Part 1 (2/2)

FIGURE 1.3 Comparison of the net returns for S&P moving average systems through May 2010 and the contribution from ”more” and ”fewer” 1-day price shocks.

Using this chart, we choose two price-shock thresholds, 3.0 and 4.0, to compare the impact of what we will call more shocks and fewer shocks. The fewer case is also larger shocks. We run a test of moving averages using calculation periods from 10 days to 200 days over the past 10 years. The rules are that a long position is entered when the moving average turns up, and a short is entered when the moving average turns down. The system is always in the market. A $25 round-turn commission is charged to cover all costs. Results are shown as Net PL in Figure 1.3, along with the net results of the 1-day price shocks. The performance pattern of the S&P begins with large losses for faster trends and finally shows profits for trend periods approaching 200 days.

The lines representing the contributions from 1-day price shocks show that in nearly all cases, the net impact of price shocks are negative returns. This can be attributed to most investors holding the same long position when there is a sustained bull market. We would caution traders not to believe that price shocks will contribute to short-term profits, even though the chart shows some net gains for the 10-day average and again for the longest calculation periods. At best, you can a.s.sume a 50% chance of a price shock in your favor. Anything else is strictly luck.

We thought it would be interesting to compare the results of these tests without the impact of the subprime crisis; therefore, we retested the data beginning at the same point, 1997, when the e-mini S&P began trading, and ending on January 1, 2008. The results are shown in Figure 1.4. The results are actually very similar for the contribution of price shocks because only a few shocks would have been added. In addition, the measurement of a price shock is relative to the previous 10 days, so that the sustained high volatility during the months from August 2008 through February 2009 made it difficult to have any shock that would have been 3 times larger. Instead, we see that the S&P was a poor performer, using a simple moving average system, and that the large downward move and the following rally from August 2008 through the current mid-2010 boosted the profits from $25,000 for the longest trends to nearly $60,000. Simple systematic trend following can perform well when traders can't.

FIGURE 1.4 S&P price shocks for the period beginning January 1991 and ending on the last day of 2007, to avoid the effects of the subprime crisis.

Interest Rates.

Interest rates are a far less volatile and more orderly market than any equity index. Using the Eurobund as the representative, we run the same tests as we did for the S&P, using 25 as the round-turn cost for each trade, and beginning in 1991. The results are shown in Figure 1.5. They differ considerably from the S&P results because more shocks total very negative returns, averaging about half of the net profits. The fewer, larger price shocks netted an impact closer to zero, but the more frequent shocks, the results of periodic economic reports, move consistently against the trend position.

FIGURE 1.5 Impact of price shocks on Euro bund moving average returns.

FIGURE 1.6 Eurobund futures prices, nearest contract, back-adjusted from 1990.

The large losses due to price shocks can be attributed to the Eurobund trend over the test period, as seen in Figure 1.6. With prices moving higher over the past 18 years, we would expect most investors to be holding long positions. Then price shocks to the downside would most often generate losses. As the trend calculation period increases, the time holding a long position increases; therefore, price shocks to the upside become a larger profit component, and shocks to the downside a larger losing component. These can be seen in Figure 1.7, where the losses due to short-side shocks far outweigh the gains from upside shocks.

FIGURE 1.7 Net effect of price shocks on Eurobund long and short positions.

Crude Oil.

Another market that has attracted a great deal of attention is crude oil, rallying from $40 per barrel to nearly $150 before falling back to $30 in just over three months, shown in Figure 1.8. A breakdown of the price shocks (Figure 1.9) shows that more shocks added to profits, while the largest shocks moved against the positions being held. This was a remarkable period for oil, and any news (more shocks) was taken as bullish. While there were big downside surprises, the market ignored them.

FIGURE 1.8 Crude oil, back-adjusted futures from 2003.

FIGURE 1.9 Crude oil effect of price shocks on the profits of a moving average strategy.

The profits from varying the calculation period of the moving average show that the slowest trends held the long position too far into the reversal that followed the peak of $150, giving back most of the gains. In hindsight, the perfect trend was about 110 days, but it's not likely we would have been trading it. Most macrotrend programs would have chosen something in the range of 60 to 80 days, all of which performed well and had small or some positive effect from price shocks.

WHY SO MUCH ABOUT PRICE SHOCKS?.

Price shocks represent the worst-case scenario for traders. They are unexpected, violent, and most often generate losses. Even more important is that most traders don't plan for a price shock. You can plan to survive a price shock by holding large reserves so that a 4-standard deviation event will not produce a loss that you can't handle. But to do that, you need to give up leverage. More often than not, without the leverage, the result will be returns that are not justified by the risk.

