Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 30, 24 January 2024
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The prediction of Apple stock price based on linear regression model and random forest model
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Abstract
In the financial market, due to various factors, the stock price fluctuation is universal. Therefore, the directional prediction of stock market price based on technical analysis is very important in stock investment. This paper conducted a regression analysis and forecasted the future trends in the close price of Apple stock through recent five years between 2018 and 2023. For the purpose of this specific study, this paper did descriptive statistical analysis of the dataset, and made graphs and analyses of regression and predictions relied on the techniques of the Linear Regression Model and Random Forest Model. Based on the three indices: MSE, RMSE, and MAE, the paper compared the advantages and disadvantages of the two machine learning methods. The result of the experiments indicated that the regression generated through employment of the Linear Regression Model outperforms the result of the Random Forest Model, leading to the conclusion that Linear Regression Model is a more effective method to forecast in this dataset.
Keywords
Stock Price Prediction, Linear Regression Model, Random Forest Model
References
1. Shivakoti, M. Jeeveth, K. Pradhan, N. R. Yesu Babu, M. 2023, Apple Stock Price Prediction Using Regression Techniques. (In International Conference on Intelligent Computing and Networking), pp. 59-75.
2. Ashfaq, N. Nawaz, Z. Ilyas, M. 2021. A comparative study of different machