Category Archives: R language
Review of “The Origin of Financial Crises” by George Cooper
The subtitle is “Central banks, credit bubbles and the efficient market fallacy”. Executive summary This is much too important of a book to remain as obscure as it is. Besides, it is quite a fun read. It talks about two subjects: Why markets for goods and services tend toward equilibrium but financial markets do not. … Continue reading
The quality of variance matrix estimation
A bit of testing of the estimation of the variance matrix for S&P 500 stocks in 2011. Previously There was a plot in “Realized efficient frontiers” showing the realized volatility in 2011 versus a prediction of volatility at the beginning of the year for a set of random portfolios. A reader commented to me privately … Continue reading
Posted in Quant finance, R language
Tagged Ledoit-Wolf shrinkage, statistical factor model, variance matrix
7 Comments
The shadows and light of models
How wide is the darkness? Uses of models The main way models are used is to: shine light on the “truth” We create and use a model to learn how some part of the world works. But there is a another use of models that is unfortunately rare — a use that should be common … Continue reading
A minimum variance portfolio in 2011
2011 was a good vintage for minimum variance, at least among stocks in the S&P 500. Previously The post “Realized efficient frontiers” included, of course, a minimum variance portfolio. That portfolio seemed interesting enough to explore some more. “What does ‘passive investing’ really mean” suggests that minimum variance should be considered a form of passive … Continue reading
Realized efficient frontiers
A look at the distortion from predicted to realized. The idea The efficient frontier is a mainstay of academic quant. I’ve made fun of it before. This post explores the efficient frontier in a slightly less snarky fashion. Data The universe is 474 stocks in the S&P 500. The predictions are made using data from … Continue reading
Posted in Quant finance, R language
Tagged efficient frontier, Ledoit-Wolf shrinkage, MACD, S&P 500
7 Comments
What does ‘passive investing’ really mean?
We know the words but what do they mean? Some definitions Here are some definitions of “passive investment management”. Investopedia says: A style of management associated with mutual and exchange-traded funds (ETF) where a fund’s portfolio mirrors a market index. Wikipedia says: Passive management (also called passive investing) is a financial strategy in which an investor (or … Continue reading
The BurStFin R package
Version 1.01 of BurStFin is now on CRAN. It is written entirely in R, and meant to be compatible with S+. Functionality The package is aimed at quantitative finance, but the variance estimation functions could be of use in other applications as well. Also of general interest is threeDarr which creates a three-dimensional array out … Continue reading
Posted in Quant finance, R language
Tagged BurStFin, Ledoit-Wolf shrinkage, statistical factor model, variance estimation
9 Comments
A slice of S&P 500 kurtosis history
How fat tailed are returns, and how does it change over time? Previously The sister post of this one is “A slice of S&P 500 skewness history”. Orientation The word “kurtosis” is a bit weird. The original idea was of peakedness — how peaked is the distribution at the center. That’s what we can see, … Continue reading
The US market will absolutely positively definitely go up in 2012
The Super Bowl tells us so. The Super Bowl Indicator The championship of American football decides the direction of the US stock market for the year. If a “National” team wins, the market goes up; if an “American” team wins, the market goes down. Yesterday the Giants, a National team, beat the Patriots. The birth … Continue reading
Posted in R language, Statistics
Tagged p-value, random permutation test, Super Bowl Indicator
4 Comments
The distribution of financial returns made simple
Why returns have a stable distribution As “A tale of two returns” points out, the log return of a long period of time is the sum of the log returns of the shorter periods within the long period. The log return over a year is the sum of the daily log returns in the year. … Continue reading
