⊵Ṡఱࢍᄓ᪓⎞ᠰᱹⳭỄಙʠ〦
ǖ ⴈЗរջṘℐ⭰ᶇἄ
ᆺࢣګ1ʳ ஊਙᆠ2ʳ ५࿆ᇛ1ʳ Ꮨ๗3ʳ ຑم՟4ʳ ႓٢ᅜ1
1խဎՕᖂᇷಛጥᖂߓ
2խ؇Օᖂತ೭८ᘜᖂߓ
3խဎՕᖂತ೭ጥᖂߓ
4ઝݾՕᖂᛜ৬ՠ࿓ᖂߓ
ၪ ⣬
யؑᎅ്ݾ։࣫լ౨ൕؑխᛧ၌ᠰܓᑮΖڼԫ്९ཚא ࠐ֭ፖ֘ኙٺڶኔᎾઔߒ֭Ζء֮༼נԫଡᎅΚؑயፖᆖᛎ࿇ሒ࿓
৫ګإֺΔڂڼڇᆖᛎ࿇ሒ࿓৫ऱഏ୮ؑயΔྤऄᛧ၌ᠰऱሟΙ
܀ڇᆖᛎ࿇ሒ࿓৫܅ऱഏ୮ؑய܅Δױ౨ױאᛧ၌ᠰऱሟΖط࣍ޢଡ
ؑᔞشऱݾ։ֱ࣫ऄױ౨լٵΔڂڼΔءઔߒאࠠڶᖂ౨Ժऱᙊႚዝ֏
壀ᆖጻሁ৬ዌࠌᛧܓ່Օ֏ऱٌ࣐ެߓอΖࠀ༼נאψؑயਐᑑωࡳၦ ᘝၦؑऱயࢤΕאԳ݁ഏփسขֻᠰᘝၦᆖᛎ࿇ሒ࿓৫Δۖאץܶٺጟᆖ ᛎ࿇ሒ࿓৫ऱ 22 ଡഏ୮ 1997~2006 ٥ڣऱैปؑᑌءΔၞ۩ኔᢞઔߒΖ ઔߒ࣠᧩قΔᆖᛎ࿇ሒ࿓৫။܅ऱഏ୮Δࠡैปؑڇەᐞٌ࣐ګءՀࠌش ݾ։࣫ᛧ၌ᠰܓᑮऱױ౨ࢤ။ΖڇྒྷᇢཚၴΔԳ݁ഏփسขֻᠰױᇞᤩ
ؑயਐᑑ 61 %ऱ᧢ฆΔF อૠሒ 0.002 %᧩ထֽᄷΔױߠؑயፖᆖᛎ
࿇୶࿓৫ڶᣂΖ
〦⼫⥱ŘؑயΔᆖᛎ࿇୶Δ壀ᆖጻሁΔᙊႚዝጩऄΖ
RELATION BETWEEN STOCK MARKET EFFICIENCY AND DEGREE OF ECONOMIC DEVELOPMENT – A GENETIC NEURAL NETWORK
APPROACH
I-Cheng Yeh1 Cheng-Yi Shiu2 Deng-Yiv Chiu1 Chi-Chuang Hsieh3 Li-Chuan Lien4 Chao-Yu Huang1
1Department of Information Management Chung Hua University
Hsin Chu, Taiwan 300, R.O.C.
2Department of Finance National Central University Taoyuan County, Taiwan 320, R.O.C.
3 Department of Finance Chung Hua University Hsin Chu, Taiwan 300, R.O.C.
4Department of Construction Engineering National Taiwan University of Science and Technology
Taipei, Taiwan 106, R.O.C.
Key Words: market efficiency, economy development, neural networks, genetic algorithms.
ABSTRACT
The market efficiency hypothesis advocates that technical analysis cannot obtain the excess profit from the market. There have been many real studies that supported and refuted to the hypothesis, respectively, for a long time. This paper proposes a hypothesis that market efficiency is proportional to the degree of economic development. In order to confirm this hypothesis, this study constructed a trading decision-making system that maximized profit by using genetic neural networks with the learning ability, and proposed “the market efficiency index” to measure the market efficiency and per capita GDP to measure the degree of economic development. Twenty two countries, representing all degrees of economic development and nine years (1997~2006) of stock market data were used as the samples to conduct the study. The findings showed that the lower the economic development, the higher the possibility of obtaining obvious excess profit by using technical analysis while considering transaction cost. In the test period, the per capita GDP may explain 61% of the variation of the market efficiency index, and the F statistics reach a remarkable 0.002% level; therefore, the economic development degree obviously affects the market efficiency.
