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Can a Nobel-prize winning economist beat the market, all the time?

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Can a Nobel-prize winning economist beat the market, all the time?

Sep 07, 2017Tyrone Wiseman
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We don’t mind a bit of history here at WFS, so let’s cast our minds back to 1998. I’d just finished school and Louise was starting Year 9. John Hewson told us the country would enter into a recession and Paul Keating delivered his “perilous moment” speech about the effects the Asian currency crisis would have on the Australian economy. 


Meanwhile, there was another major event unfolding in the US, when a large hedge fund, called Long Term Capital Management (LTCM), nearly collapsed. Had it done so, it would have set off a wobble that could have caused a global financial crisis.


LTCM was a “quant” hedge fund, run by two Nobel prize winning economists, Myron Scholes and Robert Merton, and had about $126 billion in assets under management. Investors had to contribute $10 million to even get into the fund; they weren’t allowed to take money out for three years; and they also weren’t allowed to ask about the types of investments in the fund’s portfolio. 


You’d think that last point, would be quite the warning sign.


Nope. Investors clamoured to be a part of it, and were rewarded with yearly returns of over 40%. And that was after taking 27% in management fees.


Want to know how?


In simple terms, maths. In jargon, quants.


“They were famous for not only exploiting inefficiencies, but using easy access to capital to create enormous leveraged bets on market directions.”

Source: Investopedia

 

Let me explain.


A quantitative investment strategy (quants) is all about the numbers. Analysts don’t go visiting companies and meeting management teams, to find weaknesses or a competitive edge. Instead, they look for patterns in publicly available data (i.e. numbers) to figure out their trading strategy, and then set up models to automatically buy and sell stock.


" For example, a trading strategy based on trading volume patterns may have identified a correlation between trading volume and prices. So if the trading volume on a particular stock rises when the stock’s price hits $25 per share and drops when the price hits $30, a quant might set up an automatic buy at $25.50 and automatic sell at $29.50. "

Source: Investopedia


The theory, is that the removal of emotion from the trading process improves returns. Because it’s all based on pre-determined triggers, it doesn’t matter if Bob, who might be looking after the transport asset class in the portfolio, thinks a new airline is the next big thing or not. There’s no fear. No greed. No irrational human stuff.


It sounds good, but it turns out that just as many quant strategists that succeed, also fail. And when they fail, “they fail big time”. 


The problem is, when the analysts create the models, they can’t factor in every scenario. So, when something unexpected happens, there are often catastrophic results. 


The possibility of the Russian government defaulting on its loans, was one such unexpected event, and combined with the amount of leverage LTCM was exposed to, started a chain reaction. As there was so much money involved (over $100 billion, remember), if LTCM went under, it would have caused financial chaos around the globe. It was a fund that was simply “too big to fail”. 


The good news is, the US Federal Reserve helped coordinate a bail-out, and was able to prevent world markets from being savaged.


“Had the failure of LTCM triggered the seizing up of markets, substantial damage could have been inflicted on many market participants, including some not directly involved with the firm, and could have potentially impaired the economies of many nations, including our own." 

Source: Alan Greenspan, Chairman of the US Federal Reserve


The lesson here is that even Nobel-prize economists and geniuses with PhDs are subject to the uncertainties of the markets and human foibles. At the time, Merrill Lynch said quantitative investment strategies, “may provide a greater sense of security than warranted; therefore, reliance on these models should be limited.”


We’ve noticed there seems to be a greater availability of these types of investment strategies and an increasing complacency that relying on mathematical models is a way to limit risk and maximise returns. And as part of a balanced strategy, there may be room for a quant manager in your portfolio.


But if you’re relying on a quant manager to deliver the same returns in the future as what it has done in the past, you might want to remember that old Wall Street saying, “the trend is your friend, until it ends”.


It’s certainly one of the reasons why we construct portfolios a little more conservatively than others.