Asset Selection Method Based On The VaR Adjusted Highfrequency Sharp Index

This paper uses the intraday high frequency data to construct a VaR adjusted sharp index series.Based on this exponential sequence,we propose an effective method of asset selection and combination strategy.We can transfer the mean-variance problem with the total position constraint into a penalized regression problem,which can be solved efficiently,according to this method,the newly constructed high frequency sharp index based on VaR adjustment is put into the framework of regression problem for asset selection,and we look for proper weights of selected assets to get the optimal portfolio.Asset selection and optimal combination weighting are two key steps in building a portfolio.In the empirical study,we use the daily high frequency data to construct a Sharpe index series based on VaR adjustment to carry out asset selection,which is took into account various combinations of strategies.Based on the empirical analysis of the CSI 300 market data,whether the market is down or up,compare the combination strategy constructed through high frequency VaR adjusted sharp index method with that by other stock selection methods,such as momentum stock selection method and stock selection method based on high frequency sharp index,we want to know if the new method can get better returns.To illustrate the effectiveness of the asset selection method based on high frequency sharp index,we choose momentum stock selection method and stock selection method based on high frequency sharp index as the comparative objects,we can find in the empirical research,the former gains significantly more stable than the last two,the asset selection method based on high frequency sharp index can eliminate the influence of the volatility of the assets a certain extent,and the asset selection also get more stable returns in subsequent performance,we achieved the desired effect.