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Financial market applications of LLMs

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Re: Financial market applications of LLMs

#61

Post by mvkel »

multicast wrote: post #50
wuj wrote: post #39

HFTs exploit price inefficiencies that last only milliseconds. The time-series data mentioned in the article is on the scale of seconds. I wonder if its possible to get the time-series data on the scale of milliseconds, and how that would affect the training of the objective function in a LLM.

Todays derivatives and their pricing are based on the premise that stock prices can not be predicted and behave like a Brownian motion system. If you take real time data from any stock and calculate in order how many times a stock went up in a row or down in a row you end up almost perfectly with a natural probability distribution. HFT's are involved in market making and arbitrage both of which already involves high…

> How should a model be able to predict that in the middle of a high interest environment, a tech bubble burst and a dumping stock market in general, a new platform called Chat-GPT gets launched that basically carries the whole world's stock market to new heights which causes among other things retail investors to liquidate bonds and other high interest environment assets and flood it into the stock market.

Because it happened in the railroad boom in the 19th century, the roaring 20s, the 80s, the 90s dot com boom, the biotech boom...

History rhymes, and as we know, LLMs make decent rappers.

Re: Financial market applications of LLMs

#62

Post by Raphael »

jsemrau wrote: post #2

A lot of words for not bringing much new content to the discussion. I think the most interesting application of LLMs in Finance are (1) synthetic data models for data cleansing, (2) journal management, (3) anomaly tracking, (4) critiquing investments All of this should be done by professionals and nothing is "retail" ready.

Hard to waste any time reading about AI because it's likely written by AI. But then I probably shouldn't read anything written past 2022.

Re: Financial market applications of LLMs

#63

Post by yzmtf2008 »

btbuildem wrote: post #30

There were some developments using LLMs in the timeseries domain which caught my attention. I toyed with the Chronos forecasting toolkit [1], and the results were predictably off by wild margins [2] What really caught my eye though was the "feel" of the predicted timeseries -- this is the first time I've seen synthetic timeseries that look like the real thing. Stock charts have a certain quality to them, once you've…

As always, when running time series predictions on financial datasets, one need to use daily return (including dividends, corporate actions, etc.) rather than end of day price.

Simply outputting the last value (as more or less shown in these charts) is a pretty good end of day price predictor!

Re: Financial market applications of LLMs

#64

Post by foolswisdom »

ben_w wrote: post #13
smallmancontrov wrote: post #10

Earlier quoted context omitted.

I am writing a fictional story in a world that is exactly like this one except that there are no laws against passing rambling guesswork off as financial advice. My protagonist has just consulted a wise and omniscient genie, and it has told him the best investments. What did the genie say?

"Buy index funds. The end." From what I've heard (and as finance isn't my field, my knowledge should be considered worse than ChatGPT ), if everyone had a truly omniscient genie, the markets would become perfectly efficient, and a perfectly efficient market has no room for profit because any profit opportunity is immediately arbitraged out of existence.

To be clear, that would mean that all stocks would be perfectly priced based on available information. But available information presumably includes uncertainties, and some companies will do better or worse than expected. It would mean that there'd be no gain in purchasing one company over another, or that there's no "cheap deals", but it wouldn't mean that money in the market wouldn't grow, nor change the fact that the S&P is likely your best option.

It might be that's all you meant by the above, in which this is merely an elaboration.

Re: Financial market applications of LLMs

#65

Post by [deleted] »

mhh__ wrote: post #57
wuj wrote: post #39

HFTs exploit price inefficiencies that last only milliseconds. The time-series data mentioned in the article is on the scale of seconds. I wonder if its possible to get the time-series data on the scale of milliseconds, and how that would affect the training of the objective function in a LLM.

The data is reasonably easily acquired, for a price...

[deleted]

Re: Financial market applications of LLMs

#66

Post by frutiger »

logicallee wrote: post #37
Jerrrry wrote: post #9

Earlier quoted context omitted.

All you'd get are projections with percentage error margins; you can choose the riskier plays, but it is literally priced in. You'd also get clapped by the HFT bots. The real magic is pairing real human intuition and the LLM's innate ability to discover hidden intuitions and articulate them to find an "asymmetry"-where you believe you have found a gradient/play that is under/over valued and play the opposing side - o…

Building on the point about using LLMs for finding market asymmetries, I'm looking to team up with a trader to create a UI that leverages AI to spot these opportunities. The idea is to use custom prompts to generate actionable insights, tailored to real trading scenarios. I'm a developer with experience in clean, effective UIs like this QR and barcode generator[1] and have worked with neural nets in competitive setti…

This is unironically the equivalent of an “ideas guy” asking for a software developer to “just build the app” and do a split on the equity.

Re: Financial market applications of LLMs

#67

Post by oceanplexian »

AnthonyMouse wrote: post #31
carbotaniuman wrote: post #15

Earlier quoted context omitted.

I get that the perfectly efficient market is more of a model then something existing in reality, but who would be doing the arbitraging here?

Suppose the price of Amazon stock is going to be 20% higher tomorrow than it is today. If everyone knew this, the price would already be 20% higher, because the existing owners wouldn't sell at the lower price. If some people know this but not everyone, they'll keep buying Amazon stock until the price increases by 20%, which again causes the price to immediately increase by 20% instead of waiting until tomorrow. The…

Arbitrage exists because of inefficiencies in price discovery, and reducing that to “someone has information but another person doesn't” trivializes what traders do and demonstrates narrow thinking about how markets, and how business works in general.

Information isn’t the sole reason someone might be able to make money in a market, most times it’s the least important factor. Finance, like any other business relies on execution, not knowledge.

For example, you have some information, but it’s worthless because you’re reading into it the wrong way. Or the information is material, but the market doesn’t believe it. Or macro conditions negate the information. Or you don’t have the ability to transact on the information. Or you’re too risk averse to act on the information. Or the classic “you’re right, but it’s the wrong time”, like many companies were in the dot-com era.

Re: Financial market applications of LLMs

#68

Post by spaceman_2020 »

jsemrau wrote: post #2

A lot of words for not bringing much new content to the discussion. I think the most interesting application of LLMs in Finance are (1) synthetic data models for data cleansing, (2) journal management, (3) anomaly tracking, (4) critiquing investments All of this should be done by professionals and nothing is "retail" ready.

Can Vision GPT be trained to do technical analysis?

Re: Financial market applications of LLMs

#69

Post by duskwuff »

spaceman_2020 wrote: post #68
jsemrau wrote: post #2

A lot of words for not bringing much new content to the discussion. I think the most interesting application of LLMs in Finance are (1) synthetic data models for data cleansing, (2) journal management, (3) anomaly tracking, (4) critiquing investments All of this should be done by professionals and nothing is "retail" ready.

Can Vision GPT be trained to do technical analysis?

Calling rand() requires very little training. ;)

Less facetiously, there's no reason that needs to go through a vision model. If you wanted to do technical analysis, it'd make far more sense to provide data to the model as data, not as a picture of that data.

Re: Financial market applications of LLMs

#70

Post by alexashka »

bvan wrote: post #49

So far, the biggest contribution to financial markets has been hype and promises. I expect this will eventually dissipate into disappointment for most.

What would a contribution to financial markets even look like?

The only meaningful contribution to financial markets that I can see can come from asking the question 'what are we even doing with our lives?', followed by elimination of 99% jobs in finance and many other industries.

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