I purchased the book and haven't read it yet. Stat arbing seems a bit overwhelming to me right now because I'm just learning EasyLanguage with Tradestation and even if I was completely fluent in the language, I'd think it would be very tough to find an edge just coming up with my own ideas. That being said, is there any point in reading this book if I am just going to look for isolated setups with individual stocks and not pairs trading?
Hello, and thank you very much for your post on your experience on stat arb for small investors. I have done some research and I found that looking for example at xfn, a canadian financial ETF. the spread is about 1/20 of the total size of the position to trade (this is due to the prize of the smallest weight component being about 30 $, and the weigh being about 2%). This means that an average 0.1% percent component bid/ask spread +commissions correspond to 20* 0.1=2%, i.e. 4% on a two ways trade on the ETF- components spread ( ETFCS from now). From my empirical research this spread can have some intraday oscillation of about 5% but very rarely and it would be a quite risky trade which would only earn 1% of ETFCS , i.e.about 1/(20/3)% of the capital invested considering a 30% margin account such asIB. I have not regressed the components against the ETF but simply used the weight in the ETF description. Does the regression gives much bigger oscillation of the ETFCS? How many intraday opportunities do you have on a given ETF?What are the profits? Have you checked cointegration on long multiday intervals?I read the links you provide, and one guy correctly observed that out of sample data may not cointegrate as well as the in sample used to estimate the cointegration vectors, but of course you real life experience would be the best proof. In the end this strategy is equivalent to assume a mean reverting behavior of the stocks you neglect in reproducing the ETf,but why should their weighted sum should mean revert if they are unit roots individually? Thanks Again LY
I read somewhere on ET that big quant shops use up to 19 factors for entering trades. Does anyone know what those consist of besides the obvious: cointegration, correlation, ratios, RSI?, etc
Factors analysis has nothing to do to technical analysis. Factors can be considered "factor" which are unpredictable and have influence on a given stock for example. One is the market, other can be gold, oil, inflation etc. Hedge funds like to neutralize all these factor by taking factor neutral positions, something like beta neutral portofolios , but generalized to all the factors. In these way they can exploit ( supposedly...) idiosyncratic stock movements without exposure to factor fluctuations risk. In a way it is done in the paper mentioned in the other forum, using PCA analysis or ETF as factors.
Great post, thank you very much. One question: your hint at pairing ETFs with 10 underlying and choosing the underlying based on regression. Now, in your experience is it better to choose the ones with a lower correlation to maximize the chance of profitable trading opportunities, but also the chance of divergence or would it be better to choose the ones with the highest correlation, minimizing divergence risk, but also minimizing profitable trading opportunities. Your insight is much appreciated..
You want higher correlation. Remember you are looking for the unusual variation (2+ standard deviation), because when things deviate they will have a higher probability of snapping back - as the stocks are highly correlated - as per your suggested method If you chose the lower correlated set then you don't necessarily have a higher probability things will suddenly align (so you can straddle them for the expected break), rather you just have a higher probability things won't align.
i attach a note re difference between correlation and cointegration. he says cointegration is the name of the game in pairs i don't get it, any ideas? i also thought it was correlation
Besides MATLAB, is there any other software that can analyze cointegration with reasonable programming effort? Cointegration calculation seems quite involved and MATLAB is not that accessible to average retails traders. Also, is there any recommended time frame to re-evaluate the cointegration among traded products? Supposedly, cointegration, which exists for whatever reason, may change or get stronger or weaker over time.