基于时间序列模型对中国银行股价的预测分析  

Prediction and Analysis of Bank of China Stock Price Based on Time Series Models

作　　者：王浩[1] 

机构地区：[1]贵州大学经济学院,贵州 贵阳

出　　处：《电子商务评论》2024年第2期3188-3195,共8页E-Commerce Letters

摘　　要：股价是衡量一家公司在股票市场上的价值和表现的关键指标之一,也是投资者做出买卖决策的基本依据之一,可靠的投资建议能有效提升投资者在股票市场的收益。本文尝试运用时间序列模型ARIMA模型,基于中国银行2018年12月17日至2023年7月31日日交易收盘价数据,对其股价进行分析与预测。结果显示,时间序列模型ARIMA可以很好的预测短期内的股价变化,但股市的影响因素众多,后续更需要更深入的研究,发现更适合的模型进行长期预测。Stock price is one of the key indicators used to measure a company’s value and performance in the stock market, serving as a fundamental basis for investors to make buying and selling decisions. Reliable investment recommendations can effectively enhance investors’ returns in the stock market. This study attempts to employ the time series model ARIMA to analyze and forecast the stock price of China Bank using daily closing price data from December 17, 2018, to July 31, 2023. The results indicate that the ARIMA time series model can effectively predict short-term fluctuations in stock prices. However, considering the multitude of factors influencing the stock market, further in-depth research is essential to identify more suitable models for long-term forecasting purposes.

关 键 词：股价 时间序列 ARIMA模型 

分 类 号：F83[经济管理—金融学]