ɺȮℨ ⧄
யؑᎅ്ڇஇႨயؑխΔؾছऱؑᏝ
բךٝऱ֘ᚨԱመװऱؑᏝࢬ༼ࠎऱٺጟᇷಛΔڂ ڼދᇷԳྤऄᖕመװऱᖵᇷறຘመݾ։࣫ፖቃྒྷࠐ ൕխ၌ᠰऱሟΖڼԫ്ڍڣࠐڶڍኔᢞઔߒ֭
Δᎁڇەᐞٌ࣐ګء৵Δܓشݾಛᇆࢬขسऱٌ࣐
ฃࠡሟຟྤऄԫીऱ࣍၇ၞ৵ڶฃ [1-6]Ζ܀Ո ڶڍኔᢞઔߒᏍ؞հ [7-16]Ζ
۶ᄎขسຍጟؿએऱᓵΛء֮༼נԫଡᎅΚ ψؑயፖᆖᛎ࿇ሒ࿓৫ګإֺΔڂڼڇᆖᛎ࿇ሒ࿓৫
ऱഏ୮ؑயΔྤऄᛧ၌ᠰऱሟΙ܀ڇᆖᛎ࿇
ሒ࿓৫܅ऱഏ୮ؑய܅Δױ౨ױאᛧ၌ᠰऱሟΖω ط࣍ޢଡؑᔞشऱݾ։ֱ࣫ऄױ౨լٵΔڂڼΔ լᚨᇠࠌشԫଡࡳऱݾ։ֱ࣫ऄࠐኔᢞڼԫᎅΖਚ ءઔߒ༼נאࠠڶॺᒵࢤ౨Ժऱᣊ壀ᆖጻሁ (artificial
neural networks) [17]ٽ່ࠠࠋ֏౨Ժऱᙊႚዝጩऄ
(Genetic Algorithms) [18,19]Δאൎ֏ڤᖂ (reinforced
learning) [20-22]ऱֱڤ৬ዌഗ࣍ݾ։࣫ऱٌ࣐ެߓ
อΔא൶ಘݾ։ֱ࣫ऄᛧ၌ᠰܓᑮऱױ౨ࢤΖԱᝩ
܍ขسመ৫ᔞ (overfitting) ऱംᠲΔءઔߒऱᇷறࠉழ ݧሶګಝᒭཚၴፖྒྷᇢཚၴΔᚨشಝᒭཚၴऱᇷற৬ዌ
ٌ࣐ެߓอΔۖشྒྷᇢཚၴေ۷ߓอऱய౨Ζ
ԱኔᢞՂ૪ᎅΔءઔߒ༼נא၌ᠰܓᑮഗ៕ऱ ψؑயਐᑑωڂ᧢ᑇΔۖאᜤٽഏֆ܉ऱԳ݁ഏ
փسขֻᠰ (Per capita GDP, $US, 2003) [23]ᆖᛎ࿇ሒ
࿓৫ऱਐᑑΔࠀאڼ۞᧢ᑇࠐᇞᤩؑயਐᑑຍଡڂ
᧢ᑇΖ
ʷȮᄽ᪇⤽
ڍᐋტवᕴᣊ壀ᆖጻሁਢᅮڤᖂխ່ൄشࠐ
ॺᒵࢤ৬ᑓՠࠠऱᑓڤΔڍᐋტवᕴຏൄආش່ࡕ
૾ऄऱࠐ່՛֏ᑓীቃྒྷᎄ [17]Ζ܀ءઔߒࢬ৬ مऱٌ࣐ެߓอऱᙁנ᧢ᑇਢ၇ᔄಛᇆΔڼಛᇆࠀྤբ वଖױشΔਚψᅮڤᖂωऱֱڤࠀլᔞشΖڂڼؘႊ
ࠌشψൎ֏ڤᖂωऱᄗ࢚৬ዌڼٌ࣐ެߓอΖൎ֏ڤ ᖂऱࡳᆠΚψԫጟᇢᎄ (trial-and-error) ऱመ࿓Δࠡᖲ
ࠫڇᇢᎄመ࿓խࠩڃ墅 (feedback) אᏺףᆖ᧭ีᗨΔࠀ ຘመլឰऱᇢᎄΔ່ࠩࠋ֏ऱ֘ᚨิٽΖω [18, 20, 24]
ൎ֏ڤᖂഗءᄗ࢚ڕቹ 1 ࢬق [20]Ζ
Աࠌڍᐋტवᕴࠠڶൎ֏ᖂऱ౨ԺΔءઔߒආش ᙊႚዝጩऄࠐᚌ֏ڍᐋტवᕴΔא৬ዌ່Օ֏ᛧܓऱٌ࣐
ެߓอΖڼԫਮዌጠψᙊႚዝ֏壀ᆖጻሁωΔᇡาऱዝ ጩऄߠ֮ [20, 21, 25]Ζ२ڣࠐΔ壀ᆖጻሁΕᙊႚዝ֏
ࢨ Բ ृ ऱ ٽ ᚨ ش ڇ ै ป ؑ ऱ ઔ ߒ բ ڶ լ ֟ ֮
[26-36]Δ൶ಘຍࠄԳՠཕᐝֱऄፖயؑᎅऱᣂএऱ
ઔߒՈլ֟ [37-39] Δ܀ᝫ֟൶ಘؑயፖᆖᛎ࿇ሒ
࿓৫ᣂএऱઔߒΔຍՈਢءઔߒऱૹរΖ
ߓอ
ൎ֏ڤᖂᖲࠫ
action ᛩቼ
result feedback
ߧ 1 ջೣણ∳ࡣᏕ൳ߧ
אȮᶇἄᅞᘍ
1. ⫏ᅆゝ
Աઔߒഏ୮࿇ሒ࿓৫ኙؑயऱᐙΔءઔߒᙇ ᖗشԳ݁ഏփسขֻᠰ [23] 1000 ભցאՀऱ֣ഗཎࡖΕ ཎߺᥞΕٱ؍Δ1000~5000 ભցऱဗ৳ᎏΕ֣۫Εॳ
ݪΕ್ࠐ۫ࠅΔ5000~15000 ભցऱᕠ۫ୂΕឌഏΕΔ
15000 ભցאՂऱଉཽΕᄅףࡕΕഏΕᖾΕऄഏΕᐚ
ഏΕֲءΕֺܓழΕ๛ᥞΕچܓΕભഏΕᅗՓΔԫ٥Բ ԼԲଡഏ୮ࢨچઔߒᑌءΔᙇشऱैᏝਐᑇߠॵᙕ 1Ζگႃຍࠄഏ୮ࢨچൕ 1997 ڣ 7 ִ 1 ֲ۟ 2006 ڣ 4
ִַऱैؑՕᒌᇷறٺ 2100 Δࠡխছཚऱ 1069 (પ
ڣ) ಝᒭᇷறΔ৵ཚऱ 1031 (પڣ) ྒྷᇢ ᇷறΖ
2. ༬⠛྆ᐻ᱿⤺ᾰ
ط࣍ݾਐᑑᄕڍΔءઔߒਢאࠠڶᖂ౨Ժऱᙊ
ႚዝ֏壀ᆖጻሁ৬ዌࠌᛧܓ່Օ֏ऱٌ࣐ެߓอΔࠐז ཙऴ൷ࠌشݾਐᑑΖڂڼΔءઔߒೈԱආشઌኙൎஇਐ ᑑ (RSI) ڼԫൄشऱݾਐᑑ؆ΔՈආشԫࠄഗ࣍ழၴᑇ ٨ᨠរऱਐᑑΔאܓߓอ౨ൕຍࠄഗءਐᑑΔٽଡؑ
