In the last post, Portfolio Optimization: Specify constraints with GNU MathProg language, Paolo and MC raised a question: “How would you construct an equal risk contribution portfolio?” Unfortunately, this problem cannot be expressed as a Linear or Quadratic Programming problem. The outline for this post: I will show how Equal Risk Contribution portfolio can be 
I have previously described a few examples of portfolio construction: Introduction to Asset Allocation Maximum Loss and Mean-Absolute Deviation risk measures 130/30 Portfolio Construction Minimum Investment and Number of Assets Portfolio Cardinality Constraints Multiple Factor Model – Building 130/30 Index (Update) I created a number of helper functions to simplify process of making the constraints( 
Nico brought to my attention the 130/30: The New Long-Only (2008) by A. Lo, P. Patel paper in his comment to the Multiple Factor Model – Building CSFB Factors post. This paper presents a very detailed step by step guide to building 130/30 Index using average CSFB Factors as the alpha model and MSCI Barra 
In the last post, Multiple Factor Model – Building Risk Model, I have shown how to build a multiple factor risk model. In this post I want to explain why do we need a risk model and how it is used during portfolio construction process. The covariance matrix is used during the mean-variance portfolio optimization 
This is the fourth post in the series about Multiple Factor Models. I will build on the code presented in the prior post, Multiple Factor Model – Building CSFB Factors, and I will show how to build a multiple factor risk model. For an example of the multiple factor risk models, please read following references: 
This is the third post in the series about Multiple Factor Models. I will build on the code presented in the prior post, Multiple Factor Model – Building Fundamental Factors, and I will show how to build majority of factors described in the CSFB Alpha Factor Framework. For details of the CSFB Alpha Factor Framework 
This is the second post in the series about Multiple Factor Models. I will build on the code presented in the prior post, Multiple Factor Model – Fundamental Data, and I will show how to build Fundamental factors described in the CSFB Alpha Factor Framework. For details of the CSFB Alpha Factor Framework please read 
The Multiple Factor Model can be used to decompose returns and calculate risk. Following are some examples of the Multiple Factor Models: The expected returns factor model: Commonality In The Determinants Of Expected Stock Returns by R. Haugen, N. Baker (1996) The expected returns factor model: CSFB Quantitative Research, Alpha Factor Framework on page 11, 
THIS IS NOT INVESTMENT ADVICE. The information is provided for informational purposes only. In the Time Series Matching post, I used one to one mapping to the compute distance between the query(current pattern) and reference(historical time series). Following chart visualizes this concept. The distance is the sum of vertical lines. An alternative way to map 
This is a quick post to address comments raised in the Time Series Matching post. I will show a very simple example of backtesting a Time Series Matching strategy using a distance weighted prediction. I have to warn you, the strategy’s performance is worse then the Buy and Hold. I used the code from Time 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
