Category Archives: Quant finance
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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A look at the quality of CAPM
Empirical Finance Blog has a post called “How to use the Fama French Model”. I find the first part of the post most interesting. This shows some examples of how the Capital Asset Pricing Model falls down. I’ve trashed CAPM before in the form of “4 and a half myths about beta in finance”. The … Continue reading
More S&P 500 correlation
Here are some additions to the previous post on S&P 500 correlation. Correlation distribution Before we only looked at mean correlations. However, it is possible to see more of the distribution than just the mean. Figures 1 and 2 show several quantiles: 10%, 25%, 50%, 75%, 90%. Figure 1: Quantiles of 50-day rolling correlation of … Continue reading
Posted in Quant finance, R language
Tagged correlations, equity correlations, jackknife, S&P 500, stock correlations
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On “Stock correlation has been rising”
Ticker Sense posted about the mean correlation of the S&P 500. The plot there — similar to Figure 1 — shows that correlation has been on the rise after a low in February. Figure 1: Mean 50-day rolling correlation of S&P 500 constituents to the index. For me, this post raised a whole lot more … Continue reading
Posted in Quant finance, R language
Tagged correlations, equity correlations, S&P 500, stock correlations
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An introduction to “Expected Returns” by Antti Ilmanen
The subtitle is “An Investor’s Guide to Harvesting Market Rewards”. Executive summary I don’t hold myself as being much at forecasting, but I predict that this will be a classic that many of us will go back to and consult periodically for years to come. Purpose Antti does better than I could have at describing … Continue reading
Highlights of the London Quant Group Technology Day
A summary of the high points of the day. Factor models and optimization Three of the talks formed a theme: factor models of variance — especially as applied to portfolio optimization. The basic problem is that variance matrices are created with error. A variance matrix is a key input to (some) portfolio optimizations. The optimizer … Continue reading
Selections from the R/Finance conference
The R/Finance conference happened in Chicago at the end of April. If, like me, you weren’t there, you can still benefit from it because slides from many of the talks are now online. Here is a quick synopsis (in chronological order) of some of the talks I found most interesting. Michael Kane Michael Kane and … Continue reading
Attilio Meucci starts praying
Attilio Meucci has written “The Prayer” which gives a ten-step process of quantitative analysis of the profit and loss stream. The paper is nicely laid out. Each step includes at least one “key concept” box. These give a clear, concise statement of a main idea. These allow you to quickly get the thrust without needing … Continue reading
Specific differences between Ledoit-Wolf and factor models
What can we learn about the difference in structure between a Ledoit-Wolf variance matrix and a corresponding factor model variance? Previously We’ve generated a set of random portfolios with constraints on the risk fractions of a Ledoit-Wolf variance matrix, and a corresponding set of random portfolios with risk fraction constraints from a statistical factor model. … Continue reading
Posted in Quant finance, R language
Tagged correlations, covariance matrix, Ledoit-Wolf shrinkage, risk fraction, variance matrix
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Recap of London Quant Group Spring Seminar
The London Quant Group Spring Seminar took place this Monday and Tuesday 2011 May 16-17. There were 9 talks — I give a brief (and biased) summary of each. Dan di Bartolomeo Dan talked about the information ratios that active managers have. He claims that the information ratio is upwardly biased compared to what we … Continue reading
Posted in Quant finance
Tagged information ratio, low volatility investing, Value at Risk
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