The Capital Asset Pricing Model

The Capital Asset Pricing Model is a popular asset pricing model in Finance. It is used to determine the expected rate of return of a risky asset. It says that the expected return on a risky asset is equal to the risk-free rate plus a risk premium.

The risk premium is a measure of non-diversifiable risk and is calculated using the asset’s Beta. CAPM considers only systematic risk (non-diversifiable risk) as the security specific risk (unsystematic risk) can be diversified away.

According to the CAPM model, the expected return on a security is given as follows:

E(r)=Rf+β(E(Rm)Rf)E(r)=R_{f}+\beta\left (E(R_{m}) - R_{f} \right )

Where:

  • E(Ri) is the expected return on the security
  • Rf is the risk-free rate of return
  • B is the beta of the stock
  • Rm is the expected return from the market

The key idea behind the CAPM model is that the investors should be compensated for the time value of money for holding the asset and also for the additional risk taken by them.

Assumptions of the CAPM Model

The CAPM model is a single-period model and makes a few assumptions:

  • Investors are risk-averse and want to maximize their wealth
  • All investors have same expectations from the asset returns
  • The asset returns are normally distributed
  • The investors can borrow and lend unlimited amount of money at risk-free rate
  • There are no transaction costs
  • Investors cannot influence the price, that is, they are only price takers
  • All information is available to everyone.

If the risk-free rate is 5.4%, the stock's Beta is 1.5, and the expected market return is 8.4, then the stock is expected to return 9.9% (=5.4 + 1.5 * (8.4 - 5.4)).

If the actual returns offered by the stock are less than 9.9%, then you might want to avoid the investment.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.