# Blog Archives

## Portfolio Optimization – Why do we need a Risk Model

February 26, 2012
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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

## Multiple Factor Model – Building Risk Model

February 20, 2012
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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:

## Multiple Factor Model – Building CSFB Factors

February 12, 2012
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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

## Multiple Factor Model – Building Fundamental Factors

February 4, 2012
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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

## Multiple Factor Model – Fundamental Data

January 28, 2012
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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,

## Time Series Matching with Dynamic Time Warping

January 20, 2012
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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

## Time Series Matching strategy backtest

January 17, 2012
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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

## Time Series Matching

January 13, 2012
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THIS IS NOT INVESTMENT ADVICE. The information is provided for informational purposes only. If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck. Do you want to know what S&P 500 will do in the next week, month, quarter? One way to make an

## Trading using Garch Volatility Forecast

January 5, 2012
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Quantum Financier wrote an interesting article Regime Switching System Using Volatility Forecast. The article presents an elegant algorithm to switch between mean-reversion and trend-following strategies based on the market volatility. Two model are examined: one using the historical volatility and another using the Garch(1,1) Volatility Forecast. The mean-reversion strategy is modeled with RSI(2): Long when

## Happy Holidays and Best Wishes for 2012

December 22, 2011
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This is just a quick note to wish you and your family a very healthy and happy holidays and wonderful New Year! I hope you enjoyed reading my blog and thank you for your comments and emails. Here is a short R code that implements an interesting idea from the Charting the Santa Claus Rally