Suna Reyent

In Search of Low (or Negative) Correlation Between Asset Returns

I'm sorry I can't claim much expertise or experience in Monte Carlo simulation. However I'm aware that it involves generating outcomes with a computer, which represent possible returns for the asset or portfolio. Of course they are generated using *assumptions* concerning distribution of returns, however the distribution doesn't have to be "standard normal distribution" or anything like that.

For instance to determine possible returns for a bond portfolio, the analyst needs to define the set of interest rates, or some path, which is clearly influenced by the analyst's preconceptions about the functioning of the bond market as well as international liquidity flows or the Federal Reserve and things like that (which will probably mirror the recent past, hence my objection to "forward-looking data"). What you put into the model determines what you get out of it. Of course these are categorized as "model risk".

That's all I know...

In Search of Low (or Negative) Correlation Between Asset Returns

I definitely agree. I think I overstated my case and there are instances where "forward-looking information" is a lot more applicable such as in calculating P/E ratios of companies. A trailing P/E ratio (and even 12-month forward P/E) would have been useless in calculating the value of Google when the stock was trading at $150, if I only looked at past information I wouldn't have bought it at that price, and wouldn't have decided to triple my position at around $170 (that I did because Wall Street was NOT covering it at the time.)

Of course there are models that incorporate company growth and clearly stock market takes that into account as we see certain stocks that seem very expensive in terms of traditional valuation ratios but might not be that way.

One of the conclusions I was trying to arrive at with that article was that it is possible to incorporate these so called "high risk" companies in a framework of a more diversified portfolio and clearly some of my "high-flying bets" in technology have failed (such as Sigmatel, my all-time embarrassment, and no it did NOT have a high P/E when I bought it and I did NOT increase my position as the stock fell, one of my trading rules), however if all these volatile stocks are incorporated in a portfolio that has enough oil, energy, materials and defensive consumer goods companies and such, you'll be able to absorb those losses and it will be a lot easier to carry a stock that requires an iron stomach.

I have reservations about Monte Carlo analysis however if it’s done with the knowledge that the assumptions that go into the model may be wrong, or worse biased, I think it can be a very useful tool.

But I’ve seen things in financial practice that do resemble pseudo-science rather than being a framework of hypothesis testing – we cannot “prove” something, that wouldn’t be science, theories are good to the extent that they are open to falsification.

I’m very happy with all the reactions the article is getting. I should probably announce that I hold Google and Sigmatel at this moment, though Google has become a significantly larger part of my portfolio and Sigmatel is about to go to zero, literally. Probably time to rebalance. I’m saying these to make the point that a portfolio can be successful overall even if it includes a stock that is close to extinction.

In Search of Low (or Negative) Correlation Between Asset Returns

Hi,
Thank you for all comments. Firstly, I wanted to make the article accessible and intuitive rather than a scientific treatise, I'm aware that every serious econometrician or statistician who is involved with portfolio theory will strictly look at asset returns and not even bother with the actual prices. I wanted to put these up to show the logic behind that and show the differences between the two.
Secondly, I'm aware that market concerns itself with forward-looking information, however the supposedly "forward-looking data" you put in your black box models are called "begging the question" in philosophy. That is, by the very action of putting in your "forward-looking data", you're already making predictions about the future so all these "black box" models are nothing more than glorified astrology to me. In other words, it is another version of Descartes' famous proof of the existence of God, but the assumptions he makes to come to that conclusion already presuppose what he's trying to prove, and hence the famous "Cartesian Circle" in philosophical terms.
My article does not imply that future will resemble the past, I've already stated that in the end if you read the article thoroughly.
I'm saying that a simple understanding of how correlation works will lead people to make more intelligent decisions about stock and etf ownership. And I'm showing historical data to support that idea, because I just don't think it is appropriate to show "forward-looking data" to support what I'm trying to prove, however I'm aware that's what the financial industry does.

In Search of Low (or Negative) Correlation Between Asset Returns

Hi,
The "Trend" is the index itself, or in the case of commodities, the actual futures price. I just had to differentiate betwen the index and its daily (or monthly) returns.