One time I was with a colleague in Japan interviewing managers. My colleague Patrick knew his way around Tokyo, well, somewhat, so we only spent 25% of the time lost instead of the usual 50%. This was early 2006 when Japanese hedge funds were facing a particularly horrible time. The January and February losses on the broad market were about 7% each on each downleg. Taking into account some upside volatility the market actually only lost slightly over 5% from the beginning of the year till mid February.

Hedge funds trading Japan from Japan had mostly sustained double digit losses and were facing tough questions from their investors. In the latter half of 2005, the fund of funds community, that is the people who invested on behalf of investors into hedge funds, had been very bullish about prospects for Japanese hedge funds. They had piled into Japanese hedge funds rather exuberantly.

At the annual Goldman Sachs Asian hedge fund conference usually held in November in Tokyo, the mood was upbeat and investors outnumbered hedge funds by several multiples. Some 600 over people showed up, some uninvited, hedge funds and investors both. Times like this I get nervous. I have no reason to be. No good reason at least. But human beings are like lemmings sometimes. In November 2005 we were being told that the smart money was already invested but that the party would continue. I was and am of the view that this was the correct view to take and this story is not one about contrarian investing. This story is about a particular hedge fund.

(Throughout this Blog names of people and companies are changed, so are particular circumstances so don’t bother trying to figure out who I am talking about. Usually the characters are composite characters, sometimes reflecting the schizophrenic nature of some managers, but more often because it makes for more colorful description.

John was the manager of a Japan equity long short fund. In Asian markets, equities are the most liquid and visible of markets. The dominance of bank lending has stifled the growth of corporate debt although this has changed considerably since the 1990s. Still, the majority of hedge funds in Asia will be involved in trading equities. Local currency debt is a growing market. Emerging market managers trading in Asian markets but sitting in London or New York mostly participate in the hard currency soveriegn and sometimes corporate market for debt but these rely more on macro economic analysis than bottom up stock selection.

Anyway, John was an experienced trader who had cut his teeth trading at such intitutions as HSBC, Citigroup and Merrill Lynch. He had grown up in Asia despite his African American / Japanese ethnicity. He spoke fluent Japanese and he had an excellent network in Japan.

Following a pretty good career at Citi, John joined a hedge fund launched by a big name trader who had come off the proprietary trading desk of Deutsche Bank. The fund launched with some fanfare and raised 500 million USD in capital. It traded for a year and then came unstuck. Prop desk traders are in hot demand when it comes to hedge fund start ups, but there are risks. There are always risks. With prop traders the risk is that the guy was never a very good fund manager to begin with. Maybe he was just a psychotic risk taker and his success at some investment bank was down to the quality of risk management and not to his own skill. Any prop trader leaving to set up his own fund will tell you that this is not the case. They will tell you how risk management in an investment bank is stifling and does not understand the true nature of risk, does not understand the structure of the market, or the intricacies of trading. Success at Goldman Sachs, Morgan Stanley or JP Morgan on the prop desk does not automatically translate into success at one’s own hedge fund. In any case, John’s new venture collapesed in a cloud of redemptions as investors sought to cut their losses and exposure to further losses.

When I went to see John, he had just completed his separation from the ill fated hedge fund and started his own firm. In the course of the interview I began to suspect that John was actually a very good investor. Having traded Japan myself I was in a position to discuss at the position level, his current portfolio and understand his rationale for each position. Subsequent reference checks in the days following allowed me to confirm that the damage at his previous shop was due to poor risk management on the debt side of the portfolio. John was good and the implosion of his old shop was not his fault. It was bad luck that he had saddled up with the wrong posse and got burned.

Unfazed, John picked himself up, dusted himself down and re-launched himself with his own capital. The investment in the business totalled over a million USD and he had another 3 million USD to invest in his own fund. Unfortunately for John, he launched his new business at the end of 2005. His first 3 months were horrible and saw losses totalling 17% by March. Losses do strange things to people. His natural instinct told him to stick to his knitting and he would recover but as he was at the stage of raising capital, courting funds of funds as investors, he changed the way he managed money. By June the losses were nearly catastrophic.

