I love these death of articles by people ignorant of not just the political philosophy that is their subject, but also the conditions leading to its collapse.
Let us start with his claim that, "after LTCM's collapse, it became abundantly clear to anyone paying attention to this unfortunately esoteric issue that unregulated credit market derivatives posed risks to the global financial system, and that supervision and limits of some kind were advisable." First, credit default swaps as we know them today were still in their infancy in 1998 so it would be difficult to say they were as important to LTCM's collapse as Myron Scholes' shoes were. Second, he's attacking the wrong problem, to me, one of the biggest lessons from LTCM is that risk-models and excessive leverage are a dangerous combination. Those problems were never fixed, but it is hard to say that libertarianism is or isn't the culprit. Libertarians would say that banks who lend money to institutions who use excessive leverage might fail if the bets go wrong, and they should be allowed to fail. Harping on, the author notes that "the Washington Post ran an excellent piece this week on how one such attempt to regulate credit derivatives got derailed." Again, the author fails to distinguish between a credit derivative and a derivative. That article is as much about regulating currency and bond derivatives as it is about CDS.
So here again we are faced with the theory that conservatives, liberals, and a central banker who control the government, conspired together to halt attempts to regulate derivatives. The reader is left to his or her imagination to determine how regulating derivatives would have made a difference. I agree with Ritholtz that the decision to allow investment banks to lever up to more than 30x from their original 15x was a mistake. However, I'm not quite sure what else would have or could have been done. Much of the trade in CREDIT derivatives was to get bad assets or the impact of said assets off their balance sheet, a form of regulatory arbitrage. If they threw up some more regulations, I have little doubt that the industry would have tried to find new, exciting, and complex ways around it.
The author notes that consistent libertarians, as opposed to conservatives like Gramm that he is confusing with libertarians, opposed the bail-out and then he invokes the Great Depression that many could be employed in soup-kitchens. Implicitly he is tying the libertarians with the liquidationist view of the Great Depression. L. White has done a great job explaining how Mellon wasn't a liquidationist and Hayek and Robbins weren't liquidationists.
Finally he argues, "libertarians react to the world's failing to conform to their model by asking where the world went wrong. Their heroic view of capitalism makes it difficult for them to accept that markets can be irrational, misunderstand risk, and misallocate resources or that financial systems without vigorous government oversight and the capacity for pragmatic intervention constitute a recipe for disaster."
First, there are libertarians who believe the market is efficient and there are libertarians who do not believe that. I would say that there are many many more in the latter category. I'm perfectly willing to say that markets can be irrational, misunderstand risk, and misallocate resources. However, I would also be willing to say that almost all of the times when they do this, you can point to a government regulation or a government program that is leading to this. The ABCT doesn't really describe the depth of our current situation on its own, but it sure does a good job explaining how the government encouraged the market to misallocate resources into the housing boom. The difference between the author and I is that I want to see market oversight and market regulation where he only is looking to the government for the solution. Well, I think there are plenty of cases where you can point to the government being the problem.
What's interesting to me, is that the death of socialism was predicted by Hayek and the Austrians several decades before it happened. In all reality, I'll admit that what the Soviets had and Chinese (before Deng) had wasn't really socialism. It was only really tried in the WW1 War Economy in Russia and it failed miserably, as predicted. The system that grew out of it, at least in Russia, was more of a market socialism, mostly socialism, but a little markets and freedom thrown in. Libertarians, mostly Hayekians, have predicted that the global financial system is unsustainable in its current form. Many predicted that the housing boom would lead to a situation like what we're currently experiencing. That's because what we don't have is capitalism and anyone with a brain should realize that. Even before the bail-out bill, we were on our third-way, though not as far to the socialist side as Europe. It's not that this doesn't fit with our model, but when you take our government and say we live in a capitalist country. People like me need and have stood up and said we do not live in a capitalist country. Our theories aren't to blame, our theories told us we would end up in this mess.
Sunday, October 19, 2008
Monday, October 13, 2008
Malkiel's Wambulance
"It is very tempting to try to time the market. We all have 20/20 hindsight. It is clear that selling stocks a year ago would have been an excellent strategy. But neither individuals nor investment professionals can consistently time the market." - Burton Malkiel
My problem with this statement is that it is not specific. I would agree with him that investment professionals can't time the market on a short-term or medium-term basis, for the most part. However, pretty much everyone knew without 20/20 hindsight that there were big problems in the financial sector, more than a year ago. Some people, using insights from a variety of schools of thought or just plain, old common sense, got out of the market. You don't need to time the market when it goes up, you just need to know that business cycles happen and it pays to get out of the market when the downturn is coming. The regular investor can index away in the good times, but that doesn't mean that always indexing is the proper course of action.
My problem with this statement is that it is not specific. I would agree with him that investment professionals can't time the market on a short-term or medium-term basis, for the most part. However, pretty much everyone knew without 20/20 hindsight that there were big problems in the financial sector, more than a year ago. Some people, using insights from a variety of schools of thought or just plain, old common sense, got out of the market. You don't need to time the market when it goes up, you just need to know that business cycles happen and it pays to get out of the market when the downturn is coming. The regular investor can index away in the good times, but that doesn't mean that always indexing is the proper course of action.
Wednesday, October 8, 2008
Risk and Uncertainty
What I don't like about Free Exchange is that I have no idea who the authors are who contribute to it. I don't know to always read and who to take with a grain of salt.
Here they note that modern finance "seeks to turn uncertainty into risk. You cannot quantify uncertainty, and you cannot trade it. It is pre-finance—and it can be corrosive. Risk, on the other hand, is a probability distribution. It is quantifiable. You can model it and analyse it and it has a value. Therefore, you can trade it." They are right on what modern finance seeks to do and the difference between uncertainty and risk. My problem lies with modern finance and actually turning uncertainty into risk.
I view uncertainty and risk from a Knightian lens. Risk is measurable, uncertainty is not: "The essential fact is that "risk" means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far-reaching and crucial differences in the bearings of the phenomenon depending on which of the two is really present and operating. ... It will appear that a measurable uncertainty, or "risk" proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all. We ... accordingly restrict the term "uncertainty" to cases of the non-quantitive type."
So, that leads me to wonder can you actually convert uncertainty into risk or can you only reduce and spread out risk? Knight says that risk has an ex-ante probability distribution. In trying to get life insurance, from my perspective I have uncertainty because I cannot measure my risk, but the insurance company can and from their perspective it's a problem of risk. Subjectively, after I get insurance, I would know that after my death, my family would be taken care of. I would no longer have uncertainty (on this one part of the uncertainty of my death, there's a minimum of two others, like how and when), but the insurance company has gained a risk. Actually that may not be accurate. Maybe it is also uncertainty when it hits the balance sheet of the insurance company? Perhaps it is the subjective determination of the insurance company that makes it risk rather than uncertainty? This explanation seems lacking to me. A probability distribution seems outside of value and outside of the human mind. A more satisfying explanation, to me, is that the payouts on the insurance contract are uncertain by themselves for the individual and when transferred to the insurance company. They become risk when there are enough of them that produce a probability distribution.
As an example from modern finance, if you take a bunch of MBS and pool them into a CDO, you have certainly pooled them, but the pool of assets or the structure do not become a measureable probability distribution. So what you have with CDOs is not risk diversification, but taking a bunch of assets with uncertain payoffs, pooling them in a complex structure, and then sending different levels of uncertainty to people. Risk is not diversified, but different levels of uncertainty are spread out among the owners of the tranches to the CDO.
