Should we fix it?
Up to the end of March, all the postings on this blog focused on regulatory reform; as early as January 28, one of the first postings suggested a reform that would help address this issue (see: “Efficient capital allocation doesn’t require perfect liquidity”) Nevertheless, this posting on regulatory reform is done with considerable hesitation.
When it comes to regulatory reform, nothing fits better than a quote from a Rolling Stones’ song: “You can’t always get what you want, but if you try real hard, sometimes you just might find, you get what you need.” Further, over the weekend you probably saw headline like this one from the WALL STREET JOURNAL: “Regulators Are Stumped by Drop.” “Stumped?” By what? It seems to me that regulators and politicians are going to try to obfuscate the obvious. When that happens, people get divided and emotional about anyone not going along with the party line. That’s one reason for the hesitation.
The second cause for hesitation involves disclosures. I’m not “talking my book” as the saying goes. After this posting, PART 3 will address how to make money from computer panics. It’s so easy it would be a shame to see this easy money go away. Further, mathematical models are intriguing especially when they involve financial markets. It would be equally sad to see them go away. Finally, people who develop trading systems that work can earn a rent on their effort. It’s only slightly different from people who develop any other type of systems. We let the market judge the value of other systems.
It isn’t hard to figure ways to reduce volatility. A fee per trade or circuit breakers will do the job. You might ask: why the question in the subtitle? The answer is “because any method of reducing volatility will have other consequences.”
Fees on trading have an impact that won’t be uniform across types of assets unless carefully designed. They have different implications depending on how one trades. Ultimately, they raise the cost of capital, but that could be reversed by linking them to lowering other taxes on capital.
Direct limits on vilatility will only force the volatility into other markets. That could be other markets for the same asset as occurred Thursday in selected stocks, or it could be into markets for other assets. One of the reasons for concern about limits as being discussed is a belief that many traders will shift volatility risk from equities to other asset classes where that risk can do more economic damage. Remember, equities are not the only asset class that gets volatile when computers panic, and quantitative trading cuts across asset classes. Further, limits, like fees, have to be carefully designed and cover multiple assets, not just one type of asset.
Limits also introduce another potentially dangerous risk. To illustrate how they could introduce more risk consider “trailing stop loss” orders. They are often a component of trading systems. Thursday many people learned the risk associated with those trading systems.
A “stop loss” is a limited protection. It doesn’t guarantee a price when an asset price “gaps.” That’s the same lesson learned in October 1987. Further, they don’t protect one from volatility. One can get “stopped” out of a position at a price that is lower than the price a few seconds later. In fact, the presence of a large number of “stop loss” orders increases the probability of “gapping” because once the “stop loss” thresholds have been crossed the orders all become market orders to sell.
Now, introduce limits. Limits would give people or computers a chance to go in and cancel the “stop loss” order if it had not been executed. That sounds positive. However, consider the other side of the trade. The trader who cancels the trade now has a totally un-hedged position. Granted the hedge was partial and may be failing to accomplish what the trader intended. But, as has been pointed out before in this blog and elsewhere, no hedge is ever perfect. So, by canceling the “stop loss” the trader is exposed to the risk that was hedged as well as the risk that, unbeknownst to the trader, never was hedged. That may or may not be good. But, it seems to this observer that canceling a large number of partial hedges at a time of particular volatility is a strange risk reduction strategy.
It would be naive to assume the trader whose “stop loss” has failed won’t pursue an alternative approach to limiting down side risk. When the limits kick in, would prices of protective puts shoot up to their limit? Would buying of “protective” assets spike? Would there be a flight to liquidity? Other traders would also react. Would short sellers smell blood in the water among this group of exposed longs? Would the more insightful traders find an alternative to “stop loss” orders ahead of time? If so, what would it be?
Would traders just move to a different asset class where there are no limits? All of those questions were brought up in 1987 after the October crash. Limits past after 1987 didn’t cause any disasters, but they also didn’t end computer panics. Also, historical experience seems to suggest limits didn’t reduce volatility and perhaps increased it, but, granted, other things being the same before and after 1987 would be a naïve assumption.
In any case, it seems very likely limits will be expanded to individual equities. That will shift volatility risk to other assets. Hopefully it won’t be Treasuries or currencies, but, quite frankly, what market needs to be more volatile?
There’s an interesting and informative sidebar issue resulting from Thursday. Let’s look at the issues surrounding the NYSE’s shift to slow trading on selected NYSE-listed stocks, Procter & Gamble in particular. It’s a perfect example of the shifting of volatility to markets that couldn’t accommodate it. However, that point is being overshadowed by differences in philosophy. As discussed below, it also looks like the markets that couldn’t accommodate the volatility want to change the issue to conceal their failure to provide an orderly market.
The issue surfaced in an exchange between Duncan Niederauer, CEO NYSE Euronext, and Bob Greifeld, CEO Nasdaq OMX Group Inc. Greifeld’s contention is that the overall volatility in the stock was increased by the Nasdaq’s inability to provide enough liquidity to accommodate an orderly handling of the volatility. He doesn’t say it that way since blaming it on the NYSE is so much more consistent with his interests. But, that’s the bottom line of his position. That seems reasonable. When all the volatility risk is shifted to one market, that market will be stressed.
Niederauer’s counter that the purpose of the NYSE is to provide an orderly market. Can’t argue with that. But, under the circumstances he seems to be overly professional in not pointing out that Nasdaq didn’t deliver an orderly market.
Here’s where the verbal exchange gets interesting, and it betrays each man’s philosophy and the market they serve. Another function of an exchange is to provide liquidity. Clearly, when the NYSE moved to slow mode it traded off providing instant liquidity for orderly market. Who is served by each? People don’t even sense instant liquidity. The slower mode and even a pause for a few minutes would hardly be noticed. By contrast, computers assume instantaneous, continuous liquidity. Put bluntly, Nasdaq would accommodate computers at the expense of people while NYSE leaned the other direction.
Next step in the analysis involves the exchange’s role as a method of price discovery. Clearly, that is a key function of an exchange. Again, Nasdaq was willing to “execute” any trade without regard to whether the price discovery was being compromised in order to accommodate continuous trading. NYSE wasn’t. If one needs proof, it will come when trades are unwound as is being discussed.
Finally, an exchange acts as a clearing house becoming the counterparty. NYSE slowed trading to ensure it could fill that counterparty role. We will see whether Nasdaq honors all trades. This last issue goes to the heart of the issue of whether an exchange walked away from the market. Slowing trading isn’t walking away from the market; it’s slowing it. By contrast executing erroneous trades and then not honoring them is walking away from at least two important responsibilities of an exchange.
The difference in philosophy revealed by the verbal exchange raises an interesting question. Do we need two different types of exchanges? One designed for computer trading with computer and one that accommodates people. People and computers could move between them, but the design of the assets would be different. It is less pie-in-the-sky than it sounds. It could be done by creating two different classes of stock. One would target those needing instant, continuous liquidity. The other would compromise instantaneous, continuous liquidity, but fulfill the ownership aspirations of some equity investors. If you think it is far fetched, remember there are ETFs that represent the same assets as traditional mutual funds although in that example the assets don’t differ much if at all. But, rather than explore that solution, let’s just hope limits aren’t viewed as a substitute for some common sense applied to the issue of fees.
