Optimizing Investment Strategies in Biotechnology Ventures: A Multi-Period Mean-Variance Portfolio Selection Model with Adjustable Investment Frequency

Tongyao Wang
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract:

In this study, we present a dynamic mean-variance (MV) portfolio optimization model with adjustable investment frequency, designed to enhance capital allocation strategies in biotechnology investments. This model is formulated as a constrained linear-quadratic (LQ) optimal stochastic control problem, where investment frequency is incorporated as a linear constraint. Leveraging the state separation property and a piecewise description method, we employ multiple coupled Riccati equations to derive optimal asset allocation strategies, addressing computational challenges and achieving analytical solutions. The proposed approach is validated through illustrative case studies, comparing the in-sample and out-of-sample performance of our frequency-adjusted MV portfolio strategy against the naïve 1/N benchmark and the traditional Markowitz MV strategy. Our findings underscore the importance of selecting an appropriate investment frequency to balance portfolio performance and risk exposure, particularly in the dynamic and high-risk landscape of biotechnology investments. This research provides valuable insights for investors and financial strategists in the biotechnology sector, facilitating more adaptive and efficient capital deployment strategies.