.25
72.86/70.60
61.36/72.16
66.28/54.39
68.23/67.88
69.13/60.10
56.08/55.32
68.96/62.52
74.98/65.50
56.06/71.14
65.43/63.36
76.96/75.80

9݄8೔
66.23/67.39
64.93/74.03
68.50/63.80
72.83/66.26
68.93/74.53
59.04/73.76
71.32/79.89
69.77/58.95
54.17/57.14
52.28/73.60
80.15/65.65
70.71/60.52
66.57/67.96
79.24/80.40

9 ݄ 12 ೔
68.80/62.70
44.35/62.45
56.55/51.71
74.38/72.85
74.21/78.77
55.11/66.75
74.09/72.78
66.48/65.83
53.41/66.91
67.16/74.89
72.98/67.92
62.68/61.21
64.18/67.06
76.09/78.03

9 ݄ 13 ೔
67.31/72.73
71.67/68.5570.88/56.79
68.58/71.29
63.75/70.93
46.81/72.36
41.88/75.81
59.18/59.42
64.47/51.92
68.97/56.94
67.98/71.29
48.11/65.48
61.63/66.13
75.85/77.68

9 ݄ 14 ೔
61.68/50.79
0.00/0.00
59.17/44.48
69.46/75.07
62.44/37.32
57.96/56.70
65.60/68.56
60.37/51.05
61.88/53.87
68.98/70.21
66.37/69.70
47.14/44.21
56.75/51.83
64.29/49.13

9 ݄ 15 ೔
73.38/74.41
0.00/0.00
70.38/49.59
62.47/50.60
79.54/61.03
71.53/50.75
71.94/68.39
75.74/65.37
63.55/72.33
70.94/63.66
69.53/84.29
68.45/59.8064.79/58.35
48.00/56.67

9 ݄ 19 ೔
69.86/71.41
66.68/68.34
63.02/47.49
55.82/71.67
72.07/63.38
62.36/77.11
71.76/68.49
73.63/68.08
63.13/48.35
70.16/70.91
68.56/76.08
71.70/52.87
67.40/65.35
73.53/75.87

9 ݄ 20 ೔
60.34/58.35
68.03/69.32
52.53/62.97
57.96/68.48
42.42/69.80
58.35/65.39
67.25/69.00
63.26/56.50
62.54/51.41
62.61/46.66
66.68/68.21
49.16/44.58
59.26/60.89
71.87/71.55

9 ݄ 21 ೔
70.32/66.02
75.03/71.86
65.25/57.79
45.45/76.82
51.35/60.00
67.98/70.62
63.30/78.52
75.00/66.42
67.98/68.23
65.88/83.36
67.12/54.31
77.98/74.61
66.05/69.05
76.75/84.62

9 ݄ 22 ೔
70.24/77.45
78.95/69.24
59.32/66.67
81.97/69.77
81.66/72.14
56.93/74.70
80.68/74.34
46.00/58.82
56.25/72.55
77.52/77.76
76.60/70.94
78.60/67.53
70.39/70.99
77.22/78.86

9 ݄ 26 ೔
72.50/70.61
66.14/67.20
44.03/49.27
67.34/56.02
75.11/69.85
67.38/73.05
68.22/77.35
67.20/66.27
72.45/59.93
69.20/65.01
73.98/72.25
68.19/73.92
67.65/66.73
78.55/80.36

9 ݄ 27 ೔
71.84/57.80
72.85/68.83
65.16/62.30
73.50/75.05
80.17/69.49
56.71/69.17
67.47/71.73
71.56/66.89
68.31/64.93
58.23/72.01
44.30/78.25
70.94/70.51
66.75/68.91
77.93/78.55

9 ݄ 28 ೔
62.67/70.97
68.99/68.04
57.11/53.14
71.34/73.60
79.63/77.30
66.86/67.94
67.31/70.33
51.59/71.33
66.66/72.63
66.57/70.21
68.77/72.04
72.78/58.57
66.69/68.84
79.49/78.85

9 ݄ 29 ೔
71.86/59.94
72.72/71.87
51.79/57.34
80.00/79.45
74.03/78.62
64.54/71.94
80.05/75.74
56.34/71.43
54.58/59.06
71.06/65.70
71.55/69.03
64.52/66.34
67.75/68.87
79.71/80.09

ද 2: ֤ςετ‫͚͓ʹؒظ‬Δ F ஋ [%]ɿBid(ࠨ)/Ask(ӈ)ɽ֤ྻʹ͓͍ͯ F ஋͕ߴ͍ 3 Ґ·ͰΛଠࣈͰࣔ͢ɽ
ςετ‫ؒظ‬

CP(A)
CP(B)
CP(C)
CP(D)
CP(E)
CP(F)
CP(G)
CP(H)
CP(I)
CP(J)
CP(K)
CP(L)
M ean{CP}ఏҊ๏ςετ‫ؒظ‬

CP(A)
CP(B)
CP(C)
CP(D)
CP(E)
CP(F)
CP(G)
CP(H)
CP(I)
CP(J)
CP(K)
CP(L)
M ean{CP}ఏҊ๏

9݄5೔
6.07/45.45
0.44/67.24
31.21/65.79
51.59/16.59
85.67/69.69
83.33/40.00
60.24/0.28
72.35/65.39
4.57/69.33
79.26/61.11
25.93/29.88
66.67/60.71
47.28/49.29
79.73/78.00