ऱᇷறΔ৬ዌլٵऱݾ։࣫ᑓڤΖຍࠄਐᑑץਔΚ
2 1 1
2 1
( )
( )
n
t t
t
n n
t t
C C
DW
C
= −
=
∆ − ∆
=
∆
∑
∑
(1)
ࠡխCt=ร t ֲऱگᒌᏝΙ∆Ct =Ct−Ct−1Ιn = 5,10,20Ζ
1
1
( , 0)
| |
n t t
n n
t t
Max C RSI
C
=
=
∆
=
∆
∑
∑
(2)
ࠡխ n = 5,10,20Ζ
, m m n
n
MAI MA
=MA (3)
ࠡխMAn= nֲگᒌᏝऱฝ೯ؓ݁ΙMAm= mֲگᒌᏝऱ
ฝ೯ؓ݁Ιm < n
ආش DW (Durbin-Watson) ਐᑑऱڂਢ،ױאೠྒྷ
ؑਢܡࠠڶႨΔᅝ DW ଖࣔ᧩՛࣍ 2 ழؑࠠڶ
ႨΖආش RSI ਐᑑऱڂਢ،ױאೠྒྷؑऱይၓႨΔ ԫۖߢΔRSI Օ࣍ 70 %ז।բנࣔ᧩ՂይႨΔRSI
՛࣍ 30 %ז।բנࣔ᧩ՀၓႨΖආش MAI ਐᑑऱ
ڂਢഗ࣍ Gran Ville ऱฝ೯ؓ݁ᒵᄗ࢚Δᅝ MAI Օ࣍ 1 ழΔ ז।वཚฝ೯ؓ݁Օ࣍९ཚฝ೯ؓ݁Δױ౨ਢᔞᅝऱ၇ រΙ֘հΔױ౨ਢᔄរΖ
ءઔߒەၦ၇Եរ۟ᔄנរऱᛧܓ (ࢨჾ)Δլە ၦᔄנរ۟၇Եរऱᛧܓ (ࢨჾ)Ζءઔߒەᐞٌ࣐ګ ءΔ܀ᦸ࣍ٺഏٺழཚऱٌ࣐ګءլٵΔڇڼอԫආش၇
ၞګء 0.1425 %Δᔄנګء 0.4425 %Ζ 3. ᗉᾋ⣳ԅʠ⥫̒ᅞೣ
ԱࡳၦᘝၦؑயࢤΔءઔߒ༼נψؑயਐ ᑑωڕՀΚ
ؑயਐᑑ=
n s e n
s e
C C
M M
2 /
/
(4)
ࠡխ n ᜔ٌ࣐࣍ڣᑇΙMsཚॣދᇷृࢬᖑڶऱᇷ ८ (ءઔߒࡳ 100 ᆄց)ΙMeཚأދᇷृࢬᖑڶऱᇷ ८ΙCsཚॣگᒌᏝΙCeཚأگᒌᏝΖ
ؑயਐᑑ࣍ 1.0 ז।ٌ࣐ฃྤऄᚰඓؑΔ
ؑྤᛧ၌ᠰܓᑮऱ़ၴΔؑሒࠩݙ٤யΖຍଡਐ ᑑڕڼૠऱطਢΔ։᧩قഗ࣍ݾ։࣫ऱٌ࣐ެ
ߓอࢬ౨ሒࠩڣᜎயΔ։ئਢഗᄷٌ࣐ฃऱڣᜎயΖڂ
ؑߨٻլױቃवΔڕ࣠ආشψ၇Եڶฃωഗᄷ
ٌ࣐ฃΔঞڇैؑ९ཚՂይழཚΔਢԫଡৰᣄᚰඓऱٌ
࣐ฃΙ֘հڇ९ཚՀၓழཚΔਢԫଡৰ୲࣐ᚰඓऱٌ࣐
ฃΖઌ֘چΔڕ࣠ආشψլ၇़֫ωΔฃΔঞैؑ९ཚ ՂይழཚΔਢԫଡৰ୲࣐ᚰඓऱٌ࣐ฃΙ֘հڇ९ཚՀ ၓழཚΔਢԫଡৰᣄᚰඓऱٌ࣐ฃΖڂڼإᒔऱഗᄷٌ
࣐ฃਝॺψ၇ԵڶωΔٍॺψլ၇़֫ωΔۖਢψळؾ ၇ᔄωΔڼԫฃڇٌ࣐ཚၴխऱ 50 %ऱཚၴڶΔ50 % ऱཚၴ़֫ΖࠏڕΔڕ࣠ཚॣگᒌਐᑇ 100 រΔཚآگᒌ ਐᑇ 300 រΔᖵᆖ 10 ڣΔঞڂڇڼԼڣփڶ 50 %ऱཚ
ၴڶΔਚڇૠጩࠡڣᜎயழլ౨ආش 10 ڣΔۖᚨආش 20ڣΔڂᖵᆖ 20 ڣթڶ 20 ڣ × 50 % = 10 ڣऱڶཚ
ၴΖਚٽऱڣᜎயਐᑑΚ
ळؾ၇ᔄٌ࣐ฃڣᜎயਐᑑ10×2300/100=1.056 (5)
ڂڼ (4) ڤխΔ։ئၲ 2n ڻΔۖॺ n ڻΖ 4. ᑁࠣᐉ
ءઔߒאڍᐋტवᕴ壀ᆖጻሁਮዌ (ቹ 2)Δ٦ܓش ᙊႚዝጩऄ࠷זءऱ່ࡕ૾ऄࠐᚌ֏ᦞଖΔ৬ዌ່Օ
ߧ 2 ⴈЗរջṘℐ⭰ञᲷԽᓏ྆ᄲ˅ᆞᗉᾋᑁࠣ
֏ᛧܓऱٌ࣐ެߓอΖ壀ᆖጻሁऱᙁԵᐋڶଡ壀ᆖ ցΔ։ܑז। DW (5)ΕDW (10)ΕDW (20)ΕRSI (5)ΕRSI (10)ΕRSI (20)ΕMAI (5,10)ΕMAI (5,20)ΕMAI (10,20) 9ଡݾਐᑑΖᙁנᐋڶԫଡ壀ᆖցΔז।၇ᔄಛᇆ yΚ
ᅝ y >α ழΔ၇Եಛᇆ ᅝ y <β ழΔᔄנಛᇆ
αΕβ ࡉ壀ᆖጻሁऱᦞଖԫᑌΔຟطᙊႚዝጩऄᚌ֏հΖ 5. ⴈЗរᾰᘍאᄲ⥑હ
ءઔߒࢬආشհᙊႚዝጩऄᑇࡳ (ຑم՟
2006)Κئ᧯ଡᑇࡳ 200 ଡΔዝ֏זࡳ 10 זΔ
ٌࡳ 0.9Δડ᧢ࡳ 0.001Ζ
߈Ȯᶇἄኞ
1. ≙˅ᆞໞʁʠኞ
