Interesting data on the US stock market

[srinir]

I posted in this another thread.
https://www.elitetrader.com/et/threads/what-are-the-arguments-against-index-investing.297648/page-9

Standard deviation is higher than T-bills, but lowest return for any 20 year rolling period is higher than highest T-Bill return. May be that was author trying to convey.
Source of this data is CRSP database

 

[srinir]

The US has been a stable country, so this is a proxy for a stable dataset. Today, in order to replicate that stability and achieve similar returns, focusing on the US only is probably flawled, maybe the US has been lucky. But I believe similar results can be achieved through a global ETF of stocks with exposure to countries in all 5 continents.

Infact real return is all positive for all countries in the dataset of DMS 2016 (Global Investment Returns Sourcebook 2016). There are two exceptions Russia and china (when they closed the market because of communism and seized all assets).

 

[Daal]

Infact real return is all positive for all countries in the dataset of DMS 2016. There are two exceptions Russia and china (when they closed the market because of communism).
View attachment 178880

I wanna get a hold of this multi-country dataset going back 100 years (if anyone know where to get it in excel format for free, let me know). I'm interested in the volatility part not as much in the return part of it. I want to see if by using 10 year holding periods, how many countries do stocks beat bonds in terms of downside volatility. Then do the same in 20 year holding periods. Essentially, I want to see what is a better store of wealth and how often that store fails (like it did in Russia, China and in Cuba)
I suspect it works in the vast majority of the countries and only fails ocasionally. That is, stocks are less risky than bonds once you extend your time horizon. So, not only you get more returns, but you protect your capital better. If you globally diversify, then, even more so as you would be protected against the Russia/China/Cuba scenario and other risks

 

[Martinghoul]

In an overwhelming variety of cases, stocks are better as a store of wealth.

 

[srinir]

I wanna get a hold of this multi-country dataset going back 100 years (if anyone know where to get it in excel format for free, let me know). I'm interested in the volatility part not as much in the return part of it. I want to see if by using 10 year holding periods, how many countries do stocks beat bonds in terms of downside volatility. Then do the same in 20 year holding periods. Essentially, I want to see what is a better store of wealth and how often that store fails (like it did in Russia, China and in Cuba)
I suspect it works in the vast majority of the countries and only fails ocasionally. That is, stocks are less risky than bonds once you extend your time horizon. So, not only you get more returns, but you protect your capital better. If you globally diversify, then, even more so as you would be protected against the Russia/China/Cuba scenario and other risks

This is not you requested, but best free source for US is CRSP or shiller database, free global data for each country is impossible to get. Earliest free data for International developed market ex North america index goes back to 1970 (MSCI EAFE)

Credit suise does produce impressive handbook every year, which goes back farther
http://publications.credit-suisse.c...fm?fileid=B8FDD84D-A4CD-D983-12840F52F61BA0B4

 

[beerntrading]

Yes, financial market data is unstable and vulnerable to outliers. However, some conclusions can be drawn from long samples (and IIRC the data I posted originally is from the US from 1933 to 2000), especially when the data repeats similarly on multiple countries. Maybe the US will go down a bad path and history will present a false picture of what the future will bring, however, a much higher degree of stability can be achieved by stock investors by buying, not a US focused stock ETF but a global basket of stocks from all the major parts of the world, either through a global stock ETF or by buying several continent ETFs. The latter will likely to be a lot more stable given that country risk is diversified. In a way, the US provides a proxy for what a global ETF experience would be like. To me, what is interesting is that, for very long-term capital, stocks not only beat bonds on returns (which is nothing new and everybody is aware of) but it also beats on volatility (in terms of having less return variablity over long periods of time).

Yeah, you're absolutely right that if you sample across stocks, your distribution will start to normalize--particularly if weighted by market cap. (Which was actually clear from your first post; I was reading @tommcginnis's posts and just kinda glazed over the lead in to them). It would be interesting to repeat the methodology for individual Dow components.

 

[ironchef]

He's saying that due to correlation and that the price movement begets price movement, framing the market in terms of normal (random statistical) distributions is not necessarily accurate. In statistical (and mathematical) terms, this is called regression (where the previous output is a subsequent input).

This was the basis behind FiveThirtyEight's presidential poll modelling showing 2-3x the chance in prevailing narratives of Trump winning. They explained at length (seemingly daily) that errors within their model were correlated such that under reporting in PA would mirror under reporting in FL....also, Nate Silver is a f'ing genius and a hero of mine.

So, to the extent that a stock that performed poorly in the past will continue to perform poorly, and a previously over performing stock would continue, random statistical samplings are not accurate. These are the so-called "fat tail" distributions that form the basis of long OTM option strategies...and likewise, a poor performing stock in a bull market will perform well, as will an over performing stock perform poorly in a bear market.

I suspect the majority of profitable option strategies are premised on this. Certainly profitable long strategies (false leverage strategies notwithstanding). As a premium seller, this directly influences my 'soft stops' which are subjective exits earlier than the hard stop on a stock likely to continue its decline (or advance).

Another couple other good things to point out, certain prices tend to trade more frequently (150.00 will always show more ticks than 150.47). $0 is a floor price, so the entire tail of the normal curve past there is inaccurate. A "t-Distribution" is another good one to know. Because companies maintain intrinsic value (read some Buffet shareholder letters), you won't have a true normal distribution. An undervalued company will supposedly have more people looking to buy, such that the curve's peak will still be at the current price, but substantially more than half of the area under the curve will be above the current price.

I am very impressed beerntrading. Question for you: Knowing this why are you selling premium?

Regards,

 

[ironchef]

Doesn't this apply to real prices and not so much to %? Wouldn't it be more correct to say that on longer term there's a (more) normal distribution with the top at the 7% level?

I thought the longer the time frame, the less it resembles a log normal distribution (less random)?

 

[ironchef]

https://en.wikipedia.org/wiki/Decomposition_of_time_series ???

In the short term (say, instantaneous) any trend component would not be discernible -- and any effect would be folded into the variance. In the longer term (t=100? 1000? 36,525 days?), the trend's effect on variance is diluted.

How can you see this? Compare the variance of your data before/after de-trending. ("Cool!")

(BTW, this takes me back 37 years....... and a stat course that I *hated*, but this part jumped out at me and made sense. "Whoaaaaa. Look at that!")

Are there algorithm that can extract the trend from the variance?

 

[beerntrading]

I am very impressed beerntrading. Question for you: Knowing this why are you selling premium?

Regards,

Haha, I know, right?!

The short answer is that I still win if it stagnates. Also, all those cut both ways, so there isn't an inherent disadvantage to being on the short side, and nor do they imply an advantage to the long side.

...which brings me to, knowing that it happens is not the same as knowing how. And the margins are fine enough that qualified academics with extensive data can disagree on the best way to model it with none able to prove conclusively they're right.

And the week-long time frames I deal in might be described by different curves, but not so much within the middle percentiles that one distribution gives a clear advantage vs. another. But they're all things I take into consideration...I'm more concerned about how a distribution curve is skewed than which curve I'm skewing.