Today I want to highlight a whitepaper about Adaptive Asset Allocation by Butler, Philbrick and Gordillo and the discussion by David Varadi on the robustness of parameters of the Adaptive Asset Allocation algorithm. In this post I will follow the steps of the Adaptive Asset Allocation paper, and in the next post I will show 
I want to share a brilliant idea and a great example from the You’re Looking at the Wrong Number post at the GestaltU blog. Today, I will focus on the section of this post that outlines simple steps to improve a typical 60/40 stock/bond portfolio by using risk allocation instead of dollar allocation, and targeting 
Recently, I came across quite a few examples of time series forecasting using R. Here are some examples: Time series cross-validation 4: forecasting the S&P 500 Holt-Winters forecast using ggplot2 Autoplot: Graphical Methods with ggplot2 Large-Scale Parallel Statistical Forecasting Computations in R (2011) by M. Stokely, F. Rohani, E. Tassone Forecasting time series data ARIMA 
In my last post, Factor Attribution to improve performance of the 1-Month Reversal Strategy, I discussed how Factor Attribution can be used to boost performance of the 1-Month Reversal Strategy. Today I want to dig a little dipper and examine this strategy for each sector and also run a sector-neutral back-test. The initial steps to 
Today I want to show how to use Factor Attribution to boost performance of the 1-Month Reversal Strategy. The Short-Term Residual Reversal by D. Blitz, J. Huij, S. Lansdorp, M. Verbeek (2011) paper presents the idea and discusses the results as applied to US stock market since 1929. To improve 1-Month Reversal Strategy performance authors 
Today I want to show a simple example of the 1-Month Reversal Strategy. Each month we will buy 20% of loosers and short sell 20% of winners from the S&P 500 index. The loosers and winners are measured by prior 1-Month returns. I will use this post to set the stage for my next post 
In the prior post, Factor Attribution 2, I have shown how Factor Attribution can be applied to decompose fund’s returns in to Market, Capitalization, and Value factors, the “three-factor model” of Fama and French. Today, I want to show you a different application of Factor Attribution. First, let’s run Factor Attribution on each the stocks 
I want to continue with Factor Attribution theme that I presented in the Factor Attribution post. I have re-organized the code logic into the following 4 functions: factor.rolling.regression – Factor Attribution over given rolling window factor.rolling.regression.detail.plot – detail time-series plot and histogram for each factor factor.rolling.regression.style.plot – historical style plot for selected 2 factors factor.rolling.regression.bt.plot 
I came across a very descriptive visualization of the Factor Attribution that I will replicate today. There is the Three Factor Rolling Regression Viewer at the mas financial tools web site that performs rolling window Factor Analysis of the “three-factor model” of Fama and French. The factor returns are available from the Kenneth R French: 
I have discussed Volatility Position Sizing in the Volatility Position Sizing to improve Risk Adjusted Performance post using the Average True Range (ATR) as a measure of Volatility. Today I want show how to use historical volatility to adjust portfolio leverage. Let’s start with Buy and Hold strategy using SPY and rescale it to the 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