Tuesday, May 11, 2010
Sunday, May 9, 2010
The day the computers panicked PART 1 (cont’d)
How to drive a computer mad
Although computers are emotionless, calculating machines, their behavior seems emotional at times. In that context and with tongue in cheek let me say: “Nothing irritates a computer more than violating its assumptions.” Thursday something did just that. We have as candidates “fat fingers,” Greeks, and no doubt other candidates will surface as people look into it. The exact cause of the panic isn’t really that important. But, since we have set up hearings to “investigate,” it’s probably worth pointing out a few other candidates for blame.
A posting on this blog entitled “Sometimes Wall Street provides more entertainment than Hollywood: PART 2 the losers” pointed out a few mistakes people make. These mistakes can be programmed into computers as assumptions or can be preconditions for the program to operate as intended. In fact, one of them has been advanced as a candidate.
Many programs assume instantaneous, continuous liquidity. The posting stated: “Investors get it so, so wrong. To illustrate, everyone reading this posting probably either has experienced or will experience times when most of their assets are illiquid. We aren’t just talking overnight or the mutual fund industry’s practice of redeeming at closing net asset values. Exchanges get closed down, markets dry up, and these things happen fast.” Well, an interruption to trading on selected NYSE listed stocks, Procter & Gamble in particular, has been brought up as a potential trigger for some of the volitility. Computers just don’t like the world not conforming to their assumptions.
Alternatively, the same posting stated: “Consider big, instantaneous changes in price (“discontinuities” or “gapping” in investor jargon). If one has investments, there is a good chance at least one asset experienced a gap in price during the time it takes to read this posting. It might be small, but it isn’t unusual.” Gapping due to lack of liquidity or “fat fingers” could have been a contributing factor.
However, the reason these weren’t initially mentioned was to keep the focus on the important issue. It would be a shame if the “investigation” obscures the obvious and most important point.
Although computers are emotionless, calculating machines, their behavior seems emotional at times. In that context and with tongue in cheek let me say: “Nothing irritates a computer more than violating its assumptions.” Thursday something did just that. We have as candidates “fat fingers,” Greeks, and no doubt other candidates will surface as people look into it. The exact cause of the panic isn’t really that important. But, since we have set up hearings to “investigate,” it’s probably worth pointing out a few other candidates for blame.
A posting on this blog entitled “Sometimes Wall Street provides more entertainment than Hollywood: PART 2 the losers” pointed out a few mistakes people make. These mistakes can be programmed into computers as assumptions or can be preconditions for the program to operate as intended. In fact, one of them has been advanced as a candidate.
Many programs assume instantaneous, continuous liquidity. The posting stated: “Investors get it so, so wrong. To illustrate, everyone reading this posting probably either has experienced or will experience times when most of their assets are illiquid. We aren’t just talking overnight or the mutual fund industry’s practice of redeeming at closing net asset values. Exchanges get closed down, markets dry up, and these things happen fast.” Well, an interruption to trading on selected NYSE listed stocks, Procter & Gamble in particular, has been brought up as a potential trigger for some of the volitility. Computers just don’t like the world not conforming to their assumptions.
Alternatively, the same posting stated: “Consider big, instantaneous changes in price (“discontinuities” or “gapping” in investor jargon). If one has investments, there is a good chance at least one asset experienced a gap in price during the time it takes to read this posting. It might be small, but it isn’t unusual.” Gapping due to lack of liquidity or “fat fingers” could have been a contributing factor.
However, the reason these weren’t initially mentioned was to keep the focus on the important issue. It would be a shame if the “investigation” obscures the obvious and most important point.
The day the computers panicked PART 1
A real instance of science fiction
By now you probably know something about what happened Thursday. In case you don’t, let me make a citation simply because it states the obvious. The headline is “Computers, Not Human Error, Likely Caused Market Meltdown.” It happened simultaneously in multiple markets: commodities (e.g., gold and oil), bonds (e.g., one could easily track in real time in Treasuries, but other debt markets also), currencies, equities, and derivatives. The plunge went furthest on purely automated exchanges. Do you think it might have been computer driven?
But for the sake of thoroughness, let’s include a more extensive quote:
“Computerized sell programs triggered by global events-rather than trader error or "fat finger"-appear to have caused Thursday's unprecedented market swing, according to market pros who are reconstructing the nearly 1,000-point stock sell off.
These experts think the intensely accelerated electronic trading was sparked by the Greek debt crisis and other events and not a trader who typed a "b" for billion instead of "m" for million in executing a trade on Thursday.”
If the link’s still good, you can grab the article at:
Let’s see who panicked. It wasn’t people. It happened too fast, in too many places, and in too many markets. Put bluntly, a computer or multiple computers panicked. In case you think computers don’t panic, remember this isn’t the first time. If one follows markets, one has seen a few computer panics. Think about October 1987 for a dramatic example, but it’s just one. Yes, computers can be, and have been, programmed to panic. There is probably someone programming a computer to panic right now. The people who do it are called “quants.”
This blog has pointed out the destructive impact of quantitative trading numerous times. Perhaps too subtly, but it was the topic of “On Quants” on March 9. On February 28 in “Is the Volker Plan shadow boxing or can it help?” the posting stated: “Quantitative trading did cause some of the contagion even if it wasn’t the root cause. But, hedge funds seem to have been the more important vehicle, not banks. In fact, hedge funds were probably instrumental in transmitting the downturn from debt markets to equity markets where they had an impact on a broader set of individuals through their 401k’s and IRAs.” And, again the thought scenario in a posting entitled “Beware the risk-free return” explained why quant funds blow up. Perhaps the posting should have been more explicit about the collateral damage they cause.
So, here is the science fiction. On Thursday a bunch of computers panic; after a minute or two and into Friday the panic spreads to people. Sound familiar? We know people can sense panic. Casual observation, history, behavioral economics, and the social sciences all have confirmed it. It seems computers sense each other’s panic, too. Unlike organisms, which, according to scientists, seem to have multiple methods of sensing fear, computers undoubtedly sense it from behavior. It also seems people can sense computer panic. Witness the reporting and reactions of people during Thursday’s meltdown.
Your response might be: What’s the big deal? Panics happen.” But, we remember them and record them for history because they have consequences. In the case of Thursday, the volatility of every asset class has changed. It has changed in terms of how it is measured for investment decisions, which means it will be reflected in prices, and thus it will have an impact on capital allocation. In the last step in this chain, capital allocation will influence future economic growth.
That’s damage done. The quants update their data to incorporate the revised volatility measurement. Actually the updates are preprogrammed and have often occurred already. Consequently, post-event trading reflects this dog-chasing-its-tail computer intelligence.
But, now let’s allow people into the picture. Over the weekend they’ve also had a chance to update their perceptions. Their reactions cover a much broader range of behaviors than just the multiple markets that the programmed trades influence. Most people probably aren’t feeling as secure as they did Thursday morning. If they hadn’t already thought about the consequences of a computer panicking, they should feel less secure. Realizing computers panic is quite different from panicking and warrants a very different response.
The response from a policy perspective will be PART 2, and the response from an investment perspective will be PART 3.
By now you probably know something about what happened Thursday. In case you don’t, let me make a citation simply because it states the obvious. The headline is “Computers, Not Human Error, Likely Caused Market Meltdown.” It happened simultaneously in multiple markets: commodities (e.g., gold and oil), bonds (e.g., one could easily track in real time in Treasuries, but other debt markets also), currencies, equities, and derivatives. The plunge went furthest on purely automated exchanges. Do you think it might have been computer driven?
