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...
There was a lot of buzz in the blogosphere as well as the science community about a new family of algorithms that are able to find non-linear relationships over extremely large fields of data. What makes it particularly useful is that the measure(s) it...
It is a fact that on most days, not much is going on in the stock market. When we estimate the relation of a stock with the market, or the “beta” of a stock, we use all available daily returns. … Continue reading →
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...
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...
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...
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...
Having stock market in mind, in the previous post: “Price is right, part one.”, I stated that we should not think in terms of “the price went up/down too much” but that “the current price level is wrong since…. and … Continue reading →
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...
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...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).