Posts Tagged ‘portfolio management’

The importance of the benchmark

February 24th, 2009 No comments

Business school classes and other quantitative equity readings spend a lot of time on model construction (the theoretical underpinnings of factor models, how factor models are created, the statistical issues to watch out for) and portfolio construction (mean-variance theory, efficient frontier, optimal portfolio), but hardly ever touch on benchmark selection and it’s importance in equity portfolio management. So, I’m dedicating this post to the under-appreciated benchmark…

(If you haven’t done so, you may want to read my post on Absorelative performance to better understand the performance context of this post, and to fully appreciate the effect of a bad benchmark.)

Imagine this, you’re going into the fourth quarter with an outperforming portfolio – you’ve racked up three times your excess return target for the portfolio (think “Cha-ching! There will be a great bonus this near!”). To lock in your gain, you decide to bring down your tracking error (that is, reduce the differences between your portfolio and your benchmark so that on a relative basis you neither make nor lose money going forward). You do the math and realize that in order for you to fall below the excess return target, your portfolio would have to lost a substantial percentage relative to the benchmark – an event you doubt will happen. You cross your fingers and hope to coast for the rest of the year, or maybe take a nice long vacation. I hear Monaco is nice…

Fast-forward three months: it’s the last week of December, and you’re looking at the (almost) year-end performance numbers. Your portfolio, which had earned three times the excess return target (that is, three time the amount that you were supposed to beat the benchmark by) by June now trails the benchmark by 50 basis points (one-half of a percent). That “Cha ching” you had heard a quarter earlier now sounds like the Lose-A-Turn sound on Wheel of Fortune (or any other bad event on your game show of choice) – there most certainly won’t be a bonus this year…

So what happened?! How did this great story of fortune turn into a big bust? We were entering the fourth quarter of the championship game, up by three touchdowns, and ended up losing the game by a field goal. What happened?!

What happened is that we were playing a running defense in the fourth quarter while the other team was playing a passing offense. In fact, we’d been doing the same thing during the whole game, but had just gotten lucky during the first three quarters. Heck, it was working, so why change it?

This mistake is rarely seen in football, but unfortunately is much more prevalent in portfolio management. It often happens that you start the year with a performance objective relative to some benchmark – let’s say the S&P 500 (a portfolio of the largest 500 firms in the U.S.). Now suppose that your model for selecting stocks ends up picking some firms that are in the S&P 500, but picks more firms that are smaller in size. Let’s further assume that during the first three quarters of the year, small firms on average outperform large firms. Since your portfolio is mostly small firms, it, by definition, outperforms the benchmark (which is all large firms). Finally, suppose that trend reverses in the last quarter of the year, and reverses in a big way. Since small firms perform worse than large firms in the last quarter, your portfolio under-performs the benchmark. Depending on the magnitude of the under-performance, this could cost you all of your excess return and then some (as in my example above). This is clearly a type of volatility (or tracking error to be precise) that the portfolio manager just doesn’t want!

So now you understand the source of the issue – that the benchmark is incorrectly specified – if the portfolio is comprised of smaller firms, then the benchmark should be selected to match. This way there is no upward (or downward) bias to the manager’s performance as a result of differences in firm size (or style, or any other factors). As you see in the above example, this misspecification can negate any return the manager may have earned through his skill (though in our case, the three times out-performance was a combination of skill and luck to the extent that smaller firms outperformed in the first three quarters). Thus we see that correct specification of the benchmark is just as important as alpha, or the skill of your manager.

So the issue seems easy to fix – why not just change the benchmark to match the portfolio’s attributes? There are a couple of reasons for this. If you’re benefitingfrom the misspecified benchmark, you’re not likely to have the benchmark changed since you’ll lose any benefit you may be enjoying (both from a financial bonus perspective, and from a track record perspective). If you’re disadvantaged by the misspecified benchmark, you may request it be changed, but the request is not likely to be approved. Imagine your job was to approve these requests, and you see a request on your desk. The manager, who happens to be under-performing his benchmark, wants you to change his benchmark to something that will result in his performance looking better. An immediate skepticism sets in - is this manager trying to game the system by having us choose a benchmark that favors him more? This skepticism is likely to result in a rejected request (rightfully so too, since I imagine there are many managers out there who would try just such a thing if they could get away with it!). So, changing the benchmark mid-year is just not likely to happen.