But for the sake of thoroughness, let’s include a more extensive quote:
“Computerized sell programs triggered by global events-rather than trader error or "fat finger"-appear to have caused Thursday's unprecedented market swing, according to market pros who are reconstructing the nearly 1,000-point stock sell off.
These experts think the intensely accelerated electronic trading was sparked by the Greek debt crisis and other events and not a trader who typed a "b" for billion instead of "m" for million in executing a trade on Thursday.”
If the link’s still good, you can grab the article at:
http://finance.yahoo.com/news/Computers-Not-Human-Error-cnbc-1614949039.html?x=0&sec=topStories&pos=2&asset=&ccode=But really, do you need it? Something, could have been “fat fingers” or Greece, set off a panic. It would be ironic if it was Greece because that would make the discussion of sovereign risk and the rush to post on debt on Wednesday seem prescient. However, for what follows, it is irrelevant whether it was “fat fingers” or Greece.
Let’s see who panicked. It wasn’t people. It happened too fast, in too many places, and in too many markets. Put bluntly, a computer or multiple computers panicked. In case you think computers don’t panic, remember this isn’t the first time. If one follows markets, one has seen a few computer panics. Think about October 1987 for a dramatic example, but it’s just one. Yes, computers can be, and have been, programmed to panic. There is probably someone programming a computer to panic right now. The people who do it are called “quants.”
This blog has pointed out the destructive impact of quantitative trading numerous times. Perhaps too subtly, but it was the topic of “On Quants” on March 9. On February 28 in “Is the Volker Plan shadow boxing or can it help?” the posting stated: “Quantitative trading did cause some of the contagion even if it wasn’t the root cause. But, hedge funds seem to have been the more important vehicle, not banks. In fact, hedge funds were probably instrumental in transmitting the downturn from debt markets to equity markets where they had an impact on a broader set of individuals through their 401k’s and IRAs.” And, again the thought scenario in a posting entitled “Beware the risk-free return” explained why quant funds blow up. Perhaps the posting should have been more explicit about the collateral damage they cause.
So, here is the science fiction. On Thursday a bunch of computers panic; after a minute or two and into Friday the panic spreads to people. Sound familiar? We know people can sense panic. Casual observation, history, behavioral economics, and the social sciences all have confirmed it. It seems computers sense each other’s panic, too. Unlike organisms, which, according to scientists, seem to have multiple methods of sensing fear, computers undoubtedly sense it from behavior. It also seems people can sense computer panic. Witness the reporting and reactions of people during Thursday’s meltdown.
Your response might be: What’s the big deal? Panics happen.” But, we remember them and record them for history because they have consequences. In the case of Thursday, the volatility of every asset class has changed. It has changed in terms of how it is measured for investment decisions, which means it will be reflected in prices, and thus it will have an impact on capital allocation. In the last step in this chain, capital allocation will influence future economic growth.
That’s damage done. The quants update their data to incorporate the revised volatility measurement. Actually the updates are preprogrammed and have often occurred already. Consequently, post-event trading reflects this dog-chasing-its-tail computer intelligence.
But, now let’s allow people into the picture. Over the weekend they’ve also had a chance to update their perceptions. Their reactions cover a much broader range of behaviors than just the multiple markets that the programmed trades influence. Most people probably aren’t feeling as secure as they did Thursday morning. If they hadn’t already thought about the consequences of a computer panicking, they should feel less secure. Realizing computers panic is quite different from panicking and warrants a very different response.
The response from a policy perspective will be PART 2, and the response from an investment perspective will be PART 3.
Friday, May 7, 2010
Wednesdays posting on debt markets
Thursday was interesting
Wednesday's posting on debt markets is something that won't happen too often. Most postings won't get posted without complete explanations. The posting was rushed to get it posted before leaving for a two and a half day conference. Thursday, in about 45 minutes, you saw why the rush. But, Thursday's “hissy fit” in financial markets wasn’t due just to what was going on in debt markets. Debt markets were important, but by Monday there should be a posting on some other things that Thursday dramatically illustrated. They’re relevant to investing and regulatory reform.
Wednesday's posting on debt markets is something that won't happen too often. Most postings won't get posted without complete explanations. The posting was rushed to get it posted before leaving for a two and a half day conference. Thursday, in about 45 minutes, you saw why the rush. But, Thursday's “hissy fit” in financial markets wasn’t due just to what was going on in debt markets. Debt markets were important, but by Monday there should be a posting on some other things that Thursday dramatically illustrated. They’re relevant to investing and regulatory reform.
Wednesday, May 5, 2010
Debt markets as an indicator or trade
Only relevant to traders and analysts
Quite a while back a trader asked about debt markets as an indicator. The trader focuses on equity options. In light of what is going on in Europe, an update to the response is worth posting. However, it is quite limited in depth. The trader’s question focused on not being blindsided by the end of the world while making some money trading very short term. Here’s the response; it has only been lightly edited.
Yes, debt markets were the source of the financial crash. It went way beyond being an indicator. Once debt markets realized how many people weren’t going to pay back their borrowing, debt markets froze. It started with mortgage debt, but spread via the shadow banking system. The cause of the spread was that liquidity was needed to adjust for the mis-priced Mortgage Backed Securities. The rush to liquidate (i.e., get liquid) rippled through every market in the shadow banking system. Any market and therefore any organization with a liquidity mismatch became vulnerable. That transformed a liquidity- driven contraction into a generalized fear about counterparty solvency. That was one of the unintended consequences of mark-to-market accounting as interpreted under Sarbanes Oxley.
I don’t need to go back and check. One of my kick-myself-moments was watching this happen, knowing what was happening, seeing the indicators, explaining it to people, and not trading it aggressively.
Currently, there are a few things to watch, but they are more as fire alarms than trading guidance. One should watch the relation between the short term Treasury rates and the LIBOR…the interest rates not the prices. If they start moving in different directions, it warrants a second look. It’s somewhat of a one way indicator. This can indicate when things are very bad, but most of the time it is a "no news" story. So, it generally doesn't get coverage.
If the LIBOR goes up while Treasury rates are going down it indicates something is wrong. People with money don't trust the financial system. That is exactly what happened going into this crisis.
The opposite can be timing differences as the Fed raises rates, but they shouldn't be large and shouldn't last. This is something to keep an eye on given where we are right now. If it gets large or lasts, it would indicate a reduction in the confidence in the Fed and Treasury. Specifically, it could indicate a lack of confidence in the ability of the Fed and Treasury to control inflation.
From an equity-timing perspective yield spreads between corporate debt and Treasuries are a good indicator, but the spreads have tightened enough that recently there wasn’t a lot of information in their movement. However, they generally say where we are in the cycle. When it become inconsistent with where we are in the cycle, something is going wrong in financial markets.
The problem with using debt to time equity markets in the short run is that there are so many automated trading system that rebalance big portfolios real time and daily. They kill any easy money; that leaves us manual traders with the need to interpret debt markets as well as equities. Debt markets convey a lot of information, and there is a lot less noise (i.e., random movement getting big news coverage) than in equities.