Here they note that modern finance "seeks to turn uncertainty into risk. You cannot quantify uncertainty, and you cannot trade it. It is pre-finance—and it can be corrosive. Risk, on the other hand, is a probability distribution. It is quantifiable. You can model it and analyse it and it has a value. Therefore, you can trade it." They are right on what modern finance seeks to do and the difference between uncertainty and risk. My problem lies with modern finance and actually turning uncertainty into risk.
I view uncertainty and risk from a Knightian lens. Risk is measurable, uncertainty is not: "The essential fact is that "risk" means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far-reaching and crucial differences in the bearings of the phenomenon depending on which of the two is really present and operating. ... It will appear that a measurable uncertainty, or "risk" proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all. We ... accordingly restrict the term "uncertainty" to cases of the non-quantitive type."
So, that leads me to wonder can you actually convert uncertainty into risk or can you only reduce and spread out risk? Knight says that risk has an ex-ante probability distribution. In trying to get life insurance, from my perspective I have uncertainty because I cannot measure my risk, but the insurance company can and from their perspective it's a problem of risk. Subjectively, after I get insurance, I would know that after my death, my family would be taken care of. I would no longer have uncertainty (on this one part of the uncertainty of my death, there's a minimum of two others, like how and when), but the insurance company has gained a risk. Actually that may not be accurate. Maybe it is also uncertainty when it hits the balance sheet of the insurance company? Perhaps it is the subjective determination of the insurance company that makes it risk rather than uncertainty? This explanation seems lacking to me. A probability distribution seems outside of value and outside of the human mind. A more satisfying explanation, to me, is that the payouts on the insurance contract are uncertain by themselves for the individual and when transferred to the insurance company. They become risk when there are enough of them that produce a probability distribution.
As an example from modern finance, if you take a bunch of MBS and pool them into a CDO, you have certainly pooled them, but the pool of assets or the structure do not become a measureable probability distribution. So what you have with CDOs is not risk diversification, but taking a bunch of assets with uncertain payoffs, pooling them in a complex structure, and then sending different levels of uncertainty to people. Risk is not diversified, but different levels of uncertainty are spread out among the owners of the tranches to the CDO.
Sunday, September 21, 2008
Quotations
"Bob Rubin as Secretary of the Treasury — I mean, if he was a Hindu and he was being reincarnated, he'd come back as a pail because this guy bailed out everything you can imagine." - Kevin Phillips on Bill Moyers show
HT: Big Picture
HT: Big Picture
Wednesday, September 17, 2008
Taylor Rule
If certain people used certain data series (like MacroAdvisors' Monthly GDP series and CPI) to make a Taylor Rule, they might be pleasantly surprised by investing when Fed Funds is above what is suggested by the Taylor Rule.
Thursday, September 4, 2008
Why doesn't this exist
By law, a hedge fund needs to avoid having too many (100) accredited investors in order to avoid coming under additional regulations. An accredited investor can include a pension fund, a bank, a hedge fund of funds, someone with a million dollars, and other rich persons. An investment company can also act as an accredited investor. However, people who make less than 200k dollars in either of the past two years are not accredited investors and therefore cannot invest in hedge funds. Also, a fund may require a large initial investment that more marginal investors cannot invest in. Furthermore, some of the better funds are hard to invest in, even for large investors. And I'll add in the fact that fund of funds charge an additional layer of fees that are pretty absurd.
So I think if it is legal, there should be structures like a closed end fund that solely invests in a particular hedge fund marketed to these marginal investors in hedge funds. The ideal organization to launch something like this would be an already respected fund of funds, a global investment bank, or some other organization with many contacts among large hedge funds. You could start with like the five or ten largest hedge funds that are open to investors and then expand into more.
Again it would be best sold to the marginal hedge fund investors. Someone with a 500,000+ portfolio and willing to invest 50k in a hedge fund might be willing to do it if they can buy in with a share in a closed end fund that is investing many millions more in a fund. Seems like a winning idea to me, if it's legal and the organization behind it has the relationships.
So I think if it is legal, there should be structures like a closed end fund that solely invests in a particular hedge fund marketed to these marginal investors in hedge funds. The ideal organization to launch something like this would be an already respected fund of funds, a global investment bank, or some other organization with many contacts among large hedge funds. You could start with like the five or ten largest hedge funds that are open to investors and then expand into more.
Again it would be best sold to the marginal hedge fund investors. Someone with a 500,000+ portfolio and willing to invest 50k in a hedge fund might be willing to do it if they can buy in with a share in a closed end fund that is investing many millions more in a fund. Seems like a winning idea to me, if it's legal and the organization behind it has the relationships.
Sunday, August 31, 2008
Resistance and Support
This article has been mentioned on a few sites, I saw it first at Free Exchange.
It notes that prices that end in .99 induce customers to purchase a much higher percentage of sales than would be suggested. It is particularly true for lower priced items, but a purchase like a washer/dryer wouldn't have much effect.
While it is easy to design an experiment in a retail setting to test that theory, it would be much more difficult to test it in the financial markets. However, it seems to me that it would most evidently manifest itself in support and resistance points. I don't think support and resistance really translate well into trading systems. However, they can be useful in explaining behavior in the market (though I admit more value in hindsight than at the time). For instance, a stock might test its five year high several times and after breaking through on higher volume, it will surge significantly. A more active trader could see that and place buy stop orders above the resistance level. The problem with using a system is that sometimes it will go slightly above the resistance and then drop significantly. It is done more based on feel and that's also the problem with testing the effects of support and resistance lines using standard statistical techniques. A sustained, high volume move through a resistance point is more important than a weak one.
Getting back to the BBC article, a resistance line can be thought of like a price of 8 euro. The marginal asset manager might think that a stock is worth no more than 25 dollars. He would be interested in selling at 25 and willing to buy at 24.99. However, in the real world, the decision would really be how much of his portfolio to sell at 24.99 vs. 25.00 and not whether he is buying at 24.99. Due to the same effects noted in the BBC article, he would be much more willing to sell at 25 than at 24.99. The situation works in the reverse for a support line at 25, a manager might only be willing to buy a little at 25.01, but he might be willing to buy more at 25. You may ask shouldn't it be 24.99 where he wants to buy more to be consistent with the article? However, the real meat of the article is that people don't react linearly to these price changes, the same way that portfolio managers or traders might react.
There's one problem with this analysis that I can figure out so far, the prices in the BBC article are all small. While the prices of stocks can be reasonable on the face of it, even a retail investor would probably be buying 100 share lots and a PM would purchase significantly more. So the question is, is it the dollar value that matters or the price that matters? I'm not really sure of the answer, but I would say at the very least support and resistance are important enough that every technical trader would pay attention to them. There has to be some "inefficiency" here.
Testing this would be another problem, but I'm sure some finance professor is already looking into it. I really think that the key would be to look at when it comes to resistance points with light volume or heavy volume. For instance, after identifying resistance points, I would calculate whether they are above a moving average of volume to determine whether a day is a light volume or heavy volume day (might want to do relative to the market as a whole as well) and then I would look at how the stock performs relative to the market. I would identify resistance points using something like Average True Range relative to the stock price. For instance, a 6 dollar stock that moves 25 cents a day might have have support or resistance at the $1 level, but Goldman you might look 20 dollars away for support/resistance. That way you can do all the stocks together and then compare quartiles of stocks based on price or trading volume. Finally, all you have to do is look if high volume violations of resistance points or confirmations of support lines result in prices above those points over the next month (or 3) more so than the low volume.
That's probably a publishable paper right there, biggest problem is probably identifying the resistance points. It would make sense to do it in multiple ways to avoid the criticism that you measure it wrong. If you don't remove earnings days or something, you'll also need to make some kind of assumption to deal with them.
It notes that prices that end in .99 induce customers to purchase a much higher percentage of sales than would be suggested. It is particularly true for lower priced items, but a purchase like a washer/dryer wouldn't have much effect.
While it is easy to design an experiment in a retail setting to test that theory, it would be much more difficult to test it in the financial markets. However, it seems to me that it would most evidently manifest itself in support and resistance points. I don't think support and resistance really translate well into trading systems. However, they can be useful in explaining behavior in the market (though I admit more value in hindsight than at the time). For instance, a stock might test its five year high several times and after breaking through on higher volume, it will surge significantly. A more active trader could see that and place buy stop orders above the resistance level. The problem with using a system is that sometimes it will go slightly above the resistance and then drop significantly. It is done more based on feel and that's also the problem with testing the effects of support and resistance lines using standard statistical techniques. A sustained, high volume move through a resistance point is more important than a weak one.
Getting back to the BBC article, a resistance line can be thought of like a price of 8 euro. The marginal asset manager might think that a stock is worth no more than 25 dollars. He would be interested in selling at 25 and willing to buy at 24.99. However, in the real world, the decision would really be how much of his portfolio to sell at 24.99 vs. 25.00 and not whether he is buying at 24.99. Due to the same effects noted in the BBC article, he would be much more willing to sell at 25 than at 24.99. The situation works in the reverse for a support line at 25, a manager might only be willing to buy a little at 25.01, but he might be willing to buy more at 25. You may ask shouldn't it be 24.99 where he wants to buy more to be consistent with the article? However, the real meat of the article is that people don't react linearly to these price changes, the same way that portfolio managers or traders might react.
There's one problem with this analysis that I can figure out so far, the prices in the BBC article are all small. While the prices of stocks can be reasonable on the face of it, even a retail investor would probably be buying 100 share lots and a PM would purchase significantly more. So the question is, is it the dollar value that matters or the price that matters? I'm not really sure of the answer, but I would say at the very least support and resistance are important enough that every technical trader would pay attention to them. There has to be some "inefficiency" here.
Testing this would be another problem, but I'm sure some finance professor is already looking into it. I really think that the key would be to look at when it comes to resistance points with light volume or heavy volume. For instance, after identifying resistance points, I would calculate whether they are above a moving average of volume to determine whether a day is a light volume or heavy volume day (might want to do relative to the market as a whole as well) and then I would look at how the stock performs relative to the market. I would identify resistance points using something like Average True Range relative to the stock price. For instance, a 6 dollar stock that moves 25 cents a day might have have support or resistance at the $1 level, but Goldman you might look 20 dollars away for support/resistance. That way you can do all the stocks together and then compare quartiles of stocks based on price or trading volume. Finally, all you have to do is look if high volume violations of resistance points or confirmations of support lines result in prices above those points over the next month (or 3) more so than the low volume.
That's probably a publishable paper right there, biggest problem is probably identifying the resistance points. It would make sense to do it in multiple ways to avoid the criticism that you measure it wrong. If you don't remove earnings days or something, you'll also need to make some kind of assumption to deal with them.
Wednesday, August 20, 2008
TAA and switching to bonds
First off, anybody see the ads for Crusoe on NBC during the Olympics, makes me want to break out my MES.
Second, if anyone remembers/cares I took the level 2 exam of the CFA back in June and ended up passing. So congratulations to my brothers.
Third, some ideas come to me that are rather simple, but make a lot of sense looking back on them. I had tried using bonds instead of cash in the TAA model previously, but was unimpressed due to larger volatility. However, I hadn't considered using the TAA investment in bonds. In other words, use the return series that invests in bonds when above the 10 month average and cash otherwise instead of a pure cash index for some asset classes.
The two asset classes I meant to target with this strategy were the two that historically have performed the worst on a Sharpe ratio basis, commodities and foreign equities, in the TAA strategy. I still have the TAA rule for each, but before I evaluate that I look at whether the US equity or foreign equities are below the 10 month average, if that is the case, I will have them invest in the bond TAA strategy. Then, if above the 10 month MA, they invest in that asset class, otherwise they invest in cash.
For comparison, in recent years (since 1990), the TAA strategy for commodites returned 8.8% annually (16.69% s.d., Sharpe .27 with r.f. @ period average), this simple change increases the return to 13.6% (11.8% s.d., Sharpe .79). For foreign equities, the return goes from 7.6% (12.59% s.d., Sharpe .27) to 12.2% return (12.43% s.d., Sharpe .64). The overall strategy improves from 10.7% return (6.85% s.d., Sharpe .94) to 12.5% return (7.01% s.d., 1.17 Sharpe).
Again, the reason I focused on these two was because they perform the worst. Using the strategy on US equities seems to work (Sharpe goes to 1.16) and for REITs (Sharpe goes to 1.15). Overall Sharpe goes down slightly, but for the individual asset classes the Sharpe increases suggesting the decline is due to decreased diversification and higher variances. A 5% increase in the Sharpe ratio individually doesn't impress me as much as the ones for commodities and foreign equities.
I also tested my original intention, just using the bond TAA instead of cash (and nothing more complicated like above) and it works well for REITs, but works best for equities. A marginal improvement on a risk-adjusted basis for the portfolio, but interesting nonetheless.
Second, if anyone remembers/cares I took the level 2 exam of the CFA back in June and ended up passing. So congratulations to my brothers.
Third, some ideas come to me that are rather simple, but make a lot of sense looking back on them. I had tried using bonds instead of cash in the TAA model previously, but was unimpressed due to larger volatility. However, I hadn't considered using the TAA investment in bonds. In other words, use the return series that invests in bonds when above the 10 month average and cash otherwise instead of a pure cash index for some asset classes.
The two asset classes I meant to target with this strategy were the two that historically have performed the worst on a Sharpe ratio basis, commodities and foreign equities, in the TAA strategy. I still have the TAA rule for each, but before I evaluate that I look at whether the US equity or foreign equities are below the 10 month average, if that is the case, I will have them invest in the bond TAA strategy. Then, if above the 10 month MA, they invest in that asset class, otherwise they invest in cash.
For comparison, in recent years (since 1990), the TAA strategy for commodites returned 8.8% annually (16.69% s.d., Sharpe .27 with r.f. @ period average), this simple change increases the return to 13.6% (11.8% s.d., Sharpe .79). For foreign equities, the return goes from 7.6% (12.59% s.d., Sharpe .27) to 12.2% return (12.43% s.d., Sharpe .64). The overall strategy improves from 10.7% return (6.85% s.d., Sharpe .94) to 12.5% return (7.01% s.d., 1.17 Sharpe).
Again, the reason I focused on these two was because they perform the worst. Using the strategy on US equities seems to work (Sharpe goes to 1.16) and for REITs (Sharpe goes to 1.15). Overall Sharpe goes down slightly, but for the individual asset classes the Sharpe increases suggesting the decline is due to decreased diversification and higher variances. A 5% increase in the Sharpe ratio individually doesn't impress me as much as the ones for commodities and foreign equities.
