Category Archives: R language
CambR and other upcoming events
New events CambR (Cambridge UK R user group) 2012 May 29 6:30 PM for 7:00 PM start. Pat Burns “Inferno-ish R” Abstract: While R is wonderful, it is not uniformly wonderful. We highlight a few things generally found to be confusing, and outline the forces that have driven such imperfections. Markus Gesmann “Interactive charts with … Continue reading
Posted in Events, R language
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Exponential decay models
All models are wrong, some models are more wrong than others. The streetlight model Exponential decay models are quite common. But why? One reason a model might be popular is that it contains a reasonable approximation to the mechanism that generates the data. That is seriously unlikely in this case. When it is dark and … Continue reading
Posted in R language, Statistics
Tagged exponential decay, exponential smoothing, model error
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Portfolio diversity
How many baskets are your eggs in? Meucci diversity Attilio Meucci directly addresses the adage: Don’t put all your eggs in one basket. His idea is to think of your portfolio as a set of subportfolios that are each uncorrelated with the rest. If your portfolio can be configured to have a lot of roughly … Continue reading
Posted in Quant finance, R language
Tagged asset-portfolio correlation, diversification, portfolio diversity, S&P 500
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Cross-sectional skewness and kurtosis: stocks and portfolios
Not quite expected behavior of skewness and kurtosis. The question In each time period the returns of a universe of stocks will have some distribution — distributions as displayed in “Replacing market indices” and Figure 1. Figure 1: A cross-sectional distribution of simple returns of stocks. In particular they will have values for skewness and … Continue reading
A variance campaign that failed
they ought at least be allowed to state why they didn’t do anything and also to explain the process by which they didn’t do anything. First blush One of the nice things about R is that new statistical techniques fall into it. One such is the glasso (related to the statistical lasso) which converts degenerate … Continue reading
Information flows like water
Guiding a ship, it takes more than your skill Spark David Rowe’s Risk column this month is about data leverage. The idea is that you are leveraging your data if you are using it to answer questions that are too demanding of information. The piece reminded me of a talk that Dave gave a few … Continue reading
Betas of the low vol cohorts
How did the constraints affect portfolio betas, and how did the betas change over time? Previously “Low (and high) volatility strategy effects” created 6 sets of random portfolios — the so-called low vol cohorts — as of 2007 and showed their performance up to about a month ago. “Rebalancing the low vol cohorts” looked at … Continue reading
Posted in Quant finance, R language, Random portfolios
Tagged beta in finance, low volatility investing
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Replacing market indices
If equity markets suddenly sprang into existence now, would we create market indices? I’m doubtful. Why an index? The Dow Jones Industrial Average was born in 1896. This was when computers were humans with adding machines (but they did do parallel processing). At that point boiling “the market” down to a single number had value. … Continue reading
Beta is not volatility
The missing link between beta and volatility is correlation. Previously “4 and a half myths about beta in finance” attempted to dislodge several myths about beta, including that beta is about volatility. “Low (and high) volatility strategy effects” showed a plot of beta versus volatility for stocks in the S&P 500 for estimates from 2006. … Continue reading
Low (and high) volatility strategy effects
Does minimum variance act differently from low volatility? Do either of them act like low beta? What about high volatility versus high beta? Inspiration Falkenblog had a post investigating differences in results when using different strategies for low volatility investing. Here we look not at a single portfolio of a given strategy over time, but … Continue reading
Posted in Quant finance, R language, Random portfolios
Tagged beta in finance, low volatility investing
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