TradeExpert, a trading framework that employs Mixture of Expert LLMs | Hacker News Reader

TradeExpert, a trading framework that employs Mixture of Expert LLMs

arxiv.org

114 pointswertyk136 comments

Good one, happy to add my perspective here:

DISCLAIMER: I've spent the last 8 month heavily on building a quant-based asset management app (though, still not live, currently in final steps to sync processes with broker)

a) I tried to leverage some of this AI-voodoo stuff, though not on the level as in the paper; my findings are clear (at least for me): AI-driven trading does not give you a bigger/better edge than any of the other well-known approaches

b) In fact, AI-based approaches are at best on par with traditional approaches, in lot of scenarios not even this; I havent seen any setup from anyone which actually outperformed one of the classic approaches. BUT: The AI-guys have much higher cost, be it Infra, processing time / waiting time in front of screen etc. So you have you to pick carefully, which one you choose.

c) I'm doing today only "standard approaches" with volume/statistics/vola/price action, as this approach is super-cost-efficient (i need only one cheap datastream) and a lightweight machine for 10 / 20 USD a month

d) It is clearly possible to outperform the market, though these approaches are not scalable unlimited - Ex: depending on the used instruments, there may not be enough liquidity to buy continuously for 100k, but maybe for 10k only. Apply leverage of 5-10 on an asset that moved 5% in last 10 days on a 10k position - is this outperforming? A clear >yes< in my perception?

e) People who have built & found a stable approach do not share it or talk about it, there is no real community; you will get details of working approaches only from people whom you are really "friend with"; there is a lot of unshared but working business tactics in the field.

Are you referring to LLM's when you mention AI here?

Thanks for the question:

Not really in the field of trading, rather prediction-based approaches etc.; Im not sure if LLM could be of any use here? The approaches based on statistical arbitrage are purely math/number models. from my own experience, LLM are absolutely useless when it comes to "trading ideas" (I use them for code generation, instead), this is because they are dicing together values in their output which are not really related, because of their hallucis.

Also, for fundamental analysis they are too often incorrect - so running an auto-approach based on LLM-fundamental output would be an "interesting" idea :))

you could certainly encode data into an transformer using custom tokens and fine-tune but that's not trivial.

This is what i referred to as "too high costs" :)

Im sure, there are ton of other options by applying whatever "mega-tech", but if the result is only slightly better while having much more costs (and complexity!), for me its not worth (as i'm not a company, but an individual invstor)

I mean the crazy thing is that I have implemented transformers in another language so I know exactly what needs to be done and I know why it's such a pain in the ass in python, but I just don't have the discipline to sit down and do it with a risky reward (I spend my risk tokens on other things). Now, if I had a patron, I could probably figure it out.

Interesting, so you're doing more fundamental quantitative analysis and prediction. Yes, the issue with using an LLM is that they are too often incorrect, however, a human in the loop could solve this at the expense of automation.

Your project sounds ready cool though. If you ever feel like collaborating, give me a shout at drknyt05@gmail.com.

Thats one of the primary edges: That there is _no_ human in the loop in my approach; actually, the system is build around a simple idea (regardless which approach taken): Out of 700+ stocks globally (blue chips only), there is always something in a "robust trend situation", you just have to scan the market automated and pick the ones with the highest probability. To do so, you need an application (otherwise you would die of boringness in front of the screen), and the app is submitting all trades automaticly (sure, you can cancel one, but a manual action is usually not required)

What most people dont get: This is not about "predicting" where the price will be, this is about taking momentum and weeding out the bad options - and applied on a large set of stocks, there is always something you can ride with for a couple of days (with applied leverage, there is usually a substantial profit)

I have zero knowledge of finance and trading, but when I got curious about algorithmic systems, it seemed like sentiment-based trading using current events was a more viable strategy than forecasting/regression-based analysis.

For HFT - yes, and that has been the case since before the .com bubble. Trading on news is what fuels HFT. And value trading is primarily based on insider trading / corruption.

What's left over is ETF's or luck.

Not really anymore, and for a long time: This article is more than 10 years old and describes what impressive tech & metrics they were using already back then:

https://queue.acm.org/detail.cfm?id=2536492

> AI-based approaches are at best on par with traditional approaches

This seems like quite the generalization! Wouldn't it completely depend on the approach and model?

Thats what i meant with "in my experience": For sure, i see & read a lot of content from people who are claiming, that they are using AI for their approaches - though, i havent met one or seeing someone showing some "hard facts".

It is very likely possible to find a "stable setup" with this - but it didnt work out for me, maybe i had the wrong perspective :)

With cutting edge stuff you cannot say statements like "I tried X and found that it is not good at Y".

You could not get X to be good at Y, but it's not impossible someone else can.

I have been interested in algorithmic trading for quite a while now. Everything you've said resonates with me as I ran into similar issues. I hope you don't mind that I add my own couple cents here.

(a-c) LLMs are especially difficult to use due to their knowledge cutoffs and "unpredictability". A self-trained "old-school" machine learning model can go a long way though.

(d) With Crypto the volatility is great for trading, but liquidity can quickly become a problem (even at $1000 non-leveraged positions). For me, the ultimate goal is to find a strategy that is profitable in all market conditions. I personally value consistency and reliability more than absolute profit.

(e) There's some chatting about risk management, but absolutely no discussion on profitable strategies. Resources are incredibly scarce - Systematic Trading by Robert Caver is the only book that was actually useful.

Thanks for your question:

Regarding LLM: I do use them to write code in less time, i do not use them to do anything rlated to trade analysis / execution / etc.

Regarding Crypto: No, in my location i cant use crypto as Underlying, since the crypto market is open 24/7, but the instruments im using are available only between 0800-2200 on workdays

Risk Management is key: If you have a solid hitquote, its mathemathically impossible to ruin the account; most people get to greedy and have no patience, but if you stick strictly to your risk plan, there is not that much that could go wrong.

So they used a LLM with knowledge cut in mid 2023 to evaluate 2023? Seems like a classic leakage problem.

From paper: "testing set: January 1, 2023, to December 31, 2023"

From the Llama 2 doc: "(...) some tuning data is more recent, up to July 2023."

Removing the "Market expert" which uses OHLCV (Open, High, Low, Close, Volume) also drops the sharpee from 5.01 to 1.88 while also increasing the max draw down to 13.29% (v.s. 9.70% for the index). I'd be very surprised if the pre training of the base model was the only source of leakage...

> Alpha Factors incorporates 108 technical indicators and factors with their expressions, which are believed to possess predictive power regarding stock price movements.

Examples of the indicators are in Figure 15. The ablation studies in Table 4 suggest that market and news information made a much bigger impact than the magic indicators. Makes sense if the indicators are simple enough that the LLM can reproduce them without losing processing power.

I somewhat like that they used DJI and not SPX, but 2023 was a sideways bull year with DJI +12% and SPX +23%. One year is way too short of a study.

> Hardware: NVIDIA A5000 GPU x 4, AMD Ryzen Threadripper PRO 3975WX CPU, 256 GB RAM

Seems approachable.

> The proposed TradExpert framework utilizes a Mixture of Experts (MoE) approach, where four LLMs are specialized in processing distinct sources of financial data. All these LLMs are based on the LLaMA-2-7B Touvron et al. (2023b) model and fine-tuned using the LoRA mechanism Hu et al. (2022)

Relatively small LLM.

Overall, this does seem like an interesting study, even for just comparing data sources.

> 7B, Relatively small LLM.

Possibly too large a model. (Daily) finance data is finite - 7B parameters is potentially order(s) of magnitude more than the training data.

Yes, but these are LLMs where the point is they understand English prompts and information. They aren't just fed market data.

How do people on HN think about the market?

Do you think the market is so efficient that anyone who outperforms it is merely lucky?

Or do you think the market is inefficient enough for a person smart enough to be able to outperform it by thinking?

In other words: Do you think a single person can rationally decide to invest their time into thinking about the stock market? Or would that always be a fallacy, and whatever the outcome is - we can't decide if it was just good or bad luck?

I know that due to being interested in a specific industry, lets say Video Games, I can combine a general understanding of the environment, to a specific understanding of certain companies and the games they're making. If I see in a low-key mailing list that a company is making a new game that I know will line up well with the direction the industry is going, then I can invest in that company and likely be ahead of the market.

My hunch is that if you spend more than 6 hours a week studying the mood of a industry, you would likely be "luckier" than the market (although all risks still apply). I also believe a LLM could do exactly what I do with enough investment.

This is exactly how I make money shorting (or buying puts on) companies like Ubisoft and EA. They're pretty much hated by the entire gaming community and their games do not perform well - investors actually believe the stuff they say in press conferences so often the stock prices are over inflated and get corrected about 1 week post launch.

Be careful, shorts are generally not for retail investors.

Shorting is about predicting when a stock will go down.

Not that it will go down.

Because

- there is no limit to how high a stock price can go

- the market can stay irrational longer than you can remain solvent

there are ways to cover yourself, either buy a put (like he mentioned) or if you are short shares then buy a call. both of these have defined risk. also more advanced options strategies would let you adjust where you'd profit and where your risk is in the trade

Imo long term efficient, short term could be inefficient enough

That’s a good way to put it. Short term is dominated by mood/PR/talking heads, etc, but long term seems more about fundamentals. A company not making money can survive and pull a lot of stunts but sooner or later investors want to see profits or they bail. But that “sooner or later” might be 10 years.

A lot of people say crypto bubbles are evidence of inefficiency but just because the moves are craze driven doesn't mean there's a profit opportunity, and it doesn't mean that historical price data can be used to predict future price data. It's common to conflate "market doesn't follow economic fundamentals" with "market inefficiency" but afaik those are two different concepts.

“In the short run, the market is a voting machine but in the long run, it is a weighing machine.”

Short term movements are very much sentiment driven, both humans and algorithms. Long term movements generally trend towards the consensus of fair value of any given security, which is usually modelled somehow. You can see stocks where the models struggle to reach consensus by their long term volatility.

I believe the markets are assymptotically efficient, but because of constant changes/surprises and the mechanisms being chaotic, it never reaches steady state, and thus is never actually efficient. There might be individual stocks or sectors that do reach steady state between annual reports, but as a whole: no. It's possible there's statistical signal in the inefficiency, comparing predictions to outcomes. At least HFT seems consistently profitable, and whatever Medallion is doing.

That said, judging if one specific trader is lucky or good is practically impossible.

> Do you think the market is so efficient that anyone who outperforms it is merely lucky?

> Or do you think the market is inefficient enough for a person smart enough to be able to outperform it by thinking?

The market cannot be “efficient” (though this isn't what “efficient” means in economics besides in EMH), because if it was efficient, then there would be no incentives for market makers, and as such there wouldn't be anyone working to make it efficient.

As such, the market is always in an in-between state, inefficient enough so that people spend energy to try beat the average, but still hard to beat because there are already many players out there.

    if it was efficient, then there would be
    no incentives for market makers, and as
    such there wouldn't be anyone working to
    make it efficient.

Many market makers work with others people money. So they don't need an inefficient market as an incentive.

Market makers also could have a distorted view of the market. Thinking it is inefficient while it is not. So they have an inefficient market as incentive even though it does not exist.

> Many market makers work with others people money. So they don't need an inefficient market as an incentive.

Financial institutions pay a lot of people for these tasks, and they are very well paid (and HFT infrastructures cost hundreds of millions), all of that is being paid by their own money, there must be an incentive for them to spend so much money on this.

> Market makers also could have a distorted view of the market. Thinking it is inefficient while it is not. So they have an inefficient market as incentive even though it does not exist.

This assumes that one of the wealthiest industry on the planet is entirely made out of clueless fools, which is a interesting perspective.

If you read my post again, you'll see that it was not about the question if institutions can create a positive ROI by doing HFT.

It was about the question whether the market is inefficient enough for a person to create a positive ROI by thinking.

None of the people I know in finance work in HFT. There are tens of thousands of very smart people being paid billions of dollars to make hundreds of billions for their employers with their brain.

Why don't they invest on their own instead?

Because even if the expected outcome is positive it doesn't mean that it isn't risky at an individual level. Like you wouldn't gamble your house in a coin flip game even for a 2:1 reward.

I actually had one of my finance friend quitting his job to do it on his own, but he stopped after just 3 month because the level of stress wasn't worth the additional money he was making.

But again, nobody would higher so many smart people if the optimal move was not to play and just passively investing.

Why don't they make more smaller bets so the risk cancels out?

There's only so much information an individual can gather efficiently. If you just rely on your portfolio being as loosely correlated as possible, you are just passively investing.

If you want to actively invest, you need to spend a lot of energy understanding the business and industry of the company you are investing in. (And that's actually the theory behind EMH, that in aggregate the market makers have access to all available information, because they all spend a lot of efforts gathering that info. And the underlying theory is correct, EMH is just failing on the game theory part: nobody would bother spend energy to gather that info and make decision out of it if the market was already doing that perfectly).

Then your answer to my original question:

    Do you think a single person can rationally
    decide to invest their time into thinking
    about the stock market?

Is "No". In your experience, the market is not inefficient enough that a single person can make a ROI that is worth the opportunity cost of not working for someone else instead.

Nope, all I said that the only person I know who did it wasn't able to deal with the stress (the main takeaway of this particular story is that I wouldn't advise doing that when you have a lavish lifestyle with a big house credit and a family to feed all by yourself).

"Markets can remain irrational longer than you can remain solvent"

I would argue irrational markets are easier to make money in.

Exactly. This is basically confirming that markets are, at least temporarily and sometimes for a very long time, irrational and hence not perfectly efficient.

It's easy when the market is in the 'the numbers must always go up' phase

The hard part is predicting the 'just kidding, they go down' phase.

Sort of both. I believe there are massive polarized forces that invest heavily in understanding and gaming the system. Sort of like a big multi-player game of tug of war .

The system must be understandable or people wouldn't incorporate and collaborate on extracting an edge. Slack in the system represents a lower price point, which will be corrected by a corresponding purchase order.

An individual has an effect, but it's miniscule compared to the massive forces in play. Unless you have a TRULY novel analysis of a situation, you are going to have a very low probability, success rate and out competing the market.

There is insufficient information in the market by design as evidenced by the existence of material nonpublic information.

Anyone who knows MNPI can make quite a lot of money which is why the SEC exists to regulate how that information can be used in the broader market.

To the extent that sufficient intelligence can reverse engineer or infer the private books and planning of public companies and the likely outcomes it can outperform other investors and make the market even more efficient.

Modeling global economies is hard; even regional models are hard and a large part why 20th century communism performed poorly compared to open markets (the others being internal corruption and external interference). Therefore I think individual humans have neither the capital reserves (bigger risks can be taken ~safely with more capital) nor the intelligence to beat the current markets. But it's not a zero-sum game and the global economy is still growing so if one's net outcome over several years is negative then it's probably due to bad strategy (e.g. picking stocks by hand, not diversifying) as opposed to bad luck.

Its hard to find efficiency in markets when company values seem completely unhinged from reality.

Historically, companies were valued with a heavy weight put on their financials. That doesn't seem to be the case anymore, and without any clear approximation of how we are valuing companies it feels to me more like pure gambling.

Maybe it's your understanding of reality that it's flawed.

It certainly is, everyone's understanding of reality is flawed.

Show your work here though. I'd point to P/E ratios over time [1]. That's a very common signal used when valuing a company and today those ratios are much higher than historic averages, nearing the ratios we saw before the dotcom bubble burst.

[1] https://www.currentmarketvaluation.com/models/price-earnings...

P/E ratios are probably more useful for comparing companies in the same industry at the same moment in time, rather than as a global rule of thumb.

Valuing a company needs to take risk free interest rates and future growth into account, so it's difficult to compare across vastly different companies and time periods.

Your response seems to imply that there are broad differences in potential for future growth (among other things) today relative to the dotcom era. Could you elaborate?

Alternatively, we could cherry pick a single company. How do you justify tesla's p/e relative to the other car companies? From where I'm standing it doesn't appear to make any sense.

P/E simply isn't a very good ratio other than just for a quick glance. Real analysts look at 5+ years future cash flows (not earnings), risk free interest, risk premium, cost of capital, and other factors.

Differences in any of those factors between companies or between time periods will determine why a company is valued the way it is. Interest rates are way higher now than a few years ago, and still lower than they were in the 90s. Some companies have cool business models that give them an edge, eg. Being able to generate lots of free cash, being able to invest cash at high returns, borrowing at lower rates, looking in customers, or making deals that will assure high revenue growth for the future.

I dont know much about TSLA but I don't think the market sees them as just a car company any more than Amazon was just a retailer.

Future cash flows are only predictions, you can't "look" at them. Anyone chasing future cash flows is chasing models of the future and st the whims of whatever the models did, and did not, as relevant factors.

There are certainly cases where I would agree (TSLA) that I would never be able to underwrite the investment with my mindset. I don’t think your statement is correct though. There have always been hype growth companies, appetite for risk has increased but the mental model is not foreign. The market is betting on a major shift in some way that the company is doing a lot better than it currently is. This is a lot of tech but for the rest of the market most companies trade within their expectations based on current financials.

I put a lot of weight behind P/E ratios. Today those ratios are very high compared to historic trends, and are in the ballpark of what we saw in the sitcom bubble [1].

By no means am I saying that is predictive, maybe the ratio isn't as useful as I think or maybe it is less applicable today for some reason. I do have a hard time finding any stocks today that I can justify the market valuation for, though.

[1] https://www.currentmarketvaluation.com/models/price-earnings...

Seems like current high P/E ratios could be related to having just gone through an extended period of very low interest rates.

When rates are low, some (many?) investors may decide that the stock market looks better than lending (either directly or indirectly).

And then that can be a self-reinforcing cycle as well--more money going into stock markets == stocks perform better == stocks look like an even better investment.

P/E just isn't that useful an indicator to look at. It is near useless for anything focusing on growth rather than profit generation. It's a figure that misses out on NVDA, Monster Beverage (not all growth stocks are tech!), etc. These are the names that have driven the majority of market returns over the past decades. There are entire sectors like SaaS where PE is pretty useless.

That's the first (big) reason why PE is increasingly a weak Value (in a broad sense) signal. Another huge one is that companies who are free cashflow monsters that shunt cash towards some sort of internally compounding flywheel (ex: Amazon with AWS CapEx and warehouse buildouts) screen terribly on PE ratio even though the cash they are throwing out is being plugged into a compounding engine far greater than one can find in the broad market. That's a value in and of itself, and obviously demands a premium and is well understood today.

Personally, most of the more fundamental oriented stuff I read seems to focus more on figures like sales to EV when ranking "value" in a peer group, not PE.

That said, it can be useful. I grabbed some Abercrombie because it's at a PE of 8 and all of the free cashflow goes to buybacks. Mostly chose to discount growth and focus on near term value, so there's no tricky modeling there, and again the cash is just shunted into buybacks and there is no complicated debt situation either. In an "old school" value proposition like that, PE can work decently.

But I'm also in a Canadian gold miner which is a far stronger Value proposition, yet it will screen horribly on PE... Go figure.

Why should I care about growth rather than earnings though?

The more I focus on growth the more I'm gambling on unknown future earnings. Sure I might miss out on NVDA, but whether I'm better off missing out on it depends entirely on how that company ends up doing over my time horizon.

Focusing on earnings is more predictable. That doesn't mean its better and it isn't to say that today's earnings directly predict tomorrows earnings, but it is much more tangible today and is less risky as an investment compared to focusing primarily on growth.

Edit: when considering growth rather than earnings, how do you distinguish the investment from a ponzi scheme? When I ignore earnings and focus on growth, or hype etc, I can't help but feel like I'm just trying to ride the wave and sell to the next schmuck before it fizzles out. With earnings, and ideally with dividends, I can at least point to a reason the company may be worth that value other than point at what others want to pay for it.

Because you will be missing out on return. Less risk and less reward. Ideally you would not be buying NVDA but instead an index. By focusing on dividend stocks you are taking less risk so less reward over the long term. Totally up to you and kind of the same situation when people pay off a mortgage early even with an extremely low interest rate.

Sure I might miss out on NVDA

This isn't a small point... If you are involved in the equity market and miss out on names like this, you're almost certainly going to massively underperform. The vast majority of returns comes from a small slice of names. And again, if you want to properly do value investing, you have to precisely understand the growth component of value, as well as the weird debt and cap stack situations that usually come with value names (the market is efficient and they trade at discounts for a reason).

I trade full time, and my personal long term account has no stock picking. Value or otherwise. The medium term acct does, but not the decade+ timeframe one does not. So ultimately I don't really know what to tell you. Picking the next NVDA is practically impossible, yet missing out on such a name destroys your relative returns against an impossibly simply approach (index investing). Therefore don't try. Simple conclusion.

If you are strictly focused on a very limited view of "value" such as free cashflow, asset value, and PE (which ignores important aspects like debt maturities, industry cyclicality, quality of internal compounding, etc), then you're frankly directly competing with private equity. The names that stand out on these terms get bought by private equity, and the scraps of "value" are left on public markets. PE has 500,000,000,000 in dry powder currently, and much more efficient access to Capital markets than you do, with the ability to lever up those easy "value" tilted cash flows many times and immediately sell the debt on to pension funds and such. Trust me, if the value opportunity was truly there, they would take it. What you're seeing on markets is fairly priced on a risk adjusted basis.

Just use an up to date factor overlay on top of efficient equity beta exposure if you want diversified value that won't pigeon hole you in weird value traps. Frankly. Figuring that out is going to be much easier than cracking the value investing code and somehow beating the index. I remember an interview from one of the smarter firm owners that I've heard and he uses "short junk" (which generates extra cash to deploy efficiently) to slightly lever up his equity portfolio while giving him a broad based value tilt (it's long short portfolios all the way down). Over a long time frame something like that is going to crush any form of stock picking for the vast majority of participants.

there is a case that the markets are highly efficient, just the the "information" part of the market hypothesis isn't the information a layman thinks are the market signals, and that only the HFT trading firms have the "correct" signals available to them. That certainly accounts for the seeming dissociation between company performance and market performance on certainly "blue chip" stocks

I think you're coming in with an assumption that HFTs are generally correct in their valuations, and that they are chasing efficiency or proper valuations rather than short term gains.

Maybe those would be roughly analogous in a stable situation, but my read is that the big players in the markets today are chasing short term gains despite the signals. I'd point to the housing crisis as a recent example, the biggest banks and funds were massively over valuing real estate and real estate derivatives because they were blind to the actual risks and saw what appeared to be free money.

There are so many misconceptions about what HFTs do and if anything they seem worse here than average.

HFTs do not (for the most part) have any kind of “valuation” of stocks based on the fundamentals of companies. To a first approximation most HFT strategies are market making- they buy from people who want to sell at 9 and they sell to people who want to buy at 10 and every time they successfully do that round trip they make a dollar.

There’s more to it than that because of course there is. Markets are complicated and there are a lot of market participants trying to do lots of things. So sometimes they make use of their knowledge of market microstructure to try to detect and preempt big market moves (eg index rebalances, big portfolio unwind trades etc).

But don’t think HFTs have a perspective or care about the value of TSLA or whatever as a company. They basically want people to trade TSLA a lot so they make the vig every time they do. As some sibling has pointed out, they don’t want toxic/directional flow though, they want balanced flow because that means they keep making a clip whereas directional flow leaves them holding a lot of risk one way or the other because they are on the other side of those trades.

HFTs chase two sided order flow, not valuations that are "correct" in the long term. They generally don't add information to the market.

Quantitative traders do add information.

HFTs probably don't like trading with people that are adding information, that's why order flow from retail traders is valuable.

>HFTs probably don't like trading with people that are adding information

Literally called Toxic Flow in the industry.

From this and your other replies it seems that you assume the average p/e ratio of the S&P over the last 75 years is somehow the "correct" one and therefore todays S&P's p/e is "unhinged from reality".

What would be an argument that supports this?

To me, it seems fairly easy to imagine that the p/e of the S&P could increase forever. Either because the increase of the money supply is constantly accelerating or because our ability to increase productivity is constantly accelerating.

I'm not trying to claim that there is any "correct" p/e ratio, its just an indicator or signal like anything else.

The p/e ratio is an interesting indicator because it more directly ties together the how much money the company actually makes relative to what I'm paying for it.

P/E ratios over time can be good indicators of relative value of the company. I'm not exactly inventing economic or investment theory here to point to high p/e ratios as an indicator of overvalued companies.

the flaw in P/E ratio is that it is only a snapshot of what is currently happening today.

For fast growing companies, it’s useless because they quickly add new revenue streams and profits can change quickly.

If you are purely investing off of sane P/E ratios, you might make some safe investments, but you will always be late to the party.

There's more risk in investing on growth though. You could be late for the party or you could avoid the funeral.

    high p/e ratios as an indicator
    of overvalued companies

Again, you say that as if there was some kind of agreement on what a "high" p/e ratio is. But what is it? Since you multiple times mentioned "historical averages", I guess you assume that above the historical average means "high". But that is a big statement. How do you back it up?

Imagine an investor in 1988 (the middle of the p/e chart you linked to) who took your approach. From their perspective, the historical average p/e of the S&P 500 is about 15. Would they have played the market by selling over 15 and buying under 15, they would have missed almost all of the nice long rally that came in the decades after.

Again, I'm not claiming there is a "right" p/e ratio. High is relative though, and relative to historic ratios the market is overvalued roughly on the scale of the leadup to the dotcom bubble. Are you arguing that isn't the case, or that just doesn't matter and isn't a good indicator?

> they would have missed almost all of the nice long rally that came in the decades after.

I wasn't saying to buy and sell as the ratio goes above/below some specific threshold. More importantly though, this is ultimately cherry picking. You can always look back at historic data and define a start point, or a start and end point, to make an argument work. That has little, if any, weight on predicting what will happen in the future.

I'm being pretty careful here to not claim to know what prices or the economy will look like in the future. I'm simply sharing my read on p/e ratios, what my own take away from they is, and where what I see as historic outliers are.

You said "company values seem completely unhinged from reality".

What do you mean by that?

I could have been more clear there for sure. I don't understand the multiples being offered to companies today in the stock market. Specifically I'm thinking about multiples relative to revenue, earnings, or profit.

When I'm investing in a business I want to know how quickly I can get my money back, and how risky I think the investment is relative to my expected gains.

When I'm investing in baseball cards I want to know how much the next person will pay me for it. The stock market seems much more inline with that today, and in any reality where stocks are meant to be considered investment that seems disconnected or entirely unhinged.

You don't understand the p/e ratios of any company? You mean they all seem much to high or much too low to you?

Let's talk about a specific company then. Which one have you looked into recently?

I was talking about the average p/e ratios in the market today. Though they are around 1.7 standard deviations above historical trends, so while there will be some that aren't high relative to historic trends most are.

Now we are back to the theory that above the historical average means high. I give up. We are talking in circles.

the markets are provably inefficient: companies shouldn't have their total marketcaps jostle by whole percentage points in a single trading day, when the underlying economics aren't that volatile.

but "markets" are (and have always been) "animal spirits" with all but participants leveraging their bets and other players pushing them off the table when they're slightly wrong.

Markets aren't perfectly information-efficient. They're just _very_ information-efficient. So there's at least some room for non-luck-based strategies to outperform the market.

More important than information efficiency, is the subjective nature of investments, and the inter-sectoral complexity of the modern economy. There's lots of meaningful calculations you can do to predict the longterm value of a company, but massive and mostly unseen shifts in expectations happen regularly.

A fairly small number of people in Feb 2020 saw that 1/3 of students globally were suddenly home-schooled, and connected the dots to turn that into an ultra-strong signal to invest in Moderna.

Similarly, lots of people saw that Nvidia's CUDA granted it a near absolute-monopoly over anything that requires matrix multiplication. They were able to connect the dots to turn that into an ultra-strong signal that NVDA would turn from a relatively obscure company into a significant company.

Domain specialists have an edge over the aggregate-market within their narrow field of interest.

When Meta did a name change, another stock - Meta Platforms - saw a spike

The efficient market hypothesis also implies that it's impossible to consistently lose money for a given level of risk. Yet this happens all the time.

If there’s volatility it’s not perfectly efficient, because corporate and economic fundamentals don’t change that quickly.

But playing volatility is a meta game. It’s about predicting what other players will do.