I also tested my original intention, just using the bond TAA instead of cash (and nothing more complicated like above) and it works well for REITs, but works best for equities. A marginal improvement on a risk-adjusted basis for the portfolio, but interesting nonetheless.
Monday, August 18, 2008
TAA and commodity overheating
Just wanted to do a quick blog on the TAA model noted earlier on this blog.
I created an extension to the model based on it achieving a certain return after a set number of months. After that, I looked at whether it makes sense to get out completely or to use a different exit rule (like a 5 month MA instead of 10 month MA). It doesn't get back in until the next time the 10 month MA crosses back over. The general idea is that if an asset class goes up that significantly in such a short period of time, it is unlikely that the returns in the future will be strong, despite being above the 200 day return
I started with a 20% return in a quarter and getting out completely. In that model, there is an improved return. However, closer analysis reveals that it is almost exclusively in the commodities sector. It stays out of almost five years worth trading (239 months vs. 294 months) changing an asset class with 8.8% return and 16.8% volatility to one with a 13.3% return and 13.12% volatility.
I also experimented with different combinations of returns, periods of time, and whether to use a MA average rule to get out or just permanently get out. Several of them perform better than the original TAA rule, but almost all the benefit comes from the commodities sector and the other sectors don't improve enough to be worth it.
I should note that my analysis didn't include the current period (ended in early 08), but the knowledge I take from my analysis is that when commodities rise 20% in a quarter, they historically have a correction.
Note: I also created a more complicated algorithm for the other asset classes that will get back in if the past three months did not have the quarterly 20% return which seems to help reduce volatility and improves the portfolios Sharpe ratio (though the individual ones don't appear that much better. Basically the same thing as the commodity strategy except it is willing to get back in (keeps the same returns for the commodity strategy). 11% return for the overall strategy here with 5.43% volatility. (compared to about 6.85% for the original TAA model).
I created an extension to the model based on it achieving a certain return after a set number of months. After that, I looked at whether it makes sense to get out completely or to use a different exit rule (like a 5 month MA instead of 10 month MA). It doesn't get back in until the next time the 10 month MA crosses back over. The general idea is that if an asset class goes up that significantly in such a short period of time, it is unlikely that the returns in the future will be strong, despite being above the 200 day return
I started with a 20% return in a quarter and getting out completely. In that model, there is an improved return. However, closer analysis reveals that it is almost exclusively in the commodities sector. It stays out of almost five years worth trading (239 months vs. 294 months) changing an asset class with 8.8% return and 16.8% volatility to one with a 13.3% return and 13.12% volatility.
I also experimented with different combinations of returns, periods of time, and whether to use a MA average rule to get out or just permanently get out. Several of them perform better than the original TAA rule, but almost all the benefit comes from the commodities sector and the other sectors don't improve enough to be worth it.
I should note that my analysis didn't include the current period (ended in early 08), but the knowledge I take from my analysis is that when commodities rise 20% in a quarter, they historically have a correction.
Note: I also created a more complicated algorithm for the other asset classes that will get back in if the past three months did not have the quarterly 20% return which seems to help reduce volatility and improves the portfolios Sharpe ratio (though the individual ones don't appear that much better. Basically the same thing as the commodity strategy except it is willing to get back in (keeps the same returns for the commodity strategy). 11% return for the overall strategy here with 5.43% volatility. (compared to about 6.85% for the original TAA model).
Sunday, August 3, 2008
The Economics of Registering to Vote
Well, I should say that it is more the cost/benefit analysis of registering to vote. I recently moved from Queens to Jersey City and there were some thirty-ish professionals outside the PATH entrance who wanted to register me to vote. I am registered in Indiana (where KF's parents live and went to college) and still have my Indiana driver's license.
Walking to the registration table, I figured that (outside of time wasted filling out the form) I was making a cost-less decision. I probably won't vote, but I figure that the margin difference in New Jersey in the general election will be smaller than the margin difference in Indiana. So, if my vote matters at all (probably not), it matters a fraction more in Jersey than Indiana. So the benefits side of the calculus is the expected value of me voting and that influencing the election (probably of me voting times value of my vote and also all future voting decisions and their weight discounted to the present).
However, I didn't realize the costs of voting until a man who either was an unemployed, alcoholic construction worker or homeless (probably the latter) began to convince me not to register. His early arguments weren't that convincing focusing mostly on how much the vote matters and staying off the grid (the first I already knew, the second I didn't care about). However, he mentioned that one of two places they pull jury duty from is the voter rolls. If I am pulled to do jury duty twice a decade in New Jersey that means that I earn like $3.50 (how much the lochness monster takes) and lose a vacation day, I presume.
The problem of how to value the cost is difficult for two reasons. First, the call for jury duty is random and could be modeled like a Poisson process. An easy work around would be that I have jury duty in five years and ten years and discount the costs on those dates back at 6% or so. The second difficulty is valuing a vacation day. I can assume that the value of a vacation day would increase as my income increases since leisure would become more scarce and I would imagine that my income grows significantly five to ten years from now. I could probably model it, but it shouldn't matter that much, as will be seen. My gut feeling is that, in terms of dollars, a vacation day shouldn't affect salary (I get paid the same) and you could assume that it doesn't affect your bonus. However, if you don't use all of your vacation days, you might have worked harder and deserved a higher bonus by accomplishing more work. There is some probability that it will increase your bonus by not taking the vacation day, but it is small and would probably not be a big effect after discounting*. The real place to value the vacation day is in subjective value. The proper trade off is the net benefit of sitting in the sun or skiing out west or sitting in a jury room.
The subjective benefit to skiing with friends relative to sitting in a jury room, for me, outweighs the money (from bonus or the 3.50) and the benefits of being able to vote in New Jersey. I'll stay registered in Indiana and avoid jury duty like the plague.
I'm pretty sure they don't let people who think like me on juries anyway.
*It is small on the margin because it would probably only be if you had like leftover vacation days from the day before and just dropped out from work for like a month. That would probably affect bonus.
Walking to the registration table, I figured that (outside of time wasted filling out the form) I was making a cost-less decision. I probably won't vote, but I figure that the margin difference in New Jersey in the general election will be smaller than the margin difference in Indiana. So, if my vote matters at all (probably not), it matters a fraction more in Jersey than Indiana. So the benefits side of the calculus is the expected value of me voting and that influencing the election (probably of me voting times value of my vote and also all future voting decisions and their weight discounted to the present).
However, I didn't realize the costs of voting until a man who either was an unemployed, alcoholic construction worker or homeless (probably the latter) began to convince me not to register. His early arguments weren't that convincing focusing mostly on how much the vote matters and staying off the grid (the first I already knew, the second I didn't care about). However, he mentioned that one of two places they pull jury duty from is the voter rolls. If I am pulled to do jury duty twice a decade in New Jersey that means that I earn like $3.50 (how much the lochness monster takes) and lose a vacation day, I presume.
The problem of how to value the cost is difficult for two reasons. First, the call for jury duty is random and could be modeled like a Poisson process. An easy work around would be that I have jury duty in five years and ten years and discount the costs on those dates back at 6% or so. The second difficulty is valuing a vacation day. I can assume that the value of a vacation day would increase as my income increases since leisure would become more scarce and I would imagine that my income grows significantly five to ten years from now. I could probably model it, but it shouldn't matter that much, as will be seen. My gut feeling is that, in terms of dollars, a vacation day shouldn't affect salary (I get paid the same) and you could assume that it doesn't affect your bonus. However, if you don't use all of your vacation days, you might have worked harder and deserved a higher bonus by accomplishing more work. There is some probability that it will increase your bonus by not taking the vacation day, but it is small and would probably not be a big effect after discounting*. The real place to value the vacation day is in subjective value. The proper trade off is the net benefit of sitting in the sun or skiing out west or sitting in a jury room.
