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Just like what I am asking in the title. I see nearly all the financial datas take logs before the data analysing step, Why? Dose it have nice properties?

user52897
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  • You probably need to meet certain assumptions on the distributions of the variables before applying certain models, hence the need for such a transformation. – Black Milk Aug 07 '14 at 01:06
  • This is answered here: http://stats.stackexchange.com/questions/18844/when-and-why-to-take-the-log-of-a-distribution-of-numbers. Also, look at the comments below the question there. One of the commenters provides links for further insight. Also, this might help too http://www.ats.ucla.edu/stat/mult_pkg/faq/general/log_transformed_regression.htm – Black Milk Aug 07 '14 at 01:50
  • [Variance of annual return based on variance of monthly return](http://stats.stackexchange.com/questions/64172/variance-of-annual-return-based-on-variance-of-monthly-return/64180#64180) is also relevant – TooTone Aug 07 '14 at 18:55

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Finance data tends to be money-related and as such incorporates many potential multiplicative effects - things like inflation or interest for example.

The variables tend to be right skew, in some cases close to lognormal.

The effects of things like scale changes (cents to dollars or billions to millions) are simple location shifts on the log scale. Multiplicative/percentage effects (like 10% increase, say) convert to shifts as well.

Variables often exhibit exponential growth, at least in the short term.

All these things make working on the log scale much more sensible/easy.

Glen_b
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Most common financial data (prices, returns, etc) have a Lognormal distribution. Second of all, for some modelling, it does not make sense to have negative values so by taking logs, you are ensuring that you will not get negative values in your calculations.

rocinante
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  • Though a log of a negative number is nonexistent, so modeling on those records would just throw them away, right? – gregmacfarlane Aug 07 '14 at 02:01
  • What kind of negative numbers? -12 percent GDP growth is the same as 88% of the previous quarter's GDP. This transformation preserves that data point, even under logarithmic transformation. It's inappropriate to take the log of a negative number, but that doesn't mean it's ok to throw out the data... that's a recipe for a bad/biased model. – Sycorax Aug 07 '14 at 02:11
  • After take log my data are all negative,because my original data are like all around 0.4,so shall I not take logs just use the original data? – user52897 Aug 08 '14 at 19:58
  • taking logs is usually done on level data (prices, output, money, ...); it is _not_ used on changes in level (returns, growth rates). So in this reply the 'returns' slipped in incorrectly. Note that taking logs of levels is also convenient to compute (approximate) returns/growth rates: you can just use difference of log value to obtain approximate growth rates. – Georg M. Goerg Jul 08 '15 at 10:40
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One of the major reasons in econometrics is that money tends to have diminishing marginal returns/effects. \$1 simply means less to you if you have \$1 million than if you have $10. A log transform accommodates this.

gregmacfarlane
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