The only solution may be to finish the year and take whatever gain or loss that may come with the misspecified benchmark, with the goal of fixing the issue before the start of the next year (so that “gaming the system” becomes irrelevant). Even better, rather than hastily just picking a benchmark, the manager should fully understand his strategy and model, the stocks it typically picks, and the manager’s investment universe. After carefully considering these things, an appropriate benchmark should be selected. This prudence can alleviate a lot of subsequent problems, and results in more accurate performance measurement and compensation – something that we’ll all be better off for in the long run. So next time you’re chasing that alpha and building fascinating models, don’t forget to take a step back and give that under-appreciated benchmark some consideration.

Mark-to-market: not such a simple choice

February 9th, 2009 2 comments

Lloyd Blankfein, CEO of Goldman Sachs, wrote an op-ed article in today’s Financial Timesabout seven lessons from this crisis. Amongst his lessons, Mr. Blankfein addresses fair value accounting (a.k.a. mark-to-market accounting). I think his assertions are valid, but it’s important to consider the whole picture – which is often not as simple as an op-ed piece may suggest. I have included the text of the entire article at the bottom of the post if you’re interested in reading it (which I recommend).

Fair value accounting (a.k.a. mark-to-market accounting) says that all assets should be market to their market values (as opposed to acquisition cost, or other methods) on the firms financial statements. The objective here is to make the statements more transparent, and better reflective of the true firm value. For example, consider your personal balance sheet. You have some assets and some liabilities. Amongst those assets may be a home you own, and amongst those liabilities is likely the mortgage. For simplicity’s sake, let’s assume that the home is your only asset and the mortgage your only liability, and the difference between the two is your equity. Note, means that the fundamental accounting equation (assets = liabilities + equity) is satisfied. Now, let’s assume you bought your home for $100K, and took out a mortgage of $80K. A few years later, housing prices fall, and your home is now only worth $60K. Finally, let’s suppose you’re getting married, and your spouse-to-be, like any good investor, is trying to accurately value the asset (you) before buying (saying “yes”). (No, all us finance folks don’t see everything in life through such an economic lens – hopefully the example made you smile though!)

If you were using acquisition cost accounting, the value of the asset on your balance sheet would be $100K, the liability (mortgage) would be $80K (to keep the math simple, I’m assuming you haven’t paid down any of the mortgage yet), and your equity (the net value your spouse-to-be would be getting) would be $20K. You are sufficiently capitalized and all is well. If however you are using mark-to-market accounting, the value of your asset (the home) must be marked down to its market value, and so is set at $60K. Your mortgage hasn’t changed and so your liabilities are still $80K. So, in order for the accounting equation (which, like gravity, is a law you can’t mess with) to be satisfied, your equity must be -$20K. Wow! You’re undercapitalized and your spouse-to-be will likely say no so as to not absorb the $20K hit to her own equity. In fact, you’re effectively insolvent – your liabilities are greater than your assets and you should consider bankruptcy. As Mr. Blankfein points out, and as you can clearly see, the value of fair value accounting is that it shows the actual market value of your firm, not some other number that may not mean much anymore.

So, to understand what this means in terms of lessons learned from the financial crisis and risk management. Mr. Blankfein is saying that if all firms mark their assets to market value every day, then we have a true picture of what the firm’s value is, and we can better understand how fast we are creating or destroying economic value (i.e. if you see the damage certain positions are doing to your portfolio every day, you’re more likely to respond to it by taking defensive measures than if you only look at where you stand one a month). This in turn should lead to better risk management. Sounds good, right? So maybe Mr. Blankfein is right – we all switch to mark-to-market accounting and everything is clear.

Not exactly. There are some issues that make mark-to-market accounting not such a simple choice. Keep in mind, I’m not arguing against it (I don’t personally have a view on it), but I would like to shed some light on why others may argue against it and what challenges it faces.

Firstly, there’s the question of liquidity. In the example I gave earlier, I said the house had depreciated to $60K. But how do we know/assess that? Your house is not like a stock that’s traded on the market every day and a price is well understood. In fact, homes are very illiquid - their price is not well established and they can’t be sold quickly. Who’s to say the value of the house isn’t really $80K or $40K? Similarly when credit froze up in the market late last year many investors refused to buy anything. This lack of liquidity makes it hard to sell, and when assets can’t be sold easily, their prices fall (the basic idea here is that I try to sell something at $100, if no one buys, I go to $95, and so on. I keep dropping until someone buys). This type of price reaction is temporary. Once investors regain some market confidence, they are likely to come in an buy again, driving up asset prices. So, does it make sense in the mean time to value the firm at a temporarily low amount, even if there’s a good chance it’ll rebound in the near future?

Secondly, mark-to-market may not be relevant for assets held to maturity or other generally long term assets. Again, going back to the house example. Suppose the market value is indeed $60K. But if you don’t plan on selling the house for another 25 years (by when the housing market will likely recover), then does it make sense to mark you insolvent today? Does your spouse-to-be care if she knows that you’ll both live in the house a long time and when you do sell you’ll be solvent? Similarly, in mark-to-market accounting, firms would have to mark their assets to lower prices due to the current crisis. But, if their assets are well-rated bonds that they continue to receive interest on, does it make sense? Suppose the firm holds IBM bonds – IBM will continue to pay interest for the remaining 30 years on the bond, and at the end of the period, they’ll likely pay the amount they borrowed back. So then does it make sense to say the bond holder is in a loss position?

Finally, if we use mark-to-market accounting and we mark down the asset prices, and thus the equity of the firms, we create a self-fulfilling prophecy. If we note the firm as “in trouble” by saying the firm has lost a lot of money, or has become insolvent, then creditors of the firm, its customers, and other business partners will likely stop doing business with the firm because it’s “in trouble”. Furthermore the firm’s stock price may also drop, actually reducing the value of its equity (as opposed to any temporary reductions due to fair value accounting). Thus we have taken a firm that may have otherwise been okay if the crisis were given time to play out, and caused them to fail as a result of customers and creditors fleeing, and its stock tanking.

So, is it true that mark-to-market accounting depicts the fair value of the firm and can be used to better manage risk? Sometimes is the answer. Yes, seeing how fast your positions are shrinking on a daily basis does allow you to react faster from a risk management perspective. However, it’s important to understand which assets are being marked down, and what the characteristics of those assets are (duration, likelihood of recovery, etc). There’s not much value added by deflating asset prices and creating a panic even though the assets would like have recovered. In fact, this can have grave consequences because the mark-to-market can sometimes kill off the firm before the assets are given a chance to recover.


Do not destroy the essential catalyst of risk
By Lloyd Blankfein
Published: February 8, 2009, Financial Times

Since the spring, and most acutely this autumn, a global contagion of fear and panic has choked off the arteries of finance, compounding a broader deterioration in the global economy.

Much of the past year has been deeply humbling for our industry. People are understandably angry and our industry has to account for its role in what has transpired.

Financial institutions have an obligation to the broader financial system. We depend on a healthy, well-functioning system but we failed to raise enough questions about whether some of the trends and practices that had become commonplace really served the public’s long-term interests.

As policymakers and regulators begin to consider the regulatory actions to be taken to address the failings, I believe it is useful to reflect on some of the lessons from this crisis.

The first is that risk management should not be entirely predicated on historical data. In the past several months, we have heard the phrase “multiple standard deviation events” more than a few times. If events that were calculated to occur once in 20 years in fact occurred much more regularly, it does not take a mathematician to figure out that risk management assumptions did not reflect the distribution of the actual outcomes. Our industry must do more to enhance and improve scenario analysis and stress testing.

Second, too many financial institutions and investors simply outsourced their risk management. Rather than undertake their own analysis, they relied on the rating agencies to do the essential work of risk analysis for them. This was true at the inception and over the period of the investment, during which time they did not heed other indicators of financial deterioration.

This over-dependence on credit ratings coincided with the dilution of the coveted triple A rating. In January 2008, there were 12 triple A-rated companies in the world. At the same time, there were 64,000 structured finance instruments, such as collateralised debt obligations, rated triple A. It is easy and appropriate to blame the rating agencies for lapses in their credit judgments. But the blame for the result is not theirs alone. Every financial institution that participated in the process has to accept its share of the responsibility.

Third, size matters. For example, whether you owned $5bn or $50bn of (supposedly) low-risk super senior debt in a CDO, the likelihood of losses was, proportionally, the same. But the consequences of a miscalculation were obviously much bigger if you had a $50bn exposure.

Fourth, many risk models incorrectly assumed that positions could be fully hedged. After the collapse Long-Term Capital Management and the crisis in emerging markets in 1998, new products such as various basket indices and credit default swaps were created to help offset a number of risks. However, we did not, as an industry, consider carefully enough the possibility that liquidity would dry up, making it difficult to apply effective hedges.

Fifth, risk models failed to capture the risk inherent in off-balance sheet activities, such as structured investment vehicles. It seems clear now that managers of companies with large off-balance sheet exposure did not appreciate the full magnitude of the economic risks they were exposed to; equally worrying, their counterparties were unaware of the full extent of these vehicles and, therefore, could not accurately assess the risk of doing business.

Sixth, complexity got the better of us. The industry let the growth in new instruments outstrip the operational capacity to manage them. As a result, operational risk increased dramatically and this had a direct effect on the overall stability of the financial system.

Last, and perhaps most important, financial institutions did not account for asset values accurately enough. I have heard some argue that fair value accounting – which assigns current values to financial assets and liabilities – is one of the main factors exacerbating the credit crisis. I see it differently. If more institutions had properly valued their positions and commitments at the outset, they would have been in a much better position to reduce their exposures.

For Goldman Sachs, the daily marking of positions to current market prices was a key contributor to our decision to reduce risk relatively early in markets and in instruments that were deteriorating. This process can be difficult, and sometimes painful, but I believe it is a discipline that should define financial institutions.

As a result of these lessons and others that will emerge from this financial crisis, we should consider important principles for our industry, for policymakers and for regulators. For the industry, we cannot let our ability to innovate exceed our capacity to manage. Given the size and interconnected nature of markets, the growth in volumes, the global nature of trades and their cross-asset characteristics, managing operational risk will only become more important.

Risk and control functions need to be completely independent from the business units. And clarity as to whom risk and control managers report to is crucial to maintaining that independence. Equally important, risk managers need to have at least equal stature with their counterparts on the trading desks: if there is a question about the value of a position or a disagreement about a risk limit, the risk manager’s view should always prevail.

Understandably, compensation continues to generate a lot of anger and controversy. We recognise that having troubled asset relief programme money creates an important context for compensation. That is why, in part, our executive management team elected not to receive a bonus in 2008, even though the firm produced a profit.

More generally, we should apply basic standards to how we compensate people in our industry. The percentage of the discretionary bonus awarded in equity should increase significantly as an employee’s total compensation increases. An individual’s performance should be evaluated over time so as to avoid excessive risk-taking. To ensure this, all equity awards need to be subject to future delivery and/or deferred exercise. Senior executive officers should be required to retain most of the equity they receive at least until they retire, while equity delivery schedules should continue to apply after the individual has left the firm.

For policymakers and regulators, it should be clear that self-regulation has its limits. We rationalised and justified the downward pricing of risk on the grounds that it was different. We did so because our self-interest in preserving and expanding our market share, as competitors, sometimes blinds us – especially when exuberance is at its peak. At the very least, fixing a system-wide problem, elevating standards or driving the industry to a collective response requires effective central regulation and the convening power of regulators.

Capital, credit and underwriting standards should be subject to more “dynamic regulation”. Regulators should consider the regulatory inputs and outputs needed to ensure a regime that is nimble and strong enough to identify and appropriately constrain market excesses, particularly in a sustained period of economic growth. Just as the Federal Reserve adjusts interest rates up to curb economic frenzy, various benchmarks and ratios could be appropriately calibrated. To increase overall transparency and help ensure that book value really means book value, regulators should require that all assets across financial institutions be similarly valued. Fair value accounting gives investors more clarity with respect to balance sheet risk.

