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Machine Learning for Market Prediction: Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets
Linköping University, Department of Management and Engineering, Production Economics.
Linköping University, Department of Management and Engineering, Production Economics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Forecasting procedures have found applications in a wide variety of areas within finance and have further shown to be one of the most challenging areas of finance. Having an immense variety of economic data, stakeholders aim to understand the current and future state of the market. Since it is hard for a human to make sense out of large amounts of data, different modeling techniques have been applied to extract useful information from financial databases, where machine learning techniques are among the most recent modeling techniques. Binary classifiers such as Support Vector Machines (SVMs) have to some extent been used for this purpose where extensions of the algorithm have been developed with increased prediction performance as the main goal. The objective of this study has been to develop a process for improving the performance when predicting the sign of return of financial time series with soft margin classifiers.

An analysis regarding the algorithms is presented in this study followed by a description of the methodology that has been utilized. The developed process containing some of the presented soft margin classifiers, and other aspects of kernel methods such as Multiple Kernel Learning have shown pleasant results over the long term, in which the capability of capturing different market conditions have been shown to improve with the incorporation of different models and kernels, instead of only a single one. However, the results are mostly congruent with earlier studies in this field. Furthermore, two research questions have been answered where the complexity regarding the kernel functions that are used by the SVM have been studied and the robustness of the process as a whole. Complexity refers to achieving more complex feature maps through combining kernels by either adding, multiplying or functionally transforming them. It is not concluded that an increased complexity leads to a consistent improvement, however, the combined kernel function is superior during some of the periods of the time series used in this thesis for the individual models. The robustness has been investigated for different signal-to-noise ratio where it has been observed that windows with previously poor performance are more exposed to noise impact.

Place, publisher, year, edition, pages
2018. , p. 104
Keywords [en]
Machine Learning, Finance, Financial Time Series, Support Vector Machines, Relevance Vector Machines, Multiple Kernel Learning, Simulated Annealing, SVM, RVM, MKL, SA, FSVM, TSVM, FTSVM
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:liu:diva-151459ISRN: LIU-IEI-TEK-A-18/03273-SEOAI: oai:DiVA.org:liu-151459DiVA, id: diva2:1250116
External cooperation
AP3 Third Swedish National Pension Fund
Subject / course
Applied Mathematics
Supervisors
Examiners
Available from: 2018-09-27 Created: 2018-09-21 Last updated: 2018-09-27Bibliographically approved

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ML for Market Predictions(2104 kB)282 downloads
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
  • vancouver
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  • Other style
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Language
  • de-DE
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  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • rtf