Some Observations for Portfolio Management Applications of Modern Machine Learning Methods

Abstract

Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets. FU - Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education, Science and Technology [2011-0021188] FX - This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0021188). NR - 32 PU - KOREAN INST INTELLIGENT SYSTEMS PI - SEOUL PA - RM 603, KOREA SCIENCE & TECHNOLOGY CENTER, 22, TEHERAN-RO 7-GIL, GANGNAM-GU, SEOUL, 135-703, SOUTH KOREA