Over the next few years, one should also watch long-term Treasury rates. The Fed and flight-to-quality are holding them down, but at some point the Fed will have to stop monetizing the national debt. The flight-to-quality right now is giving them a chance to start the process without having to thread a needle. There is a good chance the Fed will miss this opportunity for reasons related to global impacts. Thus, when Fed does start tightening, the speed with which long rates rise will indicate whether the recovery aborts. It could be important early next year, say March. But a lot depends on what happens in Europe.
There was an important disconnect going on for most of this year. Gold and debt markets were making two very different forecasts. Debt markets were forecasting slow growth and price stability. Gold is forecasting inflation. They were closely reflecting currency trading but with short periods where they disconnected from the dollar and started trading on their own scenarios. The movements in both were bullish for stocks over the short run. That has changed as gold stopped rising and debt markets, especially in Europe, finally realized there is risk in sovereign debt.
This is the disclosure relevant at the time. You may remember a discussion we had about gold trades. I got in below 1K for the trade I wanted. But, the other side of the trade was puts on Treasuries as a hedge for the corporate bonds I had rebalanced into. The disconnection referenced above (gold and debt markets) resulted in the bonds going up in price. The hedge wasn’t necessary. Net, I got where I wanted to be, but it was more complicated than I like.
Quite a while back a trader asked about debt markets as an indicator. The trader focuses on equity options. In light of what is going on in Europe, an update to the response is worth posting. However, it is quite limited in depth. The trader’s question focused on not being blindsided by the end of the world while making some money trading very short term. Here’s the response; it has only been lightly edited.
Yes, debt markets were the source of the financial crash. It went way beyond being an indicator. Once debt markets realized how many people weren’t going to pay back their borrowing, debt markets froze. It started with mortgage debt, but spread via the shadow banking system. The cause of the spread was that liquidity was needed to adjust for the mis-priced Mortgage Backed Securities. The rush to liquidate (i.e., get liquid) rippled through every market in the shadow banking system. Any market and therefore any organization with a liquidity mismatch became vulnerable. That transformed a liquidity- driven contraction into a generalized fear about counterparty solvency. That was one of the unintended consequences of mark-to-market accounting as interpreted under Sarbanes Oxley.
I don’t need to go back and check. One of my kick-myself-moments was watching this happen, knowing what was happening, seeing the indicators, explaining it to people, and not trading it aggressively.
Currently, there are a few things to watch, but they are more as fire alarms than trading guidance. One should watch the relation between the short term Treasury rates and the LIBOR…the interest rates not the prices. If they start moving in different directions, it warrants a second look. It’s somewhat of a one way indicator. This can indicate when things are very bad, but most of the time it is a "no news" story. So, it generally doesn't get coverage.
If the LIBOR goes up while Treasury rates are going down it indicates something is wrong. People with money don't trust the financial system. That is exactly what happened going into this crisis.
The opposite can be timing differences as the Fed raises rates, but they shouldn't be large and shouldn't last. This is something to keep an eye on given where we are right now. If it gets large or lasts, it would indicate a reduction in the confidence in the Fed and Treasury. Specifically, it could indicate a lack of confidence in the ability of the Fed and Treasury to control inflation.
From an equity-timing perspective yield spreads between corporate debt and Treasuries are a good indicator, but the spreads have tightened enough that recently there wasn’t a lot of information in their movement. However, they generally say where we are in the cycle. When it become inconsistent with where we are in the cycle, something is going wrong in financial markets.
The problem with using debt to time equity markets in the short run is that there are so many automated trading system that rebalance big portfolios real time and daily. They kill any easy money; that leaves us manual traders with the need to interpret debt markets as well as equities. Debt markets convey a lot of information, and there is a lot less noise (i.e., random movement getting big news coverage) than in equities.
Over the next few years, one should also watch long-term Treasury rates. The Fed and flight-to-quality are holding them down, but at some point the Fed will have to stop monetizing the national debt. The flight-to-quality right now is giving them a chance to start the process without having to thread a needle. There is a good chance the Fed will miss this opportunity for reasons related to global impacts. Thus, when Fed does start tightening, the speed with which long rates rise will indicate whether the recovery aborts. It could be important early next year, say March. But a lot depends on what happens in Europe.
There was an important disconnect going on for most of this year. Gold and debt markets were making two very different forecasts. Debt markets were forecasting slow growth and price stability. Gold is forecasting inflation. They were closely reflecting currency trading but with short periods where they disconnected from the dollar and started trading on their own scenarios. The movements in both were bullish for stocks over the short run. That has changed as gold stopped rising and debt markets, especially in Europe, finally realized there is risk in sovereign debt.
This is the disclosure relevant at the time. You may remember a discussion we had about gold trades. I got in below 1K for the trade I wanted. But, the other side of the trade was puts on Treasuries as a hedge for the corporate bonds I had rebalanced into. The disconnection referenced above (gold and debt markets) resulted in the bonds going up in price. The hedge wasn’t necessary. Net, I got where I wanted to be, but it was more complicated than I like.
Angel, entrepreneurs, and diversification: EPILOGUE
Just an example
In this weekend’s WALL STREET JOURNAL there was an article about small caps. The previous posting on “Angels, entrepreneurs, and diversification” made a reference to differences between small caps prices fluctuate and large caps’ prices patterns. If you want further discussion the article is at: http://online.wsj.com/article/SB10001424052748703572504575214722949936724.html?mod=WSJ_PersonalFinance_PF2
The article discusses timing; something the postings didn’t discuss since timing was incidental to the issue discussed in “Angels, entrepreneurs, and diversification.”
In this weekend’s WALL STREET JOURNAL there was an article about small caps. The previous posting on “Angels, entrepreneurs, and diversification” made a reference to differences between small caps prices fluctuate and large caps’ prices patterns. If you want further discussion the article is at: http://online.wsj.com/article/SB10001424052748703572504575214722949936724.html?mod=WSJ_PersonalFinance_PF2
The article discusses timing; something the postings didn’t discuss since timing was incidental to the issue discussed in “Angels, entrepreneurs, and diversification.”
Saturday, May 1, 2010
Sometimes Wall Street provides more entertainment than Hollywood: PART 2 the losers.
It’s unfortunate that people don’t like to talk about trades gone sour.
Behavioral economists and market observers have known for years that people take credit for their success, but the same people attribute failures to other people’s behavior or advice. In addition to people’s reluctance to face their own mistakes, much less talk about them, the media is not much help. Efforts to find scandals are all too easy; they’re the lazy man’s reporting. The result is that one can expect more stories of victims than analysis of mistakes. That’s unfortunate because other people’s mistakes are a possible source of insight; besides they’re usually cheaper than one’s own.
The Goldman Sachs story references in PART 1 may prove to be an exceptional source of information on mistakes to avoid. But, even when the facts do surface, reporters’ search for villains may obscure the potential lessons. GS clearly doesn’t come out looking like a saint. But, at the same time, it seems silly to portray them as a beneficiary. As mentioned in PART 1, loans from Buffet and TARP as well as big write-downs of assets aren’t typically what one would associate with a winning portfolio. So, how did GS and others end up needing the help?
As background, the reader will find a lengthy quote below. The quote isn’t included to explain how organizations wrecked themselves. It talks about millions which is small change when considering the scope of what happened. Rather, the quote will serve as a source of illustrations.
