In statistics, nominal data (also known as nominal scale) is a type of data that is used to label variables without providing any quantitative value. Unlike ordinal data. One of the most notable features of ordinal data is that, nominal data cannot be ordered and cannot be measured.

no natural ordering. An example of nominal data is “favorite food”. Ordinal data is ranked into a natural order so there is some form of ranking. An example of ordinal data is restaurant rating or hotel ratings.

Nominal Scale Level Data that is measured using a nominal scale is qualitative. Categories, colors, names, labels and favorite foods along with yes or no responses are examples of nominal level data.

A variable has one of four different levels of measurement: Nominal, Ordinal, Interval, or Ratio. It is important for the researcher to understand the different levels of measurement, as these levels of measurement, together with how the research question is phrased, dictate what statistical analysis is appropriate.

Examples of nominal data include country, gender, race, hair color etc. of a group of people, while that of ordinal data include having a position in class as “First” or “Second”. Note that the nominal data examples are nouns, with no order to them while ordinal data examples comes with a level of order.

Nominal Risk Free Rate = (1 + Real Risk Free Rate) / (1 + Inflation Rate)

1. Risk Free Rate = (1 + 2.5%) / (1 + 1%)
2. Risk Free Rate = 1.01%

The nominal interest rate is the periodic interest rate times the number of periods per year. For example, a nominal annual interest rate of 12% based on monthly compounding means a 1% interest rate per month (compounded).

Just remember it as equity being held long term. capm does use the NOMINAL risk-free… and yeh, you use the bond over the bill…

CAPM’s starting point is the risk-free rate–typically a 10-year government bond yield. A premium is added, one that equity investors demand as compensation for the extra risk they accrue. This equity market premium consists of the expected return from the market as a whole less the risk-free rate of return.

In practice, the risk-free rate is commonly considered to equal to the interest paid on a 3-month government Treasury billTreasury Bills (T-Bills)Treasury Bills (or T-Bills for short) are a short-term financial instrument issued by the US Treasury with maturity periods from a few days up to 52 weeks, generally the …

Most often, either the current Treasury bill, or T-bill, rate or long-term government bond yield are used as the risk-free rate. T-bills are considered nearly free of default risk because they are fully backed by the U.S. government.

Some traders, investors wanted to eliminate the risks completely. However, we note that risks cannot be eliminated, only managed. He stated that risk can only be transferred, but cannot be suppressed.

Usually, any Sharpe ratio greater than 1.0 is considered acceptable to good by investors. A ratio higher than 2.0 is rated as very good. A ratio of 3.0 or higher is considered excellent.

If you had an asset that theoretically returned 7.5 percent per year over the risk-free rate with a standard deviation of about 15 percent, your asset would have a Sharpe ratio of 0.5. Low Sharpe ratios and poor returns are primarily due to investor biases, poor decision making, and bad habits.

A Sharpe ratio of 1.0 is considered acceptable. A Sharpe ratio of 2.0 is considered very good. A Sharpe ratio of 3.0 is considered excellent. A Sharpe ratio of less than 1.0 is considered to be poor.

The Sortino ratio is a variation of the Sharpe ratio that only factors in downside risk. The Sharpe ratio is used more to evaluate low-volatility investment portfolios, and the Sortino variation is used more to evaluate high-volatility portfolios.