Top Quotations From Battle Of The Quants

Latest four stories on Hedge Fund Insight:

Top Quotations From Battle Of The Quants London
"An edge in analysis of social media is much more feasible than in analysis of news - there are only six traders globally who are successful trading off news analysis, " Rob Passarella, DataSift.

"There is a huge leap to move from getting  interesting signals to a viable investment strategy," Leigh Drogen, Estimize. Read more >>


SEB Bias Towards RV and Macro In Outlook For Hedge Funds
The market is being driven mainly by investor risk appetite and sentiment, which in turn are driven by unpredictable political decisions. Central bank actions are also driving hedge fund returns to a growing extent, as are hopes for a new round of quantitative easing from the US Federal Reserve (the Fed) or the European Central Bank (ECB)’s potential purchases of government securities from peripheral euro zone countries. Read more >>


SVM Positioned For US Recovery To Beat Expectations
SVM portfolios are currently fully invested, recognising attractive valuations in the UK and Europe and a more encouraging outlook for global growth.  In the US, news in construction, housing and retail suggests that the worst is past. US construction and housing sectors, representing in total one-sixth of the US economy, are steadily recovering.  We believe that US recovery will beat expectations.  US banks are also much better capitalised than UK and European ones, and have largely gone through their write-offs.
Read more >>


Hedge Funds’ Performance? Volenti Non Fit Injuria
The rules on investor eligibility mean hedge fund investing “is not by any enterprised nor taken in hand unadvisedly or lightly; but reverently, discreetly, advisedly, soberly and in the fear of God, duly considering the causes for which alternatives are ordained”. The hedge fund industry has no case to answer against the recurring charges of non-performance and self-enrichment at the expense of clients. Those who invest in hedge funds willingly undertake the investment and operational risks implicit in the niche money-making schemes of the stinking rich. All of the usual criticisms, e.g. the fees, the hidden beta, the lock-ups, the illiquid holdings and the spraying of chic joints’ walls with Tattinger, are all disclosed in the offering documents and/or are writ large in the industry’s track record, which is getting on for thirty years as an investment style.
Read more >>

Hedge Fund Returns Are Path Dependent - As 2011 Illustrates

One of the things that is attempted on this website is to look at market action to help explain, or comprehend hedge fund returns. For example, two years ago a commentary was distributed on the significance of the quality factor in explaining returns in 2009 (see this article), and the impact of high correlation this year was explored  (here) too. This year has been a very unusual year in the macro background and in how markets have moved - year three of a recovery does not normally look like this one in economics or markets. 

The market events of this year have been a slalom course for hedge fund managers to negotiate (risk on/risk off), and the hedge fund indices reflect that. The HFRX Global Hedge Fund Index was down 8.58% for the year up to Monday (the 5th of December), and directional funds have fared a lot worse than non-directional strategies (the former are down 18% on an index basis).

Manager letters can be a good source of market context for hedge fund returns. In particular managers taking a quantitative approach are risk aware by nature and typically have a numerically stronger way of expressing the market conditions, and the suitability of their own methodology for extracting value from them.The overview reproduced below comes from Quant Asset Management of Singapore, managers of a portfolio of global equities.

Dear Investor,

It is unusual for us to add any written text to our monthly email other than the standard text in the newsletter. Since we apply a consistent, systematic investment methodology, once familiar with the methodology, the newsletter is normally self-explanatory. But because we are currently witnessing the biggest draw-down since the inception of the QAM Global Equities fund, 71/2 years ago, we’d like to use this opportunity to share some of our thoughts on this.

We now had a period of seven consecutive negative months with the fund being down 22% for the year. The main reasons for the negative performance are:

1) We use mostly fundamental factors when selecting our stocks from a global universe of over 6000 stocks. Fundamentals haven’t been driving markets in the past seven months. Macro-economic factors were driving markets and correlations have been at an all time high.

2) We use a trend following methodology that adjust factor weightings each period for what worked well in a certain past period (dynamic) before. This didn’t work well in the past seven months due to volatility spikes and trend reversals.

3) We use a hedging methodology whereby we are either 0% or 50% net exposed mostly based on aggregate earnings revisions number and some price performance related techniques. This hasn’t added value in the past seven months.

So the question arises if our methodology is still valid and when will it work again?          

First of all; all good investment methodologies go through periods where they struggle but as long as they add value over time and make logical sense, it makes sense to stick with them in order to achieve above average returns.

