Author Archives: Pat
A sister blog is born
The Burns Statistics blog had it’s first real post today (about the corner function in the BurStMisc package). The blog will talk mainly about the R language, statistics and programming — it will not have the financial focus of the Portfolio Probe blog. The posts on the Burns Statistics blog will be announced on Twitter … Continue reading
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Clustering and sector strength
An exploration of the usefulness of sectors. Previously This subject was discussed in “S&P 500 sector strengths”. Idea Stocks are put into groups based on the sector that the company is considered to be in. Cluster analysis is a statistical technique that finds groups. If sectors really move together, then clustering should recover sectors. Will … Continue reading
US market portrait 2013 week 3
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5 — see the R commands to scrape the data) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R
My missed opportunity with random portfolios
The Observer tells of a ginger tabby named Orlando who selected a random portfolio that won an investment contest. Meanwhile I have a gray tabby here on the desk doing nothing. All that effort to write software to generate random portfolios efficiently when I could have been using cat power instead. Not a single random … Continue reading
Posted in Random portfolios
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The incoherence of risk coherence
What coherent risk measures are, why some people think coherence is important, and why I don’t. The rules A risk measure is considered to be coherent if it satisfies some mathematical properties. They are formulated in various ways — here is one set: (monotonicity) If the value of portfolio X is always bigger than the … Continue reading
US market portrait 2013 week 2
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5 — see the R commands to scrape the data) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R
Market predictions for year 2013
Calibrations of 2013 predictions for 18 equity indices — plus some publicly available predictions. Orientation The distributions are an attempt to see the variability if there were no market-driving news for the whole year. Another way of thinking: mentally moving the distribution to center on a prediction gives a sense of the variability of results … Continue reading
US market portrait 2013 week 1
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia on 2013 January 5) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R Appendix R The R commands to get the data were: require(XML) wikisptab <- … Continue reading
US market portrait 2012 final
US large cap market returns. Fine print The data are from Yahoo Almost all of the S&P 500 stocks are used (as implied by Wikipedia in 2012 April) The initial post was “Replacing market indices” The R code is in marketportrait_funs.R