The subjective benefit to skiing with friends relative to sitting in a jury room, for me, outweighs the money (from bonus or the 3.50) and the benefits of being able to vote in New Jersey. I'll stay registered in Indiana and avoid jury duty like the plague.
I'm pretty sure they don't let people who think like me on juries anyway.
*It is small on the margin because it would probably only be if you had like leftover vacation days from the day before and just dropped out from work for like a month. That would probably affect bonus.
Saturday, July 19, 2008
SEC exempts Market Makers
This big news in the market these days has been the new SEC naked short sale regulations. According to this article, market makers in equities and options have been exempted from the short sale regulations.
In my view there are three main criticisms of the original regulations. The first is resolved by this adjustment. The options market, in particular, was effected by these regulations since it can disrupt hedging operations. Since activity in the options market feeds into the equity markets, if you create regulations that make it less likely someone will make markets in some options, there will be some big effects. The second criticism is the one pointed out by Mish several times that the firms exempted from the shorts has been chosen rather arbitrarily. Finally, is the whole this prevents these companies from going quickly to a fair value and serves as a form of relief for privileged, politically well-connected banks. People lost their life savings on internet companies and rules like this weren't put in place. And I'll leave it at that.
In my view there are three main criticisms of the original regulations. The first is resolved by this adjustment. The options market, in particular, was effected by these regulations since it can disrupt hedging operations. Since activity in the options market feeds into the equity markets, if you create regulations that make it less likely someone will make markets in some options, there will be some big effects. The second criticism is the one pointed out by Mish several times that the firms exempted from the shorts has been chosen rather arbitrarily. Finally, is the whole this prevents these companies from going quickly to a fair value and serves as a form of relief for privileged, politically well-connected banks. People lost their life savings on internet companies and rules like this weren't put in place. And I'll leave it at that.
Thursday, July 17, 2008
Merger Arbitrage
*I generally don't post about specific stocks, but I haven't gotten around to some of the research I meant to do and something I am looking at is increasingly looking worthwhile.
Merger arbitrage is the art of buying companies that are getting acquired and selling companies that are acquiring. When the merger goes through, you collect the spread between them. If the merger doesn't go through, the spread widens and you lose money.
Alpha Natural Resources (ANR) is a coal stock and Cleveland-Cliffs (CLF) is an iron and coal stock. Cleveland-Cliffs announced on July 15th that it will purchase ANR for $22.23 and .95 shares of CLF. On the 16th, ANR opened up around 119 after trading around 95 the past few days and then proceeded to tank back down to a close of around 96 at the close of the 17th. CLF was trading around 110 prior to the announcement and has come down to about 97.25.
Based on current prices, 100 shares of ANR should be worth 22.23*100+97.25*95=$11,462 and only cost $9,580 on the market. Since the value of the ANR is dependent on the value of CLF, you would sell short the CLF in a merger arb situation. This way when you receive the 95 shares of CLF you can deliver them to whomever you borrowed the stock from.
For example, assuming the existing prices are where you buy and short and the merger closes, that means that ANR will be priced such that what you can buy equals 22.23*100+p*95, where p is the price of CLF. If CLF closes out at 100, ANR should be worth 117.23 per share. After your ANR shares are converted to CLF, you can close out your short (worth 95*100 dollars) and keep 2223 (22.23*100). The merger is supposed to complete at the end of the year and depending on how your margin account is handled, it looks like you could put up about 20k for an annualized return of about 20%.
That's not to say that this isn't risky. Merger arbitrage is a very risky business and it admittedly isn't mine. Given how that ANR has fallen fairly significantly since the announcement came out, the market is pricing (excluding shorting costs and TVM) that the stock is only worth three-quarters a share of CLF. I will be waiting for more details, particularly the proxy. Do your homework and certainly don't blindly follow me. I would have bought it on the open of 7/16 and have lost like 20 dollars a share already on ANR and not made it back on CLF. At these prices and this spread, I feel like it would be less risky given the potential gain.
SEC 8-K form
Press Release
edit: Harbinger Capital increased a position from 3/31 of about 8.73% to about 18.36% and announced in a 13D that they would oppose the merger.
Merger arbitrage is the art of buying companies that are getting acquired and selling companies that are acquiring. When the merger goes through, you collect the spread between them. If the merger doesn't go through, the spread widens and you lose money.
Alpha Natural Resources (ANR) is a coal stock and Cleveland-Cliffs (CLF) is an iron and coal stock. Cleveland-Cliffs announced on July 15th that it will purchase ANR for $22.23 and .95 shares of CLF. On the 16th, ANR opened up around 119 after trading around 95 the past few days and then proceeded to tank back down to a close of around 96 at the close of the 17th. CLF was trading around 110 prior to the announcement and has come down to about 97.25.
Based on current prices, 100 shares of ANR should be worth 22.23*100+97.25*95=$11,462 and only cost $9,580 on the market. Since the value of the ANR is dependent on the value of CLF, you would sell short the CLF in a merger arb situation. This way when you receive the 95 shares of CLF you can deliver them to whomever you borrowed the stock from.
For example, assuming the existing prices are where you buy and short and the merger closes, that means that ANR will be priced such that what you can buy equals 22.23*100+p*95, where p is the price of CLF. If CLF closes out at 100, ANR should be worth 117.23 per share. After your ANR shares are converted to CLF, you can close out your short (worth 95*100 dollars) and keep 2223 (22.23*100). The merger is supposed to complete at the end of the year and depending on how your margin account is handled, it looks like you could put up about 20k for an annualized return of about 20%.
That's not to say that this isn't risky. Merger arbitrage is a very risky business and it admittedly isn't mine. Given how that ANR has fallen fairly significantly since the announcement came out, the market is pricing (excluding shorting costs and TVM) that the stock is only worth three-quarters a share of CLF. I will be waiting for more details, particularly the proxy. Do your homework and certainly don't blindly follow me. I would have bought it on the open of 7/16 and have lost like 20 dollars a share already on ANR and not made it back on CLF. At these prices and this spread, I feel like it would be less risky given the potential gain.
SEC 8-K form
Press Release
edit: Harbinger Capital increased a position from 3/31 of about 8.73% to about 18.36% and announced in a 13D that they would oppose the merger.
Thursday, July 10, 2008
Tuesday, July 8, 2008
Increasing 200 day moving averages
I did a quick study of what happens if you look at whether the 200 day moving average is increasing or not. I used the same asset classes and methodology as this which through February of this year showed a return of 11.98% (6.82% std, .875 Sharpe). I looked at three improvements which probably do not have different enough results to tell a priori which is better.
I had originally assumed it would be in less. I wanted a method that would use the same entry and get you out quicker when the market begins to tank, but it appears that the benefit comes from keeping you in the market longer (roughly 70% of the months that are different are from the second method having a buy rather than a sell) and these months, particularly for commodities and stocks, generate strong returns and the handful of months avoided have relatively mixed returns. However, when they are down, they are down pretty significantly (real estate is an anomaly that acts opposite both effects). I was also surprised to find out that on average the TAA method generates on average 50 entry or exit signals per asset class whereas the second method generates about 45.
