David Shaw is a billionaire treading an alternative path to Buffett.
David E Shaw, founder of the D.E. Shaw & Co hedge fund, used his background in computing to make millions exploiting inefficiencies in financial markets with the help of sophisticated computer models. What can he teach us?
Background
Born in 1951, David Shaw is a computer scientist at heart, having worked as a professor at Columbia University where he specialised in parallel computing. He joined Morgan Stanley in 1986 working on automated analytical trading technology.
At Morgan Stanley, he worked under renowned trader Nunzio Tartaglia. A former Jesuit with a Ph.D in astrophysics, Tartaglia was an early proponent of pairs trading. He would find the stocks of two related companies whose prices had diverged from their historical relationship, and then buy the cheaper stock while shorting the overpriced one.
However, although the salary was great (six times that of a Columbia professor) Shaw missed the academic freedom.
In 1988, he started his own hedge fund with $28m from Connecticut–based Paloma Partners Management Co. and New York's Tisch family. The fund employed proprietary algorithms for quantitative trading. It pioneered the use of rapid computer trading, earning him the nickname "King Quant" and a personal fortune estimated at about $2.5bn.
The fund now manages about $30bn in assets, but he's no longer hands on in this area. In 2001 he returned to his first love, full-time scientific research. He now serves as chief scientist of DE Shaw Research, leading a research group in the field of computational biochemistry.
Technique
Shaw had a reputation for secrecy and perhaps that is hardly surprising given the importance of proprietary market-beating algorithms to the success of his hedge fund.
D.E. Shaw & Co outlines its quantitative strategies as being largely based on:
- the use of mathematical techniques to identify profit opportunities arising from subtle anomalies affecting the prices of various securities;
- the application of proprietary models designed to measure and control various forms of risk;
- the use of quantitative techniques to minimise the transaction costs associated with the purchase and sale of securities; and
- the utilisation of proprietary optimisation technology to construct dynamically evolving investment portfolios based on these profit opportunities, risk factors, and transaction costs.
Lessons
He's been quoted as saying that finance is a pure information processing game and that there are lots of jobs done in finance that should be done by computers.
I can't pretend to understand computational finance and high-frequency trading, but it is playing a large part in the way markets operate. Opinions vary on the volumes traded, but research firm Tabb Group has estimated that approximately 73% of US equity trading is now done this way.
It is certainly profitable. Tabb issued a report in 2009 estimating that the 300 securities firms and hedge funds that specialise in rapid fire algorithmic trading made approximately 21 billion in profits in 2008.
Small investors, unless they have a bent for computational finance don't really have much chance trying to adapt quant strategies to their own portfolios. Not unless they intend to run mind boggling levels of risk -- risks that can easily destroy wealth in volatile markets.
Indeed, it can be argued that such high-frequency trading, which takes place automatically, is increasing volatility in order to profit from it.
Shaw's success also highlights the difficulty of day trading and adopting full-scale quantitative analysis beloved of geeks. For that very reason, I'm much more in sympathy with Buffett's warning: "Beware geeks bearing formulas".
While amateur investors have absolutely no chance of emulating the tremendous achievements of David Shaw, they can at least aspire to follow the path set out by Warren Buffett.
For ordinary private investors our strategy should be to go with what we know and can understand, to buy good value stocks cheaply and hold for years. That way, we can make money despite the complex, computerised machinations of moneyed quants.
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