Furthermore we believe that systematically picking a large number of stocks on the basis of fundamentals (valuations, earnings growth and earnings revisions) combined with a factor adaption methodology, whilst hedging out a large part of the market risk, does add lots of value. Remember that the fund is up 154% since inception. This compares to 16% for the MSCI World in the same period.

We have always allowed volatility in our funds (around 20%, which is much more than most of our peers) in order to achieve higher returns than our peers. These high returns have been achieved and we have a strong belief that they can be achieved again. In order for this to happen one has to allow certain periods of under-performance. Draw downs are pretty natural and frequent in fundamental factor adaptation systems and one should be reminded that they can create opportunities too.

Kind regards,
The QAM Team

The letter is reproduced here to give some insight to market drivers of return this year, not to point fingers at a style or a particular manager. The general point is that the vast majority of managers take a specific approach to markets that they hope works most of the time and for most market conditions. The marketing conceit of an "all weather" hedge fund or strategy died in 2008. The returns delivered by a manager are a function of their own style and the opportunity set available from the market over the period. It is very striking  that the gyrations of markets in 2010 and 2011 made it very difficult for equity hedge fund managers to make positive absolute returns except when the equity market letter was written by the Fed and other central banks through the mechanism of QE2 (from August 2010 to March 2011). 

Hedge fund returns are path dependent, not independent of the direction of markets, nor independent of changes to intra-market or inter-market correlation, nor unaffected by the extent to which markets trend. The specific sequence of ups and downs, step-wise shifts in volatility, and how long a market regime lasts impacts the ability of the manager to harvest alpha in the way they are set up to address markets. So, for example, it would not just be relevant that markets were down 5% over a six month period, but in understanding outcomes it is more relevant that they appreciated by 11% over six weeks before losing 15-16% over 4 months (with specific volatility and correlation conditions). 

It is up to the investor in hedge funds to put together portfolios of funds which take account of the various market conditions which may occur, in full knowledge of the manager style. Building such an efficient portfolio of funds can only be achieved when investors truly understand how their capital is being applied to markets by their managers. Provided the managers stick to their expressed style, there should be a limited number of surprises to investors in hedge funds given market conditions, and how market conditions change (the specific path markets follow). For any given market conditions and sequences the better investors in hedge funds will have a range of expected return per manager in which they are invested. As yet, the path dependency of hedge fund returns is not sufficiently well appreciated  - spread the word.



UCITS III Footnote - the offshore fund from QAM was down 23.49% over the period end Feb 2011 to the end of November. The onshore equivalent  - Quant Global Equities fund, a sub-fund of the Quant AM SICAV (a UCITS III type fund) - was down  27.77% over the same period. The onshore version launched in March this year.

Chart of the Day - Extremely High Correlation of Stocks - Implications for Hedge Funds

I'm doing some work on risk measurement/management at a hedge fund management company. The investment strategy of the hedge fund is long/short equity. Most of the work revolves around measurements at the portfolio level, and the aim of measuring and controlling risk is to produce steady returns for investors. This is only possible on a sustainable basis with a diversified portfolio, unless the hit-rate is unusually high. Whilst  I have met managers with very concentrated portfolios based on very stringent selection criteria, and who have very high career hit-rates (as high as over 90% in one case), most mangers (probably more than the 80:20 rule would suggest) run portfolios diversified by stock, sector and to some extent theme.

Effective risk management is partly about being aware what has a high probability of working and when. One of the lessons of the Credit Crunch for many in hedge fund land is that there are market circumstances in which the previously assumed risk controls will not work. That is, the manager has a series of limits and stops and processes which in combination will produce the desired outcomes for most market conditions. The rub, as revealed in 2008-9, is in the conditional "most". Managers have to be aware of in what market circumstances their approach to markets will not work.

For most equity long/short managers most of the time the key decision variables at the portfolio level are about managing the net exposures to market, and specifically about managing the net beta-adjusted exposure to the market. There is a sub-set of equity managers for whom this is not true - those which have a limit on their net exposure to markets, and are structurally close to net neutral, say a band of 0-20% net long. Often the latter funds are quantitatively-driven equity long/short funds, but some discretionary managers choose to be close to net neutral. For these net-constrained funds returns have to come from stock selection to a much greater extent than funds with wider investment powers. The corollary is often a larger gross exposure to markets - consistent with the formulation of information ratios of managers. Typically, funds with a small net exposure limit target lower absolute returns, and implicitly rank risk-adjusted returns as a higher goal than absolute returns. 