ڂᙊႚዝጩऄऱᚌ֏࣠ࠠڶᙟᖲࢤΔਚءઔߒല ছ૪ԲԼԲഏैؑᇷறΔאᙊႚዝ֏壀ᆖጻሁച۩ԼڻΔ ٦࠷ؓ݁ଖ।ԫΔࠀല।ᢄګቹ 3 ፖቹ 4Ζطቹ 3 ױवΔ ڇಝᒭཚၴԳ݁ഏփسขֻᠰ။ऱഏ୮ؑயყࠋ (ؑயਐᑑᄎ൷२ 1.0)Ι֘հΔؑயለ (ؑய
ਐᑑყՕ࣍ 1.0)ΔԳ݁ഏփسขֻᠰᇞᤩؑயਐᑑ ऱ౨Ժৰൎ (ܒࡳএᑇ 0.18)Δ᧩ထֽᄷሒࠩ 0.03 % (ߠ
।Բ)Ζطቹ 4 ױवΔڇྒྷᇢཚၴ᧩ྥսڶڼԫွ (ܒࡳ
এᑇ 0.61)Δ᧩ထֽᄷሒࠩ 0.002 % (ߠ।Կ)Ζطڼױߠ ᆖᛎ࿇ሒ࿓৫ለऱഏ୮ؑለڶயΖଖܑࣹრऱ ਢڶԼଡഏ୮ऱؑயਐᑑ՛࣍ 1.0ΔڶԳ݁ഏփس ขֻᠰ່܅ऱԿଡഏ୮ (֣ഗཎࡖΕཎߺᥞΕٱ؍)ऱؑ
யਐᑑՕ࣍ 1.0Δ܀ࠀլ᧩ထ (ߠ।ԫ)Ζ
ڼ؆Δྒྷᇢཚၴፖಝᒭཚၴऱދᇷᜎயਢܡઌᣂਢܒ ឰދᇷެߓอਢܡڶய࣠ऱૹࠉᖕΔڂڕ࣠ދᇷެ
ߓอటऱ౨ജܓشಝᒭཚၴऱᇷறឯ࠷נཏሙ֏ދᇷެ
⠧ɺ ׳ߡ⊵ఱఱࢍᄓ᪓྆ᐻ (≙˅ᆞໞ)
ಝᒭཚၴ ྒྷᇢཚၴ
ؑயਐᑑ ؑயਐᑑ
ഏ୮ Log10 GDP ؓ݁ଖ ᑑᄷ
Օ࣍ 1.0
ऱ᧩ထࢤ ؓ݁ଖ ᑑᄷ
Օ࣍ 1.0 ऱ᧩ထࢤ
֣ഗཎࡖ 2.782 1.327 0.035 3.38E-06 1.091 0.098 0.190 ཎߺᥞ 2.971 1.224 0.037 8.67E-05 1.028 0.051 0.298 ٱ؍ 2.975 1.388 0.07 1.84E-04 1.066 0.065 0.168 ဗ৳ᎏ 3.025 1.319 0.01 6.47E-11 0.980 0.044 0.671
֣۫ 3.431 1.437 0.07 7.49E-05 0.961 0.078 0.685 ॳݪ 3.601 1.316 0.076 1.19E-03 0.987 0.073 0.568
್ࠐ۫ࠅ 3.626 1.433 0.083 2.70E-04 0.982 0.048 0.641 ᕠ۫ୂ 3.774 1.180 0.064 1.04E-02 0.982 0.058 0.619 ឌഏ 4.044 1.318 0.063 3.54E-04 0.933 0.038 0.943
4.098 1.188 0.039 5.05E-04 0.973 0.037 0.757 ଉཽ 4.354 1.253 0.027 3.16E-06 0.922 0.023 0.996 ᄅףࡕ 4.398 1.228 0.044 2.68E-04 0.974 0.046 0.704
ഏ 4.482 1.055 0.032 6.19E-02 0.913 0.048 0.947 ᖾ 4.500 1.054 0.011 4.12E-04 0.983 0.024 0.757 ऄഏ 4.518 1.153 0.019 1.06E-05 0.964 0.026 0.902 ᐚഏ 4.521 1.171 0.025 3.38E-05 0.947 0.034 0.921
ֲء 4.529 1.101 0.02 3.76E-04 0.934 0.033 0.963
ֺܓழ 4.530 1.150 0.012 2.76E-07 0.929 0.017 0.999
๛ᥞ 4.552 1.273 0.024 7.05E-07 0.951 0.019 0.985
چܓ 4.554 1.109 0.017 6.41E-05 0.964 0.058 0.727 ભഏ 4.567 1.051 0.031 6.96E-02 0.959 0.03 0.898 ᅗՓ 4.693 1.173 0.01 1.10E-08 0.950 0.031 0.932
⠧ʷ ⥂⅀⫏ᅆ᱿ Log10 GDPଃఱࢍᄓ᪓྆ᐻʠ ANOVAӠኔ (≙˅ᆞໞ)
ANOVA
ʳ ۞ط৫ SS MS F ᧩ထଖ
