Sep
22

## Gaussian Process Regression

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:

2: Neural network;

3: Multiple regression model;

4: A very simple model based on a simple moving average.

What interests me is

(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

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/

Hot posts:

15 Incredibly Stupid Ways People Made Their Millions

Online stock practice

Ino.com: Don't Join Marketclub until You Read This MarketClub Reviews

World Changing Mathematical Discoveries

Value at Risk xls

Random posts:

Financial Scams

Mental Math Tricks

Demystifying the Job Search Process in Quantitative Finance

Yield Curve Prediction Week in Review 150312

Three Excellent Tools To Protect Your Data

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/

**People viewing this post also viewed:**

Hot posts:

Random posts:

jzamoras

2009/09/22 11:35 [Add/Edit reply] [Clear reply] [Del comment] [Block]

abiao, can u share the paper or its name? thanks!

abiao

2009/09/23 08:50 [Add/Edit reply] [Clear reply] [Del comment] [Block]

Forecast combination model using computational intelligence/linear models for the nn5 time series forecasting.

Pages: 1/1 1