Regardless of noticeable growth in the option market’s volume on U.S. exchanges during the last 20 years, the literature covering this subject is limited in comparison to the number of books and articles written about traditional instruments such as stocks. Existing books about options focus on valuation models rather than techniques used by options strategists in their everyday work.
According to statistical data coming from option exchanges, most option positions are offset before expiration. This simple statement does not correspond with the option literature on the subject, where most strategies are analyzed by their performance upon expiration.
In March 2005, an article in this magazine introduced the Option Deviation Index (ODI), a purely statistical method of market analysis measuring market movement in consecutive time intervals, expressed as percentage of the starting value. Since its introduction, the ODI concept has proven to be a useful tool, due to its simplicity and psychological advantage. Following simple percentage readings seems to be less psychologically draining on investors, who, being familiar with technical analysis, tend to recall excessive numbers of patterns on the charts. The ODI has projected the distribution of the S&P 500 Index (CME:SPZ14) with high accuracy, allowing the application of appropriate option strategies and enhancing the chances of success.
Since the S&P 500 distribution range, as periodically calculated, is presumably correlated with volatility, attempts were made to calculate this range in relationship to volatility values; however, the results were too random. Calculations were missing an additional factor measuring the range of the S&P 500 Index movements in a similar way as was applied for the ODI. The Option Mobility Index (OMI) was the solution.
The OMI measures the range of the underlying movement for option trading purposes. Its formula is as follows:
OMI % = HiD – LoD
Where: HiD: Maximum price during analyzed period in comparison to starting value (expressed in percent)
LoD: Minimum price during analyzed period in comparison to starting value (expressed in percent)
[In a trader’s world, hunting for exploitable trends is common, but there is no indicative analytical method available to use. Some traders say that instrument X is “trendous,” which means that price distribution favors one market direction over the other for the time sufficient to capture a profit. Such market movements are commonly called “tradable trends.”
As there is no clear method to indicate tradable trends conditions, experience is the key. An experienced trader may somewhat recognize the market environment having a higher chance of potential profit, but if asked about the rationale standing behind his opinion, he may have a hard time giving a clear answer.
Many market watchers call this phenomenon “intuition,” but a more scientific answer is that the human brain has the ability to analyze more data than is obvious. We can see this effect in many areas, where people can analyze very complex issues, giving outputs of high accuracy, but at the same time their explanation of the whole rational process is lacking.
One of the areas where we observe this phenomenon is the game of chess. For centuries, chess has been a game in which certain people could analyze more data, and better, than others, but there was no simple rational explanation of this phenomenon. A famous highly publicized match, lost by the Grandmaster Kasparov to the IBM supercomputer Deep Blue in 1997, proved that the whole secret behind success in a chess game was the capacity to analyze data. But since Kasparov was competing with such a computationally powerful machine, it also showed the enormous potential of the human brain.
The financial world faces similar competition between computers and humans. A market, though, is an evolving environment, unlike a chess board. This explains the initial success of some trading systems that eventually break down. Our present technology has a high capacity for analyzing large amounts of data, but in a changing environment, the human brain naturally adapts faster to new conditions, while computerized systems need reprogramming. Today, humans still learn faster.