The majority of managers in equity long/short try to use the additional degrees of freedom they have in balance sheet disposition to produce higher absolute returns (than a net-neutral manager) though nearly always with higher volatility of returns. The tactical shape of the fund should be a function of two things: the market regime and the opportunity set for the particular investment style of the manager. There is a considerable range of understanding amongst managers of the necessity of taking these two dimensions into account in setting the net exposure of equity hedge funds. The best managers are good at both, but the majority of equity hedge fund managers are not. Yes, the majority.

The successful shaping of the hedge fund balance sheet requires two attributes in the manager: an ability to read the market regime in multi-dimensions, and a high degree of self knowledge about the applicability (and effectiveness) of their investment processes. Around the time of the Tech Bubble the first required ability was demonstrated a lot by equity hedge fund managers. The monetary stimulus provided by Greenspan on fears of the Millennium bug was read by managers as a bull market condition green light, and most managers were very net long in 1999, and investors were gorged on the excellent returns produced. The reverse happened from March 2000 onwards. By the 3Q 2000 many equity hedge funds were net short on a tactical basis, i.e . the managers jobbed from the short side.  From 2003 to mid 2008 a net long bias and a buy-the-dips mentality were positive attributes for managers. Over the same period many new hedge fund managers joined the industry, and several big names closed down, citing the lack of shorting opportunities as a reason.

So coming into the Credit Crunch phase of 2008 only a minority of equity hedge fund managers expressed an ability to read the market regime by going net neutral or net short. A majority of managers had never been net short to that point, and many did not have that available as a choice because of their offering memoranda, or because the operational limits they gave themselves precluded it.  

Current market conditions have echoes of 2008-9: large daily declines in equity prices, volatility and rising fear gauges in the price of gold and the cost of interbank borrowing. These are difficult conditions in which to manage an equity hedge fund. Quite how difficult is in part reflected in today's chart of the day. Every manager can tell you about the level of market volatility reflected in the Vix Index. This captures the current level of volatility in the market on a traded basis. The actual volatility experienced in the market is lower than the traded level, though intra-day measured volatility can be higher than that indicated by the Vix.

All equity hedge fund managers are aware of how volatility shifts impact their style because they can see it in the daily P&L changes per position, and the same at the portfolio level, and they are aware of the Vix. Those managers who take risk measurement more seriously will be aware of the Value-at-Risk of their portfolios. The same portfolio will have a different measured risk dependent on market conditions - when markets are more volatile measured risk goes up for the same portfolio. What is less well explored is the other element that feeds into the risk measure VaR, that of correlation.

The inter-relatedness of positions has an impact on measured risk. The more related the positions the less diversification there is in a portfolio. Consequently managers structurally build diversification into their portfolios by having limits on sectors/industries/macro-related themes as well as limits to specific stock risk by constraining holding size. But correlation is not stable. Cross-sectional correlation varies through time. In up-trending markets (scenario 1) volatility drops and stocks tend to become less correlated. For sideways moving markets (scenario 2) two stocks in the same sector could quite feasibly act differently - one going up and the other staying the same price, or even falling. Scenario 1 is better for producing returns from net market exposure, and scenario 2 is a richer market opportunity for returns purely from idiosyncratic stock risk (selection).

However when markets fall for a period volatility rises and correlation increases. The correlation coefficients of stocks' betas go up - the market component of stock price changes goes up, and the sector effect increases and the idiosyncratic component of stock price changes declines. The chart of the day below illustrates that we are at an extreme for measured correlation amongst S&P500 constituents.

In such a market environment portfolio returns become a product of the net market exposure, driven by the weighted average of the portfolio betas. The extreme case illustrates the point - bank shares and commodity stocks have had the highest betas in the market for some years now. The return to the net exposure to these two sectors plausibly could have been the largest component of the return of individual equity hedge funds over the last three years. For net neutral equity hedge funds the net exposure decision on these two sectors over the last three years could have even been the decision that determined return outcomes.

For market conditions with high correlation between stocks it is just about impossible to drive returns from stock selection (idiosyncratic risk) alone. This has recently been explicitly recognised by one management team -  Ralph Jainz and Jonathan Sharpe of Ratio Asset Management wrote to their investors on closing their European equity hedge fund this month that "this year stock selection has not proved profitable." History suggests that it is difficult for diversified net neutral funds to make money when there is high correlation between stocks, and only managers who are adept at shaping the balance sheet of their hedge funds will actually make money, as opposed to defending their capital.  

