Forecasting the NASDAQ Index Based on the ARIMA Model

Forecasting the stock value is an enduring topic for investors. The ARIMA model is an effective model for forecasting proposed by Box and Jenkins in the 1970s. The NASDAQ Composite provides an index for investors to analyze the market of technical companies. Based on the ARIMA model, the author uses the NASDAQ Composite from Jan.2nd, 2020 to Jun.28th, 2024 for forecasting. This paper briefly introduces the NASDAQ Composite, ARIMA model and some judging criteria. The result of the modeling process reflects that the ARIMA (0, 1, 1) can better describe the index. The following actual value of the index also proves the correction of the model. However, as the period of the data is hard to determine, the seasonality of the model is uneasy to find. In future researches, the author plans to figure out a method to determine the days of one period of the index to reveal the indexs seasonality, or use models based on ARIMA model for forecasting.

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Publication Details

Journal
Advances in Economics Management and Political Sciences
Published
2024-12-26
DOI
https://doi.org/10.54254/2754-1169/2024.ga18869
Primary Topic
Grey System Theory Applications
Type
article
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article

Forecasting the NASDAQ Index Based on the ARIMA Model

Yong‐Bing Xiang
Advances in Economics Management and Political Sciences
Grey System Theory Applications
article

Forecasting the NASDAQ Index Based on the ARIMA Model

Yong‐Bing Xiang
article en

Abstract

Forecasting the stock value is an enduring topic for investors. The ARIMA model is an effective model for forecasting proposed by Box and Jenkins in the 1970s. The NASDAQ Composite provides an index for investors to analyze the market of technical companies. Based on the ARIMA model, the author uses the NASDAQ Composite from Jan.2nd, 2020 to Jun.28th, 2024 for forecasting. This paper briefly introduces the NASDAQ Composite, ARIMA model and some judging criteria. The result of the modeling process reflects that the ARIMA (0, 1, 1) can better describe the index. The following actual value of the index also proves the correction of the model. However, as the period of the data is hard to determine, the seasonality of the model is uneasy to find. In future researches, the author plans to figure out a method to determine the days of one period of the index to reveal the indexs seasonality, or use models based on ARIMA model for forecasting.

Advances in Economics Management and Political SciencesVol. 141(1)
Shanghai Jiao Tong University (CN)
Industry, innovation and infrastructure, Partnerships for the goals
Openalex Percentile: Top 70%
Grey System Theory Applications
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Forecasting the NASDAQ Index Based on the ARIMA Model — Yong‐Bing Xiang · Advances in Economics Management and Political Sciences (2024) | TGRS Research Map | TGRS