Kusimuka kweAI zviri pachena kwakanganisa maindasitiri akasiyana, uye indasitiri yezvemari iri   . Semuyenzaniso, kuvhurwa kweruzhinji kwemamodheru akaita seGPT-3.5 gore rapfuura kwakawedzera kufarira kushandisa AI kubatsira kuwedzera kugona kwemamaneja ehomwe mukuongorora, kutonga kwenjodzi, uye kuita sarudzo. pakati peiyo yakakanganiswa zvakanyanya  Nekudaro, maturusi eAI anoshandiswa kuita kuti kuongororwa kwemusika kuve kwakaringana uye kugadzirisa njodzi zvakanyanya. Mamaneja ePortfolio anotarisirwa kuita ongororo yakajeka yemafambisirwo emusika, kudzikisira pasarudzo dzakakodzera dzekudyara, uye kugadzirisa njodzi pavanoshandisa maalgorithms ekudzidza muchina, kugadzirwa kwemutauro wechisikigo, uye maturusi ehungwaru ekugadzira mukutengesa kwavo.  Iko kubatanidzwa kwemichina yekudzidza maalgorithms, pamwe chete nematurusi ekugadzirisa mitauro mumatanho ekutengesa evatambi akakosha, anovabatsira kuwedzera kushanda kwemaitiro aya uye kuwana mukana wekukwikwidza nekukurumidza uye kwakaringana sarudzo dzekudyara uye kufungidzira analytics.  Mumakumi emakore apfuura, AI yakaitwa muzvikamu zvakasiyana zveindasitiri yezvemari. Muhofisi yekuseri, ML algorithms anoshandiswa kuwana anomalies mumatanda ekuuraya, kuona kufungidzira kutengeserana, pamwe nekugadzirisa njodzi, zvichiita kuti iwedzere kushanda nesimba uye kuchengetedzeka. Muhofisi yepamberi, AI iri kubatsira kugovera vatengi, kuita otomatiki maitiro ekutsigira vatengi, uye kukwidziridza mitengo inotorwa.  Nekudaro, iyo inonyanya kunakidza chikamu chayo kugona kweAI kwekutenga-kudivi remari - kuona masaini ekufungidzira mukati meruzha rwemusika nekuongorora huwandu hwakakosha hwe data nekukurumidza sezvinobvira. Semuyenzaniso, zvikumbiro zvakadaro zvinogona kusanganisira kufanotaura kwenguva, kupatsanura misika, uye hongu, kutonga maasset portfolio. Mikana yeAI yekugadzirisa nekuongorora akakura dhatabhesi anobatsira kuwana akajeka mapatani ayo echinyakare maitiro angangopotsa.  Portfolio optimization yanga iri tsika yakajairika kwemakumi emakore akati wandei, ichishanduka zvakanyanya pasi pekuvandudzwa kwesainzi yedata uye kuitwa kwehunyanzvi hwekuita computational. Maitiro echinyakare, akadai saMarkowitz's Modern Portfolio Theory (1952) uye Capital Asset Pricing Model (1964) akaunzwa anopfuura makore makumi mashanu apfuura asi zvichiri kushanda. Nekudaro, izvo zvavanogumira mukubata isiri-mutsara njodzi uye kutsamira pane nhoroondo data iri kuramba ichiwedzera kujeka nezuva.  Zviitiko senge njodzi yekuenzanisira, kuongororwa kwemamiriro ezvinhu, uye kutengesa kwehuwandu, kunoitwa zvakanyanya nevatambi vakakosha, seRenaissance Technologies, DE Shaw, uye Mbiri Sigma Investments zvakatungamira mukuitwa kweakanyanya kuomarara uye epamberi algorithms. Pamusoro pezvo, indasitiri iyi yakakanganiswa zvakanyanya neAI mumakore achangopfuura, sezvo kudzidza kwemuchina uye hungwaru hwekugadzira kwaita kuti kufungidzira analytics kuve kwakaringana, uye vakaita zvimwechete kune yakasarudzika nzira dzekudyara uye otomatiki akaomarara ekuita sarudzo.  