# Tag Info

27

I've worked at a hedge fund that allowed GA-derived strategies. For safety, it required that all models be submitted long before production to make sure that they still worked in the backtests. So there could be a delay of up to several months before a model would be allowed to run. It's also helpful to separate the sample universe; use a random half of the ...

21

By "cryptography" you mean information theory. Information theory is useful for portfolio optimization and for optimally allocating capital between trading strategies (a problem which is not well addressed by other theoretical frameworks.) See: J. L. Kelly, Jr., "A New Interpretation of Information Rate," Bell System Technical Journal, Vol. 35, July ...

20

I know that I have seen things like this in the past. Wasn't there something recently that used Twitter? Here are a few recent papers as examples, although I will be brutally honest that I don't know if they speak to your decent quality requirement: "Trading Strategies to Exploit Blog and News Sentiment" (Zhang, Skiena 2010) "The Predictive Power of ...

20

I think the biggest problem that genetic algorithms have are overfitting, data snooping bias and that they are black boxes (not so much like Neural Networks but still - it depends on the way they are implemented). I think they are not used very much. I guess there are a few hedge funds out there that use it but all in all they were hyped and then busted. ...

16

I've applied GA to all sorts of things. I had some success in the deterministic world where a pattern actually existed and I knew that some physical structure existed (seismic analysis, vibration analysis, inventory calcs, etc). After I found a GA model that behaved, the real work started....figuring out why it behaved. I also generated a lot of GA ...

15

I would say in the context of trading in general (for HFT see my comment above) further developments of recurrent neural networks (RNN), e.g. so called historical consistent neural networks (HCNN) together with forecasting ensembles, are state of the art. I published an article on that which will be published this month by Springer Verlag (Zimmermann, ...

13

I think you're looking for some way to test for autocorrelation in your residuals. If your model is good -- let's say you have an ARMA(1, 1) model for your forecast -- then the residuals from this model will be white noise. Which is to say that the difference between your forecast and the realization can not be predicted any better. The residual is some zero ...

13

You can forecast stock prices thru time-series models, cross-sectional, or panel models. There is considerable variation within these categories. In time-series models you would use an auto-regressive model such as an AR(1) where the independent variable is the dependent variable lagged by one period. Naturally, an AR(2) would consist of 2 lags and so on. ...

12

There's a lot of people here talking about how GAs are empirical, don't have theoretical foundations, are black-boxes, and the like. I beg to differ! There's a whole branch of economics devoted to looking at markets in terms of evolutionary metaphors: Evolutionary Economics! I highly recommend the Dopfer book, The Evolutionary Foundations of Economics, ...

12

One of the reasons the ARCH family of models is used is that you only need price data to generate the model. These data exist back to the 1800s, so ARCH is great for looking at volatility over very long periods. I don't know that I'd say that the ARCH model has a lot of problems -- it solved the problem of not allowing volatility in time or in the level of ...

11

Just FYI the Reuters product is called NewsScope. The selling point is that they provide a sentiment reading per news item so the user doesn't have to do any NLP. If you have a Reuters sales rep or contact them then they can get you several research/white papers that are interesting. Here are the ones I have been able to find online (my sales rep has ...

11

In a very, very general sense, what Renaissance Technologies does well [and others try to do, many do less well] is understand where the "true" signal is (i.e. where prices should be) and what is noise (i.e. over-/under-reactions by others in the market) in the total signal of market prices. Generally, trading profits are made by taking the opposing ...

10

Assuming you avoid data-snooping bias and all the potential pitfalls of using the past to predict the future, trusting genetic algorithms to find the "right" solution pretty much boils down to the same bet you make when you actively manage a portfolio, whether quantitatively or discretionary. If you believe in market efficiency then increasing your ...

10

I honestly think that most people who could be able to answer to this question simply won't either because they actually work for Renaissance, or because they work in a top quant hedge fund and they'll keep it a secret. I discussed this topic once during an interview and the guy said "we'll discuss this further if you get the job" lol. About papers, I'm ...