The level of global supervisory co-ordination and communication should reflect the global inter-connectedness of markets. Regulators should implement more robust information sharing and harmonised disclosure, coupled with a more systemic, effective reporting regime for institutions and main market participants. Without this, regulators will lack essential tools to help them understand levels of systemic vulnerability in the banking sector and in financial markets more broadly.

In this vein, all pools of capital that depend on the smooth functioning of the financial system and are large enough to be a burden on it in a crisis should be subject to some degree of regulation.

After the shocks of recent months and the associated economic pain, there is a natural and appropriate desire for wholesale reform of our regulatory regime. We should resist a response, however, that is solely designed around protecting us from the 100-year storm. Taking risk completely out of the system will be at the cost of economic growth. Similarly, if we abandon, as opposed to regulate, market mechanisms created decades ago, such as securitisation and derivatives, we may end up constraining access to capital and the efficient hedging and distribution of risk, when we ultimately do come through this crisis.

Most of the past century was defined by markets and instruments that fund innovation, reward entrepreneurial risk-taking and act as an important catalyst for economic growth. History has shown that a vibrant, dynamic financial system is at the heart of a vibrant, dynamic economy.

We collectively have a lot to do to regain the public’s trust and help mend our financial system to restore stability and vitality. Goldman Sachs is committed to doing so.

Hunting for negative beta

February 9th, 2009 1 comment

In my previous post on “absolrelative” performance, I spoke about absolute return and relative return, and how a money manager’s performance should be measured by one metric or the other, but never by some combination of both. Getting relative return is quite straightforward (well, not entirely, as you’ll see in my later post on the importance of the benchmark) – you invest in the benchmark, and then over-weight the stocks you think will out perform. That way you, theoretically, add value through your stock picks, while not missing out on general market movements exhibited by your benchmark. In this post I’ll talk about what you need to do to earn absolute return – that is a positive return no matter what the market does.

To fully appreciate the hunt for negative beta, it’s important to understand what beta is (besides short-form for a member of some collegiate fraternity or sorority) and how it fits in to stock returns. As mentioned in What is the Y-Factor?, investors require compensation for holding risk. For example, investors who buy IBM stock expect a return based on the risk associated with IBM. That risk (and thus the return investors demand) can be decomposed into many things: the risk of the economy slowing and computer sales falling, the risk of IBM’s products becoming inferior to Dell’s products (thus hurting sales), the risk of IBM going bankrupt, etc. In the quantitative equity world, we call each of these risks a factor. So, each stock has some exposure to any number of risks, and each risk carries some compensation with it. In quantitative finance, we call the exposure of an asset to a particular risk it’s beta (β) with respect to that risk.There are many kinds of risk that investors may require compensation for. The most notable are: risk related to the market (how much more or less risky is a particular stock than the whole market), risk associated with the firm’s size (small firms tend to be riskier than large firms), risk associated with liquidity (more liquid assets – those that trade more often, are more transparent, and have a well established price – are less risky than more illiquid assets). A typical stock will have a beta for each risk. Note that there are two general types of risks inherent in the examples I mentioned: IBM specific risk (such as it’s products being inferior) and general market-wide risk (such as the economy slowing - which effects all firms, not just IBM). The former, firm-specific type of risk is called idiosyncratic risk, and the latter, general risk is called systematic risk. Systematic risk drives all asset prices (i.e. the risk of the economy failing is priced into all stocks, which is why you see all stocks generally fall when bad economic news comes out), whereas idiosyncratic risk drives only specific firm prices (which is why only IBM would fall if IBM were to report bad earnings). Of course you may see Dell stock fall when IBM reports bad earnings – this is because investors may perceive an industry-specific risk: whatever it is that hurt IBM earnings may also hurt Dell earnings (i.e. maybe computer are becoming deprecated so all players in that industry suffer).

