This paper considers a robust optimal reinsurance-investment problem for an insurer with mispricing and model ambiguity. The surplus process is described by a classical Cramér-Lunderg model and the financial mark...This paper considers a robust optimal reinsurance-investment problem for an insurer with mispricing and model ambiguity. The surplus process is described by a classical Cramér-Lunderg model and the financial market contains a market index, a risk-free asset and a pair of mispriced stocks, where the expected return rate of the stocks and the mispricing follow mean reverting processes which take into account liquidity constraints. In particular, both the insurance and reinsurance premium are assumed to be calculated via the variance premium principle. By employing the dynamic programming approach, we derive the explicit optimal robust reinsurance-investment strategy and the optimal value function.展开更多
In this paper,we show that the increasing popularity of machine learning improves market efficiency.By analysing the performance of a set of popular machine learning-based investment strategies,we find that profits fr...In this paper,we show that the increasing popularity of machine learning improves market efficiency.By analysing the performance of a set of popular machine learning-based investment strategies,we find that profits from these strategies experience significant declines since the wide adoption of machine learning techniques,especially for profits based on the more preferred method of neural networks.These declines mainly come from long legs.Using the‘machine learning’Google search index as a proxy for machine learning-based trading intensity,we find that returns from the neural networks-based long–short and long-only strategies are weaker following high levels of machine learning intensity,while no relation is found between machine learning intensity and the short-only neural networks-based strategy.展开更多
Using rumor verification data from investor interactive platforms,we investigate the effect of stock market rumors on price efficiency.We find favorable rumors are positively correlated with stock price synchronicity,...Using rumor verification data from investor interactive platforms,we investigate the effect of stock market rumors on price efficiency.We find favorable rumors are positively correlated with stock price synchronicity,while unfavorable rumors are negatively correlated with stock price synchronicity.Both favorable and unfavorable rumors are positively correlated with stock mispricing levels,and stock price crash risk.Mechanism tests reveal that favorable rumors about industry leaders have industry spillover effects.The effect of rumors on mispricing levels and stock price crash risk are more pronounced when there are more retail investors.Further analysis shows stronger detrimental impacts of rumors on price efficiency for small-cap companies,companies with low information transparency and companies with low institutional ownership.展开更多
文摘This paper considers a robust optimal reinsurance-investment problem for an insurer with mispricing and model ambiguity. The surplus process is described by a classical Cramér-Lunderg model and the financial market contains a market index, a risk-free asset and a pair of mispriced stocks, where the expected return rate of the stocks and the mispricing follow mean reverting processes which take into account liquidity constraints. In particular, both the insurance and reinsurance premium are assumed to be calculated via the variance premium principle. By employing the dynamic programming approach, we derive the explicit optimal robust reinsurance-investment strategy and the optimal value function.
基金supported by the Key Programme of National Natural Science Foundation of China(NSFC)[Grant No.72233003].
文摘In this paper,we show that the increasing popularity of machine learning improves market efficiency.By analysing the performance of a set of popular machine learning-based investment strategies,we find that profits from these strategies experience significant declines since the wide adoption of machine learning techniques,especially for profits based on the more preferred method of neural networks.These declines mainly come from long legs.Using the‘machine learning’Google search index as a proxy for machine learning-based trading intensity,we find that returns from the neural networks-based long–short and long-only strategies are weaker following high levels of machine learning intensity,while no relation is found between machine learning intensity and the short-only neural networks-based strategy.
基金supported by the National Natural Science Foundation of China(Project Nos.71902201 and 71972189).
文摘Using rumor verification data from investor interactive platforms,we investigate the effect of stock market rumors on price efficiency.We find favorable rumors are positively correlated with stock price synchronicity,while unfavorable rumors are negatively correlated with stock price synchronicity.Both favorable and unfavorable rumors are positively correlated with stock mispricing levels,and stock price crash risk.Mechanism tests reveal that favorable rumors about industry leaders have industry spillover effects.The effect of rumors on mispricing levels and stock price crash risk are more pronounced when there are more retail investors.Further analysis shows stronger detrimental impacts of rumors on price efficiency for small-cap companies,companies with low information transparency and companies with low institutional ownership.