It’s pretty efficient at assimilating obvious revelations. You have to be quick to trade on the news, and you’re not going to find many arbitrage opportunities. There’s no reason why it would be particularly efficient at pricing long term outcomes. It reflects some sort of consensus or balance of opinions, but those things could simply be wrong. That said, even if you’re right about something, there’s so many factors to account for, it’s not going to be easy to profit. Personally I have most of my savings in retirement tracker funds (global equities), but I’m also building a portfolio of handpicked stocks/bets, aiming for 20, each with the same starting investment. Maybe it won’t be successful but at least it will be fun and minimise the regret of not acting on my insights/convictions. The diversification should stop it from being a disaster.

I hear these all the time:

    1. Non-zero sum
    2. Market efficiency 
    3. Everything is priced in

1 only makes sense if you are the market as a whole (you are not), not the one who is making the more informed decisions.

2 is true and actually a good thing for trading. It means there will be less noise vs signals.

3 doesn't matter if you are the market maker or you know the patterns caused by the "pricing in" and react to them.

IMO efficient market hypothesis is not true (assuming you have all available information you could beat it), but for an ordinary person there are so many ways the market is stacked against them that they should still just assume that it is true.

The question isn't "is it possible to outperform the market by thinking?", it is "will whatever advantage I can get by thinking weigh up for all the disadvantages I have from not being able to insider trade, being further from the exchange than other market participants, having lower ability to sustain losses than big market players, trading fees, taxes, opportunity cost, etc.?"

For any single individual, the answer to the second question is almost certainly no (unless you happen to sit in congress).

If you have industry knowledge then you can outperform market short term

Disagree unless we are talking about insider information. There are lots of experts with industry knowledge and most are not able to monetize that.

Insider information is the only durable advantage in the market. Any time you see a long running and consistently successful fund report, keep this fact in mind.

I said you can, not you will

Understanding industry allows you to not get baited by rumors, articles, dramas, bullshit comments on reddit hn etc

And I said I disagree. Having knowledge of an industry is not that valuable. It’s the bare minimum before making an investment and therefore is a very known datapoint.

>It’s the bare minimum before making an investment

Such a naivety. Try visiting r/Wallstreetbets and you'll see that people often don't have any meaningful industry understanding

Are you referring to yourself? Keyword I said was investment. Most of the folks on those types of forums are simply gamblers or wishful thinkers.

Again my only point before you took it to this level was that industry knowledge is not an edge it’s table stakes when making an investment in the public markets. Having that experience or knowledge is not niche or unique and something that can be purchased with a handful of expert interviews.

Industry knowledge is not binary value.

Some have more of it, some less.

The more you have, the more you can evaluate products, roadmaps, competition.

Of course it’s not binary but again, unless it is insider knowledge, the information is simply a commodity that many others are trying to use to make investments. “industry knowledge” is important as an operator but is simply commodity data for an investor.

N=1, I think that:

- You'd have to be either extremely lucky, or spend a lot of money and effort (e.g. to get to the bleeding edge of quant or HFI game), to be in a position where you could use your knowledge and brainpower directly to give yourself an edge over the market, and:

- Any such edge is extremely short-lived - the moment you take advantage of it, the market itself will start adjusting to correct, and on top of that, other smart players will notice the irregularity, work to exploit it or reverse engineer your approach; in the end, the "pattern" quickly disappears.

Whether one can become able to continuously find such short-lived edges and profit off them, by means other than pure random chance, I don't know, but I highly doubt it. The space is way too competitive; a sustained miracle advantage would eventually attract regulatory attention, and/or unscrupulous parties willing to lie and cheat to bury you legally, or literally. But that's just me speculating.

If the "edge" is ephemeral (I agree that it is), I always wonder how quant/HFT firms like Jane Street, RenTech continuously make insane profits out of such strategies.

I suppose it could be a combination of things that isn't necessarily just related to finding edge over markets. Having an entrenched market position or access to data faster perhaps?

Rentech is an old customer of mine, do you know if they’ve still not had a down year on the employee fund?

Not all edge is necessarily ephemeral, if you're thoroughly entrenched and have a deep understanding of a market you will usually just outperform your competitors. Take Jane Street's infamously long-lasting (and profitable!) Indian options trade for example. They had clearly put in the work and effort into understanding a market that other firms didn't do to the same extent, and resources and minds who are good at figuring this stuff out are finite.

You're totally right that edge isn't just knowing if number is more likely to go up, as an example since you mentioned faster access to data: some of the best of the best companies will hire meteorologists so that they know how reliable their microwave towers for transmitting data between e.g. Chicago and NY are (and they can lean in or widen their spreads according to how current and up-to-date their information is).

It sounds like crazy stuff to do, but when your data could be up to 10ms slower than you expect due to weather and you're so sensitive to latency you hire FPGA engineers because normal high performance CPUs aren't enough for you, it's not that crazy.

I sometimes feel like HFT is a waste of good talent and wonder what some of the people I've met who work at JS or CitSec could have done in other industries, but at the same time HFT is often the only industry that correctly prices these peoples' minds. Ultimately having a smoother financial system where risk is more correctly priced is a good thing, even if it's not the best thing they could be doing.

Nice. I also think about the amount of human brain power that goes into HFT (or trading in general) and how society would be if it were channeled elsewhere. I suppose some do it for a few years and then do other things once they've accumulated enough wealth

...let me state that we should be much more worried about the superintelligence which is entering "Ad-Tech" after attending Harvard or Stanford :-))

Jane street and others are essentially market makers. Which means in one way or another they get paid for providing liquidity. They have a different pay off profile from hedge funds, retail traders or mutual funds which usually make a directional bet. For example by having exclusive order flows (for example from robinhood traders), lower trading fees (often rebates), access to better execution (via dark pools), cheaper financing (for leveraged trades), tighter spreads in OTC deals, etc.

So is the idea that their quant researchers spend all their time finding alpha just a front/recruitment driver rather than the reality of what a quant researcher would typically do day-to-day to actually increase PnL?

