Forex Prediksjon Svm

Nåværende pris: 1.0739 Nedenfor er siste 10 predikasjoner basert på SVM: 1 Prediksjonssignal: SELLPris ved forutsigelse: 1.0739 2 Prediksjonssignal: SELLPris ved forutsigelse: 1.0739 3 Prediksjonssignal: SelgPrisen når spådd: 1.0739 4 Prediksjonssignal: SelgPrisen spådd: 1.0739 5 Prediksjonssignal: SELLPris ved forutsigelse: 1,0739 6 Prediksjonssignal: SELLPris ved forutsigelse: 1,0739 7 Prediksjonssignal: SELLPris ved forutsigelse: 1,0739 8 Prediksjonssignal: SELLPris ved forutsigelse: 1,0739 9 Prediksjonssignal: SELLPris ved forutsigelse: 1,0739 10 Prediksjonssignal: SELLPris når Forutsigbar: 1,0739 (Aktuell prediksjon) Gjeldende predikasjon for AUDUSD i de neste 15 minuttene er Prediksjonssignal: SELLPrisen når det er spådd: 1.0739 Nåværende Predikasjonsnøyaktighet: 100Support Vector Machines: Finansielle applikasjoner Listet i rekkefølge av citater per år, høyest på toppen. Sist oppdatert september 2006. PANG, Bo, Lillian LEE og Shivakumar VAITHYANATHAN, 2002. Tommelen opp Sentiment Klassifisering ved hjelp av maskinlæringsteknikker. I: EMNLP 02: Forløp av ACL-02 konferansen om empiriske metoder i naturlig språkbehandling - volum 10. sidene 79--86. Sitat av 154 (36.66 år) Sammendrag: Vi ser på problemet med å klassifisere dokumenter ikke etter emne, men ved generell følelse, f. eks. avgjøre om en vurdering er positiv eller negativ. Ved å bruke filmanmeldelser som data, finner vi at standard maskininnlæringsteknikker definitivt overgår de menneskeskapte baselinjene. Imidlertid fungerer ikke de tre maskinlæringsmetodene vi har brukt (Naive Bayes, maksimal entropi klassifisering og støttevektormaskiner) på sentimentklassifisering som på tradisjonell emnebasert kategorisering. Vi konkluderer med å undersøke faktorer som gjør følelsesklassifikasjonsproblemet mer utfordrende. fant at ved hjelp av filmomtaler som data, har standard maskininnlæringsteknikker definitivt overgått menneskelagte baselinjer. Imidlertid fant de også at de tre maskinlæringsmetodene de anvendte (Naive Bayes, maksimal entropiklassifikasjon og støttevektormaskiner) ikke fungerte så godt på sentimentklassifisering som på tradisjonell emnebasert kategorisering. VAN GESTEL, Tony, et al. . 2001. Financial Time Series Prediction bruker minste kvadrater Støtte vektormaskiner innenfor bevisrammen. IEEE-transaksjoner på nevrale nettverk. Volum 12, Nummer 4, Juli 2001, Sider 809-821. Cited by 77 (14.82 år) Abstract: Det bayesiske bevisrammene brukes i denne papirkurven til minst kvadratisk støttevektormaskin (LS-SVM) regresjon for å utlede ikke-lineære modeller for å forutse en økonomisk tidsserie og tilhørende volatilitet. På det første nivået av inngrep er et statistisk ramme knyttet til LS-SVM formuleringen som gjør at man kan inkludere den tidsvariative volatiliteten til markedet ved et passende valg av flere hyperparametere. Modellens hyperparametere utledes på det andre nivået av inngripen. De utledte hyperparametrene, relatert til volatiliteten, brukes til å konstruere en volatilitetsmodell innenfor bevisrammen. Modell sammenligning utføres på det tredje nivået av inngang for å automatisk justere parametrene for kjernen funksjonen og velge de relevante inngangene. LS-SVM formuleringen tillater en å utlede analytiske uttrykk i funksjonsområdet og praktiske uttrykk er oppnådd i dobbeltromet som erstatter det indre produktet ved den tilhørende kjernefunksjonen ved bruk av Mercers-teoremetoden. De forhåndsfortolkede forestillingene som er oppnådd ved forutsigelsen av den ukentlige 90-dagers regningstakten og de daglige DAX30-sluttkursene, viser at signifikant ut av prognoseforutsigelser kan gjøres med hensyn til Pesaran-Timmerman-testen, som er statistisk brukt på Bayesian-bevisrammen for å forutsi den ukentlige 90-dagers regningstakten og de daglige DAX30-sluttkursene. TAY, Francis E. H. og Lijuan CAO, 2001. Bruk av støttevektormaskiner i prognoser for økonomisk tidsserier. Omega: The International Journal of Management Science. Volum 29, Utgave 4, August 2001, Sider 309-317. Citerte av 67 (12.89 år) Abstract: Dette papiret omhandler anvendelsen av en ny nevralnettteknikk, støttevektormaskin (SVM), i prognoser for økonomisk tidsserier. Målet med dette papiret er å undersøke muligheten for SVM i prognoser for økonomisk tidsserier ved å sammenligne den med et flerlags back-propagation (BP) neuralt nettverk. Fem real futures kontrakter som samles fra Chicago Mercantile Market brukes som datasett. Eksperimentet viser at SVM overgår BP-neuralt nettverk basert på kriteriene for normalisert gjennomsnittlig kvadratfeil (NMSE), gjennomsnittlig absolutt feil (MAE), retningssymmetri (DS) og vektet retningssymmetri (WDS). Siden det ikke er strukturert måte å velge de frie parametrene til SVM, undersøkes variasjonen i ytelse med hensyn til de frie parametrene i denne studien. Analyse av eksperimentelle resultater viste at det er fordelaktig å anvende SVMs til å prognostisere finansielle tidsserier. Funnet at en SVM overgikk et flerlags back-propagation (BP) neuralt nettverk på fem virkelige futures kontrakter fra Chicago Mercantile Market. TAY, Francis E. H. og L. J. CAO, 2002. Modifisert støttevektormaskiner i prognoser for økonomisk tidsserier. Neurocomputing. Volum 48, Utgave 1-4, Oktober 2002, Sider 847-861. Citerte av 54 (12.86 år) Abstract: Dette papiret foreslår en modifisert versjon av støttevektormaskiner, kalt C - ascending-støttevektormaskin, for å modellere ikke-stationære økonomiske tidsserier. De C-skinnende støttende vektormaskiner er oppnådd ved en enkel modifikasjon av den regulerte risikofunksjonen i støttevektormaskiner, hvorved de siste 949-ufølsomme feilene blir straffet mer enn de fjerne 949-ufølsomme feilene. Denne prosedyren er basert på forkunnskapen om at i de ikke-stasjonære økonomiske tidsseriene endres avhengigheten mellom inngangsvariabler og utgangsvariabel gradvis over tid, spesielt de siste siste dataene kunne gi mer viktig informasjon enn de fjerne tidligere dataene. I eksperimentet testes C-skinnende vektorgravere med tre virkelige futures samlet fra Chicago Mercantile Market. Det er vist at de C-skannende støttende vektormaskiner med de faktisk bestilte prøvedataene konsekvent prognostiserer bedre enn standard-støttevektormaskiner, med den verste ytelsen når de omvendt bestilte prøvedataene blir brukt. Videre bruker de C-skinnende støttende vektormaskinerne færre støttevektorer enn de av standard-støttevektormaskiner, noe som resulterer i en sparsomere representasjon av løsnings-utviklede C-skannende støttende vektormaskiner, som straffer de siste 949-ufølsomme feilene er tyngre enn fjernt 949-ufølsomme feil, og fant at de prognostiserer bedre enn standard SVM på tre reelle futures samlet fra Chicago Mercantile Market. HUANG, Zan, et al. . 2004. Kredittvurderingsanalyse med støttevektorer og nevrale nettverk: en markedskomparativ studie. Beslutningssystemer. Volum 37, Utgave 4 (september 2004), Sider 543-558. Citerte av 21 (9.55 år) Sammendrag: Bedriftskvalitetsanalyse har tiltrukket seg mange forskningsinteresser i litteraturen. Nyere studier har vist at Artificial Intelligence (AI) - metoder oppnådde bedre ytelse enn tradisjonelle statistiske metoder. Denne artikkelen introduserer en relativt ny maskinlæringsteknikk, støtte vektormaskiner (SVM), til problemet i forsøk på å gi en modell med bedre forklarende kraft. Vi brukte backpropagation Neural Network (BNN) som referanse og oppnådde prediksjonsnøyaktighet rundt 80 for både BNN og SVM metoder for USA og Taiwan markeder. Imidlertid ble det bare observert liten forbedring av SVM. En annen retning av forskningen er å forbedre tolkbarheten av de AI-baserte modellene. Vi brukte nyere forskningsresultater i tolkning av nettverksmodell og oppnådd relativ betydning av de inngående økonomiske variablene fra de neurale nettverksmodellene. Basert på disse resultatene gjennomførte vi en markedskomparativ analyse på forskjellene i å bestemme faktorer i USA og Taiwan markets. applied backpropagation neurale nettverk og SVMs til bedrifts kredittvurdering prognose for USA og Taiwan markeder og fant ut at resultatene var sammenlignbare (begge var bedre enn logistisk regresjon), med SVM litt bedre. CAO, Lijuan, 2003. Støtte vektor maskiner eksperter for prognoser for tidsserier. Neurocomputing. Volum 51, april 2003, s. 321-339. Citert av 29 (9.08 år) Sammendrag: Dette papiret foreslår å bruke eksperter fra støttevektormaskiner (SVMs) for tidsserien prognoser. De generaliserte SVMs-ekspertene har en to-trinns nevrale nettverksarkitektur. I det første trinnet brukes selvorganiserende funksjonskart (SOM) som en klyngalgoritme for å partisjonere hele inngangsrommet til flere ubundne regioner. En trestrukturert arkitektur er adoptert i partisjonen for å unngå problemet med å forhåndsbestille antall partisjonerte områder. Deretter i andre fase, er flere SVMs, også kalt SVM-eksperter, best egnet til partisjonerte regioner konstruert ved å finne den mest hensiktsmessige kjernefunksjonen og de optimale frie parametrene til SVMs. Sunspot-dataene, Santa Fe datasettene A, C og D, og ​​de to bygningsdatasettene blir evaluert i eksperimentet. Simuleringen viser