CORN: Correlation-driven Nonparametric Learning Approach for Portfolio Selection

Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this paper, we propose a novel learning to trade algorithm termed the CORrelation-driven Nonparametric learning strategy (CORN) for actively trading stocks, which effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, in which the encouraging results show that the proposed new algorithm can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpasses a variety of state-of-the-art techniques significantly

PAMR: Passive-Aggressive Mean Reversion Strategy for Portfolio Selection

This article proposes a novel online portfolio selection strategy named “Passive Aggressive Mean Reversion” (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR’s update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art portfolio selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications. The experimental testbed including source codes and data sets is available at http://www.cais.ntu.edu.sg/~chhoi/PAMR/.Accepted versio

Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection

Singapore Ministry of Education Academic Research Fund Tier

P2PDocTagger: Content management through automated P2P collaborative tagging

As the amount of user generated content grows, personal information management has become a challenging problem. Several information management approaches, such as desktop search, document organization and (collaborative) document tagging have been proposed to address this, however they are either inappropriate or inefficient. Automated collaborative document tagging approaches mitigate the problems of manual tagging, but they are usually based on centralized settings which are plagued by problems such as scalability, privacy, etc. To resolve these issues, we present P2PDocTagger, an automated and distributed document tagging system based on classification in P2P networks. P2P-DocTagger minimizes the efforts of individual peers and reduces computation and communication cost while providing high tagging accuracy, and eases of document organization/retrieval. In addition, we provide a realistic and flexible simulation toolkit -- P2PDMT, to facilitate the development and testing of P2P data mining algorithms.</jats:p