Jaffray Woodriff – The Third Way

Jaffray Woodriff

Today’s post is a profile of Guru investor Jaffray Woodriff, who appears in Jack Schwager’s book Hedge Fund Market Wizards. His chapter is called The Third Way.

This article is part of our ‘Guru’ series – investor profiles of those who have succeeded in the markets, with takeaways for the private investor in the UK.

You can find the rest of the series here.

Jaffray Woodriff

Jaffrey Woodriff

Jaffray Woodriff is the co-founder and CEO of Quantitative Investment Management (QIM), a $5 bn hedge fund run from his home town of Charlottesville, Virgina.

Woodriff appears in Jack Schwager’s book Hedge Fund Market Wizards.

  • His chapter is called The Third Way.

Woodriff started out as a Commodity Trading Adviser (CTA) – the usual American styling for a futures trader.

Most CTAs use trend-following techniques – they follow a trend (up or down) until they get a reversal signal instead of a continuation signal.

  • A second category of CTAs use a counter-trend approach – they look for situations where an asset is a long-way from it’s trend average, and hope that there will be mean-reversion.

Woodriff belongs to the much smaller group of CTAs who follow a “the third way”.

  • He uses systems that identify patterns suggesting the very short term (24-hour) direction of the market.
Background

Woodriff picked grapes on his uncle’s farm in high school and was paid according to his output.

  • He realised that although he worked harder than local workers, the migrant pickers worked even harder than he did.

This lesson in incentives carried over to his hedge fund, which instead of the usual “two and twenty” charging scheme, uses a “zero and thirty” structure.

  • He only gets (well) paid when his clients make money.
  • Note that since he doesn’t pay back money when the fund goes down, his interests are still not aligned with those of his clients.

Woodriff was interested in odd and probability – and baseball statistics – from a young age.

  • He went to the local university and traded stocks and options through college, even buying stock during the October 1987 crash.

He decided that he wanted to be a trader before he graduated.

  • He read Schwager’s first book on Market Wizards, and attended a talk by Paul Tudor Jones, who had been to the same university (and who we’ve also covered in this series).

As soon as I learned about the efficient market hypothesis, I was on a mission to prove it wrong.

He told his parents that if he could get his predictive models to work for trading, he would later generalize the same approach into science.

In fact he stuck with trading, but he did set up a $100M foundation to improve statistical prediction methodologies and software (data mining, as others call it).

  • At the time of the interview the generalised software had not been developed, but the plan was not to release it anyway until QIM had lost its edge in the markets.
Early CTA firms

He set up a CTA partnership with a classmate, using the classmate’s family’s money.

  • But Woodriff didn’t like the other guy thinking this made him the boss, and he quit within months.

He formed another partnership with Robert Jordan in October 1991.

  • Woodriff did the trading and Jordan did the marketing.

For the first two years, they lost money.

  • Then in the first half of year three, they made 80%.

Woodriff wanted to formalize their business relationship, but his terms angered Jordan, who filed a lawsuit.

Woodriff started his own CTA in August 1994 with $50K raised from his family.

  • He lost 28% in the first 18 months.
  • Then he made 180% in 1996, but “only” 30% in 1997.
  • He ended the year with $1.5M in assets.
New York

Frustrated, he closed the fund and moved to New York to search for a proprietary trading job.

  • A friend’s uncle was a hedge fund manager and interviewed him.
  • He didn’t believe what Woodriff was doing could work, and didn’t hire him.
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A friend of a friend got him an interview at SG, where he was hired as a proprietary trader.

  • He worked there successfully from 1998 until March 2000, developing the systematic long/short style used in his hedge fund.
QIM

He left SG when his boss said he was leaving to start a multi manager hedge fund.

In fact, Woodriff was the only other person involved.

  • He didn’t take up the offer and instead started his own private equity-trading fund with a friend and $300K.

In two years he ran the fund up to $6M and moved back to Charlottesville.

  • He also diversified into futures.

The name was changed to Quantitative Investment Management (QIM) in May 2003.

  • In late 2003 they started managing other people’s money.
  • They now have 31 employees and manage $5 bn.
Performance

Futures trading accounts for 85% of the $5 bn.

From October 2003 to December 2011 the average annual compound return was 12.5%, with annualized standard deviation (ASD) of 10.5% and a Gain to Pain ratio (GPR – Schwager’s own measure) of 1.43 (a good score).

  • An older internal futures account using leverage (and not charging performance fees) has an average annual compound return of 118, with an ASD of 81% and a GPR of 1.94.

The proprietary equity account (April 2000 to September 2005) had an average annual compound return of 115%, a 69 percent ASD and a very high GPR of 2.69.

  • The client equity account (May 2008 to end 2011) had an average annual compound return of 34% with an ASD of 20% and a very high GPR of 2.38.
Trading style

Woodriff is a natural contrarian, and didn’t fancy the trend-following models that were popular.

  • He liked mean reversion more, but not enough.

He built a third class of model in the engineering school lab straight after graduation.