All the examples I gave are a precondition to even take part in the market making game. Your actual market share and profitability within the space is then still determined by the quality of your models, execution, tech stack, market access etc. for which you need good quants, traders and also lawyers (for tax and offshore structuring).

They have the best infra, lots of good properly cleaned data, and market access.

I don't know if rentech ever make markets but one way to make a _lot_ of money in finance to provide liquidity while also have good alpha models. The faster the better. This way you are earning the spread while also getting into the positions you want, and maybe even getting paid for it by the exchange.

My intuition: to the extent it's possible to systematically create an edge for yourself, someone will be doing it, and they'll have a bunch of competitors trying to outdo it, perhaps swapping the "first place" amongst themselves, but also continuously raising the costs of maintaining the edge.

There's an equilibrium there, where the top players have some profits - not necessarily insane, but enough for them to continue the race and take some of it home. Meanwhile, the up-front costs of getting all the advanced tech and expertise to try and compete with them is high enough, that you're not likely to break even for years - so you won't bother, and no one else will either, and the pressure pulling those "insane profits" down disappears. Those profits look like money being left on the table, but no one can afford to reach for it.

I spent 6 years in algo trading. I think that the market is a thing people call the US stock market and it’s 98% machine traded volume. Whenever I hear people talking about their alpha on an equity I assume it’s either insider trading or ignorance of the billions in PhDs, super computing, and dark fiber they’re up against.

There is alpha in markets. I did a lot of relatively unsophisticated SPAC relative value warrant trading during the 2020 period, and I think a big part of it is the question of scale and slippage. If you're a prop firm that makes "real money" your universe is limited by these constraints. IE "not worth the time" would be a common response to trading opportunities.

For example, there were high sophistication players in the merger/stat arb phase of the game, and they would layer out their warrants like an onion of dark liquidity (the orders were hidden/not directly listed on ARCA/etc). They were involved in just about every SPAC name out there. But when an SEC filing came out, or you learned some specifics about a certain sponsor team (maybe they have very high quality lockup partners who don't dump shares on lockup date, as shown by their last 3 SPACs), then maybe that implies a higher warrant valuation, or maybe that should be priced into the option chain. And they will happily sell to those pricing in that "hair" and sell their inventory because they don't want to deal with some 5 dollar warrant that trades 50,000 volume per day.

The profitable futures traders I know are also more or less just riding off the back of the machine volume and participating when machine trading from option flow is moving/pinning markets. They are of course just exacerbating the situation, which is partly why we see this increasingly bifurcated market where robots/options are entirely into control, interspersed with violent price discovery/mini vol events.

With time and focus any person can beat the market. The market is wildly irrational, so rational actions are not the deciding factor. It is being able to spend all your time and focus watching the data. The question is whether this can be reliably outsourced to machines and what level of effort will it take.

A very simple exercise is to go and watch the live data for a single stock for 1 hour. You will see that it moves up and down and has trends. After 1 hour you can have an idea of an entry point, pretend to pick one, continue to watch, the odds are much greater than 50% that at some point the stock will go above your entry point in the next hour. It only doesn't if you bought into the top of a solid downward trend.

And that is the very basic example, but pair that with an understanding of options and a hedge strategy for the unlikely chance that you bought in at the top of a solid downward trend.

The last piece is to watch and understand Level II data and I reliably believe anyone can "beat" the market. The issue is that you literally have to monitor it consistently with a clear focus, can you do that with more than one security?

What portion can we reliably have machines do without some risk that the machine makes a tremendous error and blows up the account?

I spend far too much time thinking about and building code for trading on betting exchanges, which isn't quite the same, but there are parallels.

Most experts agree that the hard definition of efficient markets (that all information is baked into a price immediately), doesn't hold, but the soft definition (that the price will veer towards true value), does hold. The big question is how quickly does it get there?

That means there is potential to make money (if it takes n minutes, and you are able to trade at n/2, and exit at n, you're going to make money). Insider traders can make the most money, but there is also money to be made on much longer trends too, I think.

Most of the super smart people throwing money at algos and hardware are specialising in HFT to try and trade at very high speed. They are typically not looking at much longer trends. That means there is perhaps more exploitable value for the individual in longer trend spotting compared to shorter term trends. (Note, I don't mean technical analysis when I talk about trends.)

Would I recommend you get into market making and HFT? Not without an eight figure sum to get started.

Would I recommend you get into value investing and looking at the long-term? Sure, just remember that diversification is good for a reason, and you might struggle to beat an index - most professionals do. If you enjoy it though, you might find it a good way to make your retirement savings grow, perhaps even a living.

Would I recommend you get into day trading? Probably not, but that doesn't mean there aren't successful day traders sat at home making a living trading on margin. It's hard work, and if you're smart enough to make it pay, you're probably smart enough to make more money doing something more productive for society instead.

I also don't think most people are smart enough to make money (important note [1]), if they try and work out by "thinking". Don't try and move from first principles. Learn from successful people, who aren't full of BS. The vast majority of people on YouTube and selling books are not successful. There are real success stories out there, you need to filter a lot to find them, but they exist. Use them.

[1] Your question was if people can "outperform" the market. You don't need to outperform the market, you don't need to be optimal, you "just" need to make enough money to meet your goals, and preferably more than you would get just from leaving your money in an index tracker. That's not the same thing. If you're trying to perfectly enter/exit trades and "beat the market", you're dead before you get started.

I have done, using my own real money, algorithmic swing and day trading for many years.

There are patterns that can be found and exploited, in the short term. However, I was unable to predict changes in the trading environment. My software did not even try to watch the news. So when big macro events happen, like a civil war in Syria, for example, the patterns can change overnight, so I sometimes was left holding a heavy bag.

80% of it is the world's largest and longest-running Ponzi scheme. Because it's a Ponzi scheme, it brings negative expected value (maybe the other 20% cancels it out, maybe not). But because it's the world's largest and longest-running, you'd better participate because the chance it collapses at the exact moment you didn't invest but everyone else did is pretty low, and you can't let them outcompete you in the short term. You have a significant chance of not being the bottom tier of the Ponzi, unfortunately. If you do invest and it does crash, you're no worse off than everyone else.