Iyi shanduko inofambiswa neAI yakagonesa mamaneja epodfolio kuti agadzirise akakura akatevedzana data munguva chaiyo uye kugadzirisa matambudziko matatu makuru:    Kutarisira uye kuongorora yakakura-chiyero data kubva kune akawanda midziyo uye misika yepasirese ikozvino yave nyore kuita. Scalability:    AI inogona "kuchengeta mupfungwa" zvimwe zvinhu, kusanganisira zvepfungwa uye maitiro analytics, mukuita sarudzo. Yakaoma Sarudzo-Kuita:    AI masisitimu anogona kudzidza asina kumira uye kuenderana nemamiriro matsva emusika, achibatsira mamaneja kukurumidza kugadzirisa maitiro.  Adaptability:  Maererano ne  , AI mumusika weAsset Management yaive yakakosha pamadhora mazana maviri nemakumi mashanu emadhora uye inotarisirwa kukura paCAGR ye24% mumakore gumi anotevera. Sezvineiwo, Portfolio Optimization inotungamira muGlobal musika segmentation nekushandisa, inoteverwa neData Analysis, accounting ye.  .   Global Market Insights   25% yemugove wemusika  Kuwedzera kugamuchirwa uye kudyara mune zvigadziriso zveasset zvinofambiswa neAI uye kuratidza kushandiswa kunoshanda kweAI mukugadzirisa portfolio.    AI Adoption muPortfolio Management:  Kutorwa kweAI mukati meiyo asset manejimendi indasitiri haisi maitiro matsva; yakaona kukura mumakore achangopfuura asi ichiri kungogumira kune vashoma nhamba yevatambi vemusika vanoti hedge funds, quantitative management offices, madhipatimendi makuru ekutsvagisa, uye masangano emari anoshandisa IT masevhisi.   Kune akawanda minda yekunyorera yeAI yatove:   Portfolio Optimization  AI inovandudza zvakanyanya maitiro ekugadzirisa portfolio. Semuenzaniso, maitiro echinyakare eMarkowitz's Modern Portfolio Theory, ayo anovimba neconvex optimization concepts, anoshanda seanotangira nzira dzemazuva ano dzinofambiswa neAI. Chikonzero icho dzidziso yehwaro iyi yakakosha ndechekuti inoumba hwaro kubva kune iyo AI algorithms inogona kuenderera mberi nekuchinja uye kunatsa nzira dzekudyara.  Mazuva ano, AI inowedzera pane iyi dzidziso nekuongorora zviyero zvitsva zve data uye nekubatanidza hunyanzvi hwekuongorora. Iyi yakawedzera dhata kugona inobvumira mamwe nuanced uye ane ruzivo kuita sarudzo - tsika yave kushandiswa zvakanyanya muindasitiri.   Fundamental Analysis  Dzimwe nzira dzeAI dzinonyatsoenderana nehuwandu hwekutonga, uchishandisa mavhoriyamu makuru e data nezve zvakakosha zvekambani, nharaunda yehupfumi, kana mamiriro emusika. Michina yekudzidza algorithms inogona kuwana yakaoma isina-mutsara hukama pakati pezvakasiyana zvakasiyana uye, hongu, kuona mafambiro asingagone kuitwa nevaongorori.   Kuongorora Kwemashoko  Kuongorora kwemavara ndiko kumwe kushandiswa kweAI mukuongorora kwakakosha. Uchishandisa mitauro yechisikigo kugadzirisa (NLP), AI inogadzirisa uye inoongorora zvinyorwa zvakaita semihoro yemakambani, kuburitswa kwebhangi repakati, uye nhau dzezvemari. Kuburikidza neNLP, AI inogona kutora ruzivo rwakakosha mune zvehupfumi uye mune zvemari kubva kune iyi data isina kurongeka. Nekuita izvi, inopa chiyero chehuwandu uye chakarongeka chinovandudza uye chinobatsira kududzira kwevanhu.   Kutengeserana Mabasa  Masimba eAI anobatsira zvakanyanya mukutengesa, uko kuoma kwekutengeserana uye kudiwa kwekumhanya kuri pachiyero. AI inotsigira algorithmic kutengeserana nekuita otomatiki nhanho dzakawanda dzekuita, kuvandudza mashandiro ekutengeserana anotungamirwa mumisika yemari.   Investment Advisory Services  AI yakavhura mukana wekupa kwakakura kweyakasarudzika mari yekuraira masevhisi nemutengo wakaderera. Aya masisitimu anoshandisa akaomesesa algorithms kugadzirisa chaiyo-nguva yemusika data, achiuya neakanyanya kukodzera mazano kune ega mutengi zvinodiwa zvichienderana nezvinangwa zvavo zvekudzoka uye nhoroondo dzenjodzi.   Risk Management  Mukugadzirisa njodzi, AI inobatsira nekuenzanisa mamiriro akasiyana 'angangoita asi asingadikanwi', ayo anosimudzirawo maitiro echinyakare anongotarisa pane zvingangoitika.   Artificial Intelligence (AI) Techniques uye Zvishandiso muPortfolio Management   Machine Kudzidza Algorithms:  Classical Machine Kudzidza nzira dzichiri kufarirwa zvakanyanya muPortfolio Management, uye ndeidzi: Linear Models, kusanganisira Ordinary Least Squares, Ridge Regression, uye Lasso Regression. Izvi zvinowanzosanganiswa neMean-Variance Optimization maitiro uye matrix decomposition maitiro akadai seSingular Value Decomposition (SVD) uye Principal Component Analysis (PCA), ayo ari hwaro mukunzwisisa hukama hweasset uye optimize portfolio kugoverwa.  Iri pakati peidzi nzira dzechinyakare uye dzimwe nzira dzechizvino-zvino iSupport Vector Machines (SVMs). Kunyangwe maSVM achishandiswa mukuita, haana kuwanzoiswa asi anoita basa rakakosha, kunyanya, mumapoka emabasa ane chinangwa chekufanotaura kuita kwemasheya.  Aya mabasa anowanzo sanganisira kufanotaura kana stock ichawana purofiti kana kurasikirwa, ichishandisa nhoroondo yemari data inosanganisira kuchinja kwemutengo wemasheya uye mavhoriyamu ekutengesa kuisa zvinhu muzvikamu uye kufanotaura maitiro avo.  Tichitaura nezve dzimwe nzira dzechizvino-zvino, neural network inoratidza kufambira mberi kukuru mukudzidza kwemuchina kwekutonga kwepodfolio uye inopa hunyanzvi hwekuita modhi yakaoma isina-mutsara mapatani ayo anonetsa kubata nemamodheru echinyakare. Kunze kweneural network, mamwe maitiro echinyakare akadai seanotariswa uye asina kutariswa kudzidza anowedzera kuvandudza uye kunatsiridza kuongororwa kwedata, zvichiita kuti kuwanikwa uye kushandiswa kwemasiginecha emusika kugoneke.  Maitiro matsva, akadai seKusimbisa Kudzidza uye Kudzika Q-Kudzidza kunounza hunhu uhu munzvimbo dzinokurumidza kuita sarudzo, uko mapotfolio anogona kugadziridzwa munguva-chaiyo kuti agadzirise mibairo yemari zvichienderana nehurongwa hwekudzidza kubva kumusika mhinduro.  Kugadzirisa Mutauro Wechisikigo (NLP):  Mutauro Wechisikigo Magadzirirwo maitiro sekuongorora manzwiro anogona kubatsira kusarudza uye kusarudza pfungwa dzakajairwa kubva kuzvinhu zvakaita sezvinyorwa zvepepanhau, zvinyorwa zvesocial media, uye mishumo yeanoongorora. Pamusoro pezvo, mamaneja epotfolio anogonawo kuongorora mutauro unoshandiswa munhau dzemari, kusanganisira mihoro yefemu, kunzwa manzwiro evasima mari uye kufanotaura mafambiro emusika, ese ari ruzivo rwakakosha mukuita sarudzo.  