Also, the quote was taken from a story making a bullish case for GS stocks. However, PART 1 already made the case for avoiding investments in situations involving conspiracy stories. Thus, no reader who checks out the full story could misinterpret my reason for the quote. The full story can be found at:
http://online.barrons.com/article/SB127146775162378679.html
“The SEC complaint shows the lengths to which Paulson -- and Goldman -- went to create a CDO that Paulson figured stood a good chance of collapse.
Paulson & Co., led by John Paulson, correctly anticipated that the worst subprime securities would come from adjustable-rate loans to borrowers with low credit scores in such states as Arizona, California, Florida and Nevada, where home prices had soared, the SEC said.
Paulson & Co. cleverly sought to bet via a CDO, or a collection of existing sub-prime securities, rather than simply bet on a newly created pool of loans. The securities that were the focus of Paulson's efforts were rated Triple-B by the rating agencies and stood beneath a large group of highly rated bonds.
The Abacus CDO was backed by about 90 individual Triple-B tranches from sub-prime deals. Paulson probably recognized that if losses on the underlying subprime pools hit 10% to 15%, the triple-B tranches would be wiped out, resulting in a total loss on the Abacus CDO despite Triple-A ratings on the instrument. That is indeed what happened.
The losers from the Abacus deal were a German bank, IKB (IKB.Germany), which lost $150 million, and Royal Bank of Scotland (RBS), which lost $840 million.
The Scottish bank's loss, which helped lead to a giant bailout by the U.K. government, itself is fascinating. ACA Capital, designated to select the underlying securities, originally guaranteed the $909 million top-rated tranche of the Abacus deal for just 0.50%, or about $4.5 million.
ACA subsequently got buried by mortgage losses and couldn't pay the claim. ABN Amro, the Dutch bank subsequently bought by Royal Bank of Scotland, provided a backstop to ACA on the deal for an estimated $2 million. That $2 million guarantee ended up costing Royal Bank $841 million when the CDO collapsed, and most of that money was paid to Paulson.
Royal Bank's huge loss raises questions about whether the ABN Amro managers knew what they were getting into and demonstrates the risks in the financial-guarantee business, which Buffett has described as ‘picking up nickels in front of a steamroller.’
In a statement late Friday, Goldman emphasized that it lost $90 million in the transaction, that IKB was a highly sophisticated investor and that ACA had ‘every incentive’ to select appropriate securities because it issued a $900 million guarantee on the deal.”
The quote is rich in examples of mistakes. There are mistakes both involving actions taken and interpretations of the actions. As written, it tells an interesting story. But, when one looks at it from the perspective of an analyst rather than a narrator, it’s always good to start with the most important item.
1) Asymmetric payout as a warning
The most important point is to be extra careful when asymmetric payouts are involved. From the perspective of mistakes to avoid, Buffet’s quote comparing the risks in the financial-guarantee business to "picking up nickels in front of a steamroller” is the most important point.
Why is this point most important? After all, if payouts on good trades verses losses on bad trades are markedly different, of course one would be careful. One would do the math on the payouts, calculate the odds they imply, then compare them to one’s own best guess as to what the odds really are. What’s the big deal?
First, one should remember that if the payouts are very different, any small error in the estimate of the odds will have a big, I mean BIG, impact on the payout. That in itself is a big deal. But it’s worse than that, never mind estimates. If there are even small errors in how the odds are measured , and there are aways some errors, disaster can follow.
Second, there is considerable evidence that people do funny things when large sums of money are involved. It can be seen as irrational or as evidence that a million dollars means something other than one dollar times a million. Economists say the marginal utility of money isn’t linear. Doesn’t mater what one calls it; lottery ticket sales clearly aren’t in the financial interest of the buyer from the perspective of the probable payout verse the price. Clearly, there is more to the phenomonal success of lotteries than just people shelling out a buck for a few days of the dream. It doesn’t end there. It also seems that most people don’t do extremely large numbers well. Witness how many don’t distinguish between million, billion, and trillion when discussing public policy. So, it seems important to be especially clear about what one is buying and to not confuse dollars and dimes.
Third, often asymmetric returns are associated with what is known as tail-risk in risk management and financial economics. Tail-risk involves very low probability events. This is exactly the area where traditional quantitative financial economics tends to fail. Further, the reason it fails seems to reflect basic, or at least common, perceptual predispositions of humans. We are so dependent on basing our expectations on what is common that we underestimate the probability of the uncommon. In quantitative financial economics, this surfaces in assumptions about the probability distributions associated with events. So, one has to overcome one’s own predisposition and recognize that a lot of “sophisticated” potential counterparties are in a worse situation since they are paying other people to estimate a risk that even the “professionals” are predisposed to underestimate.
The point about being paid to mis-measure the risk is only one example of why asymmetric returns are so dangerous. Making the mistake outlined above is often profitable most of the time. The Pavlovian response to the repeated positive feedback is dangerous for two reasons. First, it is a tempting trade since most of the time it makes money. Entire businesses have been built on the returns. Second, the positive feedback reinforces the tendency to underestimate the risk. Thus, there is a tendency to “up the ante.” In businesses, this takes the form of increasing the risk exposure or even ignoring risk guidelines.
“Is this relevant to the average investor?” You bet it is! People often make exactly this kind of trade. This blog has mentioned selling naked puts before, but a far more common example is not having any “rainy day fund.” As absurd as it is, we have to force unemployment insurance. Unemployment, at least once in a career, is almost inevitable. Beyond that, not insuring against risks one can’t bare is almost too common to deserve mentioning. We’re debating whether people should have to carry medical insurance and have liability insurance when they drive. Sure, most of the time one gets away without either; thus avoiding having to pay the premium. It is continuous positive feedback. But, it only takes once to wipeout any premiums previously saved.
More in the investment area as we usually define it; think about why we have margin limits. Enough people get the likelihood of unusually large moves so wrong that it makes margin limits advisable as protection for individual investors and to protect the clearing houses.
However, my favorite example is liquidity risk. Investors get it so, so wrong. To illustrate, everyone reading this posting probably either has experienced or will experience times when most of their assets are illiquid. We aren’t just talking overnight or the mutual fund industry’s practice of redeeming at closing net asset values. Exchanges get closed down, markets dry up, and these things happen fast.
The probability of more routine fluctuations, even big ones like the recent events, are underestimated. They not only happen, but they last longer than most people realize. To illustrate, think about mark-to-market accounting for a minute. Aside from the absurdity of assuming the market is always right, we’ve almost enshrined the idea of continuous liquidity into accounting.
Even the probability of lesser forms of illiquidity get mis-estimated. Consider big, instantaneous changes in price (“discontinuities” or “gapping” in investor jargon). If one has investments, there is a good chance one asset experienced a gap in price during the time it takes to read this posting. It might be small, but it isn’t unusual. Some investment advisors recommend always using limit orders as protection against discontinuities being used to the investors detriment.
A disclosure seems appropriate. I have a strong preference for avoiding asymmetric payouts totally, and when they are unavoidable, I lay the risk off with insurance.
2) Complexity as a risk
Beyond asymmetric risk issues, the quote also implies a few other mistakes to avoid. First, my reading is that complexity is its own risk We’re not talking the complexity of the instruments being traded. Seriously, the parties involved all understood the instruments being traded. They weren’t that complex to people familiar with bond markets. The complexities that probably tripped them up were the complexity of their own organizations and the complexity of the RISK embedded in the instruments.
