What’s that thing about arithmetic and geometric returns and the variance? Previously An introduction to the difference between simple and log returns is: A tale of two returns Issue Suppose you are predicting the mean annual return of an asset for some number of years. To simplify the discussion, let’s buy into the fantasy that … Continue reading →
When investment skill is simulated, it is often presented as if it is obvious how to do it. Maybe I’m wrong, but I don’t think it’s obvious. Previously In “Simple tests of predicted returns” we saw that prediction quality need not look like what you would find in a textbook. For example, there was a … Continue reading →
Using garch to learn a little about the distribution of returns. Previously There are posts on garch — in particular: A practical introduction to garch modeling The components garch model in the rugarch package garch and long tails There has also been discussion of the distribution of returns, including a satire called “The distribution of … Continue reading →
How good is the current opportunity to pick stocks relative to the past? Idea The more stocks act differently from each other relative to how volatile they are, the more opportunity there is to benefit by selecting stocks. This post looks at a particular way of investigating that idea. Data Daily (log) returns of 442 … Continue reading →
How has the distribution of correlations changed over the last several years? Previously Posts about correlation boxplots explained Data Daily returns of 443 large cap US stocks from 2004 through 2012 were used. The sample correlations — almost 98,000 of them — during each year were created. If we were actually using the correlations, then … Continue reading →
How variable are garch predictions? Previously There have been several posts on garch, in particular: A practical introduction to garch modeling The components garch model in the rugarch package Both of these posts speak about the two common prediction targets: prediction (of volatility) at the individual times (usually days) term structure prediction — the average … Continue reading →
Highlighted LondonR is soon — see the “Previously Announced” section. New Events Thirsty Quants 2013 March 21, London. Some thirsty quants will be going for a drink on the 21st of March as of 18.30 at the Lamb Tavern in Leadenhall Market. http://www.lambtavernleadenhall.com/ Rethinking the Economics of Pensions 2013 March 21 & 22 in London. … Continue reading →
What effect do predicted correlations have when optimizing trades? Background A concern about optimization that is not one of “The top 7 portfolio optimization problems” is that correlations spike during a crisis which is when you most want optimization to work. This post looks at a small piece of that question. It wonders if increasing predicted … Continue reading →
Exploring the quality of predictions using random portfolios and optimization. Previously “Simple tests of predicted returns” showed a few ways to look at expected returns at the asset level. Here we move to the portfolio level. The previous post focused on correlation. Win Vector Blog points out that gauging prediction quality using correlation can be … Continue reading →
Some ways to explore how good a method of predicting returns is. Data and model The universe is 443 large cap US stocks that have data back to the beginning of 2004. The daily (adjusted) close was used. The model that is used as an example is the default signal from the MACD function of … Continue reading →
