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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01v405sc81t
 Title: Kernel Support Vector Machine Learning of Limit Order Book Dynamics for Short Term Price Prediction Authors: Myers, Jordan Advisors: van Handel, Ramon Department: Operations Research and Financial Engineering Class Year: 2016 Abstract: Support Vector Machines (SVMs) are a prevalent methods of financial time series forecasting. Significant research has been done training SVMs on historical price data to predict future prices over daily time frames. However, the research on intraday financial time series prediction is limited. The methods used in academic studies up to this point seem disconnected from the reality of modern High Frequency Trading. In this study, we train support vector machines on high frequency order book data from the Chicago Mercantile Exchange. We use a combination of novel feature sets, prediction parameters, kernels, and data samples to train support vector machines. We find that support vector machines trained only on order book data have limited but non-trivial predictive power. Additionally, we conclude that the radial basis function and linear kernels are the most powerful for time series prediction using the order book, and that the order book contains more predictive value for short-term predictions than long term. Extent: 64 pages URI: http://arks.princeton.edu/ark:/88435/dsp01v405sc81t Type of Material: Princeton University Senior Theses Language: en_US Appears in Collections: Operations Research and Financial Engineering, 2000-2016

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