ಱូ 1 0.14328 0.14328 19.39059 0.000274
ྲྀ 20 0.147783 0.007389
᜔ࡉ 21 0.291064 ʳ ʳ ʳ
⠧ɿ ᛵ⥶⫏ᅆ᱿ Log10 GDPଃఱࢍᄓ᪓྆ᐻʠ ANOVAӠኔ (≙˅ᆞໞ)
ANOVA
ʳ ۞ط৫ SS MS F ᧩ထଖ
ಱូ 1 0.024149 0.024149 31.3822 1.75E-05
ྲྀ 20 0.01539 0.00077
᜔ࡉ 21 0.03954 ʳ ʳ ʳ
⠧߈ ⥂⅀ቅ⿵ଃᛵ⥶ቅ⿵᱿ఱࢍᄓ᪓྆ᐻʠ ANOVAӠኔ (≙˅ᆞໞ)
ANOVA
ʳ ۞ط৫ SS MS F ᧩ထଖ
ಱូ 1 0.006993 0.006993 4.297568 0.051302
ྲྀ 20 0.032546 0.001627
᜔ࡉ 21 0.03954 ʳ ʳ ʳ MAIn=(5,20)
MAIn=(10,20)
၇ᔄಛᇆ ᙁԵᐋ ឆ៲ᐋ ᙁנᐋ DWn=5
y DWn=10
DWn=20
MAIn=(5,10)
RSIn=5
RSIn=20
RSIn=10
y = -0.1298x + 1.7452 R2 = 0.1769
2.5 3.0 3.5 4.0 4.5 5.0 Log10 GDP
1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9
ؑயਐᑑ
y = -0.0533x + 1.186 R2 = 0.6108
2.5 3.0 3.5 4.0 4.5 5.0 Log10 GDP
1.2
1.1
1.0
0.9
ؑயਐᑑ
y = -0.155x + 0.7819 R2 = 0.1769
0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 ಝᒭཚၴؑயਐᑑ
1.2
1.1
1.0
0.9
ྒྷᇢཚၴؑயਐᑑ
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 log (᧩ထଖ)
250
200
150
100
50
0
᙮ (ᑓᚵ1000ڻ)
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 log (᧩ထଖ)
180 160 140 120 100 80 60 40 20 0
᙮ (ᑓᚵ1000ڻ)
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 log (᧩ထଖ)
300
250
200
150
100
50
0
᙮ (ᑓᚵ1000ڻ)
ߧ 3 ⥂⅀ቅ⿵׳ߡఱࢍᄓ᪓྆ᐻ⎞ Log10 GDPʠᄣ̡
ߧ (≙˅ᆞໞ)
ߧ 4 ᛵ⥶ቅ⿵׳ߡఱࢍᄓ᪓྆ᐻ⎞ Log10 GDPʠᄣ̡
ߧ (≙˅ᆞໞ)
ߧ 5 ⥂⅀ቅ⿵ଃᛵ⥶ቅ⿵᱿ఱࢍᄓ᪓྆ᐻʠᄣ̡ߧ (≙˅ᆞໞ)
ߧ 6 ⥂⅀ቅ⿵׳ߡఱࢍᄓ᪓྆ᐻ⎞ Log10 GDP ᱿ ANOVA Ӡኔㆴ┮ϊ Bootstrap ᑁბኞ (≙˅
ᆞໞ)
ߧ 7 ᛵ⥶ቅ⿵׳ߡఱࢍᄓ᪓྆ᐻ⎞ Log10 GDP ᱿ ANOVAӠኔㆴ┮ϊ Bootstrap ᑁბኞ (≙˅
ᆞໞ)
ߧ 8 ⥂⅀ቅ⿵ଃᛵ⥶ቅ⿵᱿ఱࢍᄓ᪓྆ᐻ᱿ ANOVA Ӡኔㆴ┮ϊ Bootstrap ᑁბኞ (≙˅ᆞໞ)
y =-0.1793x + 2.0286 R2 = 0.564
2.5 3.0 3.5 4.0 4.5 5.0 Log10 GDP
ؑயਐᑑ
1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9
⠧ʽ ׳ߡ⊵ఱఱࢍᄓ᪓྆ᐻ (ʃ≙˅ᆞໞ)
ಝᒭཚၴ ྒྷᇢཚၴ
ؑயਐᑑ ؑயਐᑑ
ഏ୮ Log10 GDP ؓ݁ଖ ᑑᄷ
Օ࣍ 1.0
ऱ᧩ထࢤ ؓ݁ଖ ᑑᄷ
