Implementacija višeagentnog sustava umjetne inteligencije za donošenje financijskih trgovačkih odluka
DOI:
https://doi.org/10.15291/oec.4863Ključne riječi:
višeagentni sustavi, umjetna inteligencija, obrada prirodnog jezika, trgovačke strategije, upravljanje rizikomSažetak
Primjena umjetne inteligencije u financijskim analizama dovodi do razvoja naprednih višeagentnih sustava koji omogućavaju učinkovitiju analizu i donošenje odluka od dosadašnjih metoda. Ovaj rad predstavlja razvoj i implementaciju višeagentnog sustava umjetne inteligencije za generiranje financijskih trgovačkih preporuka koji integrira specijalizirane agente za prikupljanje podataka, tehničku analizu, obradu vijesti i upravljanje portfeljem. Sustav kombinira kvantitativnu analizu zasnovanu na tehničkim indikatorima s kvalitativnim uvidima iz financijskih vijesti kroz tehnike obrade prirodnog jezika, poput inženjeringa upita i generiranja pomoću pretraživanja. Arhitektura ovog sustava omogućava samostalno djelovanje četiri specijalizirana agenta koji doprinose generiranju trgovačkih preporuka. Evaluacija provedena na pet reprezentativnih dionica tehnoloških kompanija tijekom šestomjesečnog perioda 2025. godine pokazuje značajno poboljšanje u upravljanju rizikom u odnosu na referentnu strategiju „kupi i drži“. Rezultati prikazuju značajno smanjenje volatilnosti i maksimalnog pada kod većine analiziranih dionica, posebno kod visoko volatilnih dionica poput Tesle gdje sustav održava maksimalni pad na 7.72% u odnosu na 47.84% referentne strategije. Sustav prilagođava trgovačke preporuke različitim tržišnim uvjetima i specifičnostima svake dionice, čime održava stabilnost portfelja u periodima kada su tržišta nestabilna. Analiza financijskih vijesti pomoću NLP-a omogućava sustavu da prepozna je li vijest pozitivna ili negativna, koliko je važna za dionicu i kako bi mogla utjecati na njezinu cijenu. Unatoč identificiranim ograničenjima u vidu opreznijeg pristupa tijekom snažnih uzlaznih trendova, modularnost ove arhitekture omogućava kontinuirani razvoj i optimizaciju pojedinih komponenti. Istraživanje potvrđuje vrijednost višeagentnih pristupa u integraciji različitih izvora podataka za poboljšanje procesa donošenja trgovačkih odluka.
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