Quantitative Trading Strategies:  Mafemu anoshanda nepamusoro-frequency trading (HFT), seaya anoshandisa AI-powered quantitative trading algorithms, anoita mari pakusashanda kunoitika kwechinguvana mumusika. Mafemu aya anoshandisa matekinoroji ekudzidza muchina kuongorora ruzivo rwemusika rwakakodzera nekumhanya kwakanyanya uye kuisa maodha ane chaiyo nguva kwenguva pfupi semillisecond.  Kukurumidza kuuraya kwakadaro kunovabvumira kuti vabatsirwe nemikana yearbitrage uye kuwedzera purofiti nekutora nhanho pamitengo yekusiyana nekukurumidza kupfuura vakwikwidzi. Nepo Renaissance Technologies ichizivikanwa nehuwandu hwayo hwekutengesa nzira, zvakakosha kuti tirambe tichifunga zano rayo rakakura rinosanganisira dzakasiyana-siyana dzekubata nguva kubva kune yechinyakare HFT maitiro, ayo anonyanya kunangana nekumhanya.  Inotsanangurwa AI (XAI):  LIME (Local Interpretable Model-agnostic Explanations) inzira ine mukurumbira yeXAI inoshandiswa kuita kuti zvinobuda zvemhando dzakaoma dzekudzidza dzemuchina dzinzwisise. Mukutonga kwepodfolio, iyi nzira inogona kuve yakakosha pakududzira kuti mhando dzebhokisi dema dzinofanotaura sei. Nekushandisa data rekuisa uye kuongorora mabatiro pane zvakabuda modhi, LIME inobatsira mamaneja epodfolio uye masayendisiti edata kutsanangura kuti ndezvipi zvinopesvedzera sarudzo dzekudyara kupfuura vamwe.  Iyi nzira inobatsira kusimudzira kujeka kweAI-yakawedzera sarudzo uye inotsigira kuedza kuratidza nekuvandudza kuti zviri nyore sei kunzwisisa aya mamodheru. Nekudaro, nepo LIME ichivandudza manzwisisiro edu emaitiro emuenzaniso, kuongorora kuvimbika kwese kwemamodhi kunosanganisira humwe hunyanzvi hwekusimbisa.  AI mukuteerera uye Kuongorora:  AI tech inobata basa rakakura mukuona kuteedzera masisitimu uye yekutarisa zvirambidzo zvekudyara mukati meindasitiri yezvemari. Nekuita otomatiki maitiro aya, masisitimu eAI anobatsira mafemu emari kunamatira kumitemo yepamutemo zvakanyanya, nemazvo, uye kusapinda mudambudziko. Iyi tekinoroji yakakosha zvikuru mukutarisa kuteedzera kwezvizhinji zvakakura zvekutengeserana uye akasiyana-siyana mapotifolio zviitiko, apo inogona nekukurumidza (pakarepo, kutaura zvazviri) kuona kutsauka kubva kune zvinodikanwa zvekutonga kana nhungamiro yemukati.  Uyezve, kushandiswa kweAI kunoderedza njodzi yekukanganisa kwevanhu, iyo yakakosha munzvimbo dzepamusoro-soro dzekutonga uko kukanganisa kunogona kutungamira kumhedzisiro yemutemo uye yemari.  Portfolio Rebalancing:  AI zvikumbiro mu otomatiki rebalancing yakakosha pakuchengetedza iyo yakakodzera kugoverwa kwemidziyo nekufamba kwenguva. Vanogona kugadzirisa mapotifoliyo mukupindura shanduko yemusika kana shanduko mune yenjodzi yemutengi, iyo inovimbisa kuenderana nehurongwa hwekudyara zvibodzwa.  Pamaonero Akawanda  Pamusoro pemaapplication akanyatso gadzirirwa mari, mukana wekuvandudzwa kweungwaru hwekugadzira mukati mebhizinesi rekutonga zvinhu unoratidzika kunge wakakura. Zvisinei, pasinei nokuti isu tinoona semusikirwo mukana wekuita otomatiki mabasa chaiwo pamatanho akasiyana-siyana echeni yekushanda, zvichiri kuoma kunyatsotarisira simba rinokanganisa rehungwaru hwekugadzira. Izvi zvinodaro nekuti AI inotarisirwa kuunza zvikamu zvitsva zvekushandisa sezvo kumwe kufambira mberi kuchigadzirwa.  