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mtsm


Total Posts: 221
Joined: Dec 2010
 
Posted: 2018-02-06 17:47
Also, what is your take on DLT jslade? Thanks.

Osiris2


Total Posts: 21
Joined: Sep 2017
 
Posted: 2018-02-07 01:28
Here's a thing ...
Lots of what we are calling "fintech" companies, which I would more say are more accurately specialty finance non-bank lenders, are selling their capability to improve on legacy credit underwriting and analysis. As of now, this remains 60% hype, 40% bullshit, and approximately 0% reality.
But that won't remain the case forever. The industry and it's biggest players have reached the scale where they will have to deliver on this promise soon, or else acknowledge they are just certain pieces of a bank with a shiny front-end and give back all the "value" they created by claiming otherwise.
That's a quant problem, and a hard one. Anyone that can improve on the predictive power of current credit scoring, do it at scale, and do it for lower marginal cost (whether in consumer unsec., real estate, or SME) should be able to turn that into real money.

NeroTulip


Total Posts: 1016
Joined: May 2004
 
Posted: 2018-02-07 12:41
It recently occurred to me that not much interesting is happening, or I am out of the loop... A friend is doing a PhD in machine learning after a dozen years in trading, and is looking for a research topic applying deep learning to finance. I was like "meh". Am I missing something?

"Earth: some bacteria and basic life forms, no sign of intelligent life" (Message from a type III civilization probe sent to the solar system circa 2016)

jslade


Total Posts: 1148
Joined: Feb 2007
 
Posted: 2018-02-07 22:32
@deeds I don't have any pointers beyond "get good at Bayesian modeling." A dumb starting point would be something like Steve Skienna's book on modeling horse races.
@mtsm I don't know what DLT means. If it's some new shitcoin, I haven't thought about it at all.

"Learning, n. The kind of ignorance distinguishing the studious."

mtsm


Total Posts: 221
Joined: Dec 2010
 
Posted: 2018-02-07 23:32
Distributed Ledger Technology. I am being told it's no longer civilized to talk about blockchain, for whatever reason.

I meant to ask if you had a take on start ups pushing this sort of tech in a proprietary way for various applications, first and foremost smart contracts for finance I suppose?

jslade


Total Posts: 1148
Joined: Feb 2007
 
Posted: 2018-02-08 18:30
Whoever told you this is an idiot. Most of the DLTs which exist in the corporeal world are blockchain.

Start ups are pushing blockchain for the same reason they're pushing "AI." Investors invest in things with those terms in the bizplan.

"Learning, n. The kind of ignorance distinguishing the studious."

katastrofa


Total Posts: 458
Joined: Jul 2008
 
Posted: 2018-02-23 21:59
Deep Learning is not successful in quant finance for several reasons:

- it's not data-efficient
- it's not suitable for online learning (you need multiple passes over a dataset), which is a problem for non-stationary data
- it's not stable: people don't talk too much about it, but you deep neural network training is very unstable, requires a lot of hand-tuning and depends on the random seed
- theoretical understanding is poor, so you're never sure about convergence, generalisation or confidence intervals

Deep Learning so far has worked best when applied to problems which humans are too lazy to do at scale (e.g. image recognition). But in trading, the incentive to analyse data is large ($$$), so anything humans *could* do in order to make a profit, they *will*. Hence, AI has to beat human performance, not just match it or replace accuracy with throughput (e.g. no image recognition network meets human performance, but we don't care because it's way cheaper to use a PC to tag 1,000,000 images than to pay a human to do it).

mtsm


Total Posts: 221
Joined: Dec 2010
 
Posted: 2018-02-24 04:21
Not wanting to play devil's advocate, but to mitigate your statements a bit:

1. not necessarily an issue, DL isn't the only technique that isn't data efficient. Data inefficiency is generally an issue on the buy side, which is mostly backward looking along a single path...

2. Alright, not much to say. It's true it's hard to make it online. It's not really the only reason non-stationarity is an issue.

3. Yes, but nobody in his right mind uses a single net to make predictions. Even computer vision applications tend to compute averaged predictions. If you spend too much time on tensorflow tutorials and such, you walk away with the impression that every learning curve is exponentially decaying and you end up with a latest and greatest trained net. That's kind of naive though...

4. That's a problem, although I would say that convergence, generalisation, confidence is generally a problem when applying statistical methods to empirical data in the absence of knowledge of the data generating process.

katastrofa


Total Posts: 458
Joined: Jul 2008
 
Posted: 2018-02-24 12:34
1. But DL is more data inefficient than others. Data efficiency is an issue in all applications.
2. Of course it's not the only one, I never said it is.
3. The problem is that if you need to train the network 40 times, and each time takes you 1h, then you either have to throw a lot of compute at the problem (which is expensive) or forget about daily recalibration.
4. You never *know* the data generating process, but you can *model* it. But DL setups are often not interpretable statistically, e.g. because they use L2 loss.

nikol


Total Posts: 573
Joined: Jun 2005
 
Posted: 2018-02-26 23:15
a lot.
finance is coming back to senses now.

nikol


Total Posts: 573
Joined: Jun 2005
 
Posted: 2018-02-26 23:16
[deleted duplicate]

nikol


Total Posts: 573
Joined: Jun 2005
 
Posted: 2018-02-26 23:16
[delete duplicate]

deeds


Total Posts: 407
Joined: Dec 2008
 
Posted: 2018-02-27 00:22
Hi Nikol,

possible to provide any color or examples?


katastrofa


Total Posts: 458
Joined: Jul 2008
 
Posted: 2018-03-02 00:19
This time it's different!

Jurassic


Total Posts: 172
Joined: Mar 2018
 
Posted: 2018-08-15 19:46
EMM?

nikol


Total Posts: 573
Joined: Jun 2005
 
Posted: 2018-08-19 15:54
> possible to provide any color or examples?

My apology. Completely, missed your question.
6 months past is difficult to reconstruct the exact meaning but I will try.

Since my feeling is subtle I should touch various 'themes'/markers. I did not formulate them clearly yet even for myself. Therefore, accept my humble apologies.

To explain that I should start from remote:
- my own, for many years, intuition tells me - cash is commodity which should be booked on 'actual basis' = when arrives/leaves account. (inspired by DLT).
- however, today, cash is subject to discount, which comes as a 'granted' thing. We should understand that discounting is supported by money generation process, where estimation of discount is not completely understood. Estimation of discount is reflected by OISs? Not sure. XVA accounts only for effects existing withing circle of first degree clients and inter-banking.
- I think global economy should be better described as an environment where banks operate. I.e. it is not only about systemics risks (networks effects etc) but also the environment of transactions, systems, regulatory/governmental borders. All of this affects our XVAs and ability to fund operations (=discounting)
- emergence of cryptoeconomy follows the same path as the history of banking:
o demand for money security ->
o secure storages ->
o wish to lend money from the storage (commitment and multiplier) ->
o note of cash (first degree of securitisation) ->
o paper money (second degree of securitisation) ->
o central banks etc.etc.
The path followed by cryptos is similar, but without those securitisations:
o wallets vs hackers creates demand for security ->
o cloud wallets ->
o facilitation of trading (brokerage fees and margins) ->
o lending is not yet there (it is difficult to lend when money mass is fixed and legal basis is not 100% stable) ->
o note of bitcoin could be there high transaction cost, but this situation seem to be smothered by LightningNetwork
o base currency and central banks will not emerge for obvious reason
Trading within crypto economy + exchange to fiat is at incredible growth. I observe see that top-notch banks are fighting for high-frequency experts (both, traders and technologists).

As a matter of coincidence, yesterday I saw this picture:
Google trends of "modelling" from 2004-now in Finance


Jurassic


Total Posts: 172
Joined: Mar 2018
 
Posted: 2018-08-19 17:55
@nikol. Worldwide is a bit much.

Try "deep learning" in finance in the USA.

nikol


Total Posts: 573
Joined: Jun 2005
 
Posted: 2018-08-19 18:59
> Try "deep learning" in finance in the USA.

I know. I wanted to make a bit of 'drama'. Smiley

When looking term Deep learning I see maximums in Venezuela and Japan. Japan is ok, but Venezuela is a bit puzzling.

Another puzzling thing is so high search rate of Kardashian in American Finance ))
Search rate of python is giving a hope.

https://trends.google.com/trends/explore?cat=7&date=all&geo=US&q=python,kardashian,modelling

anonq


Total Posts: 13
Joined: Aug 2018
 
Posted: 2018-08-19 19:11
to me the most interesting thing going on is the ease of access to cheap compute via the cloud whether for traditional machine learning or deep learning or some custom backtest optimization process... kinda silly but I always feel kinda cool when a spin up a few thousand boxes

most quant groups I know of have been making the move and the ones that haven't figured out how to make use of all the compute seem to be dying a slow death and being cut


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