Support Vector Machine for Stock Price Prediction

April 8, 2016

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A Machine Learning Technique For Predicting Stock Returns

In this experiment we implement the Support Vector Machine (SVM) method for stock prediction as the research paper “Predicting direction of stock price index movement using artificial neural networks and support vector machines- The sample of the Istanbul Stock Exchange”. 


1. Features


We use ten mathematical formulas for our features, as in the paper: 

We eliminated the 9th feature “Accumulation/Distribution Oscillator” because some values were infinity.


2. Parameter tuning


In order to choose a good parameter set, we used the same method as the paper. First we conducted a preliminary experiment by randomly choosing 20% from the entire dataset. We further divided this parameter setting data in two equal-sized training (10% of the entire) set. 


There are four parameters we need to tune in SVM: kernel ("poly", "rbf", "linear), C (regularization parameter, [1, 10, 100, 1000]) γ (gamma in kernel function, [1, 2, 3, 4, 5]) d (degree of kernel function, [1,2,3}).