at SVMs eksperter oppnår betydelig forbedring i generaliseringsytelsen sammenlignet med de enkelte SVM-modellene. I tillegg konvergerer SVMs-eksperter seg raskere og bruker færre støttevektorer. Viste at deres metode for SVM-eksperter oppnådde betydelig forbedring over single SVMs-modeller når de ble lagt til Santa Fe datasett C (høyfrekvente valutakurser mellom sveitsiske franc og Amerikanske dollar). KIM, Kyoung-jae, 2003. Finansiell tidsserien prognoser ved hjelp av støttevektormaskiner. Neurocomputing. Volum 55, Utgave 1-2 (september 2003), Sider 307-319. Citerte av 28 (8.76 år) Sammendrag: Støttevektormaskiner (SVMs) er lovende metoder for prediksjon av økonomiske tidsserier fordi de bruker en risikofunksjon bestående av empirisk feil og en regulert term som er avledet fra prinsippet om strukturell risiko minimering . Denne studien gjelder SVM for å forutsi aksjekursindeksen. I tillegg undersøker denne studien muligheten til å anvende SVM i finansiell prognose ved å sammenligne den med tilbakeproduserende nevrale nettverk og sakbasert resonnement. De eksperimentelle resultatene viser at SVM gir et lovende alternativ til aksjemarkedsprognosering. Funnet at SVM har overgått tilbakegående neurale nettverk og sakbasert resonnement når det brukes til å prognose den daglige Korea Composite Stock Price Index (KOSPI). SHIN Kyung-Shik, Taik Soo LEE og Hyun-jung KIM, 2005. En applikasjon av støttevektormaskiner i konkursmodell. Ekspertsystemer med applikasjoner. Volum 28, Utgave 1, Januar 2005, Sider 127-135. Cited by 8 (6.67year) Abstract: Denne studien undersøker effekten av å bruke støttevektormaskiner (SVM) til konkursprognoseproblem. Selv om det er et velkjent faktum at back-propagation neurale nettverket (BPN) virker godt i mønstergenkjenningsoppgaver, har metoden noen begrensninger ved at det er en kunst å finne en passende modellstruktur og optimal løsning. Videre er det nødvendig å laste så mange av treningssettet som mulig inn i nettverket for å søke i nettverketes vekt. På den annen side, siden SVM fanger geometriske egenskaper av funksjonsplass uten å avlede nettvekter fra treningsdataene, er det i stand til å utvinne den optimale løsningen med den lille treningssettstørrelsen. I denne undersøkelsen viser vi at den foreslåtte klassifiseringen av SVM-tilnærmingen overgår BPN til problemet med bedriftsforutsigelser. Resultatene viser at SVM-nøyaktigheten og generaliseringsytelsen er bedre enn BPNs, da treningssettets størrelse blir mindre. Vi undersøker også effekten av variabiliteten i ytelse med hensyn til ulike verdier av parametere i SVM. I tillegg undersøker og oppsummerer vi de mange overordnede punktene i SVM-algoritmen sammenlignet med BPN. demonstrated at SVMs utfører bedre enn tilbakeproduserende nevrale nettverk når de brukes på selskaps konkursprediksjon. CAO, L. J. og Francis E. H. TAY, 2003. Støttevektormaskin med adaptive parametre i økonomisk prognose for tidsserier. IEEE-transaksjoner på nevrale nettverk. Volum 14, Utgave 6, november 2003, Sider 1506-1518. Citerte av 20 (6.25 år) Abstract: En ny type læringsmaskin som heter support vector machine (SVM) har mottatt økende interesse for områder som spenner fra sin opprinnelige applikasjon i mønstergenkjenning til andre applikasjoner som regresjonsestimering på grunn av sin bemerkelsesverdige generaliseringsytelse . Dette dokumentet omhandler anvendelsen av SVM i prognoser for økonomisk tidsserier. Muligheten for å anvende SVM i finansiell prognose blir først undersøkt ved å sammenligne den med nevrologisk nettverk (Back-propagation (BP)) og det regulære radialbasefunksjonen (RBF) neuralt nettverk. Variasjonen i ytelsen til SVM med hensyn til de frie parametrene undersøkes eksperimentelt. Adaptive parametere blir da foreslått ved å inkorporere ikke-stationaritet i finansielle tidsserier i SVM. Fem real futures kontrakter samles fra Chicago Mercantile Market brukes som datasett. Simuleringen viser at blant de tre metodene, utvider SVM BP-neurale nettverket i økonomisk prognose, og det er sammenlignbar generaliseringsytelse mellom SVM og det regulerte RBF nevrale nettverket. Videre har de frie parametrene til SVM en stor effekt på generaliseringsytelsen. SVM med adaptive parametre kan begge oppnå høyere generaliseringsytelse og bruke færre støttevektorer enn standard SVM i økonomisk prognose. Brukte en SVM, et nevrologisk nettverk med multilayer back propagation (BP) og et regulert radialbasefunksjon (RBF) neuralt nettverk for å forutsi Fem real futures kontrakter samles fra Chicago Mercantile Market. Resultatene viste at SVM og det regulerte RBF nevrale nettverket var sammenlignbare og begge utgjorde bedre BP-neurale nettverket. CAO, Lijuan og Francis E. H. TAY, 2001. Finansiell prognose ved hjelp av støttevektormaskiner. Neural Computing amp programmer. Volum 10, Nummer 2 (mai 2001), Sider 184-192. Citerte av 26 (5.00 år) Abstract: Bruken av Support Vector Machines (SVMs) er studert i økonomisk prognose ved å sammenligne den med en flerlags perceptron utdannet av Back Propagation (BP) algoritmen. SVMs prognostiserer bedre enn BP basert på kriteriene for trend for normalisert gjennomsnittlig feilfeil (NMSE), gjennomsnittlig absolutt feil (MAE), retningssymmetri (DS), korrigere opp (CP) og korrigere ned (CD). SampP 500 daglig prisindeks brukes som datasett. Siden det ikke er strukturert måte å velge de frie parametrene til SVM, undersøkes generaliseringsfeilen med hensyn til de frie parametrene til SVMs i dette eksperimentet. Som illustrert i eksperimentet har de liten innflytelse på løsningen. Analyse av eksperimentelle resultater viser at det er fordelaktig å anvende SVMs for å prognostisere den finansielle tidsserien. Funnet at SVMs forutsier SampP 500 daglig prisindeks bedre enn en flerlags perceptron utdannet av Back Propagation (BP) algoritmen. MIN, Jae H. og Young-Chan LEE, 2005. Konkursforutsigelse ved hjelp av støttevektormaskin med optimal valg av kjernevalgsparametere. Ekspertsystemer med applikasjoner. Volum 28, Utgave 4, Mai 2005, Sider 603-614. Cited by 6 (5.00year) Abstract: Konkursforutsetningen har trukket mange forskningsinteresser i tidligere litteratur, og nyere studier har vist at maskinlæringsteknikker oppnådde bedre ytelse enn tradisjonelle statistiske. Dette papiret bruker støttevektormaskiner (SVMs) til konkursproblemet i et forsøk på å foreslå en ny modell med bedre forklarende kraft og stabilitet. For å tjene denne hensikten, bruker vi en rutenettsøkteknikk ved hjelp av 5-folds kryss-validering for å finne ut de optimale parameterverdiene for kernelfunksjonen til SVM. I tillegg, for å evaluere prediksjonsnøyaktigheten til SVM, sammenligner vi ytelsen med de med MDA, logistisk regresjonsanalyse (Logit) og tre-lags fullt koblede back-propagation neurale nettverk (BPN). Eksperimentresultatene viser at SVM overgår de andre metodene. Funnet at SVM har overgått flere diskrimineringsanalyser (MDA), logistisk regresjonsanalyse (Logit) og tre-lags fullt koblede tilbaketransplantasjonsnettverk (BPN). ABRAHAM, Ajith, Ninan Sajith PHILIP og P. SARATCHANDRAN, 2003. Modeling kaotisk oppførsel av aksjeindekser ved hjelp av intelligente paradigmer. Neural, Parallel amp Scientific Computations. Volum 11, side 143-160. Citert av 10 (4.55 år) Sammendrag: Bruken av intelligente systemer for aksjemarkedsprognoser har blitt utbredt. I dette papiret undersøker vi hvordan tilsynelatende kaotisk oppførsel av aksjemarkeder kan være godt representert ved å bruke flere sammenhengende paradigmer og myke databehandlingsteknikker. For å demonstrere ulike teknikker, vurderte vi Nasdaq-100-indeksen på Nasdaq Stock Market SM og SP CNX NIFTY aksjeindeksen. Vi analyserte 7 år8217s Nasdaq 100 hovedindeksverdier og 4 år8217s NIFTY-indeksverdier. Dette papiret undersøker utviklingen av en pålitelig og effektiv teknikk for å modellere tilsynelatende kaotisk oppførsel av aksjemarkeder. Vi vurderte et kunstig nevralt nettverk utdannet ved hjelp av Levenberg-Marquardt-algoritmen, Support Vector Machine (SVM), Takagi-Sugeno neurofuzzy-modellen og et Difference Boosting Neural Network (DBNN). Dette papiret forklarer kort hvordan de ulike forbindelsesparadigmene kan formuleres ved hjelp av ulike læringsmetoder og deretter undersøker om de kan gi det nødvendige ytelsesnivået, som er tilstrekkelig gode og robuste for å gi en pålitelig prognosemodell for børsindekser. Eksperimentresultater avslører at alle de tilknyttede paradigmene som kan vurderes, kunne representere aksjeindeksens adferd svært nøyaktig. Anvendt fire forskjellige teknikker, et kunstig nevralt nettverk utdannet ved hjelp av Levenberg-Marquardt-algoritmen, en støttevektormaskin, en forskjell som øker nevrale nettverk og en Takagi-Sugeno fuzzy inference system lært ved hjelp av en neural nettverk algoritme (neuro-fuzzy modell) til prognose av Nasdaq-100 indeksen av Nasdaq Stock Market og SP CNX NIFTY aksjeindeks. Ingen teknikk var klart overlegen, men absurd, de forsøker å forutsi den absolutte verdien av indeksene, i stedet for å bruke loggkast. YANG, Haiqin, Laiwan CHAN og Irwin KING, 2002. Støtte Vector Machine Regression for Volatile Stock Market Prediction. I: Intelligent