Dynamic Option Selection System: Analyzing Markets and Managing Risk

Dynamic Option Selection System: Analyzing Markets and Managing Risk

by Howard L. Simons


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Product Details

ISBN-13: 9780471320517
Publisher: Wiley
Publication date: 09/01/1999
Series: Wiley Trading Series , #75
Pages: 288
Product dimensions: 6.60(w) x 8.76(h) x 0.91(d)

About the Author

HOWARD L. SIMONS designs hedging strategies and trading systems and develops value-added investment products for a wide variety of clients in the agricultural, energy, and financial markets. He has served as the architect and manager of a commercial crude oil trading operation; as a commodity trading advisor and fund manager; and as a consultant specializing in risk management solutions for hedgers, professional speculators, and financial institutions. Mr. Simons holds an MBA in finance and economics from the University of Chicago and an MA in international economics from Johns Hopkins University School of Advanced International Studies. He is a contributing editor for Futures magazine and an adjunct professor of finance in the Illinois Institute of Technology's Financial Markets and Trading Program.

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Chapter 1
In the Game


Archaeologists digging and picking through the usual assortment of rubble in northern Assyria in the late 1980s kept encountering charred grains of barley in the bottom of large vessels not normally associated with baking. This led to the development of an unusual hypothesis regarding the origins of agriculture, and hence the development of modern civilizations: We started cultivating grain to ensure ourselves a supply of beer. How things change.
Of course, one cannot have the concept of ensured supply without first having mastered the concept of exchange. Although we could stretch a bit and try to convince ourselves that these first farmers were willing to donate their grain to the local brewmaster for the greater good of their community, it is far more likely that they received goods and services of value in return. Whatever method of exchange was used-probably barter-a market had to be involved.
Complex systems apparently jump from nonexistence to a near final state. In cosmology, this has been dubbed the "big bang." The parallel origin of life does not have a similar catchy name, but the end result was the same: One point in time separated the void from the universe, and another point in time separated prelife from life. Period. There was no intermediate phase or transitional state. This bright line, this sharp distinction between two very different states of affairs, and the irreducible uncertainty involved in the process, always have and always will provide us with our greatest philosophical questions: One cannot either prove or disprove either the divine or some stochastic process; one can only accept and believe.
The evolution of markets, like the evolution of living organisms, must have exploded from near nothingness to a rather complete state in a very short period of time. Just as an organism must exhibit all aspects of an organism to survive and propagate, markets must exhibit all aspects of a market to function and expand. Terms of trade, recordkeeping and accounting, storage, insurance, dispute resolution, and information transfer all had to originate quickly. Taxation did not have to originate quickly, but one suspects that it did, nevertheless.
If the concept of exchange is a prerequisite for the concept of ensured supply, and if markets require all of their multiple facets to function-just as there can be no partial life or partial universe, there can be no partial market-then human civilization arose out of a big bang of its own, the creation of markets. Everything we do in our socioeconomic lives today is but a footnote to this singular event. As a result, there are constants in market analysis, just as there are constants in the laws of physics and biology.
Although reasonable people can and will differ on the issue, we will submit that all markets are driven by common human behavior, and therefore markets can be analyzed and compared to one another on a consistent basis. In other words, it does not matter whether you are trading Eurodollars or soybeans; the behavior of traders will be the same in both markets. An immediate corollary to this is market behavior is independent of culture. Eurodollars traded by Americans will exhibit the same trading patterns as Eurolira traded by Italians, Euroyen traded by Japanese, and bank bills traded by Australians. Another corollary is trading patterns are independent of the level of technology used in a market: It does not matter whether we are using clay tablets, rice paper, an abacus, or the Internet-the resulting foot-prints of a market are the same. We can read a cotton price chart from the Civil War, stock price charts from the 1920s, and grain price charts from the early 1970s on the same basis.
The one constant between markets for different assets, between eras, and between levels of technology is the behavior of human beings. In homage to George Santayana's famous dictum that those who do not learn history are condemned to repeat it, the one thing we can learn from history is that people do not learn from history-their own or anyone else's. Maybe there is a remarkable person somewhere on this earth who has not made the same mistake twice or who has never succumbed to intuition when rational analysis was appropriate or who is immune to the emotions of fear and greed, but if so, this person does not dominate market behavior. Since the goal of this work is to both absorb the collective anxiety of the market and to minimize the need for decisions based on price forecasting, we should detour for a moment into the behavior of traders and investors.


