Hedge Fund Market Wizards

In this article I will summarize what I learnt from the book “Hedge Fund Market Wizards” by Jack Schwager. The book is based on the interviews with many great traders, such as Ray Dalio, Edward Thorp, Joel Greenblatt among others.
We can learn a lot from these interviews. This essay is my attempt to present the most remarkable points.
Colm O’Shea. The best way to trade bubbles is to go with the trend until the euphoria dies, not to short the market. Uptrend may last a long time which then is followed by a marked collapse. Since as a trader, we don’t want to take the gap risk, options (long calls) provide a good solution to trade bubbles. The example O’Shlea provides is the silver market in May 2011.
Ray Dalio. One of the most famous money managers in the world, Ray Dalio is a “big picture thinker” as Schwager puts it. His belief is that fundamental factors are not independent of the environment in which they occur. Which means that the fundamentals would imply different things under different circumstances. Schwager gives an example of this thinking which he calls “quadrant conceptualization”. If we specify two factors, namely inflation and growth, and two conditions, such as increasing and decreasing, we get four conditions which are growth increasing, growth decreasing, inflation increasing, inflation decreasing.
Most traditional portfolios tend to perform well in the growth-increasing environment while they fare badly under other three conditions. Bridgewater’s All Weather Fund is designed to perform well in all environments.
Another point that I’ve found interesting is how Dalio looks at correlations. In contrast to the conventional view, Dalio thinks that since correlations change all the time, they are not a good tool for designing a robust portfolio. Like fundamentals we discussed above, correlations also depend on the circumstances. The example the author presents is the correlation between gold and bonds. Under ordinary circumstances, these two asset classes are inversely correlated. Inflation is typically good for gold and bearish for bonds because high inflation means high interest rates. However, under some environments, such as the beginning of the deleveraging period, they can be positively correlated. In this cycle, the bond prices will rise since monetary policy will be to reduce interest rates which is bullish for bonds. On the other hand, the fear of currency depreciation will increase gold prices too.
It is also very insightful to read about his views on the differences between deleveragings and recessions. Central bank’s monetary easing (i.e, lowering interest rates and making lending cheaper so to speak) is effective in recessions. However, in deleveragings this policy of creating credit fails. The main reasons why cutting interest rates works in recessions are that there is a room for decreasing interest rates which alleviates interest burden of borrowers. Furthermore, since lower interest rates make it easier to buy assets. This results in higher asset prices which is what Schwager means when he mentioned “a positive wealth effect” that cutting interest rates creates. Due to these reasons, easing monetary policy works during recessions and is effective in stimulating economic activity.
In deflationary deleveragings, however, this policy won’t work. Since interest rates are already at 0, they cannot be cut anymore. Stimulating credit growth during deflationary depressions is not an easy task for central banks because borrowers still have a large debt burden. This is what happened in Japan’s infamous deleveraging during the 1990s. Richard Koo, who described it in his book “The Holy Grail of Macroeconomics”, calls the Japanese experience “balance sheet recession”. In deleveragings / balance sheet recessions, conventional monetary policy fails to stimulate economic activity. Private sector borrowers are overindebted and are too busy to clean their balance sheets. Therefore, they cannot be persuaded to borrow even at 0 percent. Under these circumstances, cutting interest rates fails to deliver the desired results, and less efficient ways of creating credit (e.g, fiscal stimulus) are used.
Larry Benedict. What I found most interesting in the chapter on Larry Benedict was his very stringent approach to risk management. He usually allocates a fixed amount (which can be 2 to 2.5 percent of portfolio) to a trade. When the losses increase, he takes further actions to decrease the portfolio risk. He begins to trade small; after he gets profitable again, he returns to the normal size.
Benedict emphasizes the role of trader psychology. When he talks about unsuccessful traders, he notes that the common trait of all these failed traders was “a gambler’s mentality”. When a trader starts to lose, he is in the search of that one trade that will make it all the lost money back. As he himself puts it, it is hard to find big up days on his daily P&L.
Scott Ramsey. Look at which markets performed best of all during hard times. Ramsey believes that these will be the leading markets when things begin to stabilize. Conversely, the markets that rose least of all during rallies tend to be the weakest. This is how Ramsey picks his long and shorts — always go long the strongest and short the weakest markets.
The other remarkable trait of Scott Ramsey is strict risk control. He believes that to limit the downside is the most important part of the trading business. Risk management is highly necessary not only in cutting your losses early but also to get positioned to benefit from opportunities that may follow hard times. When you keep your losses small, you have ample capital to exploit opportunities. As Mark Spitznagel’s mentor Everett Kipp would put it, a small loss is a good loss.
