School of Physics, Zhejiang University, Hangzhou, China.
College of Finance and Information, Ningbo University of Finance and Economics, Ningbo, China.
PLoS One. 2024 Jul 11;19(7):e0303707. doi: 10.1371/journal.pone.0303707. eCollection 2024.
The complex financial networks, with their nonlinear nature, often exhibit considerable noises, inhibiting the analysis of the market dynamics and portfolio optimization. Existing studies mainly focus on the application of the global motion filtering on the linear matrix to reduce the noise interference. To minimize the noise in complex financial networks and enhance timing strategies, we introduce an advanced methodology employing global motion filtering on nonlinear dynamic networks derived from mutual information. Subsequently, we construct investment portfolios, focusing on peripheral stocks in both the Chinese and American markets. We utilize the growth and decline patterns of the eigenvalue associated with the global motion to identify trends in collective market movement, revealing the distinctive portfolio performance during periods of reinforced and weakened collective movements and further enhancing the strategy performance. Notably, this is the first instance of applying global motion filtering to mutual information networks to construct an investment portfolio focused on peripheral stocks. The comparative analysis demonstrates that portfolios comprising peripheral stocks within global-motion-filtered mutual information networks exhibit higher Sharpe and Sortino ratios compared to those derived from global-motion-filtered Pearson correlation networks, as well as from full mutual information and Pearson correlation matrices. Moreover, the performance of our strategies proves robust across bearish markets, bullish markets, and turbulent market conditions. Beyond enhancing the portfolio optimization, our results provide significant potential implications for diverse research fields such as biological, atmospheric, and neural sciences.
复杂的金融网络具有非线性特征，常常受到大量噪声的影响，这对市场动态和投资组合优化的分析造成了阻碍。现有研究主要集中于在线性矩阵上应用全局运动滤波来减少噪声干扰。为了最小化复杂金融网络中的噪声并增强时间策略，我们引入了一种先进的方法，即利用互信息衍生的非线性动态网络进行全局运动滤波。随后，我们构建了投资组合，重点关注中美市场的边缘股票。我们利用与全局运动相关的特征值的增长和下降模式来识别集体市场运动的趋势，揭示在增强和减弱的集体运动期间独特的投资组合表现，并进一步提高策略表现。值得注意的是，这是首次将全局运动滤波应用于互信息网络来构建以边缘股票为重点的投资组合。对比分析表明，与源自全局运动滤波 Pearson 相关网络、完整互信息和 Pearson 相关矩阵的投资组合相比，包含互信息网络中边缘股票的投资组合的夏普比率和索提诺比率更高。此外，我们的策略在熊市、牛市和动荡市场条件下都表现出稳健性。除了增强投资组合优化外，我们的研究结果还为生物、大气和神经科学等多个研究领域提供了重要的潜在意义。
