Theinternetwas becoming mainstream in the late 90s, but Miro Mitevwas head-down exploring something thatwouldn’tbecomepopularfor decades: AI.
Now an asset manager,Mitev was anearly adopter of AI in financeafter discovering the capabilities ofneural networksin 1997 whilestudyingat theViennaUniversity of Economicsand Business.
He told CNBC he saw the potential of neural networks for financial forecasts. “I fell in love with these kinds of possibilities,” he said.
Mitevspent his 25-year career forecasting for banks and tech firms like Siemens.He foundedSmartWealthAsset Management, whosedecisions are made entirely by a network of AI systems.Its latest fund, IVAC, is eyeing $2 billion in assets under management and has an annualized returns target of 14-15%.
Despite no human involvement in the AI’s decisions, Mitev said that “humans are the most important partof the equation”asthey’rethe onesselectingtraining data,inputtingvariables,building theparameters, andconsistentlytweaking the model.
Once a model is created, “it’s very dangerous to start intervening,” Mitev said. Indeed, trusting the model is his golden rule, he added.
Instead, humans should ensure that there are no errors in the data or calculations, and introduce new data so that the model is up to date.
“The worst is to overrule the results, and this is what happens very often,” Mitev said, adding that people “don’t trust” AI at first. “Even if we, as humans, don’tsee the result now, if we look back after two months, three months, we say, ‘Oh, actually, we were wrong,'” he added.
The forces driving the market — optimism, pessimism, speculation — are very human.Even the European Central Bank has warned that the current AI bull run may be driven not by detailed technical analysis but by fear-of-missing-out.
Mitevsaid that taking the emotion out of investing proves better results; SmartWealth Asset Management has seen gains of 407.63% across a 10-year period to Nov. 1 2025, compared with anindustry benchmark of 145.34% over the same period, according to a graph a representative for the firm shared with CNBC.
It’s”not possible” to know what will happen in one year, Mitevsaid, but he can see up to one month ahead with his model.”Evaluating this information and making informed decisions based on this consistently proves to be providing better results than the human.”
The constant monitoring and introduction of new data are important points, giventhat AIsystemsdo “hallucinate”: generating false information.Mitevsaidmodels’mistakes were downto”overfitting,” data issuesor modelmisspecification.
Overfitting is where the algorithmpaystoo much attention to what Mitev called “noise.” He said this was data “which is not meaningful” because it doesn’t reveal a true cause-and-effect relationship with stock performance.
Rigorous design,validation, and live environment testing,serve as an antidote to this, Mitev added. It means that, although his fund strategy is executed entirely by a series ofalgorithms, humans still play a crucial rolein making sure it’s effective.
“It’s actually a process that evolves over years … and this is the reason why in-house development of these kind of technologies is very important,” he added – especially for anyone looking to differentiatetheirAI play.
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