Monthly Archives: February 2013
Portfolio tests of predicted returns
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
Posted in Quant finance, R language, Random portfolios
Tagged alpha generation, MACD, S&P 500
2 Comments
US market portrait 2013 week 8
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
Simple tests of predicted returns
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
US market portrait 2013 week 7
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
R for finance and other upcoming events
Featured R for Finance Workshop 2013 March 5-6 in London. The target audience are professionals and academics, who wish to learn the basics of the statistical software R and its use in Finance. The workshop is led by Ron Hochreiter, Pat Burns and Michael Sun. Details are on the Unicom website. Please reference Burns Statistics … Continue reading
Posted in Events, R language
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Variability of predicted portfolio volatility
A prediction of a portfolio’s volatility is an estimate — how variable is that estimate? Data The universe is 453 large cap US stocks. The variance matrices are estimated with the daily returns in 2012. Variance estimation was done with Ledoit-Wolf shrinkage (shrinking towards equal correlation). Two sets of random portfolios were created. In both … Continue reading
Posted in Quant finance, R language
Tagged Ledoit-Wolf shrinkage, statistical bootstrap, variance matrix
3 Comments
US market portrait 2013 week 6
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
An infelicity with Value at Risk
More risk does not necessarily mean bigger Value at Risk. Previously “The incoherence of risk coherence” suggested that the failure of Value at Risk (VaR) to be coherent is of little practical importance. Here we look at an attribute that is not a part of the definition of coherence yet is a desirable quality. Thought … Continue reading
Posted in R language, Risk
Tagged Conditional Value at Risk, Expected Shortfall, Value at Risk
3 Comments
US market portrait 2013 week 5
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
