Forecasting – Simple moving average – Example 1
Purpose of a simple moving average forecast
A simple moving average forecast’s goal is to assist stock analysts, entrepreneurs, and other professionals in choosing which stocks to buy and when to do so. More specifically, simple moving forecasting aids people in determining whether they should anticipate an upturn (price increase) or downturn (price decrease) in the price of a commodity. By doing this, people can choose wisely among their investment options based on commodities with positive trends.
What is a simple moving average?
Simple moving averages, or SMAs, are a subset of moving averages that show average prices for a given good or commodity over a given period of time. By calculating averages at regular intervals over the course of days, weeks, months, or years, moving averages are a type of calculation that is frequently used by stock market professionals to analyze price changes. The simple moving average is a.
How to calculate simple moving average
Examine the following procedures to learn how to calculate the simple moving average:
1. Establish the time frame you want to review
Consider the length of time you want to pull data from when calculating the simple moving average of a commodity. For instance, you might decide the ideal time frame is five days, fifty days, one hundred days, or two hundred days.
2. Look at the highest price points for each time interval
Say you decide to pull price data for seven days. This implies that you choose the highest price point to represent that time period (one day) each day after reviewing stock prices for that commodity. You must monitor stock prices daily and record the highest price for the remaining six trading days if you want to calculate the simple moving average for the next seven days. Here is an illustration of how the stock price of a coffee company changes:
3. Add each price point together
You must add each price point after you have noted the highest stock prices for a specific commodity over the desired time period. For example:
$8. 95 + $8. 50 + $8. 85 + $9. 00 + $8. 70 + $8. 55 + $8. 65 = $61. 20.
4. Divide the total by the number of time intervals established
You must divide the total by the number of time intervals you recorded for after adding up each price point and receiving your answer. For example:
$61.20/7 = $8.74
Therefore, $8. One week’s worth of stock prices for a coffee company average out to 74.
Simple moving average versus exponential moving average
The areas of focus and forecasting abilities of a simple moving average and an exponential moving average differ from one another. The simple moving average forecasting method, for instance, examines data over a predetermined amount of time. Individuals first compile historical data, after which they calculate the average stock price. Instead of accumulating data over time, the exponential moving average forecasting method, on the other hand, focuses specifically on current stock prices for commodities.
Due to this, simple moving averages work best for commodities that are relatively constant over time, whereas exponential moving averages work best for commodities with significant price variations that change quickly.
Advantages of simple moving average forecasts
Using a simple moving average forecast has a few benefits. Here are some examples:
Drawbacks of simple moving average forecasts
Here are a few examples of possible problems with forecasts using simple moving averages:
FAQ
How do you forecast moving averages?
The simplest forecasting method is a simple moving average (SMA). To calculate a simple moving average, add the values from the previous ‘n’ periods together and divide the result by ‘n. Therefore, the forecast for the following period is based on the moving average value.
What is the simple average forecasting method?
Moving average smoothing is used to determine the trend-cycle of previous values, whereas a moving average model is used to predict future values. Figure 8. 6: Two illustrations of moving average model data with various parameter values Left: MA(1) with yt=20+εt+0. 8εt−1 y t = 20 + ε t + 0. 8 ε t − 1 .
How do you forecast SMA?
Moving average smoothing is used to determine the trend-cycle of previous values, whereas a moving average model is used to predict future values. Figure 8. 6: Two illustrations of moving average model data with various parameter values Left: MA(1) with yt=20+εt+0. 8εt−1 y t = 20 + ε t + 0. 8 ε t − 1 .
What is moving average model forecasting?
Moving average smoothing is used to determine the trend-cycle of previous values, whereas a moving average model is used to predict future values. Figure 8. 6: Two illustrations of moving average model data with various parameter values Left: MA(1) with yt=20+εt+0. 8εt−1 y t = 20 + ε t + 0. 8 ε t − 1 .