dataneknikk og automatisert læring: IDEAL 2002. redigert av Hujun Yin, et al. . side 391--396, Springer. Cited by 19 (4.52 år) Abstract: Nylig har Support Vector Regression (SVR) blitt introdusert for å løse regresjons - og prediksjonsproblemer. I dette papiret bruker vi SVR til økonomiske prediksjonsoppgaver. Spesielt er de økonomiske dataene vanligvis støyende og den tilhørende risikoen er tidsvarierende. Derfor er vår SVR-modell en forlengelse av standard SVR som inkorporerer marginaljustering. Ved å variere marginene til SVR, kan vi gjenspeile endringen i volatiliteten til de økonomiske dataene. Videre har vi analysert effekten av asymmetriske marginer for å muliggjøre reduksjon av downside-risikoen. Våre eksperimentelle resultater viser at bruken av standardavvik for å beregne en variabelmargin gir et godt forutsigbart resultat i prediksjonen av Hang Seng Index. tryed varierer marginene i SVM-regresjon for å gjenspeile endringen i volatiliteten i finansielle data og analyserte også effekt av asymmetriske marginer for å muliggjøre reduksjon av risikoen for ulemper. Den tidligere tilnærmingen ga den laveste totale feilen ved å forutse den daglige sluttkursen for Hong Kongs Hang Seng Index (HSI). HUANG, W. Y. NAKAMORI og S. Y. WANG, 2005. Forutsi aksjemarkedet bevegelsesretning med støttevektormaskin. Datamaskiner Operations Research. Volum 32, Utgave 10, Sider 2513-2522. (Oktober 2005) Citerte av 5 (4.18 år) Sammendrag: Støttevektormaskinen (SVM) er en meget spesifikk type læringsalgoritmer preget av kapasitetsstyring av avgjørelsesfunksjonen, bruken av kjernefunksjonene og løsningen av løsningen. I dette papiret undersøker vi forutsigbarheten for økonomisk bevegelsesretning med SVM ved å prognose den ukentlige bevegelsesretningen til NIKKEI 225-indeksen. For å evaluere SVMs prognostiseringsevne sammenligner vi ytelsen med Linear Discriminant Analysis, Quadratic Discriminant Analysis og Elman Backpropagation Neural Networks. Eksperimentresultatene viser at SVM overgår de andre klassifiseringsmetodene. Videre foreslår vi en kombinasjonsmodell ved å integrere SVM med de andre klassifiseringsmetodene. Kombinasjonsmodellen fungerer best blant alle prognosemetodene som sparer evnen til SVMs, Lineær Discriminant Analysis, Quadratic Discriminant Analysis og Elman Backpropagation Neural Networks for å prognose den ukentlige bevegelsesretningen til NIKKEI 225-indeksen og fant at SVM overgikk alle de andre klassifikasjonsmetodene . Det var enda bedre en vektet kombinasjon av modellene. TRAFALIS, Theodore B. og Huseyin INCE, 2000. Støttevektormaskin for regresjon og applikasjoner til finansiell prognose. I: IJCNN 2000: Forhandlinger av IEEE-INNS-ENNS internasjonale felles konferanse om nevrale nettverk: Volum 6 redigert av Shun-Ichi Amari, et al. . side 6348, IEEE Computer Society. Cited by 19 (3.06 år) Sammendrag: Hovedformålet med dette papiret er å sammenligne støttevektormaskinen (SVM) utviklet av Vapnik med andre teknikker som RPF-nettverk (RPF) for finansielle prognoseprogrammer. Teorien om SVM-algoritmen er basert på statistisk læringsteori. Opplæring av SVMs fører til et kvadratisk programmeringsproblem (QP). Foreløpige beregningsresultater for beregning av aksjekurs er også presentert SVMs med Backpropagation og Radial Basis Function (RBF) Networks ved å forutsi IBM, Yahoo og America Online daglig aksjekurser. Merkelig, ved å bruke SVM for regresjon, gikk de frem til et valideringssett, satt epsilon til null, fast C og gjentok eksperimentet for ulike faste innstillinger av kjerneparameteren, sigma, som ga opphav til flere resultater. CAO, Lijuan og Qingming GU, 2002. Dynamiske støttevektorer for ikke-stationær prognoser for tidsserier. Intelligent dataanalyse. Volum 6, Nummer 1, Sider 67-83. Citert av 12 (2.86 år) Sammendrag: Dette papiret foreslår en modifisert versjon av støttevektormaskiner (SVMs), kalt dynamiske støttevektormaskiner (DSVMs), for å modellere ikke-stationære tidsserier. DSVMene er oppnådd ved å inkorporere problemdomenekunnskapen - ikke-stationaritet av tidsserier i SVMs. I motsetning til standard SVM som bruker faste verdier av regulariseringskonstanten og rørstørrelsen i alle treningsdataene, bruker DSVMene en eksponentielt økende regulariseringskonstant og en eksponentielt avtagende rørstørrelse for å håndtere strukturelle endringer i dataene. Den dynamiske reguleringskonstanten og rørstørrelsen er basert på forkunnskapen om at de siste datapunktene i de ikke-stationære tidsseriene kunne gi mer viktig informasjon enn fjerntliggende datapunkter. I eksperimentet evalueres DSVMene med både simulerte og virkelige datasett. Simuleringen viser at DSVMene generaliserer bedre enn standard SVM-er i prognoser for ikke-stationære tidsserier. En annen fordel ved denne modifikasjonen er at DSVMene bruker færre støttevektorer, noe som resulterer i en tynnere representasjon av løsningen. Innlemme forkunnskapen om at finansielle tidsserier ikke er stationære i deres dynamiske støttevektormaskiner (DSVMs) og bruker en eksponentielt økende regulariseringskonstant og en eksponentielt avtagende rørstørrelse for å håndtere strukturelle endringer i dataene ut fra antagelsen om at nyere datapunkter kan gi mer viktig informasjon enn fjerne datapunkter. De konkluderer med at DSVMs generaliserer bedre enn standard SVMs ved prognoser for ikke-stationære tidsserier, mens de også bruker færre støttevektorer, noe som resulterer i en tynnere representasjon av løsningen. TAY, Francis E. H. og L. J. CAO, 2002. 949-Nedadgående støttevektormaskiner for prognoser for økonomisk tidsserier. Neural Processing Letters 15 (2): 179-195. Citerte av 11 (2.62 år) Sammendrag: Dette papiret foreslår en modifisert versjon av støttevektormaskiner (SVMs), kalt 949-nedstigende støttevektormaskiner (949-DSVM), for å modellere ikke-stationære økonomiske tidsserier. 949-DSVMene er oppnådd ved å inkorporere problemdomene kunnskap 8211 ikke-stationaritet av finansielle tidsserier til SVMs. I motsetning til standard SVM som bruker et konstant rør i alle treningsdatapunktene, bruker 949-DSVMene et adaptivt rør for å håndtere strukturendringene i dataene. Eksperimentet viser at 949-DSVMene generaliserer bedre enn standard SVM i prognoser ikke-stasjonære økonomiske tidsserier. En annen fordel ved denne modifikasjonen er at 949-DSVMene konvergerer til færre støttervektorer, noe som resulterer i en tynnere representasjon av løsningen. Innlemmet problemdomenes kunnskap om ikke-stationaritet av finansielle tidsserier til SVMs ved å bruke et adaptivt rør i deres såkalte epsilon-synkende støttevektormaskiner (epsilon-DSVMs). Eksperiment viste at epsilon-DSVMs generaliserer bedre enn standard SVMs ved prognoser for ikke-stasjonære finansielle tidsserier, og konvergerer også til færre støttevektorer, noe som resulterer i en tynnere representasjon av løsningen. DEBNATH, Sandip og C. Lee GILES, 2005. En læringsbasert modell for overskriftutvinning av nyhetsartikler for å finne forklarende setninger for hendelser. I: K-CAP 821705: Utførelser av den tredje internasjonale konferansen om kunnskapsfangst. Sider 189-190. Sitat ved 2 (1.67 år) Sammendrag: Metadatainformasjon spiller en avgjørende rolle i å øke dokumentorganiseringseffektiviteten og arkiverbarheten. Nyhets metadata inneholder DateLine. ByLine. HeadLine og mange andre. Vi fant ut at HeadLine-informasjonen er nyttig for å gjette temaet for nyhetsartikkelen. Spesielt for finansielle nyheter, fant vi at HeadLine kan være spesielt nyttig for å finne forklarende setninger for større hendelser som for eksempel betydelige endringer i aksjekursene. I dette papiret undersøker vi en støttevektorbasert læringsmetode for automatisk å trekke ut HeadLine-metadataene. Vi finner at klassifikasjonsnøyaktigheten ved å finne HeadLine s forbedres hvis DateLine s er identifisert først. Vi brukte deretter den ekstraherte HeadLine s for å starte et mønster som matcher søkeord for å finne setningene som er ansvarlige for historietemaet. Ved hjelp av dette temaet og en enkel språkmodell er det mulig å finne noen forklarende setninger for en betydelig prisendring. Vis en ny tilnærming til å utvinne nyhetsmetadata HeadLines ved hjelp av SVMs og bruke dem til å finne historietemaer for å få en setningsbasert forklaring på en aksje prisendring. Van GESTEL, Tony, et al. . 