Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/147520
Title: ONLINE PORTFOLIO SELECTION
Authors: HAN EN CHOU
Keywords: Machine Learning, Data Mining, Computational Finance, Portfolio Optimisation
Issue Date: 2016
Citation: HAN EN CHOU (2016). ONLINE PORTFOLIO SELECTION. ScholarBank@NUS Repository.
Abstract: This paper proposes an online portfolio selection strategy named "Box Theory Portfolio Selection (BTPS)". There is an increased interest from the Artificial Intelligence and Machine-Learning community to apply learning techniques to perform the portfolio selection in a sequential manner over a period of time. Empirical evidences shows that stock prices follow the Mean-reversion phenomenon in the short run and are trend-following in the long-run. We borrow the trading idea of Nicolas Darvas Box System to exploit these two behaviours. We make use of an online passive aggressive learning framework to put this trading idea to practice. To more comprehensively evaluate BTPS, we benchmark its performance against existing strategies. We used six datasets from existing literature and introduced ve new datasets from Asian stock markets. Our empirical results on these real markets showed that performance of existing strategies lack consistency across various datasets. While not a top-performing strategy, BTPS has shown consistent performance and a strong ability to stop loss in adverse market conditions across all datasets. In addition, BTPS has a superior computational run-time that is desirable for online portfolio selection.
URI: http://scholarbank.nus.edu.sg/handle/10635/147520
Appears in Collections:Bachelor's Theses (Restricted)

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