王增武：以非线性数学期望破解低利率下的不确定性溢价

Core Viewpoint - The current economic and financial logic has shifted from linear to nonlinear, necessitating new approaches to wealth management and asset allocation in a low-interest-rate environment [3][4]. Group 1: Theoretical Insights - The 2008 financial crisis highlighted the inadequacy of traditional linear models in predicting significant market events, emphasizing the need for a focus on uncertainty in investment decisions [3]. - Nonlinear mathematical expectations are essential for understanding the uncertainty that characterizes current market conditions, as traditional risk premiums only address quantifiable risks [3][4]. - Embracing uncertainty can lead to excess returns, and recognizing different types of uncertainty is crucial for effective asset management [4][5]. Group 2: Practical Applications - Three main strategies for wealth management in a low-interest-rate environment include expanding service boundaries to meet client needs, diversifying asset classes to include innovative financial products, and employing various investment strategies to enhance returns [5][6]. - The importance of refined risk control and volatility management is underscored, as many institutions have failed to anticipate risks in nonlinear environments [5][6]. - Tools such as machine learning can help investors transform uncertainty into manageable risks, ultimately leading to wealth preservation and growth [6].