High correlation
How would you pick the stocks that are less highly correlated? High correlation refers to when a stock becomes more correlated with the index in a falling market.
Example:
Stock ABC has a correlation of 0.4, and stock DCE has also 0.4 correlation coefficient with index X.
The market fall more than 10% or so in a given period of time and here the correlation of ABC is 0.6, and of DCE 0.86
Indeed ABC is more desirable than DCE as it protects better the portfolio.
Anyone knows about a model that allows us to pick the right stock in such situations?
Double
=correl(return stream 1, return stream 2)
Right, but remember OP that historical correlation does not necessarily imply a future relationship. Traders have been notoriously burned by the market not behaving as expected.
Double
Fundamentals build statistics not the other way around.
Generally, companies with stable and more predictable cash flows from operations and/or earnings will be that ABC type of equity.
Example of this are companies from utility industry, AKA defensive stocks. You just can not stop using their services/products, reduce perhaps.
I like to use statistics over CFO and Earnings to define which one from a group of defensive stocks is more stable, but you still need to analyze about companies future and threats to this stream of revenue.
The DCE stock would be more a typical commodity/consume related stock.
You're talking statistical models used in conjuction with fundamental analysis, which is a sound method, this way you can avoid spurious correlation between stocks, however the very question I posted in the begining is about those statisticals tools. Basically ABC and DCE were picked from a universe of stocks and were easily screened by just plotting their returns against the main index and just using the Correl function provided by any spreadsheet to get the correlation coefficient, simple and straightforward, is there a similar way for finding the stocks with less volatile correlation?
Well this is just an idea but if you are only using returns and their statistical measures(probably to create an algorithm?), i would try to:
1 - calculate stocks average monthly or weekly betas , considering them as population samples (30 samples at least. btw thats why is so difficult to use annual betas which would be better, i think).
2 - rank stocks with low correlation to index and low beta stdev from previous step.
I've just finished reading the doc, a very interesting one, here you can find it: http://www.icmacentre.ac.uk/pdf/financialriskchapter.pdf Using standard deviation or any other tool which is based on the mean or any other weighted average is incorrect as it will cause a distrotion in the estimation of volatility, correlation will suffer even more from such a simplistic method. They do suggest exponential weighted moving average or the GARCH as a superior method. I am trying to build that algorithm, I used to plot several correlations based on moving averages of returns and then dropping one observation and going to the next, I used 20 observations for every one correlation, finally I calculated the standard deviation of the correlations. I remember vaguely the 30 observations issue, also the central theoreme etc... It is not always possible to get as much data as needed unless you move farther into the past and you will be dealing with a completly different period where underlying economic forces had changed dramatically. I am researching the Spanish main index so I can only use the last 3 years data otherwise there will be so much noise.
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