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Sep 22

Gaussian Process Regression

Posted by abiao at 10:19 | Code » Matlab | Comments(2) | Reads(10451)
I recently read a paper comparing the performances of different models to predict stock returns, at the end the authors rank the models by their out-of-sample symmetric mean absolute percentage error (SMAPE), surprisingly for me, The winning four models turned out to be:
1: Gaussian process regression;
2: Neural network;
3: Multiple regression model;
4: A very simple model based on a simple moving average.

What interests me is Gaussian process regression is the best model by the authors, as stated: "A List of different monitored learning techniques have been attempted to predict future stock returns, both for potential monetary make and because it is an interesting research problem. We use regression to capture changes in stock price prediction as a function of a covariance
(kernel or Gram) matrix. For our aims it is natural to think of the price of a stock as being some function over time. Generally, a Gaussian processes can be cosidered to be defining a distrubution over functions with the inference step occurring directly in the space of functions. Thus, by using Gaussian process regression to extend a function beyond known price data, we can predict whether stocks will rise or fall the next day, and by how much."

Should you are interested, here is a book Gaussian Processes for Machine Learning to be freely downloaded, accompanying Matlab package is also available at the website.
http://www.gaussianprocess.org/gpml/


Tags:
abiao, can u share the paper or its name? thanks!
Forecast combination model using computational intelligence/linear models for the nn5 time series forecasting.
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