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What Is Interpolation?


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    Highlights

  • Interpolation estimates unknown values using known data points in sequence, aiding investors in predicting security prices and yields
  • It differs from extrapolation by focusing on values within the known range rather than beyond it
  • Linear interpolation is the simplest form, commonly used to fill gaps in data like stock prices over time
  • Despite its utility in technical analysis, interpolation is criticized for imprecision, especially in volatile stock markets
Table of Contents

What Is Interpolation?

Let me explain interpolation directly: it's a statistical method where you use related known values to estimate an unknown value or set of values. In the world of investing, you apply it to estimate prices or potential yields of securities. You achieve this by drawing on established values that sit in sequence with the unknown one.

Understanding Interpolation

If you see a consistent trend across data points, you can reasonably estimate values at uncalculated points. As an investor or analyst, you often create line charts with interpolated points to visualize security price changes, which is key in technical analysis. Remember, this contrasts with extrapolation, which estimates values beyond the known data rather than in between.

You use interpolation to generate new data points between known ones on charts, like those showing a security's price action or volume. Computers handle this now, but the concept dates back to ancient astronomers filling gaps in planetary observations. Formal types include linear, polynomial, and piecewise constant interpolation. For instance, you might use an interpolated yield curve to graph U.S. Treasury bond yields and gauge future market and economic directions.

Example of Interpolation

Consider linear interpolation, the easiest type, useful when estimating a security's value or interest rate without direct data. Say you're tracking a stock price over time, plotting it as function f(x). You have data for August (x1), October (x3), and December (x5), but need September (x2). Using linear interpolation, you estimate f(x) at x2 within that range.

Criticism of Interpolation

One major criticism is its lack of precision, despite its long history. Ancient uses focused on predictable astronomical patterns, but stock markets are volatile and unpredictable. Still, with vast securities data, interpolations of price movements are inevitable. Most stock history charts are interpolated using linear regression to approximate curves, though you can't pinpoint exact values at specific moments with full confidence.

Interpolation FAQs

  • What Type of Interpolation Is Used in Technical Analysis? In technical analysis, you mainly see linear and exponential interpolation; linear draws a straight line between points, while exponential uses weighted averages adjusting for factors like trading volume.
  • How Is Interpolation Used in Trading? Traders use interpolation, often as smoothing, to represent high-low price ranges between closing prices, creating regression lines that approximate moving averages for predicting support levels.
  • What Is Interpolation vs. Extrapolation? Interpolation estimates values between known points to fill blanks, while extrapolation extends known points outward, carrying higher inaccuracy risks.

The Bottom Line

Interpolation is your mathematical tool to estimate unknown data points between known ones, helping fill in gaps. As a technical trader, you use it to understand past price behaviors even with incomplete info, predicting trends from a fuller picture of price action.

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