In conclusion, the TAA model can benefit by being in the market longer and not necessarily trying to avoid more periods.
- 1. If the 200 day moving average is increasing a buy signal is generated, invest in cash otherwise.
- 2. Entry order is only generated when the price is greater than the 200 day MA, only exit if the 200 MA decreases.
- 3. Same entry order, but exit if below 200 day MA and 200 day MA decreases.
I had originally assumed it would be in less. I wanted a method that would use the same entry and get you out quicker when the market begins to tank, but it appears that the benefit comes from keeping you in the market longer (roughly 70% of the months that are different are from the second method having a buy rather than a sell) and these months, particularly for commodities and stocks, generate strong returns and the handful of months avoided have relatively mixed returns. However, when they are down, they are down pretty significantly (real estate is an anomaly that acts opposite both effects). I was also surprised to find out that on average the TAA method generates on average 50 entry or exit signals per asset class whereas the second method generates about 45.
In conclusion, the TAA model can benefit by being in the market longer and not necessarily trying to avoid more periods.
Tuesday, July 1, 2008
Probit and Interest rates
I'm curious how the historical shape of the yield curve can assist in the prediction of returns for holding government bonds. This is a preliminary post that plans to detail some of the basic lines of thought I am pursuing. I am heading to DC for the 4th, so I would like to perform an out-of-sample test to look into how this line of thought actually performs when I get back. I have some skepticism and doubts about this method that can only be confirmed upon more research (more on this later).
I began by collecting total return series for 1, 2, 3, 5, 10, and 30 year government bonds along with interest data that's available for bills, bonds, and corporate debt. Some of the series are active in some time periods and not in others, so I just stuck with 3 month t-bill and 1, 3, 5, and 10 year bonds, along with BAA corporate interest rates.
I first looked at the returns for the different bonds. In general, I am interested in holding for several months, so I took the geometric average three month returns and created holding 10 year (5 years back, five years forward) windows to evaluate each time period. The evaluation was simply which (not decile or quartile, but) quintile or 20% range the return would fall into. So if a time period is ranked a five, it would perform in the top 80% relative to the performance five years prior or forward. This way the select periods of time where bonds dominate don't outweigh the whole dataset and there are still runs where it makes sense to be in bonds. The only reason I don't do the whole series is that I believe doing so would result in too much trading.
The chart above is the average monthly (not 3 month) return for each of the bonds I looked at and each decile. Below that is the same chart except if given a 4 or a 5 above, the left-most column is a 1 and 0 otherwise. Since we expect that bonds with longer maturities should have longer durations it makes sense that the 30 year has the largest spread and the greatest opportunity to profit or lose. I also looked into the correlation of the returns to the 6 bonds. For the most part correlations like 3 year vs. 5 year are very high, but as you get larger differences, there are larger differences. However, what is striking is that the correlation between many bonds, even like the 10 year vs. 2 year, are higher than 80%. That suggests that for the most part if you can build a good model for one of them, the idea should work for all (with the 10 and 30 relative to the 1 year having lowest correlations).
I think it is interesting to look at conditional means (or categories) for different statistics. For example, what are the returns like on average over the next three months when the yield curve inverts (or steepens). The only problem with that is that I have so much data and so many different yield curves to compare. I didn't want to specify one way that would work best (ie. do I only look at when the 10 year inverts relative to the 1 year, or do I look at 2 and 5, do some outperform in different central bank regimes?). To give myself as much flexibility without doing something crazy like a neural network, I decided that it would be best (at least in a preliminary sense) to look into using a probit model to categorize the returns.
I described what probit models are and how to use them to look into the probability of a crisis or recession previously. Essentially, I have the series of 0s and 1s and the goal is to use the independent variables to estimate the probability that an event will occur (in this case, the event is that it is worthwhile to invest in bonds for at least three months). I estimated the model for each bond series using two methods, in the first I focused on the interest rates mentioned above without reference to their past values, in the second I used the interest rates and each of the past 12 lags. The first method is less successful than the second, but it also avoids a lot more curve-fitting problems than the first method. The first method classifies 63% to 72% (from 30 year to 1 year) correctly whereas the second method is up to 73% to 80% (from 30 year to 1 year). For comparison, using the binary decision of greater than the 10 month MA or less, classifies at about 60% for each bond (and including it in the decision-making doesn't help). Note that I consider classifying correctly to mean a probability greater than 50%.
What will be interesting is to look at the false positive rate and the returns in situations when there is a false positive. In other words, I think the value of the probit model is identifying risk/return better than other models. If I can identify situations with good average wins relative to average losses, then I can control my risk better. Using the model incorporating 12 lags (which I am worried about), I calculated the times where there are false positives for the 10 year bond and found a 2% annual return with 3% volatility compared to a 18% return with 9% volatility for the normal (note that this is just what the returns are and ignores the fact that it will be in cash for significant periods of time, just want to get an idea of the conditional means). As expected, the periods when the model says to get out of the market, there are negative annualized returns(-10% with 7% volatility). However, I am a little worried about false negatives, but after looking at how often the model invests (since it invests for three months), it appears that the problem goes away. Obviously the problem with this is that I use the whole series to develop the probit model rather than going with information available to develop the coefficients. I would suspect that these good returns would get slightly reduced by using the actual trading model (which is what I intend to test when I am back from DC). In comparison, the 10 month MA rule, returns 8.5% with 8.5% standard deviation (ignoring interest) though it is invested more often. Incorporating commercial paper yield into the second model would reduce its return (though risk/reward stays high) though also reduce volatility by more in this model than in the 10 month MA rule.
Nevertheless, it appears to be an interesting development, I am worried that the coefficients are a bit difficult to interpret (too black boxy) and that they won't be stable enough to generate significant returns. I also don't doubt there are problems with autocorrelation, but fixing that in probit models can be a pain.
I began by collecting total return series for 1, 2, 3, 5, 10, and 30 year government bonds along with interest data that's available for bills, bonds, and corporate debt. Some of the series are active in some time periods and not in others, so I just stuck with 3 month t-bill and 1, 3, 5, and 10 year bonds, along with BAA corporate interest rates.
I first looked at the returns for the different bonds. In general, I am interested in holding for several months, so I took the geometric average three month returns and created holding 10 year (5 years back, five years forward) windows to evaluate each time period. The evaluation was simply which (not decile or quartile, but) quintile or 20% range the return would fall into. So if a time period is ranked a five, it would perform in the top 80% relative to the performance five years prior or forward. This way the select periods of time where bonds dominate don't outweigh the whole dataset and there are still runs where it makes sense to be in bonds. The only reason I don't do the whole series is that I believe doing so would result in too much trading.
The chart above is the average monthly (not 3 month) return for each of the bonds I looked at and each decile. Below that is the same chart except if given a 4 or a 5 above, the left-most column is a 1 and 0 otherwise. Since we expect that bonds with longer maturities should have longer durations it makes sense that the 30 year has the largest spread and the greatest opportunity to profit or lose. I also looked into the correlation of the returns to the 6 bonds. For the most part correlations like 3 year vs. 5 year are very high, but as you get larger differences, there are larger differences. However, what is striking is that the correlation between many bonds, even like the 10 year vs. 2 year, are higher than 80%. That suggests that for the most part if you can build a good model for one of them, the idea should work for all (with the 10 and 30 relative to the 1 year having lowest correlations).