Given that nowadays few managers can demonstrate an ability to read the market regime in multi-dimensions, and have a high degree of self knowledge about the applicability of their investment processes, I expect negative returns from the strategy for the current market. What is particularly disappointing is that the number of managers who can show they truly learned lessons from 2008-9, and can make money now, are so few. Maybe investors have to exhort their managers to take some off some of the net exposure restrictions - or do investors doubt that their managers have sufficient skills to handle wider investment powers?



   

Battle of the Quants London 2011

This year I have experienced two rounds of the Battle of the Quants. The first was the edition of the radio show "The Naked Short Club" broadcast on Monday of this week.

                           Host Dr.Stu bursts into the studio with his posse, and sets about arranging the guests

The show featured a line up of speakers that were going to feature at the conference later in the week:

Dr. Marco Fasoli- Managing Partner, Titian Global;  
Robert Passarella- MD, Dow Jones;  
Con Keating*- European Federation of Financial Analysts;
Simon Kerr- Enhance Consulting/Hedge Fund Journal;  
Bartt Kellermann- Organiser, Battle of the Quants



There was some great too-ing and fro-ing between guests as topics were debated - both sides seemingly with conviction. It was the best edition of the show I have been on myself.

                                                                  Bartt Kellerman, Rob Passarella, and guest announcer 
                                                                                        and "Dazed & Confused" Editor Rod Stanley squeeze into the studio.

                                                                              Rod Stanley reads his script, while  Dr. Marco Fasoli 
                                                                                         and Con Keating prepare to respond to a tough question.


If you would like to listen to this show you can hear it via this download link.





My second round of  Battle of the Quants was the full conference held on Thursday the 16th June, at which the Keynote Speaker was Dr.Paul Wilmott, Ph.D, publisher of the eponymous quant magazine and website. His professional background includes trading volatility at a hedge fund, so he has seen and applied quantitative methods as used in finance in practice as well as in theory. He addressed his audience of quants and investors in quant funds on some of the problems of doing this. He cited calibration and market completeness as particular problems.

An example of calibration is the the estimation of volatility for the pricing of derivatives. Models have to be fit for purpose and reflect the world as is. According to Paul Wilmott re-calibration of a model by changing parameters is a form of model risk. He was aghast to inform his audience that regulators of financial activity like to see re-calibration of banks' models used to price products and estimate risk, sometimes to the point of enforcing re-calibration.

Dr.Wilmott sees the concept of market completeness as somewhat dangerous. Markets are incomplete in reality - even fast moving markets with very large volumes have gaps in price, as fx markets show over and over, and the flash crash in the S&P showed last year. The quant maven asked rhetorically "Why then is the idea of market completeness popular?" It is because it enables market participants to use ideas of risk neutrality and a certain type of mathematics. The danger is in the fact that risk neutrality in a state of market completeness is a special circumstance, and not  the governing mode of markets.

The Keynote Speaker had a theory about quantitative techniques as used in finance - that most people who use them don't much go beyond the tools of second year undergraduate mathematics. In particular, Paul Wilmott claimed that knowledge of fluid dynamics and the maths of mechanics would replicate how most people in the market address some very complex real world topics in finance. He gave an example of coming across a group in a financial institution attempting to model hurricane activity who assumed a log-normal distribution.

My own take on this is that the application of quantitative techniques to markets is no different than, say, a fundamentally driven approach: there are a range of abilities and resources being applied within the broad category.  So for those that analyse industries and companies there will be some who rely only on street research; there will be those who carry out there own research, and those that employ expert networks. The depth of knowledge and understanding of a company or industry will vary a lot, and (relative) bet size should vary with the size of the edge (and risk/reward).

A good example of how different market participants cope with the shortcomings of modelling is in the pricing of traded options. All market participants (apart from the retail punters) are aware that the distribution of market returns is not log-normal. If you like, if you have a Bloomberg screen you will know that there is a smile in option pricing in OTM puts. If you are running money at a long only institution  and use options you will be aware that   those OTM puts are not necessarily expensive, but reflect a higher probability of a large fall in the underlying asset than is reflected in a log-normal distribution of returns. For the long only investor, who would hedge or take risk using options over a period of weeks-to-months, the assumption of the distribution of returns is flawed but sufficient for the purpose. For a market maker, who is modelling a three dimensional volatility surface and managing risk through constructing a risk book with a positive gamma, and has a time frame of intra-day and over-night risk, the assumption of log-normal returns is not adequate. No market maker uses pricing software that assumes that naive distribution of returns. So the different utility functions of the types of market participants will feed into the willingness to operate with a pricing model that is known to have shortcomings.