Օ࣍ 1.0 ऱ᧩ထࢤ
֣ഗཎࡖ 2.782 1.452 0.019 1.00E-09 1.166 0.070 0.022 ཎߺᥞ 2.971 1.310 0.012 3.47E-10 1.124 0.072 0.060 ٱ؍ 2.975 1.592 0.091 5.66E-05 1.161 0.055 0.009 ဗ৳ᎏ 3.025 1.390 0.019 4.38E-09 1.027 0.060 0.332
֣۫ 3.431 1.593 0.071 7.72E-06 1.113 0.070 0.070 ॳݪ 3.601 1.417 0.021 4.87E-09 1.111 0.067 0.067
್ࠐ۫ࠅ 3.626 1.555 0.058 2.57E-06 1.067 0.031 0.028 ᕠ۫ୂ 3.774 1.283 0.029 2.24E-06 1.082 0.048 0.059 ឌഏ 4.044 1.496 0.087 1.46E-04 1.047 0.054 0.205
4.098 1.243 0.028 5.59E-06 0.995 0.029 0.567 ଉཽ 4.354 1.346 0.042 8.24E-06 0.993 0.027 0.599 ᄅףࡕ 4.398 1.233 0.020 5.37E-07 1.001 0.048 0.490
ഏ 4.482 1.184 0.008 2.10E-09 0.996 0.033 0.547 ᖾ 4.500 1.095 0.010 1.82E-06 1.017 0.031 0.300 ऄഏ 4.518 1.243 0.038 5.77E-05 1.035 0.040 0.207 ᐚഏ 4.521 1.197 0.021 3.52E-06 0.956 0.037 0.868
ֲء 4.529 1.215 0.012 1.02E-08 0.980 0.037 0.701
ֺܓழ 4.530 1.173 0.009 4.22E-09 0.935 0.031 0.967
๛ᥞ 4.552 1.263 0.022 3.37E-07 1.002 0.046 0.480
چܓ 4.554 1.154 0.015 1.15E-06 1.057 0.065 0.200 ભഏ 4.567 1.129 0.012 9.58E-07 1.043 0.024 0.054 ᅗՓ 4.693 1.195 0.029 4.26E-05 0.996 0.051 0.531
ᑓীΔঞຍࠄᑓীᚨشڇྒྷᇢཚၴᚨᇠڶߜړऱᜎ யΖڂڼΔش।ԫऱᑇᖕᢄګቹ 5ΖطቹױवΔڇಝᒭཚ
ၴދᇷய墿။ړऱؑΔࠡڇྒྷᇢཚၴދᇷய墿Ո။ړΖ ឈྥܒࡳএᑇڶ 0.18Δ܀᧩ထֽᄷሒࠩ 5 % (ߠ।)Δ
᧩قء֮ऱދᇷެߓอᒔኔ౨ജ৬مཏሙ֏ऱދᇷެ
ᑓীΖ
Աၞԫޡᒔᎁ।Բ۟।ऱ࣠Δءઔߒආش Bootstrapֱऄ [40]Δᑓᚵ 1000 ڻ ANOVA ։࣫Δࠡऴֱ
ቹڕቹ 6~8ΖױߠΚ
(ԫ) ڇಝᒭཚၴٺഏؑயਐᑑፖ Log10 GDPऱ 1000 ڻ ANOVA ։࣫խΔྤٚ۶ԫڻࠡ᧩ထଖऱኙ 10 ࠷ ኙᑇհଖՕ࣍-1Δܛ᧩ထଖՕ࣍ 0.1Δۖڶ 97.8 %ऱᖲ
ࠡ᧩ထଖ՛࣍ 0.01Ζ
(Բ) ڇྒྷᇢཚၴঞڶ 97.8 %ऱᖲࠡ᧩ထଖ՛࣍ 0.01Ζ (Կ) ڇಝᒭཚၴኙྒྷᇢཚၴऱؑயਐᑑऱ 1000 ڻ
ANOVA։࣫խΔڶ 67.0 %ऱᖲࠡ᧩ထଖ՛࣍ 0.1Ζ
2. ʃ≙˅ᆞໞʁʠኞ
ط࣍ψळؾ၇ᔄωٌ࣐ฃऱᛧܓਢڇլەᐞٌ࣐ګ ءऱයٙՀ۷ጩנࠐऱΔڕ࣠ݾਐᑑٌ࣐ฃՈլەᐞ
ٌ࣐ګءΔᆖᛎ࿇ሒഏ୮ऱؑயਐᑑᚨᄎޓ൷२ 1.0Ζ
ԱᢞኔڼរΔڇڼאլەၦٌ࣐ګءऱයٙՀૹᄅ
⠧Ҟ ⥂⅀⫏ᅆ᱿ Log10 GDPଃఱࢍᄓ᪓྆ᐻʠ
ANOVAӠኔ (ʃ≙˅ᆞໞ)