I think it is interesting to look at conditional means (or categories) for different statistics. For example, what are the returns like on average over the next three months when the yield curve inverts (or steepens). The only problem with that is that I have so much data and so many different yield curves to compare. I didn't want to specify one way that would work best (ie. do I only look at when the 10 year inverts relative to the 1 year, or do I look at 2 and 5, do some outperform in different central bank regimes?). To give myself as much flexibility without doing something crazy like a neural network, I decided that it would be best (at least in a preliminary sense) to look into using a probit model to categorize the returns.
I described what probit models are and how to use them to look into the probability of a crisis or recession previously. Essentially, I have the series of 0s and 1s and the goal is to use the independent variables to estimate the probability that an event will occur (in this case, the event is that it is worthwhile to invest in bonds for at least three months). I estimated the model for each bond series using two methods, in the first I focused on the interest rates mentioned above without reference to their past values, in the second I used the interest rates and each of the past 12 lags. The first method is less successful than the second, but it also avoids a lot more curve-fitting problems than the first method. The first method classifies 63% to 72% (from 30 year to 1 year) correctly whereas the second method is up to 73% to 80% (from 30 year to 1 year). For comparison, using the binary decision of greater than the 10 month MA or less, classifies at about 60% for each bond (and including it in the decision-making doesn't help). Note that I consider classifying correctly to mean a probability greater than 50%.
What will be interesting is to look at the false positive rate and the returns in situations when there is a false positive. In other words, I think the value of the probit model is identifying risk/return better than other models. If I can identify situations with good average wins relative to average losses, then I can control my risk better. Using the model incorporating 12 lags (which I am worried about), I calculated the times where there are false positives for the 10 year bond and found a 2% annual return with 3% volatility compared to a 18% return with 9% volatility for the normal (note that this is just what the returns are and ignores the fact that it will be in cash for significant periods of time, just want to get an idea of the conditional means). As expected, the periods when the model says to get out of the market, there are negative annualized returns(-10% with 7% volatility). However, I am a little worried about false negatives, but after looking at how often the model invests (since it invests for three months), it appears that the problem goes away. Obviously the problem with this is that I use the whole series to develop the probit model rather than going with information available to develop the coefficients. I would suspect that these good returns would get slightly reduced by using the actual trading model (which is what I intend to test when I am back from DC). In comparison, the 10 month MA rule, returns 8.5% with 8.5% standard deviation (ignoring interest) though it is invested more often. Incorporating commercial paper yield into the second model would reduce its return (though risk/reward stays high) though also reduce volatility by more in this model than in the 10 month MA rule.
Nevertheless, it appears to be an interesting development, I am worried that the coefficients are a bit difficult to interpret (too black boxy) and that they won't be stable enough to generate significant returns. I also don't doubt there are problems with autocorrelation, but fixing that in probit models can be a pain.
Monday, June 30, 2008
Bear Stearns in Vanity Fair
The article in Vanity Fair regarding the collapse of Bear Stearns was fascinating. Particularly for an essentially anonymous trader who was able to profit (by shorting other financial stocks as Bear went down) from their collapse. The day that Bear did the final drop (I can't pull up the ticker on any of my normal methods so I can't be sure which day it was) it was up 10 dollars at about 9 o'clock only to get beaten down to flat by 9:30 and then got smoked (pretty sure it dropped at least 25%-50% the next half hour). My only insight is that most traders just see what is happening and react.
Though the entire article is worth a read, I found the following quote particularly enlightening.
The 2005 BAPCA bill was a giveaway to credit card companies, but it seems like this statement doesn't really make sense. First, it depends on if the securities are in margin account or traditional customer accounts. Margin accounts are held in the name of the brokerage, so it makes sense that those would be able to be taken in bankruptcy. Though I'm not an expert, by any means, I would assume that this hasn't changed. Within customer accounts, SIPC protects cash and securities less than numbers only lawyers remember. So based on the statement above, the 2005 BAPCA would allow the immediate seizure by creditors of the customer's cash and securities held at Bear Stearns. To me that just means that if Bear declares bankruptcy, it would be forced to liquidate. It couldn't go into bankruptcy protection and eventually hope to emerge. The equity would be worthless. In other words, senior management would never consider bankruptcy for Bear. I could be totally mistaken, but it appears that if it weren't for the BAPCPA bill, Bear could (big assumption) have tried bankruptcy and not purchase by J.P. Morgan. I'm not an expert enough to know if this is the case, but it would be interesting to look further at the influence of this bill and the collapse of the company.
Though the entire article is worth a read, I found the following quote particularly enlightening.
It was then that Gary Parr and the bankruptcy attorneys patiently explained that bankruptcy was actually not an option, not for a major securities firm. Changes to the bankruptcy code in 2005 would force federal regulators to take over customer accounts. All its securities would be subject to immediate seizure by creditors.
Thursday, June 19, 2008
Natural gas inventories
Natural Gas inventories were surveyed to change by 58 and came in at 57 (prior was 80). Natural Gas proceeded to fall 3% (at writing). For those who aren't traders, generally the natural gas inventories usually don't move the market enough to be worth trading (though they were three and four weeks ago), but I haven't seen such a strong, lasting move on this number when the inventories came in essentially in line. It looks like around 11 there was news (according to Briefing.com) that China would raise some prices of gasoline and crude, but this decision wouldn't affect natural gas. I guess I'm kind of at a loss to describe it.
Generally the trend is your friend, but I can't help put think that this is oversold (the front contract is at 12.72 and UNG is as 60.30 as of this writing). However, bottom picking this kind of strength on the downside can be vicious unless you are looking to hold for a long enough period. Thankfully my only position in UNG was in a play account on updown because I have been wanting to bottom pick this for at least an hour and it just keeps going down. The futures are at 12.65, down 4.3% from the open.
Edit: Looks like I was about ten minutes off the bottom. Also edited for grammar.
Generally the trend is your friend, but I can't help put think that this is oversold (the front contract is at 12.72 and UNG is as 60.30 as of this writing). However, bottom picking this kind of strength on the downside can be vicious unless you are looking to hold for a long enough period. Thankfully my only position in UNG was in a play account on updown because I have been wanting to bottom pick this for at least an hour and it just keeps going down. The futures are at 12.65, down 4.3% from the open.
Edit: Looks like I was about ten minutes off the bottom. Also edited for grammar.
Monday, June 16, 2008
Volatility Smile
The Black-Scholes-Merton model makes the assumption of constant volatility. In practice, we observe something called the volatility smile. Basically, options struck at the money have less volatility if you solve for volatility in BSM than options struck far in or out of the money. This is an interesting phenomenon that finance professors like to write about and hedge funds try to exploit.
I wonder whether equity index options (in particular just b/c I know that the 10 month MA strategy works on them and they have a long history) experience different volatility smiles when above the 10 month MA or below the 10 month MA. If the volatility doesn't change, I would look to a situation where calls are cheap when the market is trending up and puts are cheap when the market is trending downwards. I imagine it would take significant work to look into this. If no one else does (let me know if you do), then I might take a stab at looking into this problem sometime within the next six months. Nevertheless, I think it is an interesting question and could present arbitrage opportunities. Volatility is traditionally higher when equity markets are below 200 day moving averages, but I wonder if it is high enough given historical volatility during these times and the small (mostly negative) returns.
