October [censored] thread

[ahnuld]

I think he means to make sure they dont retain all earnings. But thats less relevant today than when it was written. If you exclude all companies that dont pay dividends you are really limiting your selections.

[Ron Burgundy]

Yeah, I certainly wouldn't exclude companies that don't plan on paying dividends, and are upfront about it. I don't think he was saying those stocks should be avoided based only on that.

But from the wording he uses, it seems that he's saying some companies that promised to pay dividends will sometimes back out and not pay them when they get into some financial trouble. As opposed to a company that never promised to pay them. And as opposed to a company that has a record of paying them even when they were in trouble.

[ahnuld]

Nothing like selling coach because you think the US economy is entering a recession and nobody will be spending money on luxury apparel and then being proven right only 2 weeks later.

[CrushinFelt]

That was a quick drop.. hovered at down 4 points for a looooong time then shot down 20 points

[technologic]

[ QUOTE ]
Yeah, I certainly wouldn't exclude companies that don't plan on paying dividends, and are upfront about it. I don't think he was saying those stocks should be avoided based only on that.

But from the wording he uses, it seems that he's saying some companies that promised to pay dividends will sometimes back out and not pay them when they get into some financial trouble. As opposed to a company that never promised to pay them. And as opposed to a company that has a record of paying them even when they were in trouble.

[/ QUOTE ]

i think he's talking about if a stock has a regularly scheduled dividend payment per quarter and sticks to that consistently, then it's solid in the case that it's not taking overaggressive bets on things that could go wrong (with "one off" losses, "unexpected market conditions", etc.)

[Messiahkid]

Financials. That is all.

[ahnuld]

Not sure if the financials are oversold or not. I bought my positions mid august for what I thought was a good price. If I learned anything though its when you are buying for value and some bad news hits a whole group that gets really sold off, dont buy the company that gets hit the hardest for apparently no really big reason (MER,C) buy the company that has the reputation for excellence that got hit as well (GS). In bad times the cream separates itself more clearly from the crap.

[dc_publius]

I like the financial stocks too, but I don't think they have hit bottom yet. There will be further reprecaussions from the credit crunch... I think... I'm holding off for now and will reevaluate in 30 days.

[Grizwold]

Hi ahnuld,

Sorry I’m late to the party. You missed a test question about global minimum variance portfolio with two risky securities:

[ QUOTE ]
The global minimum variance portfolio formed from two risky securities will be riskless when the correlation coeffcient between the two securities is..


D) -1

[/ QUOTE ]

To understand how perfect negative correlation effects portfolio variance, we may examine the portfolio standard deviation formula:



Two securities with perfect negative correlation have the same standard deviation, but the direction of the deviations is perfectly opposite (as you know). Since I can’t type the rho character (for correlation) and sigma (for standard deviation), I will refer to them as p and s respectively.

Consider the three terms under the square root (we’ll ignore square root for now, and eliminate it later). By substituting –1 for p, the third term becomes:

- 2 w ( 1 – w ) s1 s2

When analyzing the three terms together, we can factor it using the binomial factoring property:

a^2 + b^2 – 2ab = ( a – b )^2

Therefore, the term under the square root become:

[w s1 – ( 1 – w ) s2]^2

Since this is a squared term and it is under a square root, we may cancel the power. In addition, the standard deviations are equal (since it is perfectly negatively correlated), therefore if the weights are equal, the equation will equal 0. When two securities are perfectly negatively correlated, using equal weights and factoring, all terms cancel each other in the portfolio variance formula.

While you were taking the test, you made incorrect assumptions about the effect on portfolio variance of short selling a security that is negatively correlated with another. I can see how you got confused. If we look at the two extremes (perfect positive and negative correlation) you may see why you missed the question. We assume equal weights (as you did while taking the test).

Consider two stocks that are perfectly positively correlated. For simplicity, imagine it is the same stock. You short 10 shares and long 10 shares. What would you expect to happen to the portfolio variance? It will be zero (coincidentally the return will be zero since it is the same security). A move in any direction of the long security will be offset by the move of the short security.

Now consider two perfectly negatively correlated securities. You short a security, hoping it will drop. The same drop will cause your long position value to rise by the same magnitude. This does not eliminate any variance. In fact, you are causing two securities that move in opposite directions to perform equally.

Hope my explanations and examples help you and make sense.

Clark

[Phone Booth]

[ QUOTE ]
Hi ahnuld,

Sorry I’m late to the party. You missed a test question about global minimum variance portfolio with two risky securities:

[ QUOTE ]
The global minimum variance portfolio formed from two risky securities will be riskless when the correlation coeffcient between the two securities is..


D) -1

[/ QUOTE ]

To understand how perfect negative correlation effects portfolio variance, we may examine the portfolio standard deviation formula:



Two securities with perfect negative correlation have the same standard deviation, but the direction of the deviations is perfectly opposite (as you know). Since I can’t type the rho character (for correlation) and sigma (for standard deviation), I will refer to them as p and s respectively.

Consider the three terms under the square root (we’ll ignore square root for now, and eliminate it later). By substituting –1 for p, the third term becomes:

- 2 w ( 1 – w ) s1 s2

When analyzing the three terms together, we can factor it using the binomial factoring property:

a^2 + b^2 – 2ab = ( a – b )^2

Therefore, the term under the square root become:

[w s1 – ( 1 – w ) s2]^2

Since this is a squared term and it is under a square root, we may cancel the power. In addition, the standard deviations are equal (since it is perfectly negatively correlated), therefore if the weights are equal, the equation will equal 0. When two securities are perfectly negatively correlated, using equal weights and factoring, all terms cancel each other in the portfolio variance formula.

While you were taking the test, you made incorrect assumptions about the effect on portfolio variance of short selling a security that is negatively correlated with another. I can see how you got confused. If we look at the two extremes (perfect positive and negative correlation) you may see why you missed the question. We assume equal weights (as you did while taking the test).

Consider two stocks that are perfectly positively correlated. For simplicity, imagine it is the same stock. You short 10 shares and long 10 shares. What would you expect to happen to the portfolio variance? It will be zero (coincidentally the return will be zero since it is the same security). A move in any direction of the long security will be offset by the move of the short security.

Now consider two perfectly negatively correlated securities. You short a security, hoping it will drop. The same drop will cause your long position value to rise by the same magnitude. This does not eliminate any variance. In fact, you are causing two securities that move in opposite directions to perform equally.

Hope my explanations and examples help you and make sense.

Clark

[/ QUOTE ]

The answer is correct but you might want to check your assumptions - correlation of -1 does not imply that the standard deviations are the same.