I’m teaching an R workshop for the Baruch MFE program. This is the first installment of the workshop and focuses on …Continue reading »

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

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

2011 was a volatile year, no doubt about that, but was it exceptionally so from a historic point of view? To quantify the volatility, I used the Dow Jones Industrial average, which goes back to 1928 on Yahoo Finance: A volatile year no doubt, but once again confirming the fact that, in markets behaviour at

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