>> The problem with predicting absolute levels, is that there is a game theoretic aspect which undermines any mathematical trading strategy as soon as it is public.
I took finance in Business school, coming from doing a lot of statistical analysis in a research lab. I hated my finance professors and there pseudo science. Pricing formulas work great until they don't. The problem is when they don't, they really don't, in a catastrophic way. Read "When Genius Failed." Real traders know this. But some economists and finance professors act like these mathematical models are describing a predictable physical phenomena.
LTCM in being overly leveraged, relied on other market participants to maintain short term price alignment, which meant it was not arbitrage. Salomon's reduced its role as market-maker, maintaining short term price alignment, which increased short term price anomalies, and thus increased LTCM's vulnerability. The Asian financial crisis increased the frequency and extent of those pricing anomalies, and the subsequent Russian Default crisis did the same. Margin calls were made on LTCM that it couldn't cover, forcing them to close out of their positions at very unprofitable times of the trade strategy.
So I don't think "pseudo-science" is a great description for what those B-School profs are teaching. Rather the pricing formulae are just the beginnings of the financial theory you need to run arbitrage strategies, but they are not sufficient. You need to augment them with a broader picture of market dynamics and capital management, just like you'd need to learn about financial law, financial market technology, and a bunch of other stuff to run a successful market-making desk.
From what I've read elsewhere, if you had managed to hold an LTCM replica portfolio until expiry, you would have actually made money.
https://www.wiley.com/en-us/Paul+Wilmott+Introduces+Quantita...
Start by reading/running notebooks ("kernels") and winners solutions from this and previous competitions with tabular/time series data, building models and then grid searching, stacking, bagging, boosting 'em up. Here's a bit aged but still useful guide how pros do it: https://mlwave.com/kaggle-ensembling-guide/
If you want something online, check out https://www.quantopian.com/
The reason why relative valuation models are more effective is the same reason why most sports betting models use current odds as an input. Prices contain information but, in my experience, these methods aren't totally effective because they often miss important information about the company itself (big price moves happen because relative valuations are wrong). Value or quality appears to do fundamental work but is often woefully blind (for example, there are proven accounting issues with value strategies...does your average quant understand this? No. Have they ever read a set of accounts? No. They have no hope. None.)
Just imo, I think quant strategies are almost totally worthless beyond liquidity provision (even a strategy like front-running news in FX...humans do this better, and I know people who are still making tons of money doing this). I think there is massive value in that mode of analysis but the people who make the most are always going to be people who know the fundamentals better (I think firms like Marshall Wace that are doing this synthesis will move ahead) because that information is often not in the price at all.
I'm not sure about the rest of your comment, but this is mathematically the correct reason why we don't predict absolute price levels.
And the reason why this is mathematically the correct reason is because for a non-stationary process, when you predict into the future, the variance tends to infinity which means taking expectation on any statistical model is useless since the variance is ridiculously wide
This isn't true if you're fast enough. Everybody knows how to do arbitrage but it's still extremely profitable if you're faster than everyone else. HFTs are consistently more profitable than slow trading firms, earning 40%+ returns, it's just a much more capacity-constrained form of trading so absolute returns are lower.
But yeah, it might not be that useful for the funds themselves.
But isn't prediction an inherent part of valuation?
Doesn't need to be. You have wealth you have to store somehwere because it is not being spent now. Can store it as equities, debt (including a bank account), Paper cash money under the mattress, gold bars, BTC, etc etc.
Let's say you have the list of potential places to store it and can get a price/value ratio but with a common term of "X" in the value
- A = 3.1/X
- B = 2.8/X
- C = 1.9/X
- D = 4.2/X
Yes, this is a massive simplification as it ignores diversification benefits and so on but there's the basics of the usefulness of valuation where you don't and can't know the value.
"prediction of the market" here means "Do you think the S&P500 is going up or down this year?" Warren Buffet completely ignores this, takes no view on it. Timing the market is very hard. Assuming the market will reflect the economy and the economy will grow because people work hard, are smart, we invent new things and population increases, then picking the best of what's available is the normal value investors route, a la Buffet as the most famous practioner.
Burton Malkiel's "A Random Walk Down Wall St." is still, IMHO the best summary of all the theory and practise of investing - excluding the quant funds like Renaissance. Maybe he has an update for them? Maybe it's also worth reading if it exists or maybe not. Don't know.
No idea if renaissance time the market or what.
edit: punctuation and the Malkiel recommendation.
The problem with predicting absolute levels, is that there is a game theoretic aspect which undermines any mathematical trading strategy as soon as it is public. optimal game theory trading strategies don’t produce great results, and they are relatively trivial to identify. Instead strong profits in market long/short macro positions are mostly created by information advantages, which don’t really make for interesting Kaggle competitions. For example, big profits in macro trading have historically been consistently achieved by front running customer orders, by building timing advantages on top of trading infrastructure, by funding research analysts that inspect operations on the ground, by lobbying for regulations that change market directions and so on.
It’s very hard to tell if a best performing hedge funds that doesn’t have an unfair advantage, that declares its only using quantitative strategies, is in fact just a statistical anomaly with a hollow narrative.