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Due diligence on the financials of a company (what investment bankers are supposed to do) is actually really hard to get right with the algorithms we have today

Due diligence on the financials of a company (what investment bankers are supposed to do) is actually really hard to get right with the algorithms we have today. Much of the data and insight compiled by an I-banker today does not exist in an easily parse-able form for automated algorithms, and a substantial amount of the computation relies on common sense knowledge.

elliotec 10y ago
It only takes one person to prove you wrong.
signa11 10y ago
> ...actually really hard to get right with the algorithms we have today assuming, very few companies which would have publically available published financial going over a large (> 50 years) timespan, would it be safer to conjecture that it is actually the _paucity_ of data which impoverishes the algorithms ?
JumpCrisscross 10y ago
> Due diligence on the financials of a company (what investment bankers are supposed to do) is actually really hard to get right Diligence is automatable, in the long run. There is a human element to it, but it is small in most contexts. Bankers solve trust problems. An AI would need to be trustworthy in a personal way to replace bankers--this only happens with AGI. For a long time, as long as humans control capital, there will be other humans interfacing those humans with each other.
- ckastner 10y ago
  > Diligence is automatable, in the long run. There is a human element to it, but it is small in most contexts. I'm not sure I agree with the first sentence, but I agree that there is a human element. If true, then at least some investment banking jobs must remain immune.
- marcosdumay 10y ago
  > An AI would need to be trustworthy in a personal way to replace bankers--this only happens with AGI. This happens with an unbeatable public track record. We trust AIs in lots of tasks already, and that is not because they smile and speech nicely.
huac 10y ago
More to the point, most investment banking valuations are just guesses and understood to be as such. Estimating discount rates and growth rates in particular are very much gut-driven and aren't expected to be precise, no matter how complex a model the analyst comes up with. There's only so much you can ask a kid 10 months out of Harvard econ to do, no matter how many pounds of cocaine and borrowed Excel sheets they have. At the more senior levels, banking is relationship based, and making partner is a function of how much business you can bring in. This means maintaining good relationships with the clients you work with and reaching out to new ones. Show me an algorithm that can send cards on kids' birthdays, drink champagne, and play golf.
- notahacker 10y ago
  Not to mention the difficulty of an algorithm that can come up with a convincing explanation for why last quarter's predictions were way off but these ones should be fine.
deleted 10y ago
[deleted]
halomru 10y ago
That explains the current state, but has little prediction value. The availability of parsable data can change at any moment either because of regulation or through market forces (either external companies providing parsable data as a service, or enough companies realising that providing plentiful parsable data about themselves attracts capital). Once enough data is available, common sense and gut feeling can be programmed as a combination of fixed rules and machine learning.

elliotec 10y ago

It only takes one person to prove you wrong.

signa11 10y ago

> ...actually really hard to get right with the algorithms we have today assuming, very few companies which would have publically available published financial going over a large (> 50 years) timespan, would it be safer to conjecture that it is actually the _paucity_ of data which impoverishes the algorithms ?

JumpCrisscross 10y ago

> Due diligence on the financials of a company (what investment bankers are supposed to do) is actually really hard to get right Diligence is automatable, in the long run. There is a human element to it, but it is small in most contexts. Bankers solve trust problems. An AI would need to be trustworthy in a personal way to replace bankers--this only happens with AGI. For a long time, as long as humans control capital, there will be other humans interfacing those humans with each other.

ckastner 10y ago
> Diligence is automatable, in the long run. There is a human element to it, but it is small in most contexts. I'm not sure I agree with the first sentence, but I agree that there is a human element. If true, then at least some investment banking jobs must remain immune.
marcosdumay 10y ago
> An AI would need to be trustworthy in a personal way to replace bankers--this only happens with AGI. This happens with an unbeatable public track record. We trust AIs in lots of tasks already, and that is not because they smile and speech nicely.

huac 10y ago

More to the point, most investment banking valuations are just guesses and understood to be as such. Estimating discount rates and growth rates in particular are very much gut-driven and aren't expected to be precise, no matter how complex a model the analyst comes up with. There's only so much you can ask a kid 10 months out of Harvard econ to do, no matter how many pounds of cocaine and borrowed Excel sheets they have. At the more senior levels, banking is relationship based, and making partner is a function of how much business you can bring in. This means maintaining good relationships with the clients you work with and reaching out to new ones. Show me an algorithm that can send cards on kids' birthdays, drink champagne, and play golf.

notahacker 10y ago
Not to mention the difficulty of an algorithm that can come up with a convincing explanation for why last quarter's predictions were way off but these ones should be fine.

deleted 10y ago

[deleted]

halomru 10y ago

That explains the current state, but has little prediction value. The availability of parsable data can change at any moment either because of regulation or through market forces (either external companies providing parsable data as a service, or enough companies realising that providing plentiful parsable data about themselves attracts capital). Once enough data is available, common sense and gut feeling can be programmed as a combination of fixed rules and machine learning.