A machine learning approach in financial markets
Independent thesis Advanced level (degree of Master (One Year))Student thesis
In this work we compare the prediction performance of three optimized technical indicators with a Support Vector Machine Neural Network. For the indicator part we picked the common used indicators: Relative Strength Index, Moving Average Convergence Divergence and Stochastic Oscillator. For the Support Vector Machine we used a radial-basis kernel function and regression mode. The techniques were applied on financial time series brought from the Swedish stock market. The comparison and the promising results should be of interest for both finance people using the techniques in practice, as well as software companies and similar considering to implement the techniques in their products.
Place, publisher, year, edition, pages
2003. , 36 p.
Financial time series, indicator optimization, support vector machines, prediction
Computer Science Probability Theory and Statistics Software Engineering
IdentifiersURN: urn:nbn:se:bth-5571Local ID: oai:bth.se:arkivex84B8AC103A83D1CCC1256D95002E28FEOAI: oai:DiVA.org:bth-5571DiVA: diva2:832956
UppsokPhysics, Chemistry, Mathematics