2003. En støtte vektor maskin tilnærming til kreditt scoring. Bank en Financiewezen. Volum 2, mars, sider 73-82. Cited by 5 (1.56 år) Sammendrag: Drevet av behovet for å tildele kapital på en lønnsom måte og ved de nylig foreslåtte Basel II-regelverkene, blir finansinstitusjoner mer og mer forpliktet til å bygge kredittvurderingsmodeller som vurderer risikoen for mislighold av sine kunder . Mange teknikker har blitt foreslått for å takle dette problemet. Support Vector Machines (SVMs) er en lovende ny teknikk som nylig har stått fra forskjellige domener som anvendt statistikk, neurale nettverk og maskinlæring. I dette papiret eksperimenterer vi med vektorgravere med minst kvadrater (LS-SVMs), en nylig modifisert versjon av SVMs, og rapporterer betydelig bedre resultater når de er kontrasterte med de klassiske teknikkparede metodene, Ordinary Least Squares (OLS), Ordinal Logistic Regression (Ordinary Logistic Regression) OLR), Multilayer Perceptron (MLP) og minste kvadrater-støttevektormaskiner (LS-SVMs) når de brukes til kredittpoeng. SVM-metoden ga betydelige og konsekvent bedre resultater enn de klassiske lineære klassifiseringsmetodene. FAN, Alan og Marimuthu PALANISWAMI, 2000. Valg av konkursprediktorer ved hjelp av en støttevektormaskinens tilnærming. IJCNN 2000: Forhandlinger av IEEE-INNS-ENNS internasjonale felles konferanse om neurale nettverk, bind 6. redigert av Shun-Ichi Amari et al. . side 6354. Sitert av 9 (1.45 år) Sammendrag: Konvensjonell Neural Network-tilnærming har blitt funnet nyttig i å forutsi bedriftsforstyrrelser fra regnskap. I dette papiret har vi vedtatt en Support Vector Machine tilnærming til problemet. En ny måte å velge konkurspredikatorer på er vist ved hjelp av det euklidiske avstandsbaserte kriteriet beregnet i SVM-kjernen. En komparativ studie tilbys ved hjelp av tre klassiske bedriftsmodeller og en alternativ modell basert på SVM-tilnærmingen. Bruk SVM til å velge konkurspredikatorer og gi en komparativ studie. TAY, Francis Eng Hock og Li Juan CAO, 2001. Forbedret økonomisk tidsserier prognoser ved å kombinere Support Vector Machines med selvorganiserende funksjonskart. Intelligent dataanalyse. Volum 5, Nummer 4, Sider 339-354. Citerte av 7 (1.35 år) Abstract: En to-trinns nevrale nettverksarkitektur konstruert ved å kombinere Support Vector Machines (SVMs) med selvorganiserende funksjonskart (SOM) er foreslått for prognose for økonomisk tidsserier. I første fase brukes SOM som en klyngalgoritme for å partisjonere hele innspillingsplassen i flere ujevne områder. En trestrukturert arkitektur er adoptert i partisjonen for å unngå problemet med å forhåndsbestille antall partisjonerte områder. Deretter i andre fase, er flere SVMs, også kalt SVM eksperter, som passer best for hver partisjonert region, konstruert ved å finne den mest hensiktsmessige kjernefunksjonen og de optimale læringsparametrene til SVMs. Santa Fe-valutakursen og fem virkelige futures-kontrakter brukes i forsøket. Det er vist at den foreslåtte metoden oppnår både signifikant høyere prediksjonsytelse og raskere konvergenshastighet sammenlignet med en enkelt SVM-modellbasert SVM med et selvorganiserende funksjonskart (SOM) og testet modellen på Santa Fe-valutakursen og fem virkelige futureskontrakter . De viste at deres foreslåtte metode oppnår både betydelig høyere prediksjonsytelse og raskere konvergenshastighet sammenlignet med en enkelt SVM-modell. SANSOM, D. C. T. DOWNS and T. K. SAHA, 2003. Evaluation of support vector machine based forecasting tool in electricity price forecasting for Australian national electricity market participants. Journal of Electrical Electronics Engineering, Australia . Vol 22, No. 3, Pages 227-234. Cited by 5 (1.19year) Abstract: In this paper we present an analysis of the results of a study into wholesale (spot) electricity price forecasting utilising Neural Networks (NNs) and Support Vector Machines (SVM). Frequent regulatory changes in electricity markets and the quickly evolving market participant pricing (bidding) strategies cause efficient retraining to be crucial in maintaining the accuracy of electricity price forecasting models. The efficiency of NN and SVM retraining for price forecasting was evaluated using Australian National Electricity Market (NEM), New South Wales regional data over the period from September 1998 to December 1998. The analysis of the results showed that SVMs with one unique solution, produce more consistent forecasting accuracies and so require less time to optimally train than NNs which can result in a solution at any of a large number of local minima. The SVM and NN forecasting accuracies were found to be very similar. evaluated utilising Neural Networks (NNs) and Support Vector Machines (SVM) for wholesale (spot) electricity price forecasting. The SVM required less time to optimally train than the NN, whilst the SVM and NN forecasting accuracies were found to be very similar. ABRAHAM, Ajith and Andy AUYEUNG, 2003. Integrating Ensemble of Intelligent Systems for Modeling Stock Indices. In: Proceedings of 7th International Work Conference on Artificial and Natural Neural Networks, Part II . Lecture Notes in Computer Science, Volume 2687, Jose Mira and Jose R. Alverez (Eds.), Springer Verlag, Germany, pp. 774-781, 2003. Cited by 3 (0.94year) Abstract: The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well-represented using ensemble of intelligent paradigms. To demonstrate the proposed technique, we considered Nasdaq-100 index of Nasdaq Stock Market SM and the SampP CNX NIFTY stock index. The intelligent paradigms considered were an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network. The different paradigms were combined using two different ensemble approaches so as to optimize the performance by reducing the different error measures. The first approach is based on a direct error measure and the second method is based on an evolutionary algorithm to search the optimal linear combination of the different intelligent paradigms. Experimental results reveal that the ensemble techniques performed better than the individual methods and the direct ensemble approach seems to work well for the problem considered. considered an artificial neural network trained using Levenberg-Marquardt algorithm, a support vector machine, a Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network for predicting the NASDAQ-100 Index of The Nasdaq Stock Market and the SP CNX NIFTY stock index. They concluded that an ensemble of the intelligent paradigms performed better than the individual methods. YANG, Haiqin, et al. . 2004. Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression. In: Neural Information Processing: Research and Development . edited by Jagath Chandana Rajapakse and Lipo Wang, Springer-Verlag. Cited by 2 (0.91year) Abstract: Recently, Support Vector Regression (SVR) has been applied to financial time series prediction. The financial time series usually contains the characteristics of small sample size, high noise and non-stationary. Especially the volatility of the time series is time-varying and embeds some valuable information about the series. Previously, we had proposed to use the volatility in the data to adaptively change the width of the margin in SVR. We have noticed that up margin and down margin would not necessary be the same, and we also observed that their choice would affect the upside risk, downside risk and as well as the overall prediction performance. In this work, we introduce a novel approach to adopt the momentum in the asymmetrical margins setting. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average. used SVMs for regression with non-fixed and asymmetrical margin settings, this time with momentum, to predict the Hang Seng Index and Dow Jones Industrial Average. PAI, Ping-Feng and Chih-Sheng LIN, 2005. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega . Volume 33, Issue 6, December 2005, Pages 497-505. Cited by 1 (0.84year) Abstract: Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising. proposed a hybrid ARIMA and support vector machine model for stock price forecasting, and results looked very promising. ABRAHAM, Ajith, et al. . 2002. Performance Analysis of Connectionist Paradigms for Modeling Chaotic Behavior of Stock Indices. In: Second international workshop on Intelligent systems design and application . edited by Ajith Abraham, et al. . pages 181--186. Cited by 3 (0.71year) Abstract: The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketTM and the SP CNX NIFTY stock index. We analyzed 7 years Nasdaq 100 main index values and 4 years NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN). This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately. analysed the performance of an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN) when predicting the NASDAQ-100 Index of The Nasdaq Stock Market and the SP CNX NIFTY stock index. YANG, Haiqin, I. KING and Laiwan CHAN, 2002. Non-fixed and asymmetrical margin approach to stock market prediction using Support Vector Regression. In: ICONIP 02. Proceedings of the 9th International Conference on Neural Information Processing. Volume 3 . edited by Lipo Wang, et al. . pages 1398--1402. Cited by 3 (0.71year) Abstract: Recently, support vector regression (SVR) has been applied to financial time series prediction. Typical characteristics of financial time series are non-stationary and noisy in nature. The volatility, usually time-varying, of the time series is therefore some valuable information about the series. Previously, we had proposed to use the volatility to adaptively change the width of the margin of SVR. We have noticed that upside margin and downside margin do not necessary be the same, and we have observed that their choice would affect the upside risk, downside risk and as well as the overall prediction result. In this paper, we introduce a novel approach to adapt the asymmetrical margins using momentum. