Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)

<Previous Article In Volume
Next Article In Volume>

Predicting Stock Returns and Optimizing Portfolios: An Analysis of 15 Technology Companies Based on ARIMA, GARCH and Monte Carlo Simulation

Authors
Yifan Liu1, *
1International Economics and Trade, Southwestern University of Finance and Economics, Chengdu, 611139, China
*Corresponding author. Email: 42219166@smail.swufe.edu.cn
Corresponding Author
Yifan Liu
Available Online 24 February 2025.
DOI
10.2991/978-94-6463-652-9_66How to use a DOI?
Keywords
Returns Prediction; Portfolio Optimization; ARIMA and GARCH model; Monte Carlo simulation
Abstract

Traditionally, portfolio management focused on using historical averages and qualitative assessments of market trends. However, with more data available and improvements in computational power, more complex methods have been developed. This study aims to analyze the stock prices of 15 technology companies spanning from 2016 to 2023, creating a training set to calculate returns. Utilizing ARIMA and GARCH models, this study predicted stock returns for the subsequent years, 2023–2024. These models are used to capture the trends and volatility in the stock prices, providing valuable insights into potential future performance. To further enhance the analysis, this study employed Monte Carlo simulation to evaluate various portfolio combinations. This approach enabled to assess the risk and return characteristics of different investment strategies, ultimately identifying the most representative strategy for the given dataset. The findings contribute to the field of financial forecasting and portfolio optimization, highlighting the potential of advanced statistical techniques in predicting stock returns and informing investment decisions.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)
Series
Advances in Economics, Business and Management Research
Publication Date
24 February 2025
ISBN
978-94-6463-652-9
ISSN
2352-5428
DOI
10.2991/978-94-6463-652-9_66How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yifan Liu
PY  - 2025
DA  - 2025/02/24
TI  - Predicting Stock Returns and Optimizing Portfolios: An Analysis of 15 Technology Companies Based on ARIMA, GARCH and Monte Carlo Simulation
BT  - Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)
PB  - Atlantis Press
SP  - 641
EP  - 648
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-652-9_66
DO  - 10.2991/978-94-6463-652-9_66
ID  - Liu2025
ER  -