Research on Policy configuration Scheme based on improved ARIMA Model
DOI:
https://doi.org/10.54097/hbem.v17i.11480Keywords:
SARMI model; Curve fitting; Investment forecast; RMSE.Abstract
In this paper, we use the principle of different models to remove the trend from the stock time series and choose the SARIMA [1, 1, 1][0,1,0] 12 models to fit the stock price. The results show a good prediction performance. Then a ternary transaction strategy is proposed, accurately identifying value changes regarding cash as financial products (expected rate of return = 0%, cost = 0%), and establishes a ternary asset flow model. Combining the expected rate of return and volatility, we model the value of existing financial products. Based on the trend accuracy to evaluate the accuracy of stock price prediction, the SARIMA model shows high accuracy in the verification set, and the maximum root mean square error (RMSE) is 11. 5494. The final five-year return is 51973 times higher than the bank savings and fixed investment return.
Downloads
References
Choi B S. ARMA model identification [M]. Springer Science & Business Media, 2012.
Baltagi B H, Wu P X. Unequally spaced panel data regressions with AR (1) disturbances [J]. Econometric theory, 1999, 15(6): 814-823.
Ho S L, Xie M. The use of ARIMA models for reliability forecasting and analysis [J]. Computers & industrial engineering, 1998, 35(1-2): 213-216.
Valipour M. Long‐term runoff study using SARIMA and ARIMA models in the United States [J]. Meteorological Applications, 2015, 22(3): 592-598.
Horv L, Kokoszka P. GARCH processes: structure and estimation [J]. Bernoulli, 2003, 9(2): 201-227.
Ta V D, Liu C M, Tadesse D A. Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading [J]. Applied Sciences, 2020, 10(2): 437.
Zou Z, Qu Z. Using LSTM in Stock prediction and Quantitative Trading
Djankov S, Murrell P. Enterprise restructuring in transition: A quantitative survey [J]. Journal of economic literature, 2002, 40(3): 739-792.
Verbesselt J, Hyndman R, Newnham G, et al. Detecting trend and seasonal changes in satellite image time series [J]. Remote sensing of Environment, 2010, 114(1): 106-115.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
