Factor Models and Portfolio Construction [Introduction]

According to Chincarini & Kim, Quantitative Equity Portfolio Management (QEPM) is organised around the following 7 tenets (or principles)

  1.  Markets are mostly efficient.
  2.  Pure arbitrage opportunities do not exist.
  3.  Quantitative analysis creates statistical arbitrage opportunities.
  4.  Quantitative analysis combines all the available information in an efficient way.
  5.  Models should be based on sound economic theories.
  6.  Models should reflect persistent and stable patterns.
  7.  Deviations of a portfolio from the benchmark are justified only if uncertainty is small enough.

The basic premise of modern financial economics is that the average return of a stock is the payoff to the shareholder for assuming relevant risk. Factor models express this risk-reward relationship by linking average stock returns to  the

  1.  factor exposure :  the stock’s exposure to the risk that the factor represents and
  2.  factor premium the payoff for each unit of exposure to the risk .

There are three generic factor models in QEPM that are used to determine how stock returns and risk vary with factors – the fundamental factor model, the macroeconomic factor model,and the statistical factor model,- the main features of which are summarised below.

Steps toward building a quantitative portfolio :

  1. Factor Choice
  2. Data decision (timeseries vs cross section)
  3. Factor exposure
  4. Factor premium
  5. Expected Return
  6. Risk decomposition (diversifiable vs systematic risk)
  7. Forecasting
  8. Security weighting

The following flowchart is a brief overview of the portfolio construction process :

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For much more detailed information and practical implementation of portfolio optimisation in R, visit the systematic investor blog here  http://systematicinvestor.wordpress.com/

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