Optimal Daily scalping trading portfolio based on interval-valued prediction with vector autoregression approac

Interval-valued prediction includes the prediction of an interval that its boundaries determined by two random variables. The aim of this research is to design and optimize the mean-variance daily scalping trading portfolio based on interval-valued prediction for lowest and highest daily prices with the vector autoregression approach. In the present study, using the vector autoregression method, the interval related to the lowest and highest daily prices is predicted and then based on it, a daily scalping trading system is formed, including buying and selling in the forecasted prices. To reduce the risk of the trading system and increase the number of trading positions, the optimal daily scalping trading portfolio is developed in the mean-variance framework.The sample portfolio includes five shares of the Tehran Stock Exchange in a 190-day period, taking into account trading costs, shows that the average daily return is 0/0018 and the Sharpe ratio is 0/4809, which is better than the Sharpe ratio of individual daily scalping trading of portfolio assets. The daily average of the total index in the research period is 0/001 and the Sharp ratio is 0/0835, which shows that the trading system has a much better performance than the buy and hold strategy. Risk-averse investors who are interested in the daily scalping strategy are suggested to use the optimal portfolio approach introduced in the present study after carefully evaluating the profitability and risk on the set of stocks they want