Sustainable Update Investment Strategy Under Overreaction Based on Hidden Markov Models: A Case Study of Chinese Low-Carbon Policies

Over the past decade, China has achieved remarkable achievements in promoting the harmonious development of its economy and environmental protection. How to improve the effectiveness of investment strategy is one of the difficulties in achieving the next low-carbon development goal. This paper aims to explore how to formulate appropriate investment strategies in a market with investors’ reactions in the face of capital shocks caused by low-carbon policies. Based on this, we consider investors’ overreaction to information and study the impact of overreaction on investment objectives and capital constraints. The initial measurement model of the investors’ reaction characters is constructed using the RUNS test method. The Baum–Welch algorithm is used to complete the iterative parameter estimation and the trend prediction. On this basis, the sustainable update strategy is constructed according to the reaction characters of different investors. This strategy can be interpreted as one that continuously adjusts and optimizes in accordance with the fluctuations in the market environment and net returns. It fills the gap in expressing the mapping relationship between investors’ reactions and price in traditional strategies and solves the problem of updating transaction costs in practice. Through the case study, the research shows many results. First, in the face of macro policy shocks, the Markov model with investor reactions as the hidden state is more stable in price prediction than the Markov model with price as the only observation. Second, in an inefficient market, prices do not always lag behind market states. Third, when investors are in an irrational state, conservative holding is more likely to achieve relatively better returns than overreacting to the market. After general validation, we believe that the sustainable update strategy based on the hidden Markov models performs better in a volatile market environment.