Normalising Financial Data
Normalising financial data serves several important purposes. Here are some examples:
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Comparison Across Assets: It allows you to compare the performance of different assets or financial instruments on a like-for-like basis. By expressing prices as a percentage of a base value (e.g., the starting price), you can easily see which assets have performed better over time, regardless of their original price levels.
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Trend Analysis: Normalised data helps in identifying trends and patterns more clearly. It eliminates the impact of the initial price and focuses on the relative changes, making it easier to spot upward or downward trends.
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Volatility Assessment: By normalising data, you can assess the volatility of different assets. Normalised data provides a clearer picture of how much an asset’s price fluctuates relative to its base value.
- Visualization: Normalised data is often used in visualisations, without which the scale and comparison of multiple assets over time would not be possible.
In summary, normalising financial data allows for more meaningful comparisons, better trend analysis, and improved decision-making by helping to identify performance, trends, and volatility across multiple asset classes.
See how to normalise data with Python here.