We can take the lesson we’ve learned – that the returns that stocks earn represent the risk associated with the stock – and apply that to a portfolio as well. Since a portfolio is simply a collection of stocks, the return of that portfolio is simply the aggregate of the returns of each stock within the portfolio. Thus, since each stock return can be theoretically decomposed into risk factors, the sum of all stocks (i.e. the portfolio) can also be decomposed into risk factors. So, if a portfolio is simply compensation for carrying risk, what do we pay portfolio managers for? Surely if all they are doing is taking my money, putting it at risk, and earning a reward for that risk, I should be able to do the same thing myself, right? Not exactly. If you want a portfolio that has the risk profile of the S&P 500 (the aggregate risk of the 500 largest firms in the U.S.), you could simply invest in an S&P 500 index fund. However, you could also hire a portfolio manager to manage your money relative to the S&P 500. This means that he/she would generally take the same risk (and thus earn the same return) as the S&P 500, but should earn you even more by utilizing his/her skill to pick more stocks that will go up and fewer that will go down. The astute reader will realize the contradiction here: even if the portfolio manager picks more stocks that go up and fewer that go down, aren’t they really just picking more risky stocks and fewer of the lower-risk stocks (since risk and return are joined at the hip)? Well, in a way, yes, that may be true. However, you can argue that the portfolio manager’s skill is that he/she knows which risk to take at which time so as to not lose money. The other dimension to this is that the whole framework I’ve laid out is very theoretical – it assumes that assets are always priced to reflect all the risk they are exposed to. This is the (simple) definition of an efficient market. However, practically it’s not possible to know all the risk that any particular stock is exposed to, and it’s not possible to know exactly how to price each risk. Additionally, there are many other issues at hand that reduce the efficiency of the market, such that not all return is compensation for risk. A portfolio manager should be able to capitalize on these inefficiencies and make the investor money in doing so. It is for these skills that money managers should be compensated – they should not be compensated just for taking risk (though unfortunately that’s more often the case than not).

Okay, so we’ve established that the return of a portfolio is made up of the risks in that portfolio, plus some additional return generated by the portfolio manager’s ability to pick stocks. We can characterize this using the equation:

E[R] = α + β1γ1 +  β2γ2 + … + βnγn

where E[R] is the expected return of the portfolio, βi is the portfolio’s exposure to risk i (where i is a number – remember that a portfolio is exposed to many risks, so i is a number from 1 to the n, where n is the number of risks in your model), γi (γ is the Greek letter gamma) is the compensation for risk i (so that the product βiγi is the total return you get for taking risk i – for example, if IBM has an exposure of 0.9 to the economy and the compensation for risk to the economy is 2%, then IBM should earn 0.9 x 2% = 1.8% return for its risk exposure to the economy), and α (the Greek letter alpha) is the value added by your portfolio manager above and beyond any risk being taken. As you can see from the equation above, if you want to eliminate all risk in your portfolio, you have to eliminate all the βs. This will leave you with an absolute return portfolio – one that earns α% each year, no matter what.

So, getting the absolute return part of the absorelative performance we’ve been asked for at work is quite easy – just get rid of beta by finding things with negative beta and adding them to the portfolio in just the right quantity so that the overall beta is zero. Piece of cake – well not quite!

There are a couple of ways to get negative beta: 1) sell (or rather short) assets that have positive beta, or 2) buy assets that have inherently negative beta (imagine an asset that loses money when everyone makes money, and makes money when everyone loses money). These two approaches can be implemented many ways – for example, you can either short individual stocks, or short ETFs, or short futures – all of these are variants of selling assets with positive beta. To buy assets with negative beta, you can buy “short” ETFs, or buy other assets (for example, if your portfolio is exposed to oil prices sinking, you can buy oil so that when your asset loses value, your oil position makes money, effectively mitigating the risk).