*Con Keating told a good story off-air about getting through US Immigration quite a few years ago. Although UK passport holders benefited from a visa waiver scheme when travelling to the US, Brits still had to fill in the customs form. Keating completed his and waited-in-line to be seen by the customs/immigration officer. Many will know that the US immigration procedures for an alien can be tough - they don't hesitate in sending visitors back on the next flight. 

Finally Con Keating got over the yellow line and handed over his papers; the officer raised an eyebrow on reviewing them: "Mr. Keating you know we take immigration matters very seriously here. You don't appear to be doing the same: it says here under 'currency and valuable material' being brought into the United States that you are bringing in $223million. Is that correct?"

Keating confirmed that it was, and the officer went to have a consultation with his superior, eventually deciding that it was okay for the would-be visitor to enter the United States. Keating was much relieved as although he was there for a series of important business meetings he also had the job of delivering $223m of bearer bonds on behalf of his employers! 

Bob Prince, Co CEO of Bridgewater, on Alpha and Beta in HF Portfolios

Bob Prince

Now, if you look back at the history of Bridgewater, for the average market that we trade, we made 1 percent return with 3 percent risk. We've had a .3 ratio for each market. Our overall ratio is about 1. The difference between these two is diversification and portfolio structuring. So, two thirds of our performance has come from balancing risks well, and only one third has come from trying to get the bets right. Of course, if you don't get the bets right, you don't have anything to balance, so you have to start there, right? Okay. So, that's point number one. You can get a lot more mileage out of risk reduction than return enhancement.


Number two is don't believe the numbers. Now, I'm going to make an assertion here. I don't know if you've ever heard this assertion before. My assertion is that correlation is unknowable. So, we talk about portfolio mean variance, about, correlation. My assertion is that correlation is unknowable. I'm talking about the correlation of any two assets – it is unknowable. Now, I'm going to try to prove that to you on a chalkboard.


So, let's just say that I have stocks and I have bonds. Okay? And the question is, what is the correlation between stocks and bonds? Well, correlation is the way that the returns co-relate. It's how the returns relate to one another. In order to understand how the returns relate to one another, you have to really understand something about what drives returns. Okay? Well, there is more than one thing typically driving the return of any one asset. Let's just say I've got economic growth here as a factor that drives the return of stocks and bonds. Now, if the economy is strong, that will be good for stocks - generally speaking if the economy is stronger than expected. If the economy is strong, it will generally be bad for bonds. But the economy's not the only thing that drives their returns. Let's say I've got inflation here as the factor that drives their returns. If inflation falls, that will generally be good for stocks. If inflation falls, that will generally be good for bonds. So, inflation is working the same direction for stocks and bonds. Economic growth is working opposite directions for stocks and bonds.

   
So, now the question is, what is going to be the correlation of stocks to bonds? I don't think you can possibly know. You can't know unless you know what kind of environment you're going to be in. Will I be in an environment that's dominated by inflation? If I am, they'll probably have a positive correlation. If I'm in an environment that's dominated by economic growth, they'll probably have a negative correlation. But if I knew what kind of environment I was going to be in, I'd just go bet on that. But if I'm trying to passively structure a portfolio that's balanced, I don't have any idea what the correlation between stocks and bonds is going to be. And what it was in the past really has no bearing on what it will be in the future. So, now what do you do? How do we do mean variance optimization when you can't possibly know the correlation of two assets?



So, what I'm saying is you can't do this. So, what we try to do is we don't pay any attention to the numbers. We never try to look at the correlation between any two assets. What we try to do is balance our exposure to economic growth and inflation. So, given the structural characteristics of assets, they will perform a certain way given a certain economic environment. And you can look across your assets and start to think about how you're balanced against the economic environment through your asset holdings.

   
The last point is that you need to understand the fundamental characteristics of the returns that you're operating with. And the most important characteristic of those returns is the derivation of the return: is the return derived from beta or from alpha? Beta means the return is derived from the risk premium embedded in the asset. Risk premiums over time will be positive in order for the capital system to function. Risk premiums will be positive over a very long period of time, but it's very easy to buy a risk premium, so it's not a really good returning source of return. It's not a very consistent source of return because it's very attractive. A lot of people buy it and they bid up the prices, maybe has a ratio of .25 return to risk ratio. So, beta is one kind of return. It's one type or category of return. There are lots of betas.

