Stock Market Data Normalization For Time Series

Z-score normalization (Standardization)

Z-score normalization, also known as standardization, is a commonly used technique for normalizing stock price data. It scales the data to have a mean of 0 and a standard deviation of 1.

Formula:

Z = (X - μ) / σ

Where:

• Z is the Z-score (normalized value)
• X is the original stock price
• μ is the mean of the stock price for the given time period
• σ is the standard deviation of the stock price for the given time period

Min-Max Scaling (Normalization)

Min-Max scaling is another popular technique for normalizing stock price data. It scales the data to a specific range, typically [0, 1].

Formula:

X_normalized = (X - X_min) / (X_max - X_min)

Where:

• X_normalized is the normalized value
• X is the original stock price
• X_min is the minimum stock price for the given time period
• X_max is the maximum stock price for the given time period

Percentage Change

Percentage change is a simple and effective method to analyze the relative movement of stock prices in a time series. It measures the change in price as a percentage of the previous period’s price.

Formula:

Percentage Change = ((Current Price - Previous Price) / Previous Price) * 100

This method is particularly useful for comparing the price movements of different stocks, as it puts the changes in stock prices on a relative scale, independent of the stock’s actual price level.

Log Returns

Log returns are another popular method used to normalize stock price movements in a time series. This method is commonly used in finance, as it has several desirable properties, such as being additive over time and having a more symmetrical distribution.

Formula:

Log Return = ln(Current Price / Previous Price)

Where ln is the natural logarithm.

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