Since I work for an insurance company – a business that’s typically conservative in nature, and required to be conservative by regulation – we can’t do any of option 1 (short assets). So, we can’t short stocks with similar beta exposures to the ones we hold in our portfolios. We also can’t (directly) short the market, or short anything else. This is a major limitation when considering absolute return strategies. For example, suppose our models predict the best stocks from each industry and we buy those. Next, suppose that we’re greatly gifted in our stock picking ability and that one of our picks, a bank, does very well – it has earned 5% by June, whereas all other banks have earned about 1%. Now, suppose that financial services suffers great losses for some reason (lets go with a wild situation here – suppose credit freezes up). All investors are afraid of holding banks, and so bank prices immediately fall 10%. Though we picked the best bank, it is still not free from the fear of banking running through our hypothetical market, and our pick is now earning -5% (whereas all other banks are at -9%). You clearly see the problem here – since we can’t eliminate the risk of being a bank and just isolate the return associated with this particular bank, we’re always prone to losing money when the market tanks. (A hedge fund could, on the other hand, short the risk of the banking sector since they can go short, thus isolating the return derived from the quality of it’s pick).

Additionally, since we’re equity managers we can’t exercise option 2 fully. That is, we’re not able to buy other assets (like commodities, fixed income, or other things) that may serve as an effective hedge. Also, we’re not allow to use derivatives to hedge our position since we can’t use them for speculation.

So, the only option we’re left with at this time is using the emerging class of short ETFs. These securities are designed to deliver you a return equal to the negative of whatever it is you’re targeting. For example, suppose you want to short banks as in the example above, there are now ETFs available that you can buy and earn a return that’s negative to the banking sector (if banks go up 5% today, you go down, and vice-versa). This is great! Technically we’re “long” the asset – we bought it with our money, yet it gives me negative beta! So, now we can use some weighting of the various short ETFs (they have one for just about each major industry and the market in general) to neutralize most of the beta in our portfolio. Note, that it’s not possible to neutralize allof the beta – these short ETFs represent industries in general and will never allow you to short all the minute risks of individual firms. The only problem with this approach is that it’s very capital-intensive. If the investors give me $1,000 to invest, I can only invest about half of it since I have to spend the other half buying negative beta. So, if I generate 4% α each year, I’m only doing it on half of my portfolio so my return for the entire $1,000 is really only 2%. This is verycostly! Of course, if absolute return is what I’m after, this is the price that must be paid given all the other constraints on us (i.e. no direct shorting). There are other options available, but they require me to delve into derivatives, so I’ll defer to another post.

 So, though we can’t construct a portfolio that is truly risk-neutral, we can get pretty close using short ETFs. You’d basically pick the stocks your model predicts will out-perform. Then find the weight of your in each sector, and finally buy a proportionate amount of the short ETF to eliminate that weight. You’ll still be prone to some risks, but the major market movements should be covered. However, keep in mind that you now have an absolute return portfolio. So, if the market moves up 25%, you’ll miss out on that whole move since your short positions will offset any gains made by the long positions. This does allow for a cool feature called portable alpha, which I’ll talk about in another post.

Absorelative performance

January 13th, 2009 5 comments

No, it’s not a typo… “Absorelative” is the term I’ve coined for what we’ve been tasked with at work for the year 2009. Our mission is to earn an absolute return, unless the market moves up, in which case we’re expected to deliver a relative return – so we’re looking for a mix of absolute and relative performance, or “absorelative performance”. Basically, this means that if everyone loses money, we must not. But if everyone makes money, we too must make money.

To better understand the implications of this, it’s important to understand how money manager performance is measured, and how managers are compensated. Basically you can measure how your investment manager is performing in one of two ways: absolute return, or relative return. Absolute return is the total return of your portfolio. If you give someone $1 million to invest and they give you back more than $1 million at the end of the year, then they have earned a positive absolute return. Likewise, if you get less than $1 million, they have earned a negative absolute return. The fact that other investments (such as a passive index fund) could have earned 30% or lost 30% is completely irrelevant – your manager gets paid if he/she makes money. Relative return, on the other hand, depends on a benchmark. Suppose you want your manager to invest in large U.S. company stock. An appropriate benchmark could be the S&P 500 – an index of the largest 500 U.S. companies, which  you can invest in yourself – it’s pretty easy and cheap. So, you only want to pay your manager if he/she is able to beat what you can earn yourself – that is, beat the S%P 500. If the S&P 500 earns 25% and your manager earns 28%, then he/she has earned a 3% relative return. If he/she earns 22%, he/she has earned -3% relative return – he/she has still made money, but he made less than the benchmark, and so shouldn’t get paid. Clearly, the best compensation structure for you depends on what you’re looking for – if you want exposure to a particular asset class and are open to its risk and return, you want to set up a relative compensation structure – only paying your manager for picking those names that are better than average. However, if you’re looking to minimize risk and just want positive growth, an absolute structure is probably a better fit – an often small, but positive, return with very little chance of losses.