One could argue that complexity of the risk embedded in an instrument is complexity of the instrument. So, that leaves organizational complexity. The key quote is: “Royal Bank's huge loss raises questions about whether the ABN Amro managers knew what they were getting into…” That can be interpreted two ways: as either not knowing the risks implied by the guarantee or not knowing what the risk implied by the guarantee did to the Royal Bank’s overall risk. The quote addresses only the first. That’s possible, but seems less likely than the second.
But, the reader might ask: “How does that relate to an investor?” First, a direct implication is worth noting. If the second is true, it raises questions about the viability of the Basel II framework. It implies we are underestimating the level of risk in large banks operating under Basel II. Basel II could be conceptually correct, but impossible to implement in practice. There can’t be any doubt that the concept of hedging risk is a risk reduction strategy, but it is equally obvious that the more complex the risks being hedged, the greater the risk of gross mis-measurement somewhere in the process.
This reasoning would imply that growth as a risk reduction strategy has some inherent limitations. Further, it would seem logical that growth through acquisition, especially acquisition in new financial service areas, would carry the greatest risk. This could explain why banks’ initial acquisitions in non-banking areas often don’t work out. It wouldn’t be the only reason, but it probably contributes.
That would still limit the implications to investments in the financial service industry. However, if one views portfolio complexity as analogous to organizational complexity, it suggests additional implications.
First, if big financial firms with all their staff have trouble managing complexity, most investors are going to have a harder time. One should try to simplify away any complexity that one can’t clearly justify. Put, differently, know why each holding fits into the portfolio, not just why it is a good standalone investment. Balance the two: portfolio fit and standalone appeal. If an investment looks good as a standalone, but, for example, over-weights an asset class, use forced choice. Some asset in that asset class should be sold. A disclosure is appropriate because this discipline has worked so well for me that I might be overlooking limitations. A true fundamentalist would argue a total from-the-ground-up approach with each investment ONLY assessed as a standalone is better.
Second, financial firms can’t get hedges perfect: one should never assume a hedge is quantitatively right even if it is checked regularly. The quantitative values of hedges fluctuate just like the value of other assets. In other words, hedges have to be rebalanced periodically, just like any other asset in a portfolio. But, rebalancing isn’t enough. Hedges, just like any other asset, are subject to tail-risk. It even seems the tail-risks are greater with hedges than net long or net short positions. Perhaps it’s because hedges involve at least two positions. From what has been said, it should be apparent hedges don’t provide a perfect substitute for liquidity and don’t necessarily justify greater leverage risk. My disclosure is that I use traditional hedges sparingly, and always to hedge only one very specific risk associated with another position, not the position itself.
Third, when taking a position in a new asset, think of it as analogous to a corporate acquisition. If it’s new in type, expect errors. Some advisors recommend starting small; others admonish against any experimenting with a new investment style (i.e., “stick to your style”). Some advisors suggest doing it on paper first. My tendency is to only take on one new type of asset or type of trade at a time and to assume I’ll make some mistakes. My motto is “Stick to your style to make money. Experiment to learn.”
3) Know the difference between fees and profit
Perhaps it is how the transactions are reported, but one is definitely left with the impression that the pursuit of the fees that they would earn tempted these firms into trades that resulted in some losing investments. One might conclude that the point is irrelevant since fees are what financial firms are about; it’s their business. However, the point has relevance to non-institutional investors.
First, one should always remember that hedging away all risk is desirable in the financial service industry where there are fees to be made on both sides of the transaction. Most investors don’t enjoy that benefit. For most investors the fees flow out, not in. So, trying to do what the “big boys” do can be foolish. Risk isn’t something to avoid. It is inevitable for investor.
Second, fees can’t guarantee you’re not trading against your counterparty. You’re always trading against your counterparty. It is amazing that some people want to vilify GS for trading against “clients” they sold assets to. They don’t seem to realize that selling or buying an asset implies trading against one’s counterparty.
Third, fees appear to have encouraged participants to take on more risk than they could manage. For investors it is a reminder that fees are a consideration, but not THE primary consideration. Avoiding fees shouldn’t drive the investor away from a good investment any more than the pursuit of fees justified losing position by parties to the trades involved in this situation.
4) Information disparities
This is the heart of the civil suit against GS. So, it would be premature to go too far in discussing it until the suit is determined. However, there are some anomalies worth noting. One contention is that two facts weren't disclosed: (1) a short was the counterparty and (2) GS’s position. That these are an issue seems curious. They are information that one doesn’t have on any trade executed on an exchange. The anomaly is that regulatory reform would expand the role of exchanges in derivatives trading, thus extending the absence of this information to more of the derivatives market.
In the March 4th posting, The Hedged Economist argued: “Bringing more derivatives (i.e., like some standard interest rate and default swaps, some commodity hedges, etc.) onto exchanges makes sense.” Both the use of an exchange and a clearing house were endorsed. If that position is correct, one might conclude that the information at issue in the Goldman Sachs civil suit isn’t “material.” Yet, the April 23rd posting noted investors who successfully traded the same instruments “put effort into understanding whether their immediate trade was with a counterparty or a middleman.” It goes on to suggest that it’s a good idea for investors to do the same. That would suggest that the information is “material.”
The April 23rd posting, when discussing the successful traders, went on to say “Once the people making the trade understood who their counterparty would be, their primary concern wasn’t motive. Their concern was solvency.” Their concern wasn’t whether their counterparty thought the price would go up or down. They understood that their counterparty probably had an opinion different from theirs.
But interestingly, a second issue related to the suit is the fact that a short seller was involved in structuring the asset. The irony here is that a short is often the originator of many positions in any asset without it being disclosed. It’s very common in options. But even with stocks, short sellers may be selling an investor a stock or selling it to an individual’s mutual fund. Most successful investors have no reservations about buying without knowing whether a counterparty is a short seller.
Ultimately asymmetric information is a fact of life. The court will decide what information should have been disclosed. Putting limits on the information asymmetry is desirable. But, from an investor’s perspective, making assumptions about the direction of the asymmetry can be as big a mistake as assuming there is no asymmetry. Furthermore, and far more importantly, it is essential for investors to decide what information is important to their own investment decisions. The courts can’t do that for them.
It would be unfortunate if GS’s role or Paulson’s success keeps investors from looking beyond the superficial media coverage. This could be an instance where we aren’t left with only our mistakes as examples of what doesn’t work. The most obvious lesson for investors is how dangerous it is to just blame GS if the recent crisis hurt one’s portfolio. Many people won’t bother to look at how they managed their liquidity or their leverage levels, or examine the risks associated with their investment strategy. While it’s important to learn from others’ mistakes, it’s essential to think about how they compare to one’s own.
Behavioral economists and market observers have known for years that people take credit for their success, but the same people attribute failures to other people’s behavior or advice. In addition to people’s reluctance to face their own mistakes, much less talk about them, the media is not much help. Efforts to find scandals are all too easy; they’re the lazy man’s reporting. The result is that one can expect more stories of victims than analysis of mistakes. That’s unfortunate because other people’s mistakes are a possible source of insight; besides they’re usually cheaper than one’s own.
