Category Archives: R language
Predictability of kurtosis and skewness in S&P constituents
How much predictability is there for these higher moments? Data The data consist of daily returns from the start of 2007 through mid 2011 for almost all of the S&P 500 constituents. Estimates were made over each half year of data. Hence there are 8 pairs of estimates where one estimate immediately follows the other. … Continue reading
Time series equivalence of brains and markets
fMRI data from 90 locations in the brain look somewhat like daily closing prices on 116 stocks if you squint just right. Marginal Revolution was nice enough to point to “Topological isomorphisms of human brain and financial market networks”. I’ve only just glanced through the paper. I find it interesting, but I’m fairly skeptical. The … Continue reading
Beta and expected returns
Some pictures to explore the reality of the theory that stocks with higher beta should have higher expected returns. Figure 2 of “The effect of beta equal 1” shows the return-beta relationship as downward sloping. That’s a sample of size 1. In this post we add six more datapoints. Data The exact same betas of … Continue reading
Posted in Quant finance, R language
Tagged beta in finance, Capital Asset Pricing Model, CAPM, low volatility investing, S&P 500
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Solve your R problems
download ‘The R Inferno’ Update While it is still not for sale at a website near you, it is also for sale at a website near you: Buy The R Inferno Epilogue I’m not a lawyer, but here is my understanding of the rules should you want to extract images from this page: Most … Continue reading
Posted in almost wordless, R language
Tagged humor, R abnormalities, R absurdities, R anomalies, R oddities, R peculiarities, R quirks, R trouble spots, The R Inferno
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A brief history of S&P 500 beta
Data The data are daily returns starting at the beginning of 2007. There are 477 stocks for which there is full and seemingly reliable data. Estimation The betas are all estimated on one year of data. The times that identify the betas mark the point at which the estimate would become available. So the betas … Continue reading
Posted in Quant finance, R language
Tagged beta in finance, Capital Asset Pricing Model, CAPM
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Review of “Risk and Meaning” by Nicolas Bouleau
The subtitle is: Adversaries in Art, Science and Philosophy. Executive Summary Genius or madness? I haven’t decided. Irreversibility of interpretation The book drives home that once we decide how something is we can’t go back to our state of innocence. Figures 1 through 3 exhibit this idea via a randomly generated polygon. Look at Figure … Continue reading
Posted in Book review, R language, Risk
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Realized beta and beta equal 1
What does beta look like in the out-of-sample period for the portfolios generated to have beta equal to 1? In the comments Ian Priest wonders if the results in “The effect of beta equal 1” are due to a shift in beta from the estimation period to the out-of-sample period. (The current post will make … Continue reading
Posted in Quant finance, R language
Tagged beta equal 1, beta in finance, Capital Asset Pricing Model, CAPM
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The effect of beta equal 1
Investment Performance Guy had a post about beta equal 1. It made me wonder about the properties of portfolios with beta equal 1. When I looked, I got a bigger answer than I expected. Data I have some S&P 500 data lying about from the post ‘On “Stock correlation has been rising”‘. So laziness dictated … Continue reading
Posted in Quant finance, R language, Random portfolios
Tagged beta equal 1, beta in finance, Capital Asset Pricing Model, CAPM
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Things I learned at useR!2011
The title says “things” but conferences are mainly about people. Some of it can be serendipitous. For example, one day I sat next to Jonathan Rougier at lunch because I had a question for him about climate models. When Jonathan left, I started a conversation with the person on my other side. That was most … Continue reading
Random input software testing
The usual approach to testing software is to create a specific problem and see if the software gets the correct answer. Although this is very useful, there are problems with it: It is labor-intensive It almost totally neglects to test the code that throws errors There can be unconscious bias in the test cases created … Continue reading