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average. used SVM regression with a non-fixed and asymmetrical margin, this time adapting the asymmetrical margins using momentum, and applied it to predicting the Hang Seng Index and the Dow Jones Industrial Average. GAVRISHCHAKA, Valeriy V. and Supriya B. GANGULI, 2003. Volatility forecasting from multiscale and high-dimensional market data. Neurocomputing . Volume 55, Issues 1-2 (September 2003), Pages 285-305. Cited by 2 (0.63year) Abstract: Advantages and limitations of the existing volatility models for forecasting foreign-exchange and stock market volatility from multiscale and high-dimensional data have been identified. Support vector machines (SVM) have been proposed as a complimentary volatility model that is capable of effectively extracting information from multiscale and high-dimensional market data. SVM-based models can handle both long memory and multiscale effects of inhomogeneous markets without restrictive assumptions and approximations required by other models. Preliminary results with foreign-exchange data suggest that SVM can effectively work with high-dimensional inputs to account for volatility long-memory and multiscale effects. Advantages of the SVM-based models are expected to be of the utmost importance in the emerging field of high-frequency finance and in multivariate models for portfolio risk management. used SVMs for forecasting the volatility of foreign-exchange data. Their preliminary benchmark tests indicated that SVMs can perform significantly better than or comparable to both naive and GARCH(1,1) models. P201REZ-CRUZ, Fernando, Julio A. AFONSO-RODR205GUEZ and Javier GINER, 2003. Estimating GARCH models using support vector machines. Quantitative Finance . Volume 3, Number 3 (June 2003), Pages 163-172. Cited by 2 (0.63year) Abstract: Support vector machines (SVMs) are a new nonparametric tool for regression estimation. We will use this tool to estimate the parameters of a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we will show that GARCH models can be estimated using SVMs and that such estimates have a higher predicting ability than those obtained via common ML methods. used SVMs for regression to estimate the parameters of a GARCH model for predicting the conditional volatility of stock market returns and showed that such estimates have a higher predicting ability than those obtained via common maximum likelihood (ML) methods. Van GESTEL, T. et al. . 2003. Bankruptcy prediction with least squares support vector machine classifiers. In: 2003 IEEE International Conference on Computational Intelligence for Financial Engineering: Proceedings . pages 1-8. Cited by 2 (0.63year) Abstract: Classification algorithms like linear discriminant analysis and logistic regression are popular linear techniques for modelling and predicting corporate distress. These techniques aim at finding an optimal linear combination of explanatory input variables, such as, e. g. solvency and liquidity ratios, in order to analyse, model and predict corporate default risk. Recently, performant kernel based nonlinear classification techniques, like support vector machines, least squares support vector machines and kernel fisher discriminant analysis, have been developed. Basically, these methods map the inputs first in a nonlinear way to a high dimensional kernel-induced feature space, in which a linear classifier is constructed in the second step. Practical expressions are obtained in the so-called dual space by application of Mercers theorem. In this paper, we explain the relations between linear and nonlinear kernel based classification and illustrate their performance on predicting bankruptcy of mid-cap firms in Belgium and the Netherlands. used least squares support vector machine classifiers for predicting bankruptcy of mid-cap firms in Belgium and the Netherlands. CAO, L. J. and W. K. CHONG, 2002. Feature extraction in support vector machine: a comparison of PCA, XPCA and ICA. ICONIP 02: Proceedings of the 9th International Conference on Neural Information Processing, Volume 2 . edited by Lipo Wang, et al. . pages 1001-1005. Cited by 2 (0.48year) Abstract: Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, feature extraction is the first important step. This paper proposes the applications of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVM for feature extraction. PCA linearly transforms the original inputs into uncorrelated features. KPCA is a nonlinear PCA developed by using the kernel method. In ICA, the original inputs are linearly transformed into statistically independent features. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, there is better generalization performance in KPCA and ICA feature extraction than PCA feature extraction. considered the application of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVMs for feature extraction. By examining the sunspot data and one real futures contract, they showed that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, they found that there is better generalization performance in KPCA and ICA feature extraction than PCA feature extraction. CAO, L. J. and Francis E. H. TAY, 2000. Feature Selection for Support Vector Machines in Financial Time Series Forecasting. In: Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents . edited by Kwong Sak Leung, Lai-Wan Chan and Helen Meng, pages 268-273. Cited by 3 (0.48year) Abstract: This paper deals with the application of saliency analysis to Support Vector Machines (SVMs) for feature selection. The importance of feature is ranked by evaluating the sensitivity of the network output to the feature input in terms of the partial derivative. A systematic approach to remove irrelevant features based on the sensitivity is developed. Five futures contracts are examined in the experiment. Based on the Simulation results, it is shown that that saliency analysis is effective in SVMs for identifying important features. dealt with the application of saliency analysis to feature selection for SVMs. Five futures contracts were examined and they concluded that saliency analysis is effective in SVMs for identifying important features. ZHOU, Dianmin, Feng GAO and Xiaohong GUAN, 2004. Application of accurate online support vector regression in energy price forecast. WCICA 2004: Fifth World Congress on Intelligent Control and Automation, Volume 2 . pages 1838-1842. Cited by 1 (0.45year) Abstract: Energy price is the most important indicator in electricity markets and its characteristics are related to the market mechanism and the change versus the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability. In this paper, an accurate online support vector regression (AOSVR) method is applied to update the price forecasting model. Numerical testing results show that the method is effective in forecasting the prices of the electric-power markets. applied an accurate online support vector regression (AOSVR) to forecasting the prices of the electric-power markets, results showed that it was effective. FAN, A. and M. PALANISWAMI, 2001. Stock selection using support vector machines. IJCNN01: International Joint Conference on Neural Networks, Volume 3 . Pages 1793-1798. Cited by 2 (0.38year) Abstract: We used the support vector machines (SVM) in a classification approach to beat the market. Given the fundamental accounting and price information of stocks trading on the Australian Stock Exchange, we attempt to use SVM to identify stocks that are likely to outperform the market by having exceptional returns. The equally weighted portfolio formed by the stocks selected by SVM has a total return of 208 over a five years period, significantly outperformed the benchmark of 71. We also give a new perspective with a class sensitivity tradeoff, whereby the output of SVM is interpreted as a probability measure and ranked, such that the stocks selected can be fixed to the top 25used SVMs for classification for stock selection on the Australian Stock Exchange and significantly outperformed the benchmark. Van GESTEL, Tony, et al. . 2000. Volatility Tube Support Vector Machines. Neural Network World . vol. 10, number 1, pp. 287-297. Cited by 2 (0.32year) Abstract: In Support Vector Machines (SVM8217s), a non-linear model is estimated based on solving a Quadratic Programming (QP) problem. The quadratic cost function consists of a maximum likelihood cost term with constant variance and a regularization term. By specifying a difference inclusion on the noise variance model, the maximum likelihood term is adopted for the case of heteroskedastic noise, which arises in financial time series. The resulting Volatility Tube SVM8217s are applied on the 1-day ahead prediction of the DAX30 stock index. The influence of todays closing prices of the New York Stock Exchange on the prediction of tomorrow8217s DAX30 closing price is analyzed. developed the Volatility Tube SVM and applied it to 1-day ahead prediction of the DAX30 stock index, and significant positive out-of-sample results were obtained. CAO, Li Juan, Kok Seng CHUA and Lim Kian GUAN, 2003. Combining KPCA with support vector machine for time series forecasting. In: 2003 IEEE International Conference on Computational Intelligence for Financial Engineering . pages 325-329. Cited by 1 (0.31year) Abstract: Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first important step is feature extraction. This paper applies kernel principal component analysis (KPCA) to SVM for feature extraction. KPCA is a nonlinear PCA developed by using the kernel method. It firstly transforms the original inputs into a high dimensional feature space and then calculates PCA in the high dimensional feature space. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature forms much better than that extraction using KPCA per without feature extraction. In comparison with PCA, there is also superior performance in KPCA. applied kernel principal component analysis (KPCA) to SVM for feature extraction. The authors examined sunspot data and one real futures contract, and found such feature extraction enhanced performance and also that KPCA was superior to PCA. YANG, Haiqin, 2003. Margin Variations in Support Vector Regression for the Stock Market Prediction. Degree of Master of Philosophy Thesis, Department of Computer Science Engineering, The Chinese University of Hong Kong, June 2003. Cited by 1 (0.31year) Abstract: Support Vector Regression (SVR) has been applied successfully to financial time series prediction recently. In SVR, the 949-insensitive loss function is usually used to measure the empirical risk. The margin in this loss function is fixed and symmetrical. Typically, researchers have used methods such as crossvalidation or random selection to select a suitable 949 for that particular data set. In addition, financial time series are usually embedded with noise and the associated risk varies with time. Using a fixed and symmetrical margin may have more risk inducing bad results and may lack the ability to capture the information of stock market promptly. In order to improve the prediction accuracy and to consider reducing the downside risk, we extend the standard SVR by varying the margin. By varying the width of the margin, we can reflect the change of volatility in the financial data by controlling the symmetry of margins, we are able to reduce the downside risk. Therefore, we focus on the study of setting the width of the margin and also the study of its symmetry property. For setting the width of margin, the Momentum (also including asymmetrical margin control) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are considered. Experiments are performed on two indices: Hang Seng Index (HSI) and Dow Jones Industrial Average (DJIA) for the Momentum method and three indices: Nikkei225, DJIA and FTSE100, for GARCH models, respectively. The experimental results indicate that these methods improve the predictive performance comparing with the standard SVR and benchmark model. On the study of the symmetry property, we give a sufficient condition to prove that the predicted value is monotone decreasing to the increase of the up margin. Therefore, we can reduce the predictive downside risk, or keep it zero, by increasing the up margin. An algorithm is also proposed to test the validity of this condition, such that we may know the changing trend of predictive downside risk by only running this algorithm on the training data set without performing actual prediction procedure. Experimental results also validate our analysis. employs SVMs for regression and varys the width of the margin to reflect the change of volatility and controls the symmetry of margins to reduce the downside risk. Results were positive. CALVO, Rafael A. and Ken WILLIAMS, 2002. Automatic Categorization of Announcements on the Australian Stock Exchange. Cited by 1 (0.24year) Abstract: This paper compares the performance of several machine learning algorithms for the automatic categorization of corporate announcements in the Australian Stock Exchange (ASX) Signal G data stream. The article also describes some of the applications that the categorization of corporate announcements may enable. We have performed tests on two categorization tasks: market sensitivity, which indicates whether an announcement will have an impact on the market, and report type, which classifies each announcement into one of the report categories defined by the ASX. We have tried Neural Networks, a Na239ve Bayes classifier, and Support Vector Machines and achieved good resultspared the performance of neural networks, a na ve bayes classifier, and SVMs for the automatic categorization of corporate announcements in the Australian Stock Exchange (ASX) Signal G data stream. The results were all good, but with the SVM underperforming the other two models. AHMED, A. H.M. T. 2000. Forecasting of foreign exchange rate time series using support vector regression. 3rd year project. Computer Science Department, University of Manchester. Cited by 1 (0.16year)used support vector regression for forecasting a foreign exchange rate time series. GUESDE, Bazile, 2000. Predicting foreign exchange rates with support vector regression machines. MSc thesis. Computer Science Department, University of Manchester. Cited by 1 (0.16year) Abstract: This thesis investigates how Support Vector Regression can be applied to forecasting foreign exchange rates. At first we introduce the reader to this non linear kernel based regression and demonstrate how it can be used for time series prediction. Then we define a predictive framework and apply it to the Canadian exchange rates. But the non-stationarity in the data, which we here define as a drift in the map of the dynamics, forces us to present and use the typical learning processes for catching different dynamics. Our implementation of these solutions include Clusters of Volatility and competing experts. Finally those experts are used in a financial vote trading system and substantial profits are achieved. Through out the thesis we hope the reader will be intrigued by the results of our analysis and be encouraged in other dircetions for further research. used SVMs for regression to predict the Canadian exchange rate, wisely recognised the problem of nonstationarity, dealt with it using experts and claimed that substantial profits were achieved. BAO, Yu-Kun, et al. . 2005. Forecasting Stock Composite Index by Fuzzy Support Vector Machines Regression. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Volume 6 . pages 3535-3540. not cited (0year) Abstract: Financial time series forecasting methods such as exponential smoothing are commonly used for prediction on stock composition index (SCI) and have made great contribution in practice, but efforts on looking for superior forecasting method are still made by practitioners and academia. This paper deals with the application of a novel neural network technique, fuzzy support vector machines regression (FSVMR), in SCI forecasting. The objective of this paper is not only to examine the feasibility of FSVMR in SCI forecasting but presents our efforts on improving the accuracy of FSVMR in terms of data pre-processing, kernel function selection and parameters selection. A data set from Shanghai Stock Exchange is used for the experiment to test the validity of FSVMR. The experiment shows FSVMR a better method in SCI forecasting. used fuzzy support vector machines regression (FSVMR) to forecast a data set from the Shanghai Stock Exchange with positive results. CHEN, Kuan-Yu and Chia-Hui HO, 2005. An Improved Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting. ICNNB 05: International Conference on Neural Networks and Brain, 2005, Volume 3 not cited (0year) Abstract: This study applies a novel neural network technique, Support Vector Regression (SVR), to Taiwan Stock Exchange Market Weighted Index (TAIEX) forecasting. To build an effective SVR model, SVRs parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVRs optimal parameters using real value genetic algorithms. The experimental results demonstrate that SVR outperforms the ANN and RW models based on the Normalized Mean Square Error (NMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Moreover, in order to test the importance and understand the features of SVR model, this study examines the effects of the number of input node. used an SVM for regression for forecasting the Taiwan Stock Exchange Market Weighted Index (TAIEX). The results demonstrated that the SVR outperformed the ANN and RW models. CHEN, Wun-Hwa and Jen-Ying SHIH, 2006. A study of Taiwan39s issuer credit rating systems using support vector machines. Expert Systems with Applications . Volume 30, Issue 3, April 2006, Pages 427-435. not cited (0year) By providing credit risk information, credit rating systems benefit most participants in financial markets, including issuers, investors, market regulators and intermediaries. In this paper, we propose an automatic classification model for issuer credit ratings, a type of fundamental credit rating information, by applying the support vector machine (SVM) method. This is a novel classification algorithm that is famous for dealing with high dimension classifications. We also use three new variables: stock market information, financial support by the government, and financial support by major shareholders to enhance the effectiveness of the classification. Previous research has seldom considered these variables. The data period of the input variables used in this study covers three years, while most previous research has only considered one year. We compare our SVM model with the back propagation neural network (BP), a well-known credit rating classification method. Our experiment results show that the SVM classification model performs better than the BP model. The accuracy rate (84.62) is also higher than previous research. used an SVM to classify Taiwans issuer credit ratings and found that it performed better than the back propagation neural network (BP) model. CHEN, Wun-Hua, Jen-Ying SHIH and Soushan WU, 2006. Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets. International Journal of Electronic Finance . Volume, Issue 1, pages 49-67. not cited (0year) Abstract: Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention. However, most researches are for the US and European markets, with only a few for Asian markets. This research applies Support-Vector Machines (SVMs) and Back Propagation (BP) neural networks for six Asian stock markets and our experimental results showed the superiority of both models, compared to the early researchespared SVMs and back propagation (BP) neural networks when forecasting the six major Asian stock markets. Both models perform better than the benchmark AR (1) model in the deviation measurement criteria, whilst SVMs performed better than the BP model in four out of six markets. GAVRISHCHAKA, Valeriy V. and Supriya BANERJEE, 2006. Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting. Computational Management Science . Volume 3, Number 2 (April 2006), Pages 147-160. not cited (0year) Abstract: Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data. Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models. SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data. used SVMs for forecasting stock market volatility with positive results. HOVSEPIAN, K. and P. ANSELMO, 2005. Heuristic Solutions to Technical Issues Associated with Clustered Volatility Prediction using Support Vector Machines. ICNNampB3905: International Conference on Neural Networks and Brain, 2005, Volume 3 . Pages 1656-1660. not cited (0year) Abstract: We outline technological issues and our fimdings for the problem of prediction of relative volatility bursts in dynamic time-series utilizing support vector classifiers (SVC). The core approach used for prediction has been applied successfully to detection of relative volatility clusters. In applying it to prediction, the main issue is the selection of the SVC trainingtesting set. We describe three selection schemes and experimentally compare their performances in order to propose a method for training the SVC for the prediction problem. In addition to performing cross-validation experiments, we propose an improved variation to sliding window experiments utilizing the output from SVCs decision function. Together with these experiments, we show that accurate and robust prediction of volatile bursts can be achieved with our approach. used SVMs for classification to predict relative volatility clusters and achieved accurate and robust results. INCE, H. and T. B. TRAFALIS, 2004. Kernel principal component analysis and support vector machines for stock price prediction. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Volume 3 . pages 2053-2058. not cited (0year) Abstract: Financial time series are complex, non-stationary and deterministically chaotic. Technical indicators are used with principal component analysis (PCA) in order to identify the most influential inputs in the context of the forecasting model. Neural networks (NN) and support vector regression (SVR) are used with different inputs. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from technical analysis. Comparison shows that SVR and MLP networks require different inputs. The MLP networks outperform the SVR technique. found that MLP neural networks outperform support vector regression when applied to stock price prediction. KAMRUZZAMAN, Joarder, Ruhul A SARKER and Iftekhar AHMAD, 2003. SVM Based Models for Predicting Foreign Currency Exchange Rates. Proceedings of the Third IEEE International Conference on Data Mining (ICDM03) . Pages 557-560. not cited (0year) Abstract: Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e. g. neural network or ARIMA based model. SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters: regularization parameter and varepsilon - insensitive loss function. In this paper, we investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on prediction error measured by several widely used performance metrics. The effect of regularization parameter is also studied. The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed. Some interesting results are presented. investigated the effect of different kernel functions and the regularization parameter when using SVMs to predict six different foreign currency exchange rates against the Australian dollar. investigated comprehensible credit scoring models using rule extraction from SVMs. NALBANTOV, Georgi, Rob BAUER and Ida SPRINKHUIZEN-KUYPER, 2006. Equity Style Timing Using Support Vector Regressions. to appear in Applied Financial Economics . not cited (0year) Abstract: The disappointing performance of value and small cap strategies shows that style consistency may not provide the long-term benefits often assumed in the literature. In this study we examine whether the short-term variation in the U. S. size and value premium is predictable. We document style-timing strategies based on technical and (macro-)economic predictors using a recently developed artificial intelligence tool called Support Vector Regressions (SVR). SVR are known for their ability to tackle the standard problem of overfitting, especially in multivariate settings. Our findings indicate that both premiums are predictable under fair levels of transaction costs and various forecasting horizons. used SVMs for regression for equity style timing with positive results. ONGSRITRAKUL, P. and N. SOONTHORNPHISAJ, 2003. Apply decision tree and support vector regression to predict the gold price. Proceedings of the International Joint Conference on Neural Networks, 2003, Volume 4 . Pages 2488-2492. not cited (0year) Abstract: Recently, support vector regression (SVR) was proposed to resolve time series prediction and regression problems. In this paper, we demonstrate the use of SVR techniques for predicting the cost of gold by using factors that have an effect on gold to estimate its price. We apply a decision tree algorithm for the feature selection task and then perform the regression process using forecasted indexes. Our experimental results show that the combination of the decision tree and SVR leads to a better performance. applied a decision tree algorithm for feature selection and then performed support vector regression to predict the gold price, their results were positive. Van GESTEL, Tony, et al. . 2005. Linear and non-linear credit scoring by combining logistic regression and support vector machines, Journal of Credit Risk . Vol. 1, No. 4, Fall 2005, Pages 31-60. not cited (0year) Abstract: The Basel II capital accord encourages banks to develop internal rating models that are financially intuitive, easily interpretable and optimally predictive for default. Standard linear logistic models are very easily readable but have limited model flexibility. Advanced neural network and support vector machine models (SVMs) are less straightforward to interpret but can capture more complex multivariate non-linear relations. A gradual approach that balances the interpretability and predictability requirements is applied here to rate banks. First, a linear model is estimated it is then improved by identifying univariate non-linear ratio transformations that emphasize distressed conditions and finally SVMs are added to capture remaining multivariate non-linear relations. apply linear and non-linear credit scoring by combining logistic regression and SVMs. YANG, Haiqin, et al. . 