I wonder whether equity index options (in particular just b/c I know that the 10 month MA strategy works on them and they have a long history) experience different volatility smiles when above the 10 month MA or below the 10 month MA. If the volatility doesn't change, I would look to a situation where calls are cheap when the market is trending up and puts are cheap when the market is trending downwards. I imagine it would take significant work to look into this. If no one else does (let me know if you do), then I might take a stab at looking into this problem sometime within the next six months. Nevertheless, I think it is an interesting question and could present arbitrage opportunities. Volatility is traditionally higher when equity markets are below 200 day moving averages, but I wonder if it is high enough given historical volatility during these times and the small (mostly negative) returns.
Thursday, June 12, 2008
Beta and Sectors
I meant to post something about the interest rate environment and tactical asset allocation, but I haven't gotten around to it since the results aren't that spectacular. Still kind of interesting. Anyway, I've been reading Eric Falkenstein lately over at the Falkenblog and his website DefProb.
One point that he makes is that historically buying low Beta stocks has better returns than buying high Beta stocks (though there are periods of significant underperformance such as the internet bubble). I found this result interesting (risk is inversely related to returns, how could that not be interesting?) and so decided to look into a similar strategy using sectors.
I used weekly dividend adjusted data of the 9 Sector Spiders since they began at the end of '98 along with SPY. I calculated Beta vs. SPY using at least a year's worth of data and no more than 5 years worth of data. At the start of every year I ranked the Sectors on the basis of Beta and formed a high Beta and low Beta portfolio with three Sectors each. I also calculated a portfolio investing equally in each Spider to serve as comparison (SPY is market-weighted).
Over this period (from Jan. 2000 to the end of last week), the equal-weight portfolio return 2.4% annually (15.7% std), the high beta portfolio returned -.14% (20.7 std), and the low beta portfolio returned 4.3% (14.3% std).
I then calculated the 40 week (200 day) moving average and considered a signal at the beginning of the month good through the end of the month (since that is how the 10 month TAA works and I wanted it to be somewhat comparable). The results are reported below:
(After accidentally inflating the returns of the high portfolio) The results indicate that the low Beta portfolio outperforms when the market is above the 40 week moving average and slightly outperforms when the market is below the 40 week moving average which confirms the argument that Mr. Falkenstein had made (note that the Sharpe ratio is higher for the high than the low in the below 40 week, I think that the Sharpe ratio is an incorrect method of comparison when returns are below 0). That doesn't change the fact that investing when below the 200 day moving average is very risky.
CFO advisory posted yesterday regarding the sector momentum strategy (which I have covered before on this site) and noted that a significant portion of the return has been due to XLE. The low Beta portfolio included XLE from 2000 to the beginning of 2007; however, from 2007 until recently XLE return 35% annualized compared to 11% from 2000 to 2007. So I would argue that the performance of the low Beta portfolio doesn't suffer from the XLE criticism (note that I really agree with it anyhow).
One point that he makes is that historically buying low Beta stocks has better returns than buying high Beta stocks (though there are periods of significant underperformance such as the internet bubble). I found this result interesting (risk is inversely related to returns, how could that not be interesting?) and so decided to look into a similar strategy using sectors.
I used weekly dividend adjusted data of the 9 Sector Spiders since they began at the end of '98 along with SPY. I calculated Beta vs. SPY using at least a year's worth of data and no more than 5 years worth of data. At the start of every year I ranked the Sectors on the basis of Beta and formed a high Beta and low Beta portfolio with three Sectors each. I also calculated a portfolio investing equally in each Spider to serve as comparison (SPY is market-weighted).
Over this period (from Jan. 2000 to the end of last week), the equal-weight portfolio return 2.4% annually (15.7% std), the high beta portfolio returned -.14% (20.7 std), and the low beta portfolio returned 4.3% (14.3% std).
I then calculated the 40 week (200 day) moving average and considered a signal at the beginning of the month good through the end of the month (since that is how the 10 month TAA works and I wanted it to be somewhat comparable). The results are reported below:
(After accidentally inflating the returns of the high portfolio) The results indicate that the low Beta portfolio outperforms when the market is above the 40 week moving average and slightly outperforms when the market is below the 40 week moving average which confirms the argument that Mr. Falkenstein had made (note that the Sharpe ratio is higher for the high than the low in the below 40 week, I think that the Sharpe ratio is an incorrect method of comparison when returns are below 0). That doesn't change the fact that investing when below the 200 day moving average is very risky.CFO advisory posted yesterday regarding the sector momentum strategy (which I have covered before on this site) and noted that a significant portion of the return has been due to XLE. The low Beta portfolio included XLE from 2000 to the beginning of 2007; however, from 2007 until recently XLE return 35% annualized compared to 11% from 2000 to 2007. So I would argue that the performance of the low Beta portfolio doesn't suffer from the XLE criticism (note that I really agree with it anyhow).
Thursday, June 5, 2008
TAA and avoiding pullbacks
I should probably be studying, but this didn't take me that long to work out and was pretty interesting.
This could be considered another extension of the tactical asset allocation system developed by Mebane Faber that I have blogged about several times. The original strategy is to invest in five asset classes (US bonds, US stocks, Foreign stocks, Commodities, Real Estate) when they are greater than their 200 day moving average and commercial paper otherwise.
I modified the system slightly, maintaining the 200 day moving average requirement, but I added an additional constraint: it could not be the case that it was above the 5 month (4 and 6 have similar results) moving average and the return over the previous month was negative. The point of this was to take into account periods being overbought and then getting back in quickly. The periods above the five month that have negative prior month returns have very poor risk to reward ratios, most significantly for stocks and REITs. So making this simple addition can take a system with an 11.9% historical return with 6.8% standard deviation (.867 Sharpe at 6%) to 11.8% with 5.68% standard deviation (1.026 Sharpe). Though there is not a statistically significant difference in means, there is a significant difference in standard deviations according to an F test.
Not only is this improvement a significant, easy to implement improvement, but it is based on logic. Most trends do not continue up continuously. There tend to be pull backs. This strategy maintains the idea that the trend is your friend and attempts to stay out of a pull back if it happens two months in a row.
Note: the portfolio leveraged 50% has a 14% return with 8.5% standard deviation compared to 13.5% with 9% standard deviation for the original version. The best benefit in reducing standard deviation is in keeping the risk to reward statistics strong when using leverage.
This could be considered another extension of the tactical asset allocation system developed by Mebane Faber that I have blogged about several times. The original strategy is to invest in five asset classes (US bonds, US stocks, Foreign stocks, Commodities, Real Estate) when they are greater than their 200 day moving average and commercial paper otherwise.
I modified the system slightly, maintaining the 200 day moving average requirement, but I added an additional constraint: it could not be the case that it was above the 5 month (4 and 6 have similar results) moving average and the return over the previous month was negative. The point of this was to take into account periods being overbought and then getting back in quickly. The periods above the five month that have negative prior month returns have very poor risk to reward ratios, most significantly for stocks and REITs. So making this simple addition can take a system with an 11.9% historical return with 6.8% standard deviation (.867 Sharpe at 6%) to 11.8% with 5.68% standard deviation (1.026 Sharpe). Though there is not a statistically significant difference in means, there is a significant difference in standard deviations according to an F test.
Not only is this improvement a significant, easy to implement improvement, but it is based on logic. Most trends do not continue up continuously. There tend to be pull backs. This strategy maintains the idea that the trend is your friend and attempts to stay out of a pull back if it happens two months in a row.
Note: the portfolio leveraged 50% has a 14% return with 8.5% standard deviation compared to 13.5% with 9% standard deviation for the original version. The best benefit in reducing standard deviation is in keeping the risk to reward statistics strong when using leverage.
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