ANOVA
ʳ ۞ط৫ SS MS F ᧩ထଖ
ಱូ 1 0.27317 0.27317 25.87633 5.63E-05
ྲྀ 20 0.211135 0.010557
᜔ࡉ 21 0.484305 ʳ ʳ ʳ
⠧ɼ ᛵ⥶⫏ᅆ᱿ Log10 GDPଃఱࢍᄓ᪓྆ᐻʠ
ANOVAӠኔ (ʃ≙˅ᆞໞ)
ANOVA
ʳ ۞ط৫ SS MS F ᧩ထଖ
ಱូ 1 0.055016 0.055016 37.77944 5.26E-06
ྲྀ 20 0.029125 0.001456
᜔ࡉ 21 0.08414 ʳ ʳ ʳ
⠧Ҝ ⥂⅀ቅ⿵ଃᛵ⥶ቅ⿵᱿ఱࢍᄓ᪓྆ᐻʠ
ANOVAӠኔ (ʃ≙˅ᆞໞ)
ANOVA
ʳ ۞ط৫ SS MS F ᧩ထଖ
ಱូ 1 0.03726 0.03726 15.89557 0.000725
ྲྀ 20 0.046881 0.002344
᜔ࡉ 21 0.08414 ʳ ʳ ʳ
ߧ 9 ⥂⅀ቅ⿵׳ߡఱࢍᄓ྆ᐻ⎞ Log10 GDPʠᄣ̡ߧ (ʃ≙˅ᆞໞ)
ച۩ᙊႚዝ֏壀ᆖጻሁΔ࣠ڕ।ն۟।Զፖቹ 9 ࠩቹ 11 ࢬقΖ
طቹ 9 ױवΔڇಝᒭཚၴԳ݁ഏփسขֻᠰ။ऱഏ ୮ؑயਐᑑ။൷२ 1.0Δֺەᐞٌ࣐ګءणउޓࣔ
᧩ΖԳ݁ഏփسขֻᠰᇞᤩؑயਐᑑऱ౨Ժৰൎ (ܒ
ࡳএᑇ 0.56)Δ᧩ထֽᄷሒࠩ 0.006 % (ߠ।ք)Ζ طቹ 10 ױवΔڇྒྷᇢཚၴ᧩ྥսڶڼԫွΔႨ
y =-0.0805x + 1.3649 R2 = 0.6539
2.5 3.0 3.5 4.0 4.5 5.0 Log10 GDP
1.2
1.1
1.0
0.9
ؑயਐᑑ
y =-0.2774x + 0.6785 R2 = 0.4428
0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 ಝᒭཚၴؑயਐᑑ
1.2
1.1
1.0
0.9
ྒྷᇢཚၴؑயਐᑑ
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 log (᧩ထଖ)
200 180 160 140 120 100 80 60 40 20 0
᙮ (ᑓᚵ1000ڻ)
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 log (᧩ထଖ)
140 120 100
80 60 40 20 0
᙮ (ᑓᚵ1000ڻ)
-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 log (᧩ထଖ)
250
200
150
100
50
0
᙮ (ᑓᚵ1000ڻ)
ߧ 10 ᛵ⥶ቅ⿵׳ߡఱࢍᄓ᪓྆ᐻ⎞ Log10 GDPʠᄣ
̡ߧ (ʃ≙˅ᆞໞ)
ߧ 11 ⥂⅀ቅ⿵ଃᛵ⥶ቅ⿵᱿ఱࢍᄓ᪓྆ᐻʠᄣ̡ߧ (ʃ≙˅ᆞໞ)
ޓףࣔ᧩ (ܒࡳএᑇ 0.65)Δ᧩ထֽᄷሒࠩ 0.0005 % (।
Ԯ)ΖࠡխڶֲءΕଉཽΕΕഏΕᐚഏΕֺܓழΕᅗ
ࠢԮଡؑॺൄڶய (ؑயਐᑑ՛࣍ 1.0)Δ֣ഗ ཎࡖΕٱ؍Ε್ࠐ۫ࠅԿଡؑለྤய (ؑயਐ ᑑ᧩ထՕ࣍ 1.0)Δࠡ塒ԼԲଡؑؑயਐᑑՕ࣍ 1.0Δ
܀լ᧩ထΖ
ڼ؆Δطቹ 11 ױवΔڇಝᒭཚၴދᇷய墿။ړऱؑ
Δࠡڇྒྷᇢཚၴދᇷய墿Ո။ړΔࠡܒࡳএᑇሒࠩ 0.44Δ
᧩ထֽᄷሒ 0.07 % (ߠ।Զ)Δ᧩قء֮ऱދᇷެߓอ ᒔኔ౨ജ৬مཏሙ֏ऱދᇷެᑓীΖ
Աၞԫޡᒔᎁ।ք۟।Զऱ࣠Δءઔߒආش Bootstrapֱऄ [40]Δᑓᚵ 1000 ڻ ANOVA ։࣫Δࠡऴֱ
ቹڕቹ 12~14ΖױߠΚ
(ԫ) ڇಝᒭཚၴٺഏؑயਐᑑፖ Log10 GDPऱ 1000 ڻ ANOVA ։࣫խΔڶԫڻࠡ᧩ထଖऱኙ 10 ࠷ኙ ᑇհଖՕ࣍ -2Δܛ᧩ထଖՕ࣍ 0.01Δܛڶ 99.9 %ऱᖲ