2004. Outliers Treatment in Support Vector Regression for Financial Time Series Prediction. Neural Information Processing: 11th International Conference, ICONIP 2004, Calcutta, India, November 2004, Proceedings not cited (0year) Abstract: Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting outliers and deflating their influence are important but hard problems. In this paper, we propose a novel 8220two-phase8221 SVR training algorithm to detect outliers and reduce their negative impact. Our experimental results on three indices: Hang Seng Index, NASDAQ, and FSTE 100 index show that the proposed 8220two-phase8221 algorithm has improvement on the prediction. proposed a novel two-phase SVR training procedure to detect and deflate the influence of outliers. The method was tested on the Hang Seng Index, NASDAQ and FSTE 100 index and results were positive. However, its not clear why the significance of outliers (such as market crashes) should be understated. YU, Lean, Shouyang WANG and Kin Keung LAI, 2005. Mining Stock Market Tendency Using GA-Based Support Vector Machines. Internet and Network Economics: First International Workshop, WINE 2005, Hong Kong, China, December 15-17, 2005, Proceedings (Lecture Notes in Computer Science) edited by Xiaotie Deng and Yinyu Ye, pages 336-345. not cited (0year) Abstract: In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on the historical data. To evaluate the forecasting ability of GASVM, we compare its performance with that of conventional methods (e. g. statistical models and time series models) and neural network models. The empirical results reveal that GASVM outperforms other forecasting models, implying that the proposed approach is a promising alternative to stock market tendency exploration. applied a random walk (RW) model, an autoregressive integrated moving average (ARIMA) model, an individual back-propagation neural network (BPNN) model, an individual SVM model and a genetic algorithm-based SVM (GASVM) to the task of predicting the direction of change in the daily SP500 stock price index and found that their proposed GASVM model performed the best. HARLAND, Zac, 2002. Using Support Vector Machines to Trade Aluminium on the LME.. Proceedings of the Ninth International Conference, Forecasting Financial Markets: Advances For Exchange Rates, Interest Rates and Asset Management . edited by C. Dunis and M. Dempster. not listed Abstract: This paper describes and evaluates the use of support vector regression to trade the three month Aluminium futures contract on the London Metal Exchange, over the period June 1987 to November 1999. The Support Vector Machine is a machine learning method for classification and regression and is fast replacing neural networks as the tool of choice for prediction and pattern recognition tasks, primarily due to their ability to generalise well on unseen data. The algorithm is founded on ideas derived from statistical learning theory and can be understood intuitively within a geometric framework. In this paper we use support vector regression to develop a number of trading submodels that when combined, result in a final model that exhibits above-average returns on out of sample data, thus providing some evidence that the aluminium futures price is less than efficient. Whether these inefficiencies will continue into the future is unknown. used an ensemble of SVMs for regression to trade the three month Aluminium futures contract on the London Metal Exchange with positive results. Van GESTEL, T. et al. . 2005. Credit rating systems by combining linear ordinal logistic regression and fixed-size least squares support vector machines, Workshop on Machine Learning in Finance, NIPS 2005 Conference, Whistler (British Columbia, Canada), Dec. 9.not listeddeveloped credit rating systems by combining linear ordinal logistic regression and fixed-size least squares SVMs. Taking a big step in machine learning: Profitable historical results across multiple Forex pairs In the past I have been able to use machine learning to create profitable trading systems successfully, this includes my Neural Network implementations (which generated the Sunqu, Tapuy and Paqarin strategies, later building the AsirikuyBrain) as well as my attempts at linear classification and other types of algorithms. However, one of the things that all of these developments have in common is that they have traded on EURUSD daily data and have failed to generate decent results across other pairs andor time frames. This means that although I have tackled this particular pairtimeframe successfully (several of these systems have been traded live with profitable outcomes) I still hadn8217t been able to develop anything for other instruments. On today8217s post I am going to talk about one of my latest developments (in big part due to an Asirikuy member I will be mentioning later on) which has allowed me to achieve profitable machine learning results across other pairs besides the EURUSD. Note that all back-testing results showed are non-compound (so that they can be easily judged by linearity). The fact that machine learning techniques seem to be so 8220easy8221 to develop on the EURUSD daily, yet so hard to develop on other pairs on the same timeframe has always bugged me. Why is the EURUSD daily so special, that previous data seems to easily predict future daily bar outcomes while in other pairs this simply does not work The answer seems to be this exact same point of view 8212 what I am trying to predict. Fabio 8211 a member of our community 8211 pointed to me that it would be interesting to attempt to classify whether a certain trade outcome would be successful, rather than trying to classify simply whether the next bar would be 8220bullish or bearish8221. Predicting whether a certain trade entry would be successful is an interesting route, because you8217re trying to predict whether your actual trade within some exit boundaries will reach a profit or loss, rather than whether the overall directionality will be for or against you. When implementing the above idea in F4, I saw that not all trade outcome predictions were equally successful, while predicting big edges didn8217t work at all (for example attempting to predict where a 1:2 risk to reward trade would be successful), predicting smaller edges worked much better. Different algorithms also gave markedly different results, while linear classifiers were extremely dependent on the feed data (changed significantly between my two FX data sets), Support Vector Machines (SVM) gave me the best overall results with reduced feed dependency and improved profit to drawdown characteristics. Simple mean keltner clustering techniques also gave interesting results, although the profitability was reduced compared with the SVM. As in all my machine learning implementations, training is done on each new daily bar using the past X bars and therefore the machine learning technique constantly retrains through the whole back-testing period . 8211 8211 Interestingly this technique achieves profitable results (25 year back-tests) across all 4 Forex majors (same settings), with particularly good results on the EURUSD and GBPUSD and worse but still profitable results on the USDCHF and the USDJPY. The ability to predict outcomes seems to be lost most significantly on the USDJPY, where there is a significantly long period (about 10 years) where the strategy is unable to achieve any significant level of success. I would also like to point out that the current machine learning test uses just a single machine learning instance and I haven8217t attempted to increase profitability by building committees or such other 8220tricks8221 that might help improve and smooth results when using machine learning techniques. In this case trying different trade range predictions within a committee or even only putting SVM and mean Keltner techniques to work together might significantly improve the results. For me the fact that this technique has finally 8220broken the multi-pair barrier8221 has been quite significant as it reveals something fundamental about using machine learning which, up until now, I seem to have missed. This also reinforces the fact that output selections are absolutely critical when developing machine learning strategies as attempting to predict the wrong outputs can easily lead to unprofitable techniques (as it happened to me when attempting to create ML strategies on other symbols). Choosing outputs that are meaningful for trading but still predictable within a good accuracy, leads to the development of more successful machine learning strategies. In this case in particular, changing the focus to a prediction that had direct implications in trade profitability had a good impact. 8211 8211 Although the results up until now are quantitatively nothing to 8220party about8221, the fact that there is now a road open towards developing profitable ML strategies that might work across the board (not just only on one pair) gives me confidence in the fact that I am walking the correct path (thanks to Fabio for his suggestions). After reaching this milestone my goal now is to polish and study this machine learning implementations to find better predictors and improve the results on non-EURUSD pairs, my end-goal would be to have a machine learning strategy that can deliver highly linear historical results (alike the AsirikuyBrain) across at least the 4 majors (hopefully even more pairs) so that I can have a source of diversification that is constantly being adapted to new market conditions. If you would like to learn more about machine learning strategies and how you too can easily build linear classifiers, random forests, keltner mean clustering, neural network and SVM strategies in F4 please consider joining Asirikuy, a website filled with educational videos, trading systems, development and a sound, honest and transparent approach towards automated trading in general. I hope you enjoyed this article. o)