Algorithmic traders are also guilty of ignoring price shocks. They test their programs on historic data that contain past shocks, and when they design their strategies and risk controls, the acc.u.mulated result of the way they traded during those past shocks can often produce net gains-a situation not likely to happen in real trading. The biggest offender was Long Term Capital Management, which, we are told, removed the price shocks from the data history because they believed them to be unrealistic (in the context of their trading) and not likely to be repeated. While it's true that the next shock is never a repeat of another past event, there are countless new market surprises.

What can you do to avoid price shocks, even if you can't predict them? The practical solutions are: Don't trade.

Stay out of the market as much as possible.

Choose a faster system so that you're not holding the same position as everyone else.

Trade a hedged or market-neutral position.

Of course, no one reading this book would choose not to trade, so we'll ignore that point. On the other hand, staying out of the market as much as possible is very practical. That can be done easily with faster trading systems, where you target profits and have specific timing requirements to entering a new trade. If you end up holding positions for one-third of the number of trading days, then you have a 66% chance of missing a price shock.

Trading a faster system is also better. By not holding the same position as everyone else, and getting net long or short frequently, you have a good chance of a positive price shock 50% of the time.

Our choice is the last point, trading a spread or market-neutral position, which takes price shocks out of the picture completely. Of course, there is a chance that the two markets that are spread will not react as expected, but that is a very small chance. In addition, mean reverting strategies, which represent most of the market-neutral programs, are fast trading and require two or more markets to diverge; therefore, they are in the market for a relatively short period of time. As we will discuss later, long holding periods are not compatible with a mean reverting method because you then fight with the trend.

COMPLEXITY AND CONTAGION RISK.

This generation of traders has recently experienced a series of contagion risks, where one seemingly modest financial event causes another seemingly controlled financial event, and in the end, we have a very large event. The first of these was the subprime meltdown. It first appeared to be limited to the U.S. housing market, then started to affect lending liquidity, and then moved to bank reserve requirements, until it had spread throughout the United States, Europe, and finally Asia. Equity markets all plunged, and investors ran to the safety of government treasuries.

We are now teetering on the problem of sovereign debt, first with Greece and probably some other countries in the European Union, but possibly spreading back to the United States. It's not yet a reality, but that's not because the financial news networks aren't trying to scare everyone into believing that it will happen.

The problem stems from a structural change in the way markets are used. Investors now diversify into a wide range of programs, placing money in hedge funds, bond funds, and real estate trusts. If one link in the chain fails, then investors must liquidate other holdings to cover losses. That leads to further liquidation. It is the complexity of the markets that we don't understand, the way money interacts with it all, causing the possibility of sequential failures.

THE UGLY SIDE.

No discussion of uncertainty would be complete without recognizing that a different type of price shock occurs when we find out that we've been fooled. Of these, the greatest of all scams was Bernie Madoff, who fooled everyone into thinking that he had a lock on trading profits. Of course, he refused to discuss the specifics of his trading method because it was proprietary.

That brings up the issue of black boxes. A black box is usually a fully systematic, algorithmic trading model that is used for trading but not disclosed to anyone outside the company. Naturally, we can understand why a money manager would need to keep the specifics of a strategy secret. But then we don't really know what they're using or if they are using anything systematic at all. Fortunately, most investment managers, hedge fund operators, and commodity trading advisors (CTAs) are very credible. They give investors a general idea of what methods they use for trading and how they control risk. These can most often be verified by watching their performance under various market conditions. Unlike Madoff's results, which were up 15% each year regardless of market conditions, we expect to see periods of loss but more gains than losses over time.

Even in the best times, investing in someone else's trading program involves added risk---what we would normally call counterparty risk, the risk of one of the parties failing to perform their fiduciary responsibility. If you are the investor, that could be the stock or commodity exchange, but more likley the person or company managing your money.

If it is at all possible, you need to manage your own investment portfolio. Your common sense is enough to keep it on a safe track. If you're a trader, then all the better, because you can decide what strategy and how much leverage to use. And there is no penalty for stopping and withdrawing your money, and no waiting period. During the subprime crisis, many hedge funds refused to allow investors to withdraw their funds ”for their own good” and because there was no market to liquidate their positions.

TAKING DEFENSIVE ACTION.

We propose that the solution to all this risk and uncertainty is to trade in a way that removes exposure to directional risk. That include all forms of statistical arbitrage (stat-arb), from spreads to yield-curve trading to program trading, and from the simplest pairs trade to large-scale market-neutral strategies. These methods extract alpha from the market, profits over and above what might be gained, or lost, by a pa.s.sive investment in the stock or bond market. Alpha is considered a measure of cleverness, although you will see that some of the methods do not require much more than common sense.

<script>