Most of us assume, as a matter of course, that people trade to make money, since that is the reward of a successful trading program. However, many risky activities have a negative expected return. The classic example of this is gambling. A simple stroll down the Strip in Las Vegas is a testament to our willingness to lose money; those gaudy casinos were not financed by allowing the customer to win. An economist of the behavioral school would counter quickly that gamblers must derive some additional utility other than expected winnings, such as camaraderie, free food and drink, other forms of entertainment, or simply the thrill of being in the game.
This argument carries a strong appeal for an entertainment center like Las Vegas, but does it carry equal appeal for a video poker game at a truck stop in rural South Carolina? What external utility does this activity provide? The same question can be asked in regard to state lotteries, most of which offer very negative expected returns. The behavioral answer in both cases is a short-term dream, the fleeting hope that in return for a low-cost lottery ticket or a few coins in a video poker machine, the player's life will be changed forever for the better. Never mind that the probability of dream realization is quite low, that the cumulative cost of playing the game can be a rather stiff tax on naiveté, or that the trap of gambling addiction can be worse than the life the player is trying to escape: The expected utility from playing the game must exceed the cost of playing. In more general terms, a player should purchase entry into the game if expected utility, the sum of all probability-weighted returns, exceeds the cost of entry.
Of course, this relationship breaks down quickly as the cost of entry rises. A lottery player may assign internally a certain utility to his warm feeling on purchasing a ticket for $1, because the pain of losing $1 is, one hopes, less than any pleasure received from just being in the game. Our player may stay in at a cost of $5 or $10, but as the ticket prices rise, his willingness to accept an actuarially fair bet will decrease. You don't see very many $100 slot machines, do you? This phenomenon, known as the Bernoulli paradox, will come into play later in our discussion of appropriate strike selection for long option positions. One of the central assumptions of utility analysis is that more is better than less, so that the relationship between utility and wealth is positively sloped.
The philosophical implication of this assumption is that we are all doomed to frustration and unhappiness because no matter what level of utility we achieve; no matter how large our bank accounts, we could always have more. Fortunately, most of us make choices in our life to address utility functions that cannot be measured in dollars and cents. For example, we may choose to spend more time with our children or volunteer for a nonprofit organization. Economic analysis can be extended to comparing the utility of these activities to that of income-producing activities; Gary Becker was awarded a Nobel Prize in economics for his efforts in this regard. Although our total utility function for all of our activities-our personal portfolio, as it were-is positively sloped, the mix of activities associated with generating income frequently declines as higher wealth levels are reached. In other words, the marginal utility of income declines with wealth for most of us. This is obvious at the extremes; a person struggling to survive will need to devote an extraordinary portion of his efforts to providing for necessities, whereas a wealthy person is free to pursue many non-income generating activities and may even be able to retire from the labor force at an early age.
Declining marginal utility of income, which defines risk-averse behavior, is a simple concept with immense ramifications. Let us take two investors, one with a net worth of $10,000 and one with a net worth of $10,000,000, and present them both with an opportunity to make an additional $1,000. This potential income represents a gain of 10% in wealth for the first investor but a gain of only 0.01% for the second investor. On this basis, we can assume that the first investor would value the additional $1,000 more than would the second. The opposite is true as well. A loss of $1,000 would be devastating for our first investor, but we should hope that the second investor would not be upset badly. Succinctly, what could the second investor do with $10,001,000 that he could not do with $10,000,000, or what could he not do with $9,999,000 that he could have done with $10,000,000?
We can categorize the risk preference of investors by the second derivative of their utility functions (Figure 1.1).

U''( W) < 0 Risk-averse (1.3)
U''( W) > 0 Risk-seeking (1.4)
U''( W) = 0 Risk-neutral (1.5)


