| Abstrakt: |
Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. But the fact is that forecasting stock prices by using various models has been suffering from low accuracy, slow convergence, and complex parameters. This study aims to employ a mixed model to improve the accuracy of stock price prediction. We present how to use a random walk based on jump-diffusion, to obtain stock predictions with a good-fitting degree by adjusting different parameters. Aimed at getting better parameters and then using the time series model to predict the data, we employed the time series model to smooth the sequence utilizing logarithm and difference, which successfully resulted in drawing the auto-correlation figure and partial the auto-correlation figure. According to the comparative analysis, which focuses on checking the mean absolute error, including root mean square error and R square evaluation index, we have drawn a clear conclusion that our mixed model prediction effect is relatively good. In the context of Chinese stocks, the hybrid random walk model is very suitable for predicting stocks. It can "interpret" the randomness of stocks very well, and it also has an unparalleled prediction effect compared with other models. Based on the time series model's application in this paper, the abovementioned series is more suitable for predicting trends. [ABSTRACT FROM AUTHOR] |