I kept in touch with John throughout and followed his investment strategy through the months. They were sound and would eventually turn his way. Unfortunately, as the great Lord Keynes said, the markets can stay irrational longer than one can stay solvent. I have lost track of John now and he may have thrown in the towel. I hope he hasn’t because he was a skilful investor on a bad roll. At least that was my opinion.

The first question is, how do you distinguish between bad luck and poor skill? If one has traded the same markets or strategies one can empathize with the manager. What if the strategy is alien and one is learning about it for the first time. Technical competence and skill are very different things. Competence can be learnt. Skill takes experience to learn, sometimes painful experience. Here again, skill could be discerned if one knew enough of the strategy and had sufficient information (transparency) to understand the rationale behind the positions.

A more difficult question as an investor is how long do you tolerate bad luck?

A word about alpha and betas in investment returns

The Math

In mathematics, half the problem is giving things names. The investment management industry has borrowed a naïve model, applied it to very complicated problems and then expect to make sense of the results.

What is alpha and what are betas?

The terms alpha and beta come from the linear model of statistical modelling.

yi = b xi + a + ei

Where yi is the dependent variable, xi is the independent variable, a is an intercept term and ei is an error term which by construction has a mean value of zero.

The standard example is where yi are the returns of a particular stock, xi are the returns of the market (using some suitable stock index as proxy). The coefficient b is the beta and represents the systemic risk, the coefficient a is alpha which represents specific risk.

In order to make statistical inferences from the model a distribution needs to be assigned to the error term ei. There is a theorem that says that subject to some assumptions about how ei behaves, not only is it possible to estimate what the betas and alpha look like but we can make inferences from them.

The model is easily extended and generalized to :

yi = sum of (bj xi +a + ei)

Where there is not one market factor but k of them. The industry applies this model to hedge fund returns often with one factor, usually an equity index, and sometimes to several factors. Natural candidate factors are, bonds, yield curve shape, equity vol, swaption vol, credit spreads, interest rates, currencies.

Observations and comments:

An assumption is being made about the relationship between y and the x’s. If the assumptions are wrong, the betas and alpha measured are meaningless. The industry will sometimes apply the model to a credit manager, or a fixed income arbitrageur, or an asset based lender with equity market returns as an explanatory variable, despite the lack of causality.

There has to be sufficient data. The more complicated the model, the more data you need. Hedge funds publish monthly performance numbers. A manager with a 5 year track record has only got 60 data points. Having enough data is the first point. The data has also to be well behaved.

Proper estimation of betas and alpha require that the x’s have certain properties. One of them is that the x’s should not cluster, mean revert or converge. This is clearly a problem. Basically what the methodology requires, in simple terms, is that if you want to measure a manager’s alpha, you need to have all sorts of market conditions from bull and bear trends to choppy sideways markets. It makes sense. A 5 X levered position in a rising market looks very much like alpha. What does one call a 0.5X levered position in a rising market? Negative alpha?

The two previous points suggest also that the data has to come from a sufficiently diverse set of states. Enough data and enough variation in data imply that a manager has to be tested over all phases of the cycle in their particular market. Ideally, the performance should be measured over several cycles.

The necessary conditions for meaningful inference make this methodology intractable for hedge fund analysis. Track records are rarely sufficiently long to include several iterations of the market cycle.

Comments about industry implementation:

Seeking to buy alpha is only relevant if one is willing to invest over sufficient cycles for the alpha to manifest.

Beta is cheap. Alpha is not priced. It may be expensive or not, but current performance fees are not directly linked to alpha.

Alpha and beta are thought of as constructive concepts when they are illustrative concepts. Unless one is happy to invest over a sufficiently long horizon.

Alpha can be negative even as returns are positive and outperforming the market.

Alpha and Betas are convenient language for active risk and passive risk as long as we don’t take them too seriously.