Accounting recognizes the difference between stock concepts and flow concepts, the former being a snapshot, a balance sheet, and the latter an accumulation, an income statement. Wealth, or the amount of equity in a trading account, should be a stock concept, a simple declaration of the funds available at any given moment. However, we treat our wealth level on a flow basis as well. Mentally, we give it a history.
For example, a trader who bought soybeans at $6.50 per bushel and then watched them go down to $6.25 per bushel is far more likely to jump at the opportunity to sell them out at a subsequent recovery to $6.60 per bushel than is a trader who had bought soybeans at $6.55 the day before. Why is this the case? The only thing that should matter to either trader is his expectation for future soybean prices based on supply and demand fundamentals. The recent price history of soy-beans and the trader's own equity history should be irrelevant. However, the first trader, who sat through a loss of $0.25 per bushel on his position-$ 1,250 per futures contract on the Chicago Board of Trade-probably is very relieved that both his monetary loss and his personal anxiety, if any, are now things of the past. The second trader has no such experience and therefore no sense of relief that a loss has been avoided.
This reversion from risk aversion to risk seeking as a function of recovered equity produces one of the more common price patterns, the double top or double bottom. This is illustrated in Figure 1.2 for the June 1988 treasury bond contract. A first top formed in early February near the 94: 00 price level; this top was the continuation of a rally that began in December 1987. Trading was characterized by a series of strong closes, which can be interpreted as buyers' being eager to own the contract prior to the next day's opening. The market pulled back quickly toward the 91: 00 level, giving a loss of close to $3,000 per contract for anyone who had bought near the highs. A subsequent recovery within days to the 94: 00 level produced a different set of results and a different character of trading. Not only did the market fail to make a new high, but the closes were consistently near the lows of the day, which can be interpreted as sellers' being eager to take advantage of perceived weakness.
The principles of investor psychology were encapsulated neatly by Daniel Kahneman and Amos Tversky. The first trader in our soy-bean example illustrates two of the Kahneman-Tversky principles. The first is that traders are risk averse in the domain of profits; we are too eager to grab a gain for the simple satisfaction of having won the trade. The second is that traders are risk seeking in the domain of losses; we are too willing to sit through a loss in the hopes that our original decision will be vindicated. Neither principle should exist if the first trader were the classical risk-adjusted profit maximizer he was supposed to be.


The notion that any individual trader's risk preference can change from seeking to aversion and back again should not be surprising. We encounter this preference in many aspects of our lives, both individually and collectively. For example, most of us are unbelievably risk averse when it comes to medical expenses and are thus overinsured. Why should a person carry insurance for normal medical expenses that might amount to no more than a grocery or car repair bill, and why have we, as a society, created a mind-numbingly complex system of billing and review to administer these routine payments? The standard answer is that we fear the expense of a catastrophic illness, but another of the Kahneman-Tversky principles is that we overestimate the probability of such an event's happening and then underestimate its actual impact when it does. The result of this principle is that we are underinsured for the catastrophic event. Although our societal overinsurance for mundane medical expenses indicates risk aversion, our habits regarding life insurance or saving for retirement indicate risk seeking. Even though mercifully few among us relish a conversation on our own mortality, even fewer of us will win this game. The same holds true for its complement, growing old. A huge cottage industry has emerged to remind the baby boom generation that they are not saving enough to cover the risk that they will outlive their money, and the life insurance industry struggles to remind people that they are leaving their loved ones at too great a risk of the bread-winner's untimely demise. The very same people who would never consider employment with an organization whose medical plan was deemed inadequate probably avoid succe ssfully the necessities of financial planning.
Lamenting this state of affairs is pointless. This is what we as human beings are, have been, and will be. Our job as investors, traders, and market analysts is to take advantage of the situation as it is presented to us and not to remake the world into some impossible ideal. This means recognizing our own vulnerability to the Kahneman-Tversky principles and sculpting a trading system, as we will later do, to turn them to our advantage:

  • Since traders are risk averse in the domain of profits, even if the original logic behind the trade is still valid, a mechanical and quantitative method of taking profits is likely to be superior over time to an arbitrary profit-taking methodology.
  • Since traders are risk seeking in the domain of losses, even if the original logic behind the trade has been shown to be invalid, a mechanical and quantitative method of reducing losses is likely to be superior over time to an arbitrary stop-loss methodology.
  • Since all of us tend to overestimate the probability of the catastrophic and then underestimate its impact, a mechanical and quantitative methodology of structuring trades and managing positions to both capture the anxiety embedded in the first condition and the profit contained in the extreme event is likely to produce superior returns.

Any market is composed of traders, both buyers and sellers, who have different utility functions. At a given point in time, two traders with long positions and identical fundamental analyses may possess entirely different urges to hold their position, add to their position, or exit their position. Accommodating these different trades produces short-term noise in the market, but this noise can be analyzed in turn for clues as to where the underlying economic value is. Even more important, the combination of underlying signal and noise can provide clues as to the short-term anxieties of buyers and sellers, and this is often the most useful information a market produces.

Table of Contents


In the Game.

For What It's Worth.

Taking Care of Business.

Take My Risk, Please.

The Beating Heart.

The Shape of Things to Come.

The Original Sin.


But Not the Obligation.

Choose Your Weapons.

So Many Positions, So Little Time.

Do the Right Thing.


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