Moore Research Center, Inc.

A PDF presentation can be found here.

To remain in the vanguard of constructive research is forever to test the outer edges of knowledge, to validate or debunk current assumptions, to create new perspectives. In our constant effort to expand ideas and test theory, MRCI wishes to update our customers with our proprietary Scenario^SM, where we profile certain aspects of futures market conditions or behavior.

No attempt is made to advise or recommend. Rather the emphasis is, as always, to calculate, organize, detect, and present historical data from a perspective that may spur further thought and analysis. Some readers may draw conclusions, some may wish to integrate the information into their own trading programs, some may devise trading stratagems. At any rate, for those with an interest in statistical analysis there will be found plenty to consider.

What is Scenario^SM?

Scenario^SM analysis uses decision-logic to investigate and quantify market behavior after a higher monthly close or lower monthly close. Simply put, decision-logic tests for a condition and then the results of a specific action taken because of that condition. In this particular study, the question(s) being asked, and quantified, are as follows:

• If a contract closes higher in any given month A than in the previous month, what has been the potential for the market to trade higher than month A's high in either of the two subsequent months?
• If a contract closes lower in any given month B than in the previous month, what has been the potential for the market to trade lower than month B's low in either of the two subsequent months?

Price Specifics

Monthly data is derived from daily open-high-low-close (OHLC) data. For studies on futures contracts, the data is contract-specific rather than continuous. Price data for as far back as 35 years is used, as are 12 calendar months prior to expiration for futures contracts or any 12-month period for cash or index data.

Instead of grouping all delivery months into a single basket and attempting to determine a perspective that would apply to the market in general - and, therefore, to no month in specific - Scenario^SM analysis inspects the market by calendar-month alignment.

Table Description

Scenario^SM studies are generated in two parts. "Condition One" (the upper table) quantifies the results of the first question stated above. "Condition Two" (the lower table) quantifies the results of the second.

Recent monthly OHLC data is provided as an addendum to "Condition Two." All prices are specific to the designated futures contract.

Helpful Hint!
To bring up a Scenario^SM study, simply click on the desired "Market" in the left hand column of the
"Monthly Scenario Summary".

Application

The benefits of this type of study accrue primarily in a more "macro" time-frame rather than for market timing. Seasonal traders may find a sense of over-all trend strength from which to base their decisions. Traders may also find previous month's highs/lows as more legitimate objectives against which to buy pullbacks/sell rallies.

For instance, a trader may notice in a scenario month, that when this contract has closed higher than the previous month, it has followed through and exceeded the high of the scenario month in either of the subsequent two months 90% of the time (Scenario Percentage). Should it become apparent that scenario month will close higher than its previous month again this year, a trader may look to buy any pullbacks with the expectation that it will follow through again. He may further notice that when this contract has done so in the past, it has

• (1) exceeded that month's close by an average amount (Avg Max Increase) and
• (2) by a maximum amount (Max Increase) and
• (3) suffered adversity only by the average amount(Avg Max Decline) but
• (4) by as much a maximum amount (Max Decline).

Therefore, the trader has a minimum price objective (scenario month's high) which has (in this example) a 90% historical reliability of being exceeded, and he has a historically average and maximum price objective. He further has a maximum time objective (2 months) and a historically average time objective (Avg Days to Max Increase). Finally, he knows what both the historically average and maximum price drawdowns have been and the amount of time (Avg Days to Max Decline) it has typically taken to complete those drawdowns.