Tinofanira kurangarira zvipimo zvehungwaru hwekugadzira pamwe nenjodzi dzahunoisa kune mamwe maficha ekutonga kwepotfolio, zvisinei nekuti zvaita kuti zvikwanisike kufambiswa mberi kwetekinoroji uye kubudirira kwechigadzirwa uchishandisa hungwaru hwekunyepedzera. Chekutanga, hungwaru hwekugadzira uye nzira dzekudzidza dzemuchina dzinotsamira pane data rinoshandiswa kudyisa maalgorithms ekudzidza.  Izvo zvinodikanwa kuti iyi data ive yemhando yepamusoro maererano nekuvandudzwa, chokwadi, kukwana, uye kumiririra.  Pamusoro pezvinodiwa zvehuwandu hwakakura kwazvo hwe data, iyo isingawanzo kuwanikwa, inyaya yekuti iyi data inofanirwa kuve yemhando yakanaka. Mune chero imwe mamiriro ezvinhu, zvakawanikwa zvinowanikwa uchishandisa mienzaniso yekufungidzira hazvina kuvimbika kana kusimba.  Uyezve, maalgorithms anogonawo kuita fungidziro dzenhema nekutora maitiro asina basa kubva mudhatabheti inoongororwa, izvo zvinogona kutungamirira kumhedzisiro isiriyo. Izvi zvinogona kuguma nekubata zvakakomba, kusvetuka kwakapinza zvakanyanya, uye kubondera kudiki kudiki. Kurasikirwa kwemakwikwi emusika kunogona kuitika nekuda kwekuti vazhinji vashandisi vemusika vanobata zvakafanana AI algorithms vanogona kuita sarudzo isiriyo panguva imwe chete kana kuita nenzira yakafanana kune chaiyo-nguva mamiriro. Ngozi yakadaro inogona kuuraya.  Kunyangwe paine mabhenefiti angangoita eAI mukutonga kwepodfolio, semumunda chero upi zvake, kune akawanda matambudziko atinofanira kurangarira uye pakupedzisira - kero. Rimwe rematambudziko makuru kushaikwa kwekujeka uye kududzira nyaya dzeAI modhi, izvo zvinogona kuita kuti zviome kune mamaneja kutsanangura mhedzisiro yekubatana kwavo neAI. Uku kuoma kwekushandisa kungave chimwe chezvikonzero nei kutorwa kweAI mumari dzeEurope kwakadzikira. Kubva munaGunyana 2022,  yakavakirwa muEuropean Union yaiti inoshandisa AI mukuita kwavo kwekudyara.   65 chete kubva pamari ye22,000  Sangano reEuropean Financial Markets Authority (ESMA)  zvinhu zvinogona kubatsira kune yakaderera kugamuchirwa mwero, sekushaikwa kweakajeka ekudzora masisitimu uye hunyanzvi hweAI pakati pevakuru vemahomwe. Nekudaro, dambudziko rekutsanangura mhedzisiro yeAI nekuda kwekuoma kwemuenzaniso kunogona zvakare kuve chimwe chezvikonzero zvinotsigira mwero wakaderera wekutora. Ndinofunga tichazviziva nekufamba kwenguva.   akaziva  Panguva ino, zvinoita sekunge hungwaru hwekugadzira huchiri kure kubva pakutsiva zvachose vanhu chaivo muindasitiri yekutarisira maasset. Izvo zviri kutaurwa, pachena, hukama hwekuvimba, uye kuonana pakati pevatengi uye manejimendi nyanzvi dzinoramba dziri dzakakosha hunhu, kupfuura nakare kose.  Asi, isu hatirambe kuti hungwaru hwekunyepedzera hunounza maturusi matsva uye anonakidza anogona kushandiswa mucheni yekukosha, uye kugona kwezvishandiso izvi kunogona kunyatso shandura maitiro anoita indasitiri nhasi.

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