What is Weighted Moving Average?

A Weighted Moving Average (WMA) is a statistical technique used to smooth data by assigning different weights to different data points in a time series. Unlike a Simple Moving Average (SMA), where all data points carry equal weight, the WMA gives more importance to recent data, making it more responsive to new trends.

How Weighted Moving Average Works:

1. 

   Choose a Window (Period): Decide how many past data points (e.g., 3-day, 5-day, 10-day) to include.

2. Assign Weights: Assign higher weights to more recent data points and lower weights to older ones.

3. Calculate the Average: Multiply each data point by its weight, sum them up, and divide by the sum of the weights.

Example Calculation (3-Day WMA):

Suppose we have closing stock prices over 3 days:

Calculation:

$$WMA=\frac{(50\times 1)+(52\times 2)+(54\times 3)}{1+2+3}=\frac{50+104+162}{6}=\frac{316}{6}\approx 52.67$$WMA=1+2+3(50×1)+(52×2)+(54×3)=650+104+162=6316≈52.67

Key Features of WMA:

✔ More sensitive to recent data than SMA.
✔ Helps in trend identification and reducing lag.
✔ Commonly used in technical analysis (stock trading, forecasting).

Comparison with Other Averages:

Applications:

• Stock trading (identifying buy/sell signals).

• Economic forecasting.

• Inventory management.