On the afternoon of November 18th, Professor Jin Cai from the Department of Statistics and Actuarial Science at the University of Waterloo, Canada, delivered a lecture entitled "Worst-Case Values of Target Semi-Variances with Applications to Robust Portfolio Selection" at Room 414, North District Integrated Building. The lecture was hosted by Vice President Zhang Hua, and some faculty and students from the School of Statistics and Data Science attended the event.

At the beginning of the lecture, Professor Cai Jun introduced the most famous principle of feature combination theorem - the principle of minimizing the variance of the portfolio. Professor Cai analyzed the advantages and disadvantages of minimizing the portfolio variance from the perspective of variance, and then proposed the main content of the lecture - the goal semi-variance, indicating that the goal semi-variance is not to control the two-sided loss, but to control the one-dimensional risk risk measure.

During the interactive session, faculty and students from the college actively asked questions, creating an extremely academic atmosphere on the spot, sparking a strong interest in the research on the worst-case target semi-variance and robust portfolio selection method. With thunderous applause, the lecture came to a successful conclusion.

(By Li Pijian, Tan Yuhua, Liu He)