Jan Szilagyi
speaker
232 appearances
1 recordings
1 series
first heard Aug 2026
last heard 3 Aug
Jan Szilagyi’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Aug 2026 with 1.
Appearances
OK, oil prices going up, inflation is going to be higher, yield curves can be steeper.
It's good for drillers, maybe good for oil companies unless their fuel's right there.
But there's a lot more, right?
There's a production of plastics.
It impacts, for example, sugar growers because the ethanol-ethanol-gasoline blend might be changing.
It impacts, for example, the amount of fertilizer that's available, and therefore the harvest in the Western Hemisphere might be less than it otherwise would have been.
There are all these, and that's what makes financial markets ultimately so exciting, is that it is an incredibly complex system.
And without a mapping of sort, it's really hard to organize the relative...
importance of these consequences and relationships and so on.
Yeah, I think that's I think that's exactly the right analogy, because if you pull on the spider web right in any corner, you can kind of see which other parts are moving.
And that's exactly how a knowledge graph ultimately works, is that you have all of these edges that are connected and traversing them lets you figure out really quickly how and where the ripple effects are traveling.
Scenario analysis actually is a great example of how and where this is particularly beneficial because, as I think has become pretty clear from the discussion we've already had so far, financial markets, by their very nature, the ripple effects and the connectedness of the system means that the implications will be far and wide.
And what you ultimately are trying to figure out as a portfolio manager is under a certain set of assumptions, under a certain macro or microeconomic scenario, you really can only correctly guess the implications and the consequences for your portfolio if you have fully modeled all of the different connections, right?
Like if you are assuming that, I don't know, there's going to be a scenario in 27 where the U.S.
economic growth slows considerably, it's extremely important that you know
what the economic sensitivities are of your banking stocks, of your tech stocks.
What does that mean actually for the funding environment and so on?
Those are very complex relationships.
And what large language models really are very good at and humans tend to be less good at is this kind of multidimensional synthesis, right?
It doesn't run just one question in parallel.
Showing 101–120 of 232 · page 6 of 12
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