Alpha

Alpha is totally different. Alpha is a bet. I've got a view. There's timing involved. I'm long. Now I'm short. Now over time, people might make bets, but they might on average be long. Then on average they have a beta in their return, right? But ... so the question is, are you generating your return through beta or through alpha, through risk premiums or through timing of bets? Because the characteristics of those is radically different, and the ability to produce a very high return is inherently limited if you're basically holding betas because betas tend to be pretty expensive. You've only got about a .25 ratio. No matter what the historical numbers are ... don't believe the numbers. No matter what the historical numbers are, the return to risk ratio of a beta is probably not above .3. It may have an option characteristic that makes it look that way, but it's not above a .3.

   
And they tend to be very highly correlated because betas are all related to the same economic environment, so it's hard to get a lot of diversification in betas. Therefore, it's hard to get a really high ratio -- high consistent return -- from betas. On the other hand, alphas are very uncorrelated, but you never know if you're going to make or lose money because alpha is a zero sum game. So, it's entirely a bet of can you bet on and find the right manager who can take money from somebody else. And if you know ... if you think you can, you probably should ... as a test, you might want to think about who they're taking money from as a cross check on your process because somebody's got to take money from somebody else when it comes to alpha. For every winner there's got to be a loser.

Beta in Hedge Fund Returns

Now, I just want to show you a quick example of the importance of understanding the composition of beta in the returns of a manager that you might hire. One of the things that we did was that we looked at our database of 2,700 hedge fund managers - we ran the calculations for how much beta is approximately in the returns of these various managers. Well, we did that up to the beginning of the crisis in July 2007, quantified how much beta was in every single one of these 2700 managers. And then, we looked at how those managers then performed over the crisis period.


And so, what this work shows is the managers with more beta lost a lot of money, and that those with not very much beta in their portfolio up to July 2007 lost less. Hardly anybody made money. And the observations are right on the line of best fit (though you should also look at their return to risk ratio).

So, the amount of beta in a hedge fund's portfolio was something like, 96 percent correlated to what their performance has been since July 2007. So, if you just knew that one thing -- how much beta is in their portfolio -- you would have been able to identify with a 96 percent correlation how they would have done it through the financial crisis. The same thing is happening going forward because if you actually monitor this through the crisis for those managers, that beta hasn't changed. They still have that beta.

Now, so if their particular beta does well now, they are going to look good. But it's because that beta is in there. So, are you betting on the manager or are you betting on the market that they're involved in? It's absolutely crucial for you to understand that. And if you're betting on the market they're involved in, no matter what their historical ratio is, the beta ... the ratio of the beta is inherently limited, so something like .25, .3.


So if you look at what Bridgewater does, right now we're long bonds. There's risk premium there that we're earning today by being in a long bond position. But if you look back at what we've done historically, we're long half the time and we're short half the time. There's no systematic bias to be long bonds. And if you understood our process, you would know how hard we try to make sure we don't have that in there, right? So, literally indicator by indicator, market by market, we are approaching it with an expressed purpose of not having beta in our alpha. And we try hard to not let it get in there.

At any point in time, we could be long or short a market. Funds don't have to always be market neutral. But what I'm referring to is a systematic orientation toward beta. What I'm saying is that the amount of beta that was in those 2700 manager's returns was measured by a statistic, that a static holding of asset classes over many years was 80 percent correlated to their return, so that they are, over time, largely in a beta position.


 

Bridgewater Equity Mandates

I think the question of relative versus absolute is always trying to get at the real question of "what is value added?". And there's a lot focus on the industry on the quest for alpha, so to speak. But I think if you were to ask three people in a room of experts what the definition of alpha is, you'd probably get about four answers.


So, the real question is, what are you trying to do in getting value add other than exceed a simple passive benchmark? And, secondly, how is that going to influence people behaviorally?


That tend to lead you in a couple of directions, one in terms of more complexity -- I'll give you an example of that- our equity mandates. Our benchmark not against an overall equity index, but every security selection decision is against a sector in a country. So, you get six major countries, ten S&P sectors. So, we have something called a 60 cell matrix; you can imagine the operational complexity behind that. But every active choice is done against a very specific subsector so that the sector's taken out.


The question is, though -- and it gets back to that behavioral one -- how is that going to influence people, and can you even think intuitively about that when you're in 60 different dimensions? And so, there is something to be said for simplicity because the reality is that the very best tech fund manager in 2002 might have exceeded his peer group by 5 percent, but that meant they were only down 90 percent as opposed to 95 percent. And that's not going to solve anybody's problem.



