Question

Which is not a characteristic of simple moving averages applied to time series data?

1) Smooths real variations in the data
2) Weights each historical value equally
3) Lags changes in the data
4) Smooths random variations in the data
5) Has minimal reliance on historical data

Answer 1

The characteristic not true for simple moving averages is that they have minimal reliance on historical data, as SMAs are calculated using historical data and each data point is equally weighted.

Step-by-step explanation:

The characteristic of simple moving averages (SMA) applied to time series data that is not true is: 'Has minimal reliance on historical data'. SMA, by definition, relies on historical data to calculate the average.

In a simple moving average, each data point in the series is averaged over a specified period of time, which smooths real variations in the data. All historical values are equally weighted, which means that each value in the period contributes equally to the result.

However, this can also lead to a lag, as SMAs do not react quickly to recent price changes. Lastly, SMAs do smooth out random variations in the data by averaging out the series over time.

Answer 2

In simple moving averages, characteristic 5) 'Has minimal reliance on historical data' is incorrect as SMAs rely heavily on historical data to smooth out variations and identify trends.

Step-by-step explanation:

The characteristic that is not a feature of simple moving averages applied to time series data is: 5) Has minimal reliance on historical data. Simple moving averages (SMAs) are used to smooth out data series to identify trends. They take the total sum of past data points over a fixed period and divide it by the number of data points to calculate the average. This mechanism means that SMAs do:

• Smooth real variations in the data
• Weight each historical value equally
• Lag changes in the data - because they are based on past data, any sudden changes in the underlying data will only be reflected after the lag period
• Smooth random variations in the data by averaging them out over time

However, SMAs do have a substantial reliance on historical data, which is critical to their calculation. Therefore, option 5 is the characteristic that is not representative of simple moving averages.