rudi

A Simple Momentum System for Beating the Market

Besides the 8-9% that stock markets make, one can only win what others loose. So who are the loosers when everybody outperformes by 10%?

This is what EMH supporters tell hard cases.

Matt:
"Naturally all analysis is post"
You can develop a strategy with old data, say 1900 to 1980, and then validate the strategy with the data from 1980 until today. There are still some difficulties with that but you test the model on data which you didn't use to build the model, thats essential.
I can't believe I answered that seriously.

If you guys like, we can do an experiment, just say: "I'd like to".

cheers
rudi

A Simple Momentum System for Beating the Market

Ikkyu:
Ok I read the paper a second time:

If you assume a daily standard deviation of say 1%, then you get an approximate standard deviation (ignoring fat tails, using a hundret years, etc.) of sqrt(1*250*100) = 158%. If you would like to do a simple test you would compare the difference of both charts (Exhibit 2 and 3) to twice that standarddeviation. The cumulated returns of the traded series shoud be 300% larger than the buy and hold strategy to be 95% sure, not to have an incidental phenomenon.

To get the mentioned "scientific proof", a lot more would be neccesary. One thing that did never happen is a test of the hypothesis on true validation data. i.e. you apply an (appropriate) econometric test to data, that you never used for your analysis and which you don't use a second time!

"you guys act like Mr. Faber is some kinda snake-oil salesman!! "
Exactly. Because he does marketing for his book and he is unscientific.

I bet he would never put all his money in that strategy and lever. Neither would he (and wouldn't be able to) administer larger sums of money with that strategy.

I am very willing to discuss this further, if it helps to clarify!

A Simple Momentum System for Beating the Market

Those are all ex-post analysis, aren't they? Comparing winners to loosers afterwards for sure shows an outperformance ;)

You didn't reveal any scientific proof because you can't.

cheers
rudi

31 Country P/E and PEG Ratios

Hi Richard,

thanks for the research! 1.9 for germany seems to be a typing error, it should be around 12 I guess.
Where did you get the data from?

cheers
Rudi

Adding Wood to Your Portolio: A Worthwhile Investment

DEL is also not a diversification to stocks. Its correlation with SPY is 0.66. There is no correlation to commodities.
I used data from around 12.11.2007 (listing of CUT) until today.

@mkreisel: as far as I know, the capital in ETFs is secured. Otherwise one would see deviations between different ETF providers, since the ETF price would include the risk of bankruptcy. e.g. YYY never showed such a behavior, when there were rumours about their situation.

Better look for some other interesting asset classes, not timber.

cheers
Rudi

Adding Wood to Your Portolio: A Worthwhile Investment

Hi,

I created the table stated below (I hope its not too screwed up), showing correlations of CUT and PCL to commodities and S&P500, based on daily returns.

Mind that CUT (0.68) and even more PCL (0.78) represent the movement of the stock market and are both not correlated with commodities as one would suspect.


CUT RJI RJN SPY PCL
CUT 1.00 0.22 0.07 0.68 0.48
RJI 0.22 1.00 0.89 0.02 -0.15
RJN 0.07 0.89 1.00 -0.09 -0.22
SPY 0.68 0.02 -0.09 1.00 0.78
PCL 0.48 -0.15 -0.22 0.78 1.00

I suggest to use RJI to diversify a portfolio. It includes 1% lumber, but I think that wood prices are anyhow highly correlated with the rest of RJI, especially oil and gas.

Where do you get historic data of wood prices?

Rudi

El-Erian's Recommended Allocation vs. Harvard, Yale

I completely agree with you, Foust. I admit, the sentence you quoted was not the best I wrote ;)
Of course most asset classes does not mean the best allocation. To be more concrete, I like the high yield bonds in the harvard portfolio, and the higher percentage of inflation-linked bonds. Also, they have less stocks then El-Erian.
All portfolios in my opinion underweight bonds drastically!

