Trend lines are significant for investment. They help you identify the upward or downward trend in the market. By analyzing types of trend lines, you can make effective investment decisions. For example, you can enter a long-term strategic position to bid or buy items in an uptrend. If the trend is downwards, you can shift to a short-term position to sell your offerings. There are different types of trend lines, like linear, logarithmic, and polynomial. So what is the exemplary scenario for using them? Which one is the right choice for rapidly changing values? In this post, you will find details.

What Is A Trend Line?

Trend lines are reference horizontal or vertical lines that help you interpret data. They are simply diagonal lines that join two or more price points with a straight line. Trend lines highlight a trend or price range. They give you a sense of how high or low the price might go in a given timeframe. Trend lines are used to identify the historical trend of price movements. Also, they are used to indicate support and resistance levels. There are different types of trend lines, such as linear, logarithmic, polynomial, etc. Investors and analysts use them in different scenarios. For example, you should use the linear trend line if the data values increase or decrease at a constant rate.

What Do Trend Lines Tell You?

Trend lines give you approximate areas of support and resistance. Instead of looking at past business performance, you can look for trends in price action. It helps determine the current direction in market prices, regardless of the time frame. For example, your company is trading at $35. Then it moves to $40 in two days and $45 in three days. Hence, you have three points to plot on the chart. You start at $35 and then move to $40 and $45. You will get an upward trend if you draw a line between the three price points. The trend line will then have a positive slope. Therefore, it will tell you to buy in the direction of the trend. However, the trend line will have a negative slope if the price goes down from $35 to $25. Hence, you should sell your products.

Are There Different Types Of Trend Lines?

There are different types of trend lines, including linear, logarithmic, and polynomial.

Linear Trend Lines

A linear trend line is a best-fit straight line. You use it when the data sets are linear. It can be linearly increasing or decreasing. On the other hand, a linearly increasing trend line describes a rise in the data. On the other hand, a linearly decreasing trend line represents a fall in the data. Here is an example:

This linear trend line shows the Sydney railway usage for work travel to various routes. Notice the fall in the trend line.

Logarithmic Trend Lines

A logarithmic trend line is a best-fit curved line. It is used for datasets that either increase or decrease and then level out. You can use the logarithmic trend line for both positive and negative values. This type of trend line is the inverse of the exponential trend line. Here is an example:

Polynomial Trend Lines

The polynomial trend line is a curved line. You use it when the datasets are changing data values. It is categorized based on its order. You can determine the order by the number of fluctuations in the data. Let’s take a look at this example:

The graph shows profit in millions in terms of the number of years. It uses an Order 2 polynomial trend line to illustrate the profit fluctuations.

Power Trend Lines

A power trend line is another curved line. You use it mostly for datasets that increase at a particular rate. But you can’t use it with data containing zero or negative data values. This type of trend line is more symmetrical. It is similar to the exponential type of curve. Here is an example:

The chart shows a power trend line illustrating a chemical reaction that changes every 10 seconds. As you can see, the line has moved upward. That means there is an increase in the response every second.

Exponential Trend Lines

The exponential trend line is a curved line. You use it when there is an exponential rise or fall in the dataset. Like the power trend line, you cannot use this type of trend line for data with negative or zero values. Here is an example:

The chart shows the number of tigers in terms of years. It utilizes an exponential trend to illustrate the population that falls with time.

Moving Average Trend Lines

The moving average trend line clearly shows a pattern or trend by smoothing out fluctuations in data. Instead, you use it on datasets with rapidly increasing and decreasing values. The points in a moving trend line are the average value of specific data points. The period option sets it. If you set the period to 2, the average of the first two data points is then used as the first point in the moving average trend line. Here is an example:

This is a chart that shows fluctuating stock prices. It uses the moving average trend line to illustrate a clear pattern. Read: A Beginner’s Guide To Charts

What Are Trend Line Model Terms?

There are different trend line model terms that you should know about. So let’s take a look at them. Model Formula: The formula for the entire trend line model. It reflects on excluding factors from the model. Number of Modeled Observations: IThe number of rows used in the view. Number Of Filtered Observations: Defines the number of observations excluded from the trend line model. Model Degrees Of Freedom: The number of parameters required to specify the model. For example, the model degree of freedom of linear, logarithmic, and exponential trend lines is 2. Residual Degrees of Freedom (DF): Defined by subtracting the number of observations from the number of parameters estimated in the model. SSE: Sum Squared Error, the difference between the model’s observed and predicted values. MSE: Mean Squared Error, defined by dividing the quantity of SSE by its corresponding degree of freedom. R-Squared: Ratio of the unexplained variance to the total variance of the data. You use it to understand how well the data fit the linear model. Standard Error: Estimate the normal variability of the random error in the model formula. It is defined by the square root of the MSE of the whole model. P-Value: Measure of significance for the trend line. A smaller value leads to a more significant model. For example, a p-value of 0.05 or less is often considered necessary. Analysis of Variance (ANOVA): The trend line model lists information for each factor. Individual Trend Lines: Provide information about each trend line in the view. They help you easily identify the lines that are the most statistically significant. StdErr: The measure of the spread of the sampling distribution’s coefficient estimate. The error shrinks as the quality and quantity of the information used in the forecast continue to increase. T-Value: It measures the size of the difference relative to the variation in your sample data. When the t-value is 0, it indicates that the sample results exactly equal the null hypothesis.

How Can I Assess If A Trend Line Is Statistically Significant?

If you get values of r2 ≥ 0.65 and p ≤ 0.05 after performing regressions concerning time and values in a time series, then the trend line is statistically significant. The same thing applies to deterioration concerning time and means values from intervals into which the series has been divided.