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visits member for 2 years, 7 months
seen Apr 16 at 20:48

Feb
12
comment Is the number of outstanding shares a stationary series?
Could I save it by instead of using number of outstanding shares, the market cap? (Which is price * number of outstanding shares (for the moment I bypass freefloat adjusment). Note that one of the other explanatory variables is price. So I 've got P and P*N as explanatory variables, is that allowed?
Feb
12
comment Is the number of outstanding shares a stationary series?
Thanks for thinking along. Now as I simply take the differenced series most of them become a series that is 0 most of the time and now and then makes a sudden jump to come back to 0 (which was expected). This could very well be stationary right? It passed the unit root tests :). But the disadvantage that I'll lose knowledge about the influence of a absolute level of outstanding shares and only examine the effect a change in outstanding shares has. Am I right?
Feb
11
comment Is the number of outstanding shares a stationary series?
How? I am analyzing stock-liquidity as a dependent variable, number of shares is one of the explanatory variables...
Feb
11
comment Is the number of outstanding shares a stationary series?
No, it's not clear.
Nov
6
comment How to properly take averages to reduce data in regression/panel data analysis
My dependent variable is the liquidity of a stock (several proxies are possible, for now I am using the ILLIQ measure of Amihud). For my independent variables I tried several combinations out of {Marketcap, Liquidity of the index it belongs to (3 indices), Number of Shares, Number of Freefloat share, percentage of shares that belongs to the freefloat}. Another thing I tried is to average vertically (over time) instead of horizontally (over stocks), which avoids the problem above of how to sort. Still my regression gives way too high p-values.
Oct
16
comment Time Series or Regression
Am I not looking a fixed effect panel data regression of some sorts?
Oct
16
comment Time Series or Regression
Thanks for your reply. According to Wikipedia: A VAR model describes the evolution of a set of k variables (called endogenous variables) over the same sample period (t = 1, ..., T) as a linear function of only their past values. Is this what I want? The concept of regression came up because I'd figure the liquidity would depend on certain characterstics (that in turn could change over time) and not only depend on previous values.