On the usage of support vector machines for the short-term price movement prediction in intra-day trading
Date
2013-06-11
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Publisher
Tartu Ülikool
Abstract
The aim of the current thesis is to research the prediction of future stock prices by using the implementation of support vector machines, also to find possible technical solutions and to interpret the gained results. In order to consider the problem of forecasting future stock prices for a short period of time, the market data of the British multinational telecommunications company Vodafone Group Plc and the British-Swedish multinational pharmaceutical and biologics company AstraZeneca Plc is being used to fit the models and verify how good their predictive power is. The opportunities of packages e1071 and kernlab of programming language R are being used in the current thesis. The implementation of the predictions to trading algorithms is not being considered due to it is not relevant to the underlying thesis. The thesis consists of three chapters. The first chapter is dedicated to support vector machines, because this particular method is used in developing prediction algorithms. For better understanding of the principle of this method, certain fundamentals are being explained. The first chapter introduces what is machine learning, explains finding the regression function by using support vector machines and mentions the problems which may arise during finding the regression function. The concept of regression estimation is being explained with theoretical and graphical examples.
The second chapter is dedicated to kernels, because that gives an opportunity to use non-linear functions as regression functions. In this chapter, the classification of kernels is being introduced. In addition, it is explained to the reader why does the usage of kernel functions simplify the finding of the regression function. The short overview of technical opportunities of programming language R packages is also being introduced in the second chapter. Finally, such statistical method of evaluating and comparing learning algorithms as cross-validation is being briefly mentioned in the chapter. Unlike from the first two chapters, which give a theoretical overview, the third chapter is the practical part of the thesis. It introduces the implementation of support vector machines on the short-term price movement prediction in intra-day trading. The algorithm of the price prediction is being explained in the third chapter. Given data is also described in this chapter. Due to similar data involved, the author also presents the comparison with the master’s thesis of Andrei Orlov [1]. In addition, at the end of the thesis, the reader can find Appendices which consist of data frame, the diagram explaining the relations between functions in a code of algorithm, the codes of figures and the CD containing the code of the algorithm.