9݄6೔
73.82/63.18
71.95/73.77
70.33/48.77
4.96/75.86
70.46/70.91
47.47/36.52
75.70/73.73
23.80/66.84
38.71/65.79
75.01/68.15
77.03/68.65
52.65/69.25
56.82/65.12
77.91/76.89

9݄7೔
64.43/63.72
57.32/31.18
68.21/76.89
70.92/74.78
64.26/75.42
63.00/56.22
28.93/79.15
69.36/57.11
57.19/27.62
69.70/12.42
74.98/63.20
62.98/71.65
62.61/57.45
76.25/74.76

9݄8೔
59.14/74.22
65.58/1.19
17.57/69.05
83.96/78.49
81.06/84.36
9.41/72.22
19.72/84.45
78.64/31.32
67.81/71.26
46.51/84.38
84.96/71.41
72.78/37.62
57.26/63.33
78.07/80.07

9 ݄ 12 ೔
69.13/60.02
61.45/66.63
34.18/40.93
62.83/75.89
74.67/77.81
68.27/59.87
71.40/74.98
59.79/44.40
18.52/29.64
68.56/78.62
74.81/72.64
54.39/47.05
59.83/60.71
75.76/76.81

9 ݄ 13 ೔
67.10/71.31
68.10/72.40
41.81/40.26
68.23/73.09
11.60/78.91
40.97/69.09
43.54/13.24
6.08/44.42
29.34/35.01
68.34/8.24
55.37/67.36
45.97/74.79
45.54/54.01
75.29/76.72

9 ݄ 14 ೔
63.44/51.69
0.00/0.00
69.97/2.00
63.81/0.85
56.20/25.07
71.23/25.46
62.48/45.62
63.29/63.30
65.46/64.27
7.59/15.13
50.04/77.70
39.19/47.25
51.06/34.86
73.68/65.89

9 ݄ 15 ೔
74.65/85.14
0.00/0.00
30.99/61.13
68.59/63.72
77.31/5.12
70.81/28.22
77.21/5.51
60.20/79.03
43.07/24.80
74.39/76.01
73.33/87.36
52.58/46.67
58.59/46.89
62.86/72.34

9 ݄ 19 ೔
73.26/72.68
65.78/65.47
37.18/52.17
57.32/65.09
79.17/77.44
47.83/75.78
69.95/66.51
57.95/79.71
31.05/59.76
66.03/16.36
14.11/80.43
82.88/62.36
56.88/64.48
72.66/75.34

9 ݄ 20 ೔
56.50/32.47
63.01/68.81
31.17/68.78
65.92/67.01
45.01/56.18
44.78/59.56
72.35/81.28
75.10/59.54
73.92/55.74
54.43/35.07
57.32/12.42
42.46/34.52
56.83/52.62
71.27/70.74

9 ݄ 21 ೔
81.77/48.96
41.38/1.29
0.18/10.87
45.45/14.63
10.00/60.00
66.94/0.35
7.59/76.37
76.16/65.30
80.83/0.90
69.91/40.50
25.00/60.74
1.00/58.00
42.18/36.49
71.87/84.03

9 ݄ 22 ೔
5.26/77.17
74.10/68.71
66.76/78.79
55.17/45.83
60.08/80.14
71.46/82.82
50.61/71.68
52.63/60.51
48.15/28.61
77.57/71.97
74.32/48.49
1.86/64.08
53.16/64.90
76.90/77.87

9 ݄ 26 ೔
73.74/81.78
15.66/60.96
34.91/57.46
69.72/32.26
74.90/70.10
22.93/67.69
74.90/77.14
24.98/44.77
56.65/72.51
69.58/77.67
74.29/71.71
71.41/25.75
55.31/61.65
77.86/79.58

9 ݄ 27 ೔
35.10/54.79
11.96/61.49
76.20/62.81
77.48/75.48
86.15/66.98
30.95/66.98
68.81/46.25
64.73/68.62
70.36/56.40
18.54/65.35
55.78/47.74
48.29/69.66
53.70/61.88
77.66/78.01

9 ݄ 28 ೔
76.77/68.57
81.25/62.4226.56/48.11
67.08/5.00
79.87/72.87
16.78/43.58
64.57/54.63
55.77/68.96
60.65/19.83
71.31/19.44
4.32/61.29
8.70/27.18
51.14/45.99
79.16/77.83

9 ݄ 29 ೔
72.71/68.26
53.71/73.77
28.85/12.37
80.20/72.64
81.98/77.36
47.83/59.40
50.33/48.59
9.10/19.94
68.67/69.51
73.27/76.27
47.38/78.15
36.64/68.35
54.22/60.38
78.30/79.00

˞ʮ0.00ʯ͸༧ଌ஋͕શͯ ±σ ͷൣғ಺ͱͳΓɼෆมͱ൑ผ͞ΕͨͨΊͰ͋Δɽ

経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)

ਤ 5: Mean(‫ݸ‬ผ๏ (ฏ‫ۉ‬஋)) ͓Αͼ Stacking(ఏҊ๏) ʹΑΔ Accuracy(ਖ਼౴཰) ͷൺֱਤ 6: Mean(‫ݸ‬ผ๏ (ฏ‫ۉ‬஋)) ͓Αͼ Stacking(ఏҊ๏) ʹΑΔ F–measure(F ஋) ͷൺֱ