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Volume 5, 2024 - Issue 1

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Forecasting the NASDAQ Composite Index Using Artificial Neural Network

by Ruiyi Liu 1,*
1
College of Business and Economics, The Australian National University
*
Author to whom correspondence should be addressed.
JIEM  2024 5(1):71; https://doi.org/10.69610/j.iem.20240624
Received: 1 June 2024 / Accepted: 19 June 2024 / Published Online: 24 June 2024
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Abstract

In this study, we evaluated the performance of various machine learning and time series models for predicting Nasdaq stock prices. The models used included Random Forest, Support Vector Machine (SVM), Bayesian Regression, ARIMA, Recurrent Neural Network (RNN), and Decision Tree. We assessed the models using Mean Squared Error (MSE) and Mean Absolute Error (MAE). Random Forest showed promising results with an MSE of 980.67 and an MAE of 18.46. ARIMA and Bayesian Regression exhibited higher errors, with ARIMA showing an MSE of 9763.61 and an MAE of 56.46, and Bayesian Regression with an MSE of 9937.30 and an MAE of 56.63. SVM and Decision Tree had the highest errors, with SVM at an MSE of 28651.85 and an MAE of 136.49, and Decision Tree at an MSE of 439626.21 and an MAE of 518.28. RNN excelled with the lowest errors, achieving an MSE of 4.94869e-05 and an MAE of 0.00464 on the training set, and an MSE of 7.81471e-05 and an MAE of 0.00538 on the test set. The results indicate that the proposed RNN algorithm is effective and outperforms the other models.

Keywords: Stock price prediction; Nasdaq; machine learning; time series; ARIMA; Random Forest; SVM; Bayesian Regression; RNN;
Copyright: © 2024 by Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Liu, R. Forecasting the NASDAQ Composite Index Using Artificial Neural Network. Journal of Innovations in Economics & Management, 2024, 5, 71. doi:10.69610/j.iem.20240624
AMA Style
Liu R. Forecasting the NASDAQ Composite Index Using Artificial Neural Network. Journal of Innovations in Economics & Management; 2024, 5(1):71. doi:10.69610/j.iem.20240624
Chicago/Turabian Style
Liu, Ruiyi 2024. "Forecasting the NASDAQ Composite Index Using Artificial Neural Network" Journal of Innovations in Economics & Management 5, no.1:71. doi:10.69610/j.iem.20240624

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