9

A few thoughts. Yes, your return series are autocorrelated (i.e., stocks don't exactly follow a random walk), so you should use Newey-West standard errors. If you do this as a univariate regression $$R_{i,t} = \alpha_i + \beta_i R_{j,t-1} + \epsilon_{i,t}$$ then there's almost certainly an omitted variable inside $\epsilon$ that is moving both $R_i$ and ...

8

You are correct: evaluating volatility forecasts is quite different from evaluating forecasts in general, and it is a very active area of research. Methods can be classified in several ways. One criterion is to consider evaluation methods for single forecasts (e.g., for the time series of returns of a specific portfolio) vs multiple simultaneous forecasts ...

8

GARCH(1,1) is a "standard approach for modeling volatility" mainly in academic literature. Most of us in the real world don't use it. Volatility forecasting tends to come more from looking at more-liquid comparables for future market volatility than from fitting fancy retrospective models. As for ignoring the dependence of residuals, well, folks are ...

8

Upon close reading, this appears to be 3 (interesting) questions, not one. I'm not sure if the mods have the tools needed to split it up, so I'm just going to write down the three questions as I see them and then deal with them one by one. Note, it is simpler for me to talk about variance instead of volatility. This has no material impact on the answer. ...

7

CXO-Advisory investigate the claim of this paper and conclude that evidence indicates that changes in open interest in futures markets are strong predictors of returns for associated asset classes, even after controlling for a number of conventional predictors. They state that investors may be able to exploit these predictive powers via tactical asset class ...

7

A cautionary tale on all these approaches it told by Tim Loughran and Bill MacDonald in the Journal of Finance, 2011 (When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks, here). In their analysis they show that the commonly used Harvard Psychosociological Dictionary is inadequate for sentiment classification in a financial ...

7

Deutsche Bank's Quantitative Strategy (US) team put together the following piece on this topic (note: their research is available for clients, but I found that somebody uploaded the piece to a sketchy web site). In case the link dies, some of the academic papers they site are: Akbras, F., E. Kocatulum, and S. Sorescu, 2008, “Mispricing following public ...

7

The only "indicators" that I believe add value in academic research are time series smoothing functions. ( I don't call them indicators because they are all lagging thus do not indicate anything into the future). There is clear empirical evidence and a number of academic papers have been published that show that none of the common indicators (common ...

6

Ensemble methods, or ensemble learning are a class of statistical methods that, loosely speaking, operate on many rather than a single instance of the data. Think bootstrapping, but then combine the estimates for an aggregate. The Wikipedia link has more. Combining two forecasters is something else that is sometimes called pooling forecasts or, more ...

6

Maybe a better question title is "Can futures market open interest predict commodity, treasury, and equity returns"? I saw this paper in an earlier form and it still baffles me. Superficially, it makes sense that price*quantity holds more information than just price when quantity can change quickly (i.e., outstanding futures contracts changes more quickly ...

6

I just made a Genetic Algorithms calculator you can try at http://www.gregthatcher.com/Stocks/GeneticAlgorithmCalculator.aspx I'm not a "quant expert" like all of you (I'm just a programmer), but here is what I've found. 1.) If you set the constraints up correctly, the results are amazing. e.g. you can get portfolios that have very high return and low ...

6

Have you considered fitting ARIMA with exogenous regressors model? Linear regression with autocorrelated errors might be appropriate. R can do this with the arima() function via specifying the xreg argument.

6

I think the answer to your question is very dependent on the respective indicators. It can be argued for example that moving averages not only smooth out time series but because they are a shifted version of the original series signals on crossovers make use of the momentum factor. In general you might want to check out the book Evidence Based Technical ...

5

Relevant paper: Efficient Estimation of Volatility using High Frequency Data (Zumbach, Corsi, and Trapletti 2002) http://www.olsen.ch/fileadmin/Publications/Working_Papers/020221-efficientVolEstimator.pdf

5

Personally, I've dealt with volatility estimation using wavelets with HF data. Estimations seem reasonable and also it's fairly quick computation wise compared to other methods. There's quite a bit of literature on the subject, I would recommended starting off with An Introduction to Wavelets and Other Filtering Methods in Finance and Economics

Only top voted, non community-wiki answers of a minimum length are eligible