In this thesis, using a finance lens, we investigate various aspects of political actions of the Chinese National Social Security Fund (CNSSF). The thesis comprises three empirical papers. In the first essay, we analyze the trading behavior of the CNSSF that operates in a highly political environment. We show that the CNSSF adopts a rebalancing strategy that maintains both portfolio liquidity and growth opportunities. Further, we find direct and indirect evidence that the CNSSF actively intervenes in the stock market by providing liquidity to mutual funds in distress. This liquidity provision can be primarily explained by public information quality. Most notably, CNSSF can profit by assisting distressed mutual funds, especially if such liquidity provision relates to private information. In addition, this bailout-like behavior is not speculative and can positively improve the performance of the distressed mutual funds.
In essay 2, we propose a new measure of the policy information advantage available to the CNSSF. Specifically, we assess the impact of this policy-linked “information advantage” on stock performance. Our results show that in the short run, there is a positive and significant information advantage-stock performance linkage. Moreover, our findings support the view that CNSSF promotes the absorption of inside information into prices. In contrast, in the long run, the policy information advantage of the CNSSF negatively decreases the firm operation performance. Finally, we document that there is an information network spillover of the information advantage across the Chinese mutual fund industry, which also affects mutual fund performance.
In the third essay, we propose a smooth non-parametric estimation to explore the safety-first portfolio optimization problem. As an empirical application, we simulate optimal portfolios and display return-risk characteristics using the CNSSF strategic stocks. We obtain a non-parametric estimation calculation formula for loss (truncated) probability using the kernel estimator of the portfolio returns’ cumulative distribution function, and embed it into two types of safety-first portfolio selection models. We numerically and empirically test our non-parametric method to demonstrate its accuracy and efficiency. Cross-validation results show that our non-parametric kernel estimation method outperforms the empirical distribution method.