The Goldman Sachs story references in PART 1 may prove to be an exceptional source of information on mistakes to avoid. But, even when the facts do surface, reporters’ search for villains may obscure the potential lessons. GS clearly doesn’t come out looking like a saint. But, at the same time, it seems silly to portray them as a beneficiary. As mentioned in PART 1, loans from Buffet and TARP as well as big write-downs of assets aren’t typically what one would associate with a winning portfolio. So, how did GS and others end up needing the help?
As background, the reader will find a lengthy quote below. The quote isn’t included to explain how organizations wrecked themselves. It talks about millions which is small change when considering the scope of what happened. Rather, the quote will serve as a source of illustrations.
Also, the quote was taken from a story making a bullish case for GS stocks. However, PART 1 already made the case for avoiding investments in situations involving conspiracy stories. Thus, no reader who checks out the full story could misinterpret my reason for the quote. The full story can be found at:
http://online.barrons.com/article/SB127146775162378679.html
“The SEC complaint shows the lengths to which Paulson -- and Goldman -- went to create a CDO that Paulson figured stood a good chance of collapse.
Paulson & Co., led by John Paulson, correctly anticipated that the worst subprime securities would come from adjustable-rate loans to borrowers with low credit scores in such states as Arizona, California, Florida and Nevada, where home prices had soared, the SEC said.
Paulson & Co. cleverly sought to bet via a CDO, or a collection of existing sub-prime securities, rather than simply bet on a newly created pool of loans. The securities that were the focus of Paulson's efforts were rated Triple-B by the rating agencies and stood beneath a large group of highly rated bonds.
The Abacus CDO was backed by about 90 individual Triple-B tranches from sub-prime deals. Paulson probably recognized that if losses on the underlying subprime pools hit 10% to 15%, the triple-B tranches would be wiped out, resulting in a total loss on the Abacus CDO despite Triple-A ratings on the instrument. That is indeed what happened.
The losers from the Abacus deal were a German bank, IKB (IKB.Germany), which lost $150 million, and Royal Bank of Scotland (RBS), which lost $840 million.
The Scottish bank's loss, which helped lead to a giant bailout by the U.K. government, itself is fascinating. ACA Capital, designated to select the underlying securities, originally guaranteed the $909 million top-rated tranche of the Abacus deal for just 0.50%, or about $4.5 million.
ACA subsequently got buried by mortgage losses and couldn't pay the claim. ABN Amro, the Dutch bank subsequently bought by Royal Bank of Scotland, provided a backstop to ACA on the deal for an estimated $2 million. That $2 million guarantee ended up costing Royal Bank $841 million when the CDO collapsed, and most of that money was paid to Paulson.
Royal Bank's huge loss raises questions about whether the ABN Amro managers knew what they were getting into and demonstrates the risks in the financial-guarantee business, which Buffett has described as ‘picking up nickels in front of a steamroller.’
In a statement late Friday, Goldman emphasized that it lost $90 million in the transaction, that IKB was a highly sophisticated investor and that ACA had ‘every incentive’ to select appropriate securities because it issued a $900 million guarantee on the deal.”
The quote is rich in examples of mistakes. There are mistakes both involving actions taken and interpretations of the actions. As written, it tells an interesting story. But, when one looks at it from the perspective of an analyst rather than a narrator, it’s always good to start with the most important item.
1) Asymmetric payout as a warning
The most important point is to be extra careful when asymmetric payouts are involved. From the perspective of mistakes to avoid, Buffet’s quote comparing the risks in the financial-guarantee business to "picking up nickels in front of a steamroller” is the most important point.
Why is this point most important? After all, if payouts on good trades verses losses on bad trades are markedly different, of course one would be careful. One would do the math on the payouts, calculate the odds they imply, then compare them to one’s own best guess as to what the odds really are. What’s the big deal?
First, one should remember that if the payouts are very different, any small error in the estimate of the odds will have a big, I mean BIG, impact on the payout. That in itself is a big deal. But it’s worse than that, never mind estimates. If there are even small errors in how the odds are measured , and there are aways some errors, disaster can follow.
Second, there is considerable evidence that people do funny things when large sums of money are involved. It can be seen as irrational or as evidence that a million dollars means something other than one dollar times a million. Economists say the marginal utility of money isn’t linear. Doesn’t mater what one calls it; lottery ticket sales clearly aren’t in the financial interest of the buyer from the perspective of the probable payout verse the price. Clearly, there is more to the phenomonal success of lotteries than just people shelling out a buck for a few days of the dream. It doesn’t end there. It also seems that most people don’t do extremely large numbers well. Witness how many don’t distinguish between million, billion, and trillion when discussing public policy. So, it seems important to be especially clear about what one is buying and to not confuse dollars and dimes.
Third, often asymmetric returns are associated with what is known as tail-risk in risk management and financial economics. Tail-risk involves very low probability events. This is exactly the area where traditional quantitative financial economics tends to fail. Further, the reason it fails seems to reflect basic, or at least common, perceptual predispositions of humans. We are so dependent on basing our expectations on what is common that we underestimate the probability of the uncommon. In quantitative financial economics, this surfaces in assumptions about the probability distributions associated with events. So, one has to overcome one’s own predisposition and recognize that a lot of “sophisticated” potential counterparties are in a worse situation since they are paying other people to estimate a risk that even the “professionals” are predisposed to underestimate.
The point about being paid to mis-measure the risk is only one example of why asymmetric returns are so dangerous. Making the mistake outlined above is often profitable most of the time. The Pavlovian response to the repeated positive feedback is dangerous for two reasons. First, it is a tempting trade since most of the time it makes money. Entire businesses have been built on the returns. Second, the positive feedback reinforces the tendency to underestimate the risk. Thus, there is a tendency to “up the ante.” In businesses, this takes the form of increasing the risk exposure or even ignoring risk guidelines.
“Is this relevant to the average investor?” You bet it is! People often make exactly this kind of trade. This blog has mentioned selling naked puts before, but a far more common example is not having any “rainy day fund.” As absurd as it is, we have to force unemployment insurance. Unemployment, at least once in a career, is almost inevitable. Beyond that, not insuring against risks one can’t bare is almost too common to deserve mentioning. We’re debating whether people should have to carry medical insurance and have liability insurance when they drive. Sure, most of the time one gets away without either; thus avoiding having to pay the premium. It is continuous positive feedback. But, it only takes once to wipeout any premiums previously saved.
More in the investment area as we usually define it; think about why we have margin limits. Enough people get the likelihood of unusually large moves so wrong that it makes margin limits advisable as protection for individual investors and to protect the clearing houses.
However, my favorite example is liquidity risk. Investors get it so, so wrong. To illustrate, everyone reading this posting probably either has experienced or will experience times when most of their assets are illiquid. We aren’t just talking overnight or the mutual fund industry’s practice of redeeming at closing net asset values. Exchanges get closed down, markets dry up, and these things happen fast.
The probability of more routine fluctuations, even big ones like the recent events, are underestimated. They not only happen, but they last longer than most people realize. To illustrate, think about mark-to-market accounting for a minute. Aside from the absurdity of assuming the market is always right, we’ve almost enshrined the idea of continuous liquidity into accounting.
Even the probability of lesser forms of illiquidity get mis-estimated. Consider big, instantaneous changes in price (“discontinuities” or “gapping” in investor jargon). If one has investments, there is a good chance one asset experienced a gap in price during the time it takes to read this posting. It might be small, but it isn’t unusual. Some investment advisors recommend always using limit orders as protection against discontinuities being used to the investors detriment.
