Forecasting Nasdaq Price Index: A Comparative Study of Regression and Time Series Analysis

Ziheng Li

2023

Abstract

The Nasdaq Stock Market, one of the world’s premier stock exchanges, serves as an imperative indicator of economic activity and investor sentiment. Accurate forecasting of the Nasdaq Price is of paramount importance for a myriad of stakeholders, ranging from policymakers to individual investors. This study embarks on an exhaustive journey to discern the most efficacious forecasting method for this critical indicator. We systematically compare the predictive prowess of several techniques: the (Autoregressive Integrated Moving Average) ARIMA models, linear regression, cubic spline regression, and a decomposition approach that identifies and leverages underlying trends and seasonality. The culmination of our rigorous analyses revealed that the cubic spline regression outperformed the other contenders, marking itself as the most apt model for forecasting the Nasdaq Price within the scope of this study if without any significant and unexpected events. This article provides an analysis of various forecasting methods for predicting the Nasdaq Price. The article compares the predictive accuracy of different techniques, including ARIMA models, linear regression, cubic spline regression, and a decomposition approach that identifies and leverages underlying trends and seasonality. This article provides valuable insights into effective forecasting methods for economic indicators and investor sentiment.

Paper Citation

in Harvard Style

Li Z. (2023). Forecasting Nasdaq Price Index: A Comparative Study of Regression and Time Series Analysis. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 515-521. DOI: 10.5220/0012814400003885

in Bibtex Style

@conference{daml23,
author={Ziheng Li},
title={Forecasting Nasdaq Price Index: A Comparative Study of Regression and Time Series Analysis},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={515-521},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012814400003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Forecasting Nasdaq Price Index: A Comparative Study of Regression and Time Series Analysis
SN - 978-989-758-705-4
AU - Li Z.
PY - 2023
SP - 515
EP - 521
DO - 10.5220/0012814400003885
PB - SciTePress