Markowitz, MPT, and Market Efficiency

  • Modern Portfolio Theory (MPT) is rooted in the mean-variance analysis research performed by Harry Markowitz conducted to allocate assets through a portfolio optimization process.

  • The portfolio concepts presented in section I trace their roots to Markowitz groundbreaking work.

  • The Efficient Market Hypothesis (EMH) contents that the market correctly prices all securities.

  • MPT argues that all investors should hold the same portfolio of diversified securities correctly priced by the efficient market.

  • The conclusion of MPT would argue for passive management of investor portfolios.

    • With this in mind, some portfolio managers have consistently out-returned a recognized market index over time.
    • Further, sometimes asset mispricings are correctly identified by portfolio managers and these anomalies have been exploited to generate excess returns above the market index benchmark. These issues can serve as a foundation for justifying active portfolio management over passive management.
  • CAPM assumes that investors face no restrictions on lending and (more importantly) borrowing at the risk free rate, as well as the ability to short sell.

  • In reality these limitations frequently exist.  Any investor can lend at the risk free rate buy purchasing a bond deemed as having extremely low risk.

  • Investors lack the ability to command the low borrowing rates of say the US, Japanese, or German sovereign governments (which would be the proxies for a risk free rate, despite a total risk free nature).

  • Many investors cannot short sell (even some big institutional types, such as pensions).

  • Those investors who can short sell could opt to borrow at their cost and sell short some of the portfolio.

  • Ultimately, with a spread between the lending (lower) and borrowing (higher) rates imposed on investor reality and restrictions on short selling, the market can exhibit inefficiencies.

  • Inefficiency causes a distortion in the linear nature of the relationship between expected returns and beta.

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.

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

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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.