Edited Transcript from The Greenwich Roundtable

Information Edge Five - Speed & Selectivity

Last year I wrote four times* on hedge funds utilising an information edge. The edge has to be exploitable and and through repetition become significant to the return series of the fund. This applies whether it is an edge in understanding (say the ability to extrapolate from few data points or read across from related industries) or more pertinently an edge in receiving proprietary information not generally available. There can also be an edge in hedge funds in the speed of reciept of information.


This last (superficially unlikely) edge is exploited by high frequency trading algorithms and stat arb managers. The extreme case of this need for speed is evident in the clustering of server farms near the exchanges. There can be a milli-second advantage of signals being received and sent over short physical distances between the computers generating trades and the exchanges fulfilling the orders down the pipes, whether on dark pools or electronic books on the bourses.  These systems are blind with no emotional or subjective inputs, but rely on accurate, logical and continuous pricing. As such, automatically traded capital is subject to event risk - if you like, the news on companies and sectors can get in the way of treating shares as trading chips for playing with minute-by-minute. 


This topic came to mind in reading a press release from Selerity, which describes itself as a low latency, real-time fact aggregation and event data company that caters to sophisticated investment firms including hedge funds, banks and proprietary trading firms. The Selerity technology searches and extracts "event data" from real-time primary sources, and delivers to clients machine-readable, actionable input.


In a new development Selerity software can now work with earnings pre-announcement event data as part of its content offerings to allow traders to use or avoid unexpected, market-moving events in their trading strategies. Corporate preannouncements are real-time, breaking updates released by companies regarding changes in their outlook or guidance ahead of scheduled earnings announcements, and as they are unscheduled fundamenal inputs these announcements can be disruptive to cluster trading, pairs trading and other high frequency trading strategies.


One of the essential tasks managers of high-frequency trading strategies (and nearly all quant managers) is to define their universe to exclude the outliers, anomalous stocks - those subject to corporate activity, and those having scheduled news releases for example - and make an effort to leave in their programmes only suitably qualified stocks by their criteria. Promptly identifying stocks that are subject to unexpected changes in earnings guidance from company management would help algorithmic traders and traders who deal in whole portfolios from being left marooned with unintended idisyncratic risk in the holdings/portfolios. 

There are many hedge funds that engage in event-driven strategies. The larger and more sophisticated ones use software systems to trawl through court documents, SEC Filings and other lengthy documents to find the few nuggets of information they seek. It is the document equivalent of conference delegates that come away with two or three quality pieces of insight or new understanding from a whole day sitting listening to presentations. The more deep and diverse the information inputs a manager uses in their process the more applicable are these tools.

The Selerity product is a specific example of a delivering appropriately framed and focussed information on a timely basis to a hedge fund or  investor of risk capital. But nearly all hedge fund managers need information of that sort, whether it is delivered via human vectors or software crawlers and spiders. The information edge has to supported and developed, and have a thorough process behind it.


* http://simonkerrhfblog.blogspot.com/2009/12/creative-way-to-build-hedge-fund-brand.html
http://simonkerrhfblog.blogspot.com/2009/12/information-flows-to-hedge-funds-2.html
http://simonkerrhfblog.blogspot.com/2009/12/podcast-2-discussion-with-uk-equity.html
http://simonkerrhfblog.blogspot.com/2009/11/galleon-edge-illegal-but-information.html

PODCAST FOUR – CTA Beach Horizon

A Discussion with Head of Research of Beach Horizon, Dr. Paul Netherwood.
Dr. Netherwood spent four years in the Nineties in trading systems research and development at AHL (Adams, Harding and Lueck, now part of Man Group), and for the last 9 years he has been at Beach Capital Management and Beach Horizon LLP, a systematic fund management partnership. Beach Horizon is based in the City of London.

 
Clicking on the link will open a page containing the sound file - download or play in your browser



Part One (Link Here) (15 minutes)


 0.00 Introduction to Beach Horizon and sytematic CTAs
 5.50 Portfolio construction and diversification including milk and pork bellies
 9.20 Targets for outputs return and volatility
 10.50 Upside volatility
 11.55 Margin-to-equity as a proxy for risk

Part Two (Link Here) (13 minutes)

 0.00 The team - an advantage in being able to tap into a trader with a discretionary background for idea creation  and assessing research ideas.
 4.45 An FX research project
 6.20 Prioritising research, co-opting the sciences
7.35 Research productivity
10.00 People power - intellect is not scalable
12.05 Beyond trend-following - different frequencies

Part Three (Link Here) (8 minutes) 

0.00  Different time horizons of investors - a dominant frequency
1:06 Performance of CTAs in 2008
3.40 Performance in 2009
5.06 Current Drawdown - when will we see outperformance of CTAs? Influence of QE?
 