  • He worked a double all-nighter on 20 computers back-testing his model in many markets.

He soon realised that it was better to use multiple models than a single “best” model.

  • After he set up his fund, he further realised that it was best to use the same models across all markets, rather than a specific model for each market.
  • By 1994 he was trading 20 markets.

Of course, neither of these approaches is unique amongst CTAs.

Secondary variables

Woodriff uses combinations of “secondary variables”, each generated from the daily open / high / low / close price data.

  • An example would be volatility, which is not related to price direction.

He has tried using macroeconomic data as well, but can’t get it to work.

Woodriff tests “trillions of combinations”, despite warnings from other system builders against “burning the data”.

I knew I could find a way to try any number of combinations and not overfit the data. …

I was using the data up to a year before the current date as the training data set, the final year data as the validation data set, and the ongoing real- time data as the test.

He combines the secondary variables into “trend-neutral” models that are neither trying to project a continuation of the trend or a reversal of the trend.

  • Instead, they try to predict the probable market direction over the next 24 hours.

He has over a thousand models, but many share common characteristics.

  • Schwager couldn’t convince him to explain any of them in detail.

Woodriff quotes David Shaw from one of Schwager’s earlier books:

The more variables you have, the greater the number of statistical artifacts that you’re likely to find, and the more difficult it will generally be to tell whether a pattern you uncover actually has any predictive value.

We take great care to avoid methodological pitfalls associated with overfitting the data. … We typically start by formulating a hypothesis based on some sort of structural theory or qualitative understanding of the market, and then test that hypothesis.

Woodriff ignores this advice:

Instead, I blindly search through the data. I want to automate the process.

The secondary variables have to make sense, though.

  • It’s reasonable to think that indicators like volatility or price acceleration might logically provide important information.
Data mining

To avoid the problems of data mining from the billions of possible combinations of secondary variables, Woodriff generates random numbers that have the same distribution characteristics as real price data.

  • The performance of the best model on the fictitious data provides a baseline that other models have to beat on the real data.

The performance difference between the models using real data and the baseline is indicative of expected performance on future data.

You are really getting somewhere if the out- of-sample results are more than 50 percent of the in-sample.

Sometimes we give a little more weight to more recent data, but it is amazing how valuable older data still is. The stationarity of the patterns we have uncovered is amazing to me.

It takes a tremendous amount of deterioration to drop a model. We don’t react to the short-term results. … What is predictive is how the model performed over the entire 31 years. The extra 3% provided by the most recent year doesn’t make much difference.

Capacity

Woodriff admits that the increase in the size of his fund has made trading more difficult.

  • The fund now trades through the day, instead of just at the opening.
  • They also give greater weight to more liquid markets – they have found that their edge is greater in more liquid markets.
  • He estimates that the fund could grow to $6 bn to $9 bn before it runs into further problems.
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Risk management

The core of the fund’s risk management process is

evaluating the risk of each market based on an exponentially weighted moving average of the daily dollar range per contract.

  • As volatility increases, the number of contracts traded in that market drops.

The other process is a leverage reduction policy, which hasn’t worked as well.

Whenever there was an intra month drawdown of 6% from the monthly peak, they cut their exposure to 75%.

  • On an 8% drawdown, exposure was cut to 50%.
  • On a 10% drawdown, it was cut to 25%.

In 2010 and 2011, this policy hurt returns because when the models were working best, the exposure was lowest.

  • If a system doesn’t work well for a period, mean reversion suggests a greater-than-normal probability that it will do well in the subsequent period.
  • By reducing your risk after a drawdown, that means you have a small exposure when you expect good returns.

Reducing exposure after losses will mitigate the chances of a catastrophic loss, but it will do so at the cost of adversely impacting performance.

OTC Markets

Woodriff is not a fan of the lack of regulation in OTC markets.

The OTC markets are very often used to take advantage of clients who are “sophisticated” in the legal definition, but are naive in practice.

The OTC markets have been built to maximize asymmetries of information and are an example of how markets should not operate.

Markets should be fair and transparent, like the futures and equities markets.

Conclusions

This is another strange interview.

Woodriff is clearly very successful and the chapter makes an interesting read, but he reveals very little about the details of his systems.

  • Even if he had done, they would be very difficult for a private investor to implement.

We can take his success as more evidence that it is perfectly possible to build models that predict short-term market movements.

  • And that these models can be constructed simply by back-testing sets of secondary price variables against historical price data.

But we don’t know what these models are, and we don’t have the computing power to work them out for ourselves.

Perhaps the best takeaway is Woodriff’s spin on the regular advice to scale back your trading when things are going against you.

  • As well as reducing his trading when he has a monthly downturn, Woodriff also scales back trading in a market when the volatility of that market increases.

Until next time.

Mike is the owner of 7 Circles, and a private investor living in London. He has been managing his own money for 35 years, with some success.

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Jaffray Woodriff – The Third Way

by Mike Rawson time to read: 6 min