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29

I would offer the distinctions are i) pure statistical approach, ii) equilibrium based approach, and iii) empirical approach. The statistical approach includes data mining. Its techniques originate in statistics and machine learning. In its extreme there is no a priori theoretical structure imposed on asset returns. Factor structure might be identified thru ...


13

I think you have the correct dichotomy here. Things started in the late 1980s and through the 1990s with analytical approaches particularly to derivative pricing (as in "hey, let's create yet another exotic option we can sell to the buy side"). The risk modelling "fashion" of the 1990s (when regulated entities such as banks needed to beef up reporting) ...


9

Yes, you use an implementation of each signal and then use a statistical package like sas to generate a factor model for you. It generates a mathematical formula, with coefficients, and signals(variables) and even tells you the efficacy(R^2) However you quickly find yourself exposed to data snooping bias by choosing this approach. Similar to the results ...


6

No - clearly you've not seen the licensing agreements the exchanges force you to sign (one way or the other). Generally such firms and individuals have greater utility from the money they'll make working with the data than risking going to jail. Market data is a 5bn / yr business. You're pushing the proverbial up-hill. Anyway, you can get financial index ...


5

Is your question more about approaches taken on the buy side vs. sell side? If so, you may want to read Attilio Meucci's paper, P vs. Q, on this topic. He breaks down the dichotomy as derivatives pricing (the "Q" world), which uses a lot of very sophisticated modeling involving Ito calculus and PDEs, and portfolio management (the "P" world), which makes ...


3

Here is an example of the 75% trading rule coded in R: Can one beat the random walk This is how the author describes the rule: The following script will generate a random series of data and follow the so called 75% rule which says, Pr[Price>Price(n-1) & Pr<(n-1) < Price_median] Or [Price < Price(n-1) & Price(n-1) > Price_median] = 75%. ...


3

Maybe I completely misunderstood the question, but it seems to me that you are looking to find a model structure as opposed to fit a specified/known model. In your context the model specification (the trading rules) are unknown... Am I right? If that Is the case, maybe genetic programming: http://en.wikipedia.org/wiki/Genetic_programming Is what you need? ...


3

I think both approaches don't answer question of profitability. The most algo systems are more sophisticated than this. I would extend your list to adaptive algorithms, stat models and knowing something that other overlook.


2

I don't see the difference in the 'statistical' and 'empirical' approaches. Statistical or Data Mining or Machine Learning approaches, which mostly are under the same umbrella, rely on inductive inference. On the other hand, the analytic approach, relies on some prior axioms which we assume to be true by definition, and beyond this step, the theory is ...


1

As best I can tell, the primary difference between traditional approaches (be they classical or Bayesian) and the newer "predictive analytics" is that the traditional approaches make a few explicit (and testable) assumptions, and then give you quantified estimates of your potential errors. The newer methods exchange a chance at more sophisticated (and ...



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