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 Yearling
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May
13
comment Regressing NYSE returns: Lagged intercept term & efficient market hypothesis
I agree with Quadtopic about the test. Also, I used weekly S&P 500 data to test this. In the first equation, I get that the first two parameters are significant. It's just that the quadratic one isn't. I get the same result in the second equation. I don't see what the quadratic terms get you. I'm disinclined to include quadratic terms in a regression unless I have a clear reason to do so.
May
11
revised Portfolio Selection formulation
added 1 character in body
May
11
answered Portfolio Selection formulation
May
10
comment How to deal with missing returns when creating value (equal) weighted returns
Yes. I'd be worried about delisted firms, but also firms that go bankrupt.
May
9
comment What are the main market anomalies/inefficiencies detected in quantitative finance?
Here's a recent paper detailing over 80 anomalies: papers.ssrn.com/sol3/papers.cfm?abstract_id=2508322
May
8
answered How to deal with missing returns when creating value (equal) weighted returns
May
8
comment What are the main market anomalies/inefficiencies detected in quantitative finance?
@Quantopic vonjd presently has the top answer on a post discussing the difference between risk factors and anomalies. I'm not a particularly big fan of the answer, but it's consistent with academic finance. To me, it doesn't really matter what they're called. Size and value used to be considered anomalies by some, but now the profession calls them risk factors. Oh well.
May
1
awarded  Yearling
Apr
18
comment Monte Carlo simulation returns not normal distributed
If you divide a lognormally distributed variable by a constant, then it will still be lognormally distributed.
Apr
17
comment Monte Carlo simulation returns not normal distributed
Geometric Brownian motion is lognormally distributed in levels. en.wikipedia.org/wiki/Geometric_Brownian_motion
Apr
15
answered Is a stationary process necessarily mean-reverting?
Apr
15
comment Is a stationary process necessarily mean-reverting?
Seems weakly stationary to me.
Apr
9
comment Inferences with non-normal data
I understood what you meant, and I answered all of your questions. In regression, the assumptions are more related to the errors than the actual data. You can use Newey-West if you're worried about heteroskedasticity or autocorrelation. You don't need to make any adjustments for normality to make inferences, but there are techniques you can use regardless.
Apr
8
revised Inferences with non-normal data
deleted 7 characters in body
Apr
8
answered Inferences with non-normal data
Apr
7
comment Inferences with non-normal data
Do you mean percent changes of index closing values?
Apr
7
awarded  Necromancer
Apr
6
comment Please give a step-by-step explanation on how to build a factor model
You're asking for links after not asking for links....All the references the writer used are to classic papers or one of the most well-known finance textbooks.
Mar
30
answered Calculate efficient frontier using fPortfolio with incomplete set of returns
Mar
12
comment MLE estimate of normal distribution
The MLE variance estimator for normal distributions is biased because it divides by $n$ rather than $n-1$, see ee.columbia.edu/~dliang/files/mle_biased.pdf. Not sure how much that relates to the above quote.