A disclosure seems appropriate. I have a strong preference for avoiding asymmetric payouts totally, and when they are unavoidable, I lay the risk off with insurance.
2) Complexity as a risk
Beyond asymmetric risk issues, the quote also implies a few other mistakes to avoid. First, my reading is that complexity is its own risk We’re not talking the complexity of the instruments being traded. Seriously, the parties involved all understood the instruments being traded. They weren’t that complex to people familiar with bond markets. The complexities that probably tripped them up were the complexity of their own organizations and the complexity of the RISK embedded in the instruments.
One could argue that complexity of the risk embedded in an instrument is complexity of the instrument. So, that leaves organizational complexity. The key quote is: “Royal Bank's huge loss raises questions about whether the ABN Amro managers knew what they were getting into…” That can be interpreted two ways: as either not knowing the risks implied by the guarantee or not knowing what the risk implied by the guarantee did to the Royal Bank’s overall risk. The quote addresses only the first. That’s possible, but seems less likely than the second.
But, the reader might ask: “How does that relate to an investor?” First, a direct implication is worth noting. If the second is true, it raises questions about the viability of the Basel II framework. It implies we are underestimating the level of risk in large banks operating under Basel II. Basel II could be conceptually correct, but impossible to implement in practice. There can’t be any doubt that the concept of hedging risk is a risk reduction strategy, but it is equally obvious that the more complex the risks being hedged, the greater the risk of gross mis-measurement somewhere in the process.
This reasoning would imply that growth as a risk reduction strategy has some inherent limitations. Further, it would seem logical that growth through acquisition, especially acquisition in new financial service areas, would carry the greatest risk. This could explain why banks’ initial acquisitions in non-banking areas often don’t work out. It wouldn’t be the only reason, but it probably contributes.
That would still limit the implications to investments in the financial service industry. However, if one views portfolio complexity as analogous to organizational complexity, it suggests additional implications.
First, if big financial firms with all their staff have trouble managing complexity, most investors are going to have a harder time. One should try to simplify away any complexity that one can’t clearly justify. Put, differently, know why each holding fits into the portfolio, not just why it is a good standalone investment. Balance the two: portfolio fit and standalone appeal. If an investment looks good as a standalone, but, for example, over-weights an asset class, use forced choice. Some asset in that asset class should be sold. A disclosure is appropriate because this discipline has worked so well for me that I might be overlooking limitations. A true fundamentalist would argue a total from-the-ground-up approach with each investment ONLY assessed as a standalone is better.
Second, financial firms can’t get hedges perfect: one should never assume a hedge is quantitatively right even if it is checked regularly. The quantitative values of hedges fluctuate just like the value of other assets. In other words, hedges have to be rebalanced periodically, just like any other asset in a portfolio. But, rebalancing isn’t enough. Hedges, just like any other asset, are subject to tail-risk. It even seems the tail-risks are greater with hedges than net long or net short positions. Perhaps it’s because hedges involve at least two positions. From what has been said, it should be apparent hedges don’t provide a perfect substitute for liquidity and don’t necessarily justify greater leverage risk. My disclosure is that I use traditional hedges sparingly, and always to hedge only one very specific risk associated with another position, not the position itself.
Third, when taking a position in a new asset, think of it as analogous to a corporate acquisition. If it’s new in type, expect errors. Some advisors recommend starting small; others admonish against any experimenting with a new investment style (i.e., “stick to your style”). Some advisors suggest doing it on paper first. My tendency is to only take on one new type of asset or type of trade at a time and to assume I’ll make some mistakes. My motto is “Stick to your style to make money. Experiment to learn.”
3) Know the difference between fees and profit
Perhaps it is how the transactions are reported, but one is definitely left with the impression that the pursuit of the fees that they would earn tempted these firms into trades that resulted in some losing investments. One might conclude that the point is irrelevant since fees are what financial firms are about; it’s their business. However, the point has relevance to non-institutional investors.
First, one should always remember that hedging away all risk is desirable in the financial service industry where there are fees to be made on both sides of the transaction. Most investors don’t enjoy that benefit. For most investors the fees flow out, not in. So, trying to do what the “big boys” do can be foolish. Risk isn’t something to avoid. It is inevitable for investor.
Second, fees can’t guarantee you’re not trading against your counterparty. You’re always trading against your counterparty. It is amazing that some people want to vilify GS for trading against “clients” they sold assets to. They don’t seem to realize that selling or buying an asset implies trading against one’s counterparty.
Third, fees appear to have encouraged participants to take on more risk than they could manage. For investors it is a reminder that fees are a consideration, but not THE primary consideration. Avoiding fees shouldn’t drive the investor away from a good investment any more than the pursuit of fees justified losing position by parties to the trades involved in this situation.
4) Information disparities
This is the heart of the civil suit against GS. So, it would be premature to go too far in discussing it until the suit is determined. However, there are some anomalies worth noting. One contention is that two facts weren't disclosed: (1) a short was the counterparty and (2) GS’s position. That these are an issue seems curious. They are information that one doesn’t have on any trade executed on an exchange. The anomaly is that regulatory reform would expand the role of exchanges in derivatives trading, thus extending the absence of this information to more of the derivatives market.
In the March 4th posting, The Hedged Economist argued: “Bringing more derivatives (i.e., like some standard interest rate and default swaps, some commodity hedges, etc.) onto exchanges makes sense.” Both the use of an exchange and a clearing house were endorsed. If that position is correct, one might conclude that the information at issue in the Goldman Sachs civil suit isn’t “material.” Yet, the April 23rd posting noted investors who successfully traded the same instruments “put effort into understanding whether their immediate trade was with a counterparty or a middleman.” It goes on to suggest that it’s a good idea for investors to do the same. That would suggest that the information is “material.”
The April 23rd posting, when discussing the successful traders, went on to say “Once the people making the trade understood who their counterparty would be, their primary concern wasn’t motive. Their concern was solvency.” Their concern wasn’t whether their counterparty thought the price would go up or down. They understood that their counterparty probably had an opinion different from theirs.
But interestingly, a second issue related to the suit is the fact that a short seller was involved in structuring the asset. The irony here is that a short is often the originator of many positions in any asset without it being disclosed. It’s very common in options. But even with stocks, short sellers may be selling an investor a stock or selling it to an individual’s mutual fund. Most successful investors have no reservations about buying without knowing whether a counterparty is a short seller.
Ultimately asymmetric information is a fact of life. The court will decide what information should have been disclosed. Putting limits on the information asymmetry is desirable. But, from an investor’s perspective, making assumptions about the direction of the asymmetry can be as big a mistake as assuming there is no asymmetry. Furthermore, and far more importantly, it is essential for investors to decide what information is important to their own investment decisions. The courts can’t do that for them.
It would be unfortunate if GS’s role or Paulson’s success keeps investors from looking beyond the superficial media coverage. This could be an instance where we aren’t left with only our mistakes as examples of what doesn’t work. The most obvious lesson for investors is how dangerous it is to just blame GS if the recent crisis hurt one’s portfolio. Many people won’t bother to look at how they managed their liquidity or their leverage levels, or examine the risks associated with their investment strategy. While it’s important to learn from others’ mistakes, it’s essential to think about how they compare to one’s own.
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