Radial Basis Functions With Adaptive Input and Composite Trend Representation for Portfolio Selection

We propose a set of novel radial basis functions with adaptive input and composite trend representation (AICTR) for portfolio selection (PS). Trend representation of asset price is one of the main information to be exploited in PS. However, most state-of-the-art trend representation-based systems ex...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 29; no. 12; pp. 6214 - 6226
Main Authors: Lai, Zhao-Rong, Dai, Dao-Qing, Ren, Chuan-Xian, Huang, Ke-Kun
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive input
Adaptive systems
Basis functions
composite trend representation
Learning systems
Mathematics
Microsoft Windows
portfolio selection (PS)
Portfolios
Radial basis function
Radial basis function networks
radial basis functions (RBF)
Representations
Risk management
State of the art
Switches
System effectiveness
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:We propose a set of novel radial basis functions with adaptive input and composite trend representation (AICTR) for portfolio selection (PS). Trend representation of asset price is one of the main information to be exploited in PS. However, most state-of-the-art trend representation-based systems exploit only one kind of trend information and lack effective mechanisms to construct a composite trend representation. The proposed system exploits a set of RBFs with multiple trend representations, which improves the effectiveness and robustness in price prediction. Moreover, the input of the RBFs automatically switches to the best trend representation according to the recent investing performance of different price predictions. We also propose a novel objective to combine these RBFs and select the portfolio. Extensive experiments on six benchmark data sets (including a new challenging data set that we propose) from different real-world stock markets indicate that the proposed RBFs effectively combine different trend representations and AICTR achieves state-of-the-art investing performance and risk control. Besides, AICTR withstands the reasonable transaction costs and runs fast; hence, it is applicable to real-world financial environments.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2018.2827952