Construction And Empirical Study Of Stock Volume And Price Trend Line

In real investment,the moving average theory as a stock market technical analysis method is widely used by investors to find and capture opportunities.In academia,many scholars also have done a lot of research based on the moving average.But the main idea of the study is limited to the technical parameters of the moving average size,length and short cycle,cross-behavior,the combination of technical optimization or trading strategy and other aspects of research.Few scholars study the flaws of moving average theoretical,and this dissertation just study from this perspective.First we point out the shortcomings of the simple moving average,and analyze the formation of moving average elements which should include the volume as an important element.And then we establish moving average trend line that includes stock price and volume,and the empirical analysis is carried out.Based on the Shanghai Composite Index and the Small and Medium Enterprise Board Index,the ARIMA model was used to fit the trend line of the price and the short-term forecast was made after the model parameters were optimized,and the predicted values were compared and analyzed.From the analysis results,the average forecast error rate of the Shanghai Composite Index is only 0.7%,the Small and Medium Enterprise Board Index forecast average error rate is 0.8%.This error level,are far less than 5% of the standard error,fully meet the model accuracy requirements.At the same time,from the two indices of different error rate,but also shows the small board market volatility is greater than the motherboard market,the typical characteristics of the stock market volatility in emerging markets.This dissertation improved the composition of the moving average,established stock price trend line which contains stock price and volume.Through empirical research shows that it is not only the rationality and validity of the construction of price and trend line,but also can provide a reference for investors to make short-term forecast and investment activities.