That's for general market trends. Individual stocks also make no sense. You could buy and hold some very stable company's stock but you're making a relative pittance compared to just, like, changing jobs or something. Expected compound interest is not high enough to justify the risk of a catastrophic crash; also if you think a crash will happen any time in the next forever, you're better off holding cash or low-risk bonds until after the crash and jumping in then, as that will more than make up for the compound interest you make up on.

In the past, compound interest has been significantly higher than expected, and very much worth it, but only due to survivorship bias. There are many reasons to think the next 60 years won't be anything like the last 60.

>Expected compound interest is not high enough to justify the risk of a catastrophic crash

That's just not the case. It may be a lower yield world, but you can still find companies with relatively stable and growing 10-15% cashflow to EV ratio even in the US. Outside of the US old-school "easy" Value is still very much alive as well.

What you were advocating for is market timing, and market timing demonstrably does not add alpha unless it's done very mindfully, mostly because the opportunity cost of sitting out is great. If you look at core alpha sources through a factor lens like trend following, most of the profit comes from participating in the big trends (read, most of the easy returns come from "expensive" getting "more expensive/"overbought"). From another lens that takes into account fragility/crashes - vol trading - selling vol is paradoxically highest sharpe when vol is low and most vulnerable to severe disruption.

Part of what you're disregarding is how market participants are far more sophisticated today than they were even 20 years ago. If you're building a 60/40, it doesn't look attractive, but even accounting for survivorship bias, the baseline has risen.

The "crash case" remains relatively similar when it comes to portfolio planning - you need to prepare for 60% crashes, and 10-year "lost decades." The tools to manage that equity risk and still access smooth returns are far more powerful and accessible than they were in the past though.

I stick with the dog walker analogy. A man walks down the street at a steady pace, and the dog on the leash runs back and forth and side ways and sits and jumps.

The man is the economy, the dog is the market. Unless you have access to HFT systems & models, there is no point in trying to guess where the dog will go. You have to look at the man, which is the aggregate health of the economy. If I start to see the man walk backwards, I sell, and vis versa. But *mostly*, I just stick things in ETF's and don't worry about it unless I need the money in the near future.

If I understand this correctly we have come full circle on what MoE means.

MoE started out as some form of multi model approach.

Afaik in current architectures it’s basically a load balancing method that while it increases latency makes the model better suitable for distributed operations.

To me this reads as if the author uses the term closer to Urs original meaning than its current.

Does anyone understand how the Market Expert works? It takes in numerical OHLC data and converts it to embeddings for use by the LLM… but embedding are also numbers so I don’t see how that’s any easier for the LLM to process since it’s a language model.

> The Market Analyst LLM focuses on analyzing historical OHLCV (Open, High, Low, Close, Vol- ume) data to predict stock movements. However, time series data is inherently continuous and lacks the discrete token structure that LLMs are designed to process. This misalignment poses a signifi- cant challenge in effectively utilizing LLMs on time series. To this end, we utilize a reprogramming mechanism Jin et al. (2024) to reprogram the input financial time series into text prototype repre- sentations.

The market is like an ecosystem. There are huge mammals (investment banks, hedge funds) that look at certain type of preys, and there are smaller rodents that only eat very tiny worms.

In high volatility regimes, ie. stocks with low market cap, the market is far from efficient. Hedge funds are not even looking at stocks with 100M market cap.

There are traders that act in these regimes that beat the market, exactly because they play small.

Anyhow most people would be better of by assuming the market is completely efficient.

fwiw, I tried something similar about 5–10 years ago. I wasn’t using LLMs like the abstract here suggests, and honestly, I’m not sure how you'd act on a signal fast enough with them. When I gave it a shot, there was some slight predictive value, but in the end it felt like noise and gambling, so I moved on.

Do is it "tradExpert" or "tradeExpert" ?

Shortening variable names from words by a vowel or two is a hge pt peev.

How did it perform against a boglehead portfolio? Were fees and commissions included? Seems weird to evaluate performance over a single year for trades. Much more interested in long-term growth over one or more market cycles.

I think if you're talking on portfolio level a lot of these things are used as signals and parts of a greater whole.

In that sense, they can indeed add value. My current project is a modern version of a classic Harry Browne portfolio with even asset allocations to gold/bonds/equity/commodities, with optional layers of sophistication according to spec needs.

Something like systematic macro could be analyzed as a standalone return stream, but it's more useful when considered as an input into allocation/leverage adjustments (ex: if geopolitical uncertainty readings are high, cut down the trend following exposure). Even the more robot/quant stuff like vol trading feeds back into the wider portfolio management and portfolio construction level to some degree.

So did it make any money?

It doesn't look like it. There isn't a clear comparison to a "human trader".

If it did, I think it's safe to assume they would have sold it to a quant firm and we never would have heard about it.

This. It is a gimmick.

one drawback here is the backtesting. They backtesting only 1 year in 2023.

I feel like applying MoE to LLMs is a just a brute-force prediction approach that's neither efficient or very accurate.

Not a very good study. I did not look at any of the researchers background but it’s like they did not consult their respective finance school departments.

A sharpe of 5 is ludicrously good for a long only equities strategy

Did they release the code or the model itself?

I want to coin a new law: if a news title has "revolutionizing" in the title, its bullshit.

The phrase "changing the [something] game" is the most obnoxious.

I'm curious at what point stock and derivatives trading becomes something entirely for AI, and thus restricted to companies rich enough to buy tons of GPUs. Are we near that point yet?

We've been past that point for more than a decade. This is a gimmick.

Then why are there still brokerage firms targeting small people?

lol 5 sharpe

classic 'tell me you overfit without telling me' tell

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