K-Ratio
Looks at how consistently an equity's return has increased over time.
What Is the K-Ratio?
A valuation tool called the K-ratio looks at how consistently an equity's return has increased over time.
A value-added monthly index (VAMI), which employs linear regression to follow the development of a $1,000 initial investment in the securities under consideration, provided the information for the ratio.
This ratio is determined using the value-added monthly index to gauge an equity's consistency of returns over time (VAMI).
The computation includes linear regression on a Value-Added Monthly Index (VAMI) curve's logarithmic cumulative return. When calculating risk, the K-ratio considers both the sequence of returns and the returns themselves.
The ratio, which considers the return trend, calculates the security's return over time and is a valuable tool for evaluating the performance of stocks.
- The rise of return and the consistency of that growth over a given period are two aspects that the ratio helps us gauge.
- A greater ratio can predict better performance than a lower ratio. Therefore, this ratio can be used in finance to assess the performance and long-term viability of stocks and other assets.
- The ratio is calculated by building a time series of data relevant to an occasion or tactic. For example, it was created so that a financial manager or investor might examine a data set and attempt to draw a straight line with a minimum slant.
History of the K-Ratio
In 1996, Lars Kestner developed the K-Ratio to assess a strategy's general profitability. The ratio was created so that a financial manager or investor may examine data collection and attempt to draw a straight line with low variance.
This would aid in indicating that they are looking at a product or asset with a profitable return on investment.
According to Kestner, the ratio was developed to supplement the Sharpe Ratio. Kestner made small modifications to the ratio throughout time, including correction factors for various return observations and return times.
Formula and Calculation of the K-Ratio
The ratio is calculated by assembling a time series of data relating to an occasion or tactic. First, the returns are compounded, then their sums are calculated geometrically.
The formula is:
K-Ratio = [Slope(LogV AMI Regression Line)]/ n(Standard Error of Slope)
Lars Kestner, a derivatives trader and statistician, created the ratio to fill a perceived vacuum in previous return analyses. Kestner developed his K-ratio to assess risk vs. return by examining how consistent a securities, portfolio, or manager's returns are over time.
This is because returns and consistency are the main concerns of an investor.
When calculating risk, the ratio considers the returns and their chronological sequence. In addition, the computation includes linear regression on a Value-Added Monthly Index (VAMI) curve's logarithmic cumulative return.
The formula is then applied to the regression findings. The standard error of the slope reflects the risk, whereas the slope is the return, which should be positive.
Kestner revised his initial K-ratio in 2003, changing the calculation formula to incorporate the quantity of return data points in the denominator. In 2013, he adjusted by including a square root computation in the numerator.
Example of How to Use the K-Ratio
Because it considers the return trend rather than just point-in-time snapshots, the ratio is a helpful instrument for evaluating the performance of stocks. It quantifies the return on the investment over time.
The K-ratio makes it possible to compare the cumulative returns of several stocks (and equity managers) across time. Considering the sequence of returns, it varies from the popular Sharpe measure.
In reality, the K-ratio is intended to be compared against and used in conjunction with other performance indicators.
K-ratios may be computed for bonds and their usage in examining individual stock returns, style classes, and fund managers. Depending on the Asset type (domestic stocks versus bonds versus cash), K-ratios will vary.
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