Abstract
Price prediction plays a crucial role in portfolio selection (PS). However, most price prediction strategies only make a single prediction and do not have efficient mechanisms to make a comprehensive price prediction. Here, we propose a comprehensive price prediction (CPP) system based on inverse multiquadrics (IMQ) radial basis function. First, the novel radial basis function (RBF) system based on IMQ function rather than traditional Gaussian (GA) function is proposed and centers on multiple price prediction strategies, aiming at improving the efficiency and robustness of price prediction. Under the novel RBF system, we then create a portfolio update strategy based on kernel and trace operator. To assess the system performance, extensive experiments are performed based on 4 data sets from different real-world financial markets. Interestingly, the experimental results reveal that the novel RBF system effectively realizes the integration of different strategies and CPP system outperforms other systems in investing performance and risk control, even considering a certain degree of transaction costs. Besides, CPP can calculate quickly, making it applicable for large-scale and time-limited financial market.
Highlights
The target of portfolio selection (PS) is to achieve some long-term financial goals by constructing an effective investment strategy that can reasonably allocate wealth among a set of assets
We proposed a new comprehensive price prediction (CPP) system based on inverse multiquadrics (IMQ) radial basis function with an integration of three different aggressive and moderate strategies for effective and robust PS
Instead of using a traditional GA function, here we chose a more stable and accurate function that is IMQ for the novel radial basis function (RBF) system, which centers on multiple strategies
Summary
The target of PS is to achieve some long-term financial goals by constructing an effective investment strategy that can reasonably allocate wealth among a set of assets. The other is Kelly investment [2] [3] which focuses on maximizing the expected log return and is suitable for multiple-period PS These two theories are the cornerstone of modern PS research and are constantly exploited and innovated. A comprehensive price prediction (CPP) system based on IMQ radial basis function is constructed. This paper’s main contributions are as follows: 1) Propose a novel RBF system based on IMQ radial basis function and centered on multiple price predictions, which form a comprehensive price prediction. This is equivalent to maximizing the increasing factor vtΤst Note that this optimization problem does not require statistical assumption about the changes in asset price
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
