What is INTERMARKET ANALYSIS? What does INTERMARKET ANALYSIS mean? INTERMARKET ANALYSIS meaning - INTERMARKET ANALYSIS definition - INTERMARKET ANALYSIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Intermarket analysis is a relationship, or a measurable correlation between certain markets. It is a form of fundamental analysis, without a time delay. Supporters of intermarket analysis state that it can be done by applying the statistical methods like correlation. Critics of intermarket analysis refute statistical methods and use only price indicators for fundamental analysis.
In John Murphy's first book, published in 1991 on Intermarket analysis he used the crash of 1987 to lay out his Intermarket hypothesis. Murray Ruggiero published Intermarket based trading systems in 1994.
Hence, intermarket market analysis can be thought of as a type of instantaneous fundamental analysis and is not really meant to work on a tick by tick basis. It gives you a general bias and direction. Thus, your intermarket work looks for times that these underlying relationships are moving opposite to the market you are trading.
There are many approaches to intermarket analysis like mechanical, rule based (while not mechanical via a different angle). One can use intermarket analysis of Oil along with the intermarket analysis of other key markets to help with profitable day trading of the Russell 2000 Emini ER2.
There are many supporters and detractors for this theory and the points to consider regarding the Intermarket Relationship have been clarified in the following sections.
1) They are tried and time-tested intermarket relationships with easily available free data and a simple spreadsheet or charting program. The quickest function to use is the simple correlation study, wherein one variable is compared with a second variable i.e. the correlation between two data series.
If it is positive; the correlation value shall go as high as + 1.0 – representing a perfect and positive correlation between the two series of prices. Moreover, a perfect inverse (negative) correlation depicts a value as low as - 1.0. Readings near the zero line would show no discernible correlation between the two samples.
Moreover, it is rare to have a perfect correlation between any two market for a very long period of time, but most analysts would probably agree that any reading sustained over the +0.7 or under the –0.7 level (which would equate to approximately a 70% correlation) would be statistically significant. Also, if the correlation value went from a positive to a negative correlation frequently, the relationship would most likely be unstable, and probably useless for trading.
2) The most widely accepted correlation is the inverse correlation between stock prices and interest rates; for it is a general assumption that as interest rates go higher, the stock prices go lower, and conversely, as interest rates go down, the stock prices go up.
3) A really simple concept for Intermarket-based systems. For positively correlated markets, the concept is:
If Intermarket is in uptrend and traded market in down trend then buy; or
If Intermarket is in down trend and traded market is in uptrend then sell.
Various concepts are used to define an up and down trend; if we use the price relative to a moving average, then: For negatively correlated market we have as follows:
If Intermarket is in uptrend and traded market in uptrend then sell; or
If Intermarket is in down trend and traded market is in down trend then buy.
There are 3 major pitfalls with reaching consensus around the outcome of an event.
These fully specified, manipulation resistant, and publicly verifiable events form a necessary foundation for sound prediction markets. Without this foundation, prediction markets are subject to confusion, manipulation, and abuse. Though sometimes tricky, many prediction events exist that satisfy all of the criteria listed above. As the world moves forward into the realm of decentralized prediction markets, it will be important to keep in mind the pitfalls associated with many naive prediction events.
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