Seafarer: The domestic stocks are in ACWI (international stocks). As far as I remember they hold 40% US stocks.
To your question: I would add stocks, which are quite unrelated to S&P500 for example. This could be "defense", like LMT. Other ideas are holdings, e.g. BRK.B and shipping, e.g. OSG. Secondly, I would add carry trades to the portfolio. Check out DBV. Since DBV is too expensive, I would make the carry trades manually via forex trading. Thirdly, I would have a look at covered calls, like BEO. If you have some more ideas, please let me know, I am alway looking for some >6% return investments, which are a stand-alone asset class, to diversify with. Back to the question: I would then substract from ACWI mostly and from real estate.

By the way, I am not just guessing these numbers, I retrieve them from statistical portfolio optimization. I can for sure tell you, that >30% stocks is way too much. 10-15% is healthy.

best regards
rudi

El-Erian's Recommended Allocation vs. Harvard, Yale

I'd find it interesting to know, how these portfolios are being created. As I am involved into asset allocation a lot, I wonder why there is so little allocation into bonds. I think the difficulty is, what expected returns to assume, when optimizing the portfolio by the best sharpe ratio (or some other ratio). As you can see, all portfolios assume >10% commodities to be appropriate. I find results like that, when I imply the empirical returns of commodities for the last years. Minding the efficient market hypothesis, there is no reason to assume more than the inflation rate plus some increased demand due to worlds economic growth, as an expected return. This would be 3-4% in my opinion. This leads me to a portfolio with maximum 5% commodities.

I conclude the harvard portfolio to be defenitely the best, since they are best diversivied, i.e. they have the most asset classes. High yield bonds and inflation-linked bonds should be included in every good portfolio.
In spite of that, the harvard portfolio could be easily outperformed (risk adjusted) by a portfolio like that:

-International Stocks (weighted by marketcap) 20% (ACWI)
-International inflation-linked-bonds 30% (TIP)
-High yield bonds 3% (HYG)
-Bonds with short avg. maturity 18% (SHY)
-International Bonds 12% (BWX)
-Commodities 5% (DBC)
-Real Estate 7% (VNQ)
-Private Equity 5% (PPE?)

Keep in mind, that private equity is correlated with stocks. So there is a lot of emphasis on stocks market in all mentioned portfolios.

The idea of having hedge funds is in my opinion misleading as well, since they are either doing some similar allocation, or they apply some strategy, which includes short selling (not sticking to efficient market hypothesis), what means, you are long and short in the same assets at the same time, just wasting fees.

By the way, there are even more asset classes, that should be added to the portfolio, but I wanted to show a portfolio, which doesnt't include more than the harvard one and is already more developed.

best regards
rudi

EFA More Attractive Than SPY

The market is always right! Maybe market expects the US economy to be more competitive and growing. I wouldn't base an argument on this!

Relying on the theory of efficient markets, I would chose the product with less fees, that is SPY.

best regards
Rudi

E-Z Stealth ETF Portfolio

Hi Thomas,
I hope you don't mind me posting a few comments on the portfolio. I agree that one shouldn't try to play the market. This is in fact, however, also good advice for experts.
The portfolio as described above is obviously not a passive portfolio, but bets on a badly performing US economy. I would go so far as too say it is de facto an extremely active approach, and certainly cannot be reconciled with efficient market theory. (Which I hope you believe in, considering you are giving advice to a growing community here.)


While historically the stock market has outperformed bonds marginally, the volatility of equities is three times that of bonds.
And regarding the negative correlation between these asset classes and relying on Markowitz, one should have about 60%-90% bonds.
I would therefore suggest adding a very substantial position of WIP, for example. It is well diversified and inflation linked. Secondly, I don't understand why you arbitrarily buy different sectors and SPY as well. If you don't want to bet but to invest, buy ACWI.
Thus reducing fees, whilst establishing a greater level of diversification.
In order to fine-tune the portfolio, I would suggest reducing the RJI position, as the expected return is around the rate of inflation. So it just works as a hedge (which you do not need if you do not lever).