Jaffray Woodriff. Woodriff, a quantitative trader, gives some useful insights in his interview. First, unsurprisingly, having many different models is much more preferable to using a single model. Second, the risk of overfitting the theory to the past data is higher with the market-specific models than with the ones that perform well across multiple markets. An important positive change in his career occurred in the early 1990s when he left the specific models for more general models. Another change during this period was related to the shift to general models mentioned above. With the growth of AUM (assets under management), he increased the diversification of his portfolio. Since he started to using the models that were robust across many markets, it was possible to trade many markets, not only 2–3 models with which he started to trade.
Edward Thorp. One of the most colorful figures in the hedge fund industry, Edward Thorp developed a winning strategy for blackjack, discovered the option pricing model before Black, Scholes and Merton, and founded one of the most successful funds, Newport Princeton Partners.
In his interview, he talks a lot about position size, namely about the popular Kelly formula. Thorp notes that position size is as important as the entry criteria. Not trading at all or trading with a small position size when the odds are not in your favor, and trading big when the probability is high can significantly improve the results. He believes that this can even turn a losing strategy into a winning one though I find it difficult to agree with him on it.
It’s very important to have a conviction in a trade. How much you are confident in a trade determines not only the position size but also your risk management approach. Schwager contrasts Thorp’s arbitrage trades with his trend-following strategies. In the former trades the maximum risk is possible to approximately calculate. Therefore, when the position went against him, Thorp almost never reduced the position size. Conversely, in a trend-following strategy bets are directional which makes the estimate of the edge uncertain. This was the reason why Thorp cut the position size when he employed trend-following strategies.
Jamie Mai. Mai is known from Michael Lewis’ book “The Big Short” where he is one of traders betting against the subprime mortgage market. His interview is the one I enjoyed the most in the book. Schwager describes Mai being in the search of bets with asymmetric risk-return profile, i.e the trades where the potential return is the several multiples of the risk amount. Most of his trades mentioned in the book involved mispriced situations in the options markets.
One type of these mispricings Mai emphasizes is that the market participants tend to assign normal distribution probabilities to the events that are strictly binary. In 2003, there were several litigations against tobacco companies which could settle in very high amounts. (9- or 10-figure amounts). When he looked at one of these companies, Altria, they found that its option prices imply a normal distribution while they (Mai and his traders) were confident that it was a bimodal event. Since out-of-money options were too cheap, they exploited the situation by buying out-of-money calls because they believed in a bullish result. The fund made more than 2 times of its money on the position.
Another pattern that Mai observed in the options markets is that in low volatility market environments options are priced too low. (I think one of the reasons is that volatility has a positive relationship with the option value in option-pricing formulas). He believes that the best time to long volatility is when volatility is too low because 1) volatility is inexpensive and 2) markets tend to extrapolate low volatility to the future which leads to higher than normal volatility.
This is not the only mispricing related to option volatility though. Mai also notes the assumption that volatility increases with the square of time interval usually results in the underestimation of options volatility with longer time intervals. For shorter periods, this assumption can scale but for long-dated options it doesn’t work.
And the last insight Mai shares about option volatility assumptions is that trend is dismissed in volatility calculations in theoretical models. Price changes in option pricing models are dependent on volatility and time horizon not on the trend. Since most of these theoretical models assume random price moves, sustained trends can lead to unlikely results which these models imply that shouldn’t have happened. The example Mai gives concerns Canadian dollar versus US dollar. In 2007, USDCAD fell from 1.1000 to 0.9200 gradually while the volatility was declining. As Mai himself puts it, it was “a nonsensically improbable event” if we look at it from the volatility aspect. Under these circumstances, out-of-money options may be too cheap.
Michael Platt. When the book was being written, BlueCrest Capital Management, the hedge fund that Platt founded in 2000 traded mostly a discretionary strategy and a trend-following strategy. In 2014, the fund spun off more than $8 billion of its assets and thus Systematica Investments was created. Though I couldn’t find more information, I believe that trend-following strategy is being traded by Systematica Investments at the moment.
What is most remarkable about Platt is his excellent risk management. Schwager notes that during 11 years (2000–2011), the fund’s maximum drawdown was only 5 percent. What is more surprising though is that this figure includes the results of one of the most difficult years for hedge fund industry, 2008.
Platt mentions some risk control approaches in the interview. For example, for the trend-following strategy, they exit “overextended trends”. Platt doesn’t like the idea of shorting out-of-money options since the downside risk is unlimited. The only exception was in 2009. After the financial crisis, out-of-money were very rich (i.e, overpriced). They shorted them and hedged their positions by going long at-the-money options. He believes that it was a good idea because it was unlikely that another crisis would happen six months after 2008. And he was right; another crisis didn’t occur, and out-of-money options expired worthless. This trade was one of the reasons why the fund performed well in 2009.
His strict risk control also expresses itself in how Platt implements trades. If he finds an investment theme, e.g., exchange rate of a currency pair will increase, most likely he will not make a directional bet on that exchange rate. Platt will try to find options or spread strategies with the similar return potential to that of the directional bet but which lowers the downside risk.