Portfolio optimization for insurance companies under different scenarios (A case study of Tehran stock exchange)

The capital market is one of the most influential institutions in the country when it comes to the economic improvement of society, and the optimal investment in this market has always been the main concern of investors and researchers in this field. Nowadays, a lot of research has been done to form an investment portfolio and various models have been introduced to optimize the investment portfolio. This study aims to investigate the performance of different investment portfolio optimization models under different scenarios in the insurance industry and pension funds as one of the main pillars of the capital market.

This study investigates the performance of different investment portfolio optimization models using variance, semivariance, standard deviation, semistandard deviation, conditional value at risk and conditional drawdown at risk under different scenarios. The optimized investment portfolio using the beta return-based mean-variance model and the CAPM model is also presented as an alternative consideration for investors with higher accuracy and as a basis for decision making. The data used for this research were collected from April to March 1401 on a daily basis from Tehran Stock Exchange.

Monthly stock returns indicate that the respective stocks were not stable and did not show a steady trend during the analyzed period. By forming an investment portfolio consisting of shares of insurance companies and pension funds, the return and risk of each investment model were determined, providing investors with comprehensive information. Depending on their risk appetite or risk aversion, investors can choose different investment strategies to maximize the benefits of their investment.

This research shows that the portfolio formed with the Conditional Drawdown at Risk measure has a return of 22%, the highest return among the analyzed models in the insurance industry in the Tehran Stock Exchange market.