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Jun
11
comment Reproducing levels when PCA has been done on changes
You might want to add some more details about what kind of analysis you want to do about the levels. For instance, do you want to forecast the future levels of the time series.
Jun
4
comment Beta Constrained Markowitz Minimum Variance Portfolio - Closed Form Solution
The constraints can be grouped together to something like $Aw=b$, so that the lambdas are a vector. This set-up is actually pretty common. I typically refer to the derivation in edoc.hu-berlin.de/master/jiao-wei-2003-12-16/PDF/jiao.pdf
Jun
3
comment Regression model when samples are small and not correlated
@cogolesgas It wouldn't surprise me that it's very low. However, it might or might not be a statistically significant relationship. Moreover, they could also test applying the equation to hundreds of stocks and see if it makes sense to try to trade on it.
Jun
2
comment Regression model when samples are small and not correlated
I think you should give a few more details as to the motivation of using the Kalman filter and why it would be useful in this case.
Jun
2
comment Regression model when samples are small and not correlated
I'm finding your notation a little confusing. D represents date, T represents intraday time. The (dt, dt+1) and (dt-1, dt) are a little confusing. It might be better to put all of that explanation in Latex like the formula. Anyway, I'm not sure about any "machine learning" technique, but I would recommend a mixed effects model (see lme4 for R) to allow you to group the dates.
May
27
comment Compare performance buy-and-hold strategies after stock-split
That formula looks like a good start.
May
27
comment Compare performance buy-and-hold strategies after stock-split
My recommendation would be to focus on the cumulative difference in returns around the splits and to ignore the longer-term.
May
26
comment R package for portfolio
Auxiliary variables can be tricky though when you extend them to other circumstances, like turnover constraints or transaction costs. You sometimes have to impose additional constraints to ensure that $w_{i}y_{i}=0$, which makes the problem no longer fit for quadratic optimizers.
May
26
comment Compare performance buy-and-hold strategies after stock-split
I'm not sure I understand what you're talking about here, but the fact that two share classes don't have a statistically significant difference in returns is not surprising.
May
26
comment Compare performance buy-and-hold strategies after stock-split
This approach doesn't make much sense to me. If instead of looking at the difference in prices, you look at the difference in log prices, then it won't continue to get larger over time. The fact that it grows larger over time is an artifact of compounding.
May
21
comment Is R being replaced by Python at quant desks?
On statistics, Python just doesn't have as much developed as R does on those fronts, but statsmodels is my goto. One option is to just call R functions with Python (RPy, but not apparently well tested with Windows). On derivatives, pyql seems to be more developed than the R version. On machine learning, scikit.learn. One other benefit of Python is that iPython Notebook is better than Rstudio. However, the Jupyter project looks interesting (and should work with R).
May
21
comment Is R being replaced by Python at quant desks?
I'm more-or-less with @vonjd on this. I've used a bunch of languages, but I'm most productive for statistics and analytics in R. Python is a great language and numpy and pandas are a fantastic combination. However, the community for R is just so much better. I once spent several hours over a few days to get ipopt to work on Python, and then it just worked without any real effort with R. I also don't like the conda package manager because I can't get it to work behind a corporate firewall.
May
21
comment How to identify the orders p and q for ARIMA model using least squares method?
You might find this informative: coolstatsblog.com/2013/08/14/using-aic-to-test-arima-models-2
May
21
comment R package for portfolio
@AntoniusGavin What you're really hoping is that someone is going to hide the implementation details from you. The way all these optimizers work is by implementing some algorithm(s). These algorithms work for some types of problems and not for others. An algorithm that can solve a simple quadratic problem can do it very fast and it is better just to use that. However, for your type of problem a standard quadratic optimizer will not work. So you have to use a different algorithm. nloptr allows you to choose one that works (slsqp).
May
21
comment Z-Score calculation for a win-loss streak
@Lyrk Glad you figured it out.
May
20
comment R package for portfolio
Good answer. nloptr would allow either an L1-norm or an L2-norm.
May
18
comment constrained portfolio optimization by fmincon
As a general rule, I would recommend starting with the simplest possible case and then making it more complicated. The only thing that sticks out to me is that when you use the portrisk function in fmincon you may not be passing the covmat variable with it. See the answer here: stackoverflow.com/questions/18946407/…
May
13
comment Regressing NYSE returns: Lagged intercept term & efficient market hypothesis
Oh yeah, and the significant coefficient on the first slope parameter should be negative over a long period of time, consistent with the one month reversal effect.
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
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.