I would increase the carry trade position as it further diversifies the portfolio, whilst the returns are similar to those of bonds. Bear in mind, that DBV (if i remember correctly) charges 0.65% fees, which is definitely too much. One can easily look up the monthly composition of DBV and buy the currencies manually and save 90% of the fees.

There could be more fine-tuning, by adding some more asset classes and adjusting the percentages. But I am afraid I would be divulging trade secrets if I were to go any further.
Finally, a portfolio with 50% WIP added and levered by 1.5-2.0 will certainly and consistently outperform your portfolio!

best regards
Rudi

Capitalization Coupling: Major Indices at Correlation Highs

As you asked me for some links to studies of copulas:

www3.interscience.wile...

This here is about what is called tail-dependency (stock crash
together but in positive return times, they are less correlated
(indexes as well).

db.riskwaters.com/publ...
This is interesting as well but german.

www.imw.tuwien.ac.at/f...
This is an introduction to the topic, stating that conjunct negative
returns are more likely than conjunct positive returns. Also german,
sorry.

papers.ssrn.com/sol3/p...
This is a good study about copulas, but not within stocks, although interesting.

Googeling "tail-dependency" seems promising if you want to find more

Nevertheless: All this stuff is based on Embrechts (1999)
Embrechts P., McNeil A. and D. Straumann (1999), Correlation: Pitfalls
and alternatives, RISK

Link: www.ma.hw.ac.uk/~mcneil/ftp/risk.pdf !!


To d_teller:

This seems an interesting idea, as a non-parametric approach may deal
with the problem of non-normality of return distributions.
Have you had any success with that or any of the links to the studies?


In spite of that I have to repeat, that no approach is worth spending
time on, as long as it does not generate cash!

For this purpose, one should always lag the independent variables AND
split the sample in a test-sample and validation-sample.
Fit your model with lagged variables and apply it to the unused data
of the validation sample. It is only worth money if you find some
relation in the validation sample.
Keep in mind, that you bias the study if you use knowledge from the
validation-sample to alter your model. Best is to keep a (third) final
validation-sample until you are really sure with your model.

Michael, have you tried adding more variables and maybe
interaction terms to your model? E.g. include the lagged returns in
interaction with the correlation. I also would use a smaller time
frame than 100 days to calculate the correlation, to have a more
sensitive variable.


As an impetus: To generate cash, you could spend time to build models
that predict future correlations. One could use that to better
minimize the portfoliorisk and therefore absorb a higher lever. There
you get your free lunch. GARCH Models and Kalman filters are suitable for this purpose.

best regards
Rudi

Capitalization Coupling: Major Indices at Correlation Highs

Michael,

I replicated a part of your study. I selected DIA/IWM since it has the highest volatility and correlation. I used the past 100 days to calculate the correlations. In another replication of your study, I also used weekly data und based the correlations on the past 20 weeks.
On both occassions, there wasn't any significant relationship.
If there would have been one, I would have done the following (and suggest you try that for the set of data where you indeed found something): Lag the correlation-variable (differentiated) and do a regression analysis with the differences in returns.(Thus regarding the temporal structure.) Btw: A regression is not the best way in this case. A VECM would be a better choice, but it means a lot of work. Then try it vice versa. Lag the variable containing the returns and regress it on the future period of correlation. This may work. It should be even more significant if you shorten the period, to calculate the correlation. (Like 30 days e.g.)

Your R² statistics are random. You did not post any p-values and the negative (-1%) number for daily-based analysis suggests, that you are not able to reject the null-hypothesis. R² also increases with a shrinking N, so your increasing percentages cannot form the basis of any serious argument.

Capitalization Coupling: Major Indices at Correlation Highs

"In other words, as the correlation between indexes fell, the markets rose on average."
This is more likely to be explained by a higher correlation when markets crash. Your interpretation does not bear in mind the temporal causality, the interpretation should be the opposite, it is therefore worse than tassology.
The increasing correlation has been well established in conjunction with copulas.
en.wikipedia.org/wiki/...