 
Drawdown Analysis for Beach Horizon May 2005 to December 2009

   

Source: Beach Horizon Database

 

My thanks go to Dr. Paul Netherwood for his contribution to this podcast.

Quality Factors in Equity Hedge Fund Returns This Year

In 2008 one of the disappointments was the returns of market-neutral quantitatively-driven equity strategies. The difficulties were focused as much as anything else in the seemingly illogical behaviour of quality factors last year. I recently inquired to a senior researcher in equity quant about factor returns this year, and again quality factors have driven returns in unexpected ways. The behaviour of quality factors helps to explain equity hedge fund returns across different styles this year, and so I have included the following abstract from the research of this quant published at the beginning of this month:

Overview

Discerning the direction of the Quality trade remains the key issue for investors. Discussions of investors seeking to take money off the table heading into year-end and positioning themselves in a more defensive posture are not borne out by the behaviour of our Quality index.

Market Commentary

It has been a tough year for Quality. And it has been a tough year for stock picking. And these two facts are not unrelated. Indeed, over the past 9 to 10 months, the key to successful stock picking has been to understand the direction of the Quality trade.


The big news in the quantitative factor space, and really the market as whole, this month has been the underperformance of High Quality stocks relative to Low Quality Stocks. For the month, we saw High Quality stocks underperform Low Quality stocks by approximately 2.9%. For our quality index, this is a big move since, in general, our quality index runs at approximately 1/3rd the volatility of the Russell 1000. In other words, if our quality index were scaled to have the same volatility as the Russell 1000 index, we would have seen an approximately 8.5% down move in our Quality index this month. Clearly something is happening.


Backing up for a second: as most people are aware, the rally in the market that started around mid-to-late-July coincided with a very strong move upwards by low quality stocks. Specifically, we saw low quality names outperform high quality names by approximately 5.2%. Whereas the low quality junk rally in March was primarily focused around companies with distressed balance sheet and low stock prices, the July rally was much more broadly focused. Here we saw low quality companies of all stripes outperform. Companies with poor historical profitability outperformed. Companies with low quality of earnings (i.e. non-repeatable earnings) outperformed companies with high quality of earnings. And companies with low quality balance sheets outperformed those with high quality balance sheets. In short, the late summer rally was coincident with low quality stocks of all stripes outperforming.


Since that time, we have seen a reversal in quality and then this month a subsequent continuation of the low quality trend.


Now this stopped around September 16th as we headed into earnings season. From there to the end of October we saw High Quality outperform Low Quality stocks by approximately 4.5% as investors positioned their portfolio defensively and braced for what by all respects they feared would be a tough earnings season. Note, again these are large moves, if scaled to equivalent Russell 1000 vol, this would be a 13.5% move in a month and half.


This turned around again on October 28th, with another strong low quality rally emerging coincident with strong earnings announcements by banks, basic materials and commodity producers, consumer goods companies and REITs – in short, bellwether companies for any weakness in the recovery. As these decidedly good earnings numbers provided assurance of the recovery's continuation, investors took off defensive positions and piled back into the early stage cyclical recovery trade names. This trend has continued virtually unabated ever since, with the Quality index experiencing positive returns on only 6 of the 20 trading days last month (November). Discussions of investors seeking to take money off the table heading into year-end and positioning themselves in a more defensive posture are not borne out by the behaviour of our Quality index.


Perhaps the most surprising element to us was that the quality index has continued its downward march late last week even on the news coming out of Dubai, which instilled some measure of fear in the market. We would have expected a flight to quality on Friday as the story broke and world equity markets lurched down. But it didn't happen. Our Quality index continued to inch downward on Friday (-4 bps) and then again on Monday (-23 bps), as even geopolitical fears did not send investors fleeing to safety.


As we head into year-end, the pressing question is: do we anticipate a rally in Quality? Absent a high-impact geopolitical event, we see two scenarios under which the Quality rally can end. Namely, investors can become concerned about slowing growth and thereby move into a more defensive posture in their portfolios.


We believe the most likely cause of a pull-back in Quality is the likelihood that investors take money off the table from the strategy as valuation of low Quality stocks is no longer compelling. By most metrics that we use to judge, low quality stocks appear to be fairly to slightly richly priced.