Quantitative Finance Events Random Portfolios The R Language in Finance The Technical Analysis Challenge Other Sections of the Website A Glimpse at Quantitative Finance is one perspective on the subject. Events Note that events listed here may have nothing to do with Burns Statistics. • The London Quant Group Investment Seminar will be held 2010 September 12-15. Details at http://www.lqg.org.uk/ • Finance with R, a two-day training class, will be held 2010 November 15-16 in London. Patrick Burns will be a guest lecturer. For details see: http://www.optirisk-systems.com/events/rtraining.asp Random Portfolios Random portfolios -- a form of Monte Carlo -- are an extremely useful, but very under-valued technique for finance. The random portfolios page is meant to help remedy the lack of appreciation. In particular random portfolios dramatically improve the performance measurement of investment funds. The R Language in Finance The R language has rapidly been gaining acceptance in the finance community. It is clearly a suitable environment for many quantitative tasks in finance. The body of specifically financial functionality is growing all the time. Almost any task that is done in a spreadsheet but is non-trivial to do there would more productively be done in R. Introduction to R R is a language designed for the interactive analysis of data. Some hints for the R beginner is an introduction to using R. An Introduction to the S Language provides a quick introduction to the ideas (R is a dialect of the S language). An example of the power of R is the ease with which resampling techniques can be done in it. The Statistical Bootstrap and Other Resampling Methods shows some simple financial examples. Financial Specifics There is a number of "Task Views" available for different applications of R. There is the Task view on Finance and also the Task view on Econometrics. Rmetrics provides quite a large amount of financial functionality. There is a mailing list for those interested both in finance and in R. You can sign up to it via: https://stat.ethz.ch/mailman/listinfo/r-sig-finance Resources for R that are not specific to finance may be found on the Links page. Alternatives to R Spreadsheets are over-used in finance (and elsewhere). Spreadsheet Addiction discusses some of the challenges posed by spreadsheets to error-free computation as well as highlighting several specific problems with Microsoft Excel. R is a very good antidote to the problems of spreadsheets. The C language can (and often does) perform tasks that are often done in R. C does calculations very fast, and so is often a good tool. The downside of C is that it can take a substantial amount of time to write the code. Perhaps the best approach is to think of C and R as complements rather than competitors. It is very easy to call C functions from R. Doing data manipulation in R and numerical computation in C is a very efficient model. When developing new functionality, it is quick to try out ideas in R. For ideas that pan out, the computationally intense portions can then be moved into C. Another alternative to R is Matlab. In many respects these two are very similar. A key difference is that Matlab was made for mathematics while R was made for data analysis. The result is that R has a much richer set of objects available. That extra complexity means that Matlab is somewhat easier to learn initially. However, solutions to the complex problems of finance generally end up being simpler in R than in Matlab. The Technical Analysis Challenge In the autumn of 2003 the Technical Analysis Challenge was held. The challenge was to select an actual price series from some random alternatives. There is an explanation and access to the data as well as a working paper on the results. You can take the test yourself and then check your answers. Other Sections of the Website Other areas of the Burns Statistics website that have financial applications are: Software Products Focuses on generating random portfolios and portfolio construction. Working Papers The strongest theme running through the papers is on asset management. The utility of an investor's entire portfolio should be the driving force behind the investment strategy. The papers tend to discourage managing funds relative to benchmarks -- a focus on tracking errors is found to be counter-productive in many cases. Information ratios relative to a benchmark are very noisy. Unconstrained fund management is encouraged, especially since random portfolios can be used to assess the skill of the investment manager. Performance fees for fund managers is also encouraged. It is suggested that minimum variance portfolios could be used to produce another type of passive investment. The evaluation of predictions, and the creation of variance matrices with factor models are also studied. Risk management is addressed with an out-of-sample study comparing various estimators of Value at Risk. Statistical aspects include the combination of p-values, an introduction to random permutation tests, and a study of the rank Ljung-Box test. There is a brief note on computing multivariate GARCH estimates. Training Courses Links