# What is a Beta Coefficient?

The beta coefficient is a measure of an asset's risk and return in relation to a broad market, meaning that it will show, more or less, how the asset or a portfolio of assets will respond as the market moves up or down. It is used in the capital asset pricing model (CAPM) and regression analysis. Basically, the CAPM is used in portfolio management to calculate the expected return of an asset. Essentially, regression analysis is a statistical method used in finance to estimate a link that might exist between two variables, such as a single stock and an entire stock market. This is why, when computing the beta coefficient of an asset in question, the historical returns will be used when measuring its connection to the performance of a broader market.

A beta coefficient will show how an asset's performance is sensitive to systematic risk, which is the risk that can affect an entire market. An investor who is seeking to measure the expected return of a particular stock, for example, will use a stock market index to represent the broad market. The stock market index will normally have a beta coefficient of 1.0, and in theory, a security whose beta is 1.4, for example, will move 1.4 times the move of the index. This means that if the stock market index was to move up or down by 20 percent, the security would move 28 percent accordingly.

On average, many securities have a beta coefficient of 1.0, which means that they move more or less in line with the market. A security with a beta coefficient of more than 1.0 is more risky than the average market and is fit for more aggressive investment strategies. On the other hand, those whose beta coefficient is below 1.0 are considered to be less risky, because their performance is less tied to the systematic risk. Moreover, there are assets whose beta is negative, and these tend to have dull returns when the economy is robust, but in a downturn, they have a tendency of outperforming most other investments.

The asset with a negative beta is inherently less sensitive to systematic risk, and for this reason, an investor might use this type of asset to hedge his or her portfolio. To hedge, in this sense, is to try to offset losses that might result if a systematic event arises. Moreover, when performing a regression analysis, an individual might use historical data of returns in order to estimate the link between an asset's performance and that of the wider market.

The beta of an asset can change over time; for example, the beta of a particular asset can be 1.2 for about a decade, then for various reasons, it might change to 1.4 in the following decade. Thus, in regression analysis, the beta coefficient is meant to be the same for the period being sampled. That is, if an individual was to use a sample from two decades where in one it was 1.2 and the other 1.4, the resulting information will most likely be misleading.

Moreover, the estimation of an asset's return compared to the market can also be represented graphically in regression analysis. The graph typically will be a scatter diagram, with the X-axis dedicated to the market performance, and the Y-axis is for the asset whose performance is being measured. The graph will have points scattered about it which represent specific historical returns for a particular period. Additionally, there will be a line drawn to best fit the points, and the steeper the slope of the line, the greater the beta of the asset, or the riskier the asset will be.

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