Certain types of money managers typically have certain kinds of compensation. Hedge funds can go long and short (that is, they can buy assets they think will make money, and borrow and sell assets they think will lose money, expecting to repurchase the sold asset later at a cheaper price to repay the loan, and keep the difference between the sale and repurchase prices), and so typically employ strategies that can profit in up markets and down markets. These strategies should make money no matter what, and so are called absolute return strategies. As you would expect, since hedge fund managers employ absolute return strategies, they are typically compensated on an absolute return basis.

Traditional money managers (think of insurance companies, mutual funds, etc) aren’t allowed to go short, and so can only buy whatever asset class they are managing (bonds for bond managers, stocks for equity managers). Thus, if you’re a long-only equity manager, and stocks are falling, your portfolio will fall too since all you can do is buy the falling asset class. The performance of these strategies is tied to the benchmark, and so they’re called relative return strategies, and the manager is compensated relative to some benchmark.

Getting back to the situation at work… Allstate is an insurance company, and as such we’re a long-only shop. Until now our performance has been measured on a relative basis. In 2008, equity markets fell sharply, as did our portfolios. By definition, if we beat our benchmarks we should still get paid, and if not, we shouldn’t. As you can imagine, the investors who give us capital may not be thrilled with this setup. On one hand they were looking for exposure to equities (and by that I mean the risk and return that comes with equities), and should be glad that we were able to provide a return greater than, albeit still negative, the benchmark they had selected. On the other hand, when 35% of your portfolio disappears, it’s hard to be excited and pay your money manager for making you one third poorer than you were at the start of the year. So, going into 2009 things changed – the investors decided that they didn’t want to take this much risk, and that they didn’t want to pay their managers if they lost money – all the ingredients of an absolute performance setup. (As a side note, I would have argued that if the investors aren’t comfortable with the risk of equities, the simpler, more direct solution is to simply not invest in equities. They should invest in a safer asset class. However, this is a suicidal argument for me since if the investors choose not to invest in equities, I’d be unemployed!)

Earning an absolute return in itself is not a big issue – see my upcoming post on negative beta. The problem comes in when the investor wants the relative dimension as well – when the investor says “don’t lose money, but if the market is up x%, you must be up about x% too”. The issue here is that in order to get absolute return, we have to eliminate all beta exposure (market beta, size beta, style beta, sector beta, etc). This way, no matter what happens in the markets, our stocks earn only alpha – their unique return after accounting for all systematic risk. This is a great positive in down markets – in fact zero beta is what allows us to not lose money (assuming we’re right about our stock predictions – our alpha). However, not having this beta exposure in up markets means that your portfolio is missing out on systematic return. That is, if everyone feels more confident about things and stock markets recover, you’ll miss the recovery because it is market-wide compensation for positive sentiment, not company-specific compensation. Thus, you will under-perform the market in up markets.

So, given that we can’t earn absolute return by neutralizing beta since that also eliminates our return in up markets, the only choice we’re left with (as far as I can see) is to be in stocks in good times (so that we’re earning alpha and beta), and be in cash (or cash equivalents) in bad times so we’re not losing money tied to our stocks’ betas. This strategy is called market timing, and it’s something that no manager that I’m aware of has ever been able to do consistently and successfully. Basically you have to know more often than not when the market will go up so you can buy stocks, and when it’ll go down so you can sell stocks.

Now, my friends, you understand the dilemma for 2009. In the absence of a crystal ball that tells us what will happen in the markets tomorrow, how do we know when to switch from stocks to cash? If you have any ideas, or see something I missed, please leave a comment and educate me. In the mean time, I’m going to roll my dice – even means market, and odd means cash!