LINEAR REGRESSION MODEL

A linear regression, or straight-line fit, could be the basis for a simple trading strategy similar to a moving average. For example, an n-day linear regression, applied to the closing prices, could be used with the following rules:

1. Buy when the closing price moves above the forecasted value of today’s close.

2. Sell when the closing price moves below the forecasted value of today’s close.`

There is an important difference between a model based on linear regression and one founded on a moving average. There is no lag in a regression strategy. If prices continue higher at the same rate, a moving average system will initially lag behind, then increase at the same rate. The lag creates a safety zone to absorb some changes in the direction of prices, without getting stopped out. A regression model, on the other hand, identifies a change of direction sooner by measuring future movement against a straight-line projection in which the current price value has little influence. A steady price move, however, will place the fitted line right in the center of market movement, subject to frequent whipsaws. The area at which a uniform trend changes from one direction to another is a difficult case for a linear regression system and points out the need for using bands. Even with bands, the turning point of an orderly trend will appear to have much greater variance than during the direction period over the same calculation interval.