ࠡ᧩ထଖ՛࣍ 0.01Ζ
ߧ 12 ⥂⅀ቅ⿵׳ߡఱࢍᄓ᪓྆ᐻ⎞ Log10 GDP ᱿ ANOVA Ӡኔㆴ┮ϊ Bootstrap ᑁბኞ(ʃ≙
˅ᆞໞ)
ߧ 13 ᛵ⥶ቅ⿵׳ߡఱࢍᄓ᪓྆ᐻ⎞ Log10 GDP ᱿ ANOVAӠኔㆴ┮ϊ Bootstrap ᑁბኞ (ʃ≙
˅ᆞໞ)
ߧ 14 ⥂ ⅀ ቅ ⿵ ଃ ᛵ ⥶ ቅ ⿵ ᱿ ఱ ࢍ ᄓ ᪓ ྆ ᐻ ᱿ ANOVAӠኔㆴ┮ϊ Bootstrap ᑁბኞ(ʃ≙
˅ᆞໞ)
(Բ) ڇྒྷᇢཚၴঞڶ 98.8 %ऱᖲࠡ᧩ထଖ՛࣍ 0.01Ζ (Կ) ڇಝᒭཚၴኙྒྷᇢཚၴऱؑயਐᑑऱ 1000 ڻ
ANOVA։࣫խΔڶ 91.8 %ऱᖲࠡ᧩ထଖ՛࣍ 0.01Ζ
え⻞ɺ ᶇἄⴆח᱿⊵ъ྆ᄲ
ഏ୮ ैؑټጠ ैؑזᇆ
ֲء NIKKEI 225 N225
ٱ؍ Composite Index JKSE
֣۫ IBOVESPA SAO PAULO BVSP
್ࠐ۫ࠅ KLSE Composite KLSE
ᕠ۫ୂ IPC MXX
ឌഏ KOSPI Composite Index KS11
ભഏ DOW JONES COMPOSITE INDEX DJA
ഏ FTSE 100 FTSE
ଉཽ HANG SENG INDEX HSI
TSEC weighted index TWII
֣ഗཎࡖ Karachi 100 KSE ཎߺᥞ All Share CSE
ᄅףࡕ Straits Times Index STI
چܓ ATX ATX
ᐚഏ DAX GDAXI
ᖾ All Ordinaries AORD ᅗՓ Swiss Market SSMI ဗ৳ᎏ PSE Composite PSI
๛ᥞ AEX General AEX
ॳݪ Merval Index MERV
ऄഏ CAC 40 FCHI
ֺܓழ BEL-20 BFX
ʽȮ ⧄
ኙ࣍ءઔߒ༼נऱԲଡᎅΔኔᢞ࣠ᓵڕՀΚ 1. ༬⠛Ӡኔ⎞⬢㆜Ӵ។᱿〦
ኔᢞ࣠᧩قΔڇەᐞٌ࣐ګءՀΔԲԼԲଡؑխ
ڶԿଡؑऱैؑױ౨ױشॺᒵࢤᑓীᛧ၌ᠰܓᑮΖ
܀ڇլەᐞٌ࣐ګءՀΔԲԼԲؑխڶԼնଡؑױش ॺᒵࢤᑓীᛧࣔ᧩ऱ၌ᠰܓᑮΔࠡխڶԿଡױሒ 5 %ऱ
᧩ထֽᄷΖڂڼΔڇەᐞٌ࣐ګء৵Δܓشݾಛᇆࢬข سऱٌ࣐ฃࠡሟྤऄԫીऱ࣍၇ၞ৵ڶฃΖڼ
؆ΔאಝᒭཚၴऱދᇷᜎயࠐᇞᤩྒྷᇢཚၴऱދᇷᜎயΔ
ࠡ F อૠၦլᓵڇەᐞࢨլەᐞٌ࣐ګءՀΔ݁ױሒ 5 % ऱ᧩ထֽᄷΔ᧩قء֮ऱދᇷެߓอᒔኔ౨ജ৬مཏሙ
֏ऱދᇷެᑓীΖ
2. ఱࢍᄓ᪓⎞ᠰᱹଭỄಙ᱿〦
ኔᢞ࣠᧩قΔڇەᐞٌ࣐ګءൣउՀΔڇྒྷᇢཚၴ
Գ݁ഏփسขֻᠰױᇞᤩؑயਐᑑ 61 %ऱ᧢ฆΔF อ ૠၦሒ 0.002 %ऱ᧩ထֽᄷΖආش Bootstrap ֱऄᑓᚵ 1000 ڻ ANOVA ։࣫Δঞڶ 97.8 %ऱᖲࠡ᧩ထଖ՛࣍ 0.01Ζ
ڇլەᐞٌ࣐ګءൣउՀΔڇྒྷᇢཚၴԳ݁ഏփسขֻᠰ ױᇞᤩؑயਐᑑ 65 %ऱ᧢ฆΔF อૠၦሒ 0.0005 %ऱ
᧩ထֽᄷΖආش Bootstrap ֱऄᑓᚵ 1000 ڻ ANOVA ։࣫Δ ঞڶ 98.8 %ऱᖲࠡ᧩ထଖ՛࣍ 0.01Ζᢞኔء֮ऱψؑ
யፖᆖᛎ࿇୶࿓৫ڶᣂωհᎅΖ
ط࣍ءઔߒشԲԼԲଡؑᅝઔߒᑌءΔآࠐױല ᑌءᏺףΔࠀྒྷᇢլٵழཚᇷறΔאၞԫޡ᧭ᢞՂ૪ᎅΖ
⦒ ⨀
ءઔߒࢭ፞ഏઝᄎᢥܗ (ૠᒳᇆ 97-2221-E-216- 038)ΔڼીΖ
א≙ᄽ᪇
1. Fama㖣E. F.㖣“Random Walks in Stock Market Prices,”
Financial Analysis Journal㖣 Sep-Oct 1965, pp.55-59 (1965).
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