Why it is good to take log on Finance data? Does it have nice properties? 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?
 A: 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.
A: 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. 
A: 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.
