According to Chincarini & Kim, Quantitative Equity Portfolio Management (QEPM) is organised around the following 7 tenets (or principles)
- Markets are mostly efficient.
- Pure arbitrage opportunities do not exist.
- Quantitative analysis creates statistical arbitrage opportunities.
- Quantitative analysis combines all the available information in an efficient way.
- Models should be based on sound economic theories.
- Models should reflect persistent and stable patterns.
- 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
- factor exposure : the stock’s exposure to the risk that the factor represents and
- 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 :
- Factor Choice
- Data decision (timeseries vs cross section)
- Factor exposure
- Factor premium
- Expected Return
- Risk decomposition (diversifiable vs systematic risk)
- Security weighting
The following flowchart is a brief overview of the portfolio construction process :
For much more detailed information and practical implementation of portfolio optimisation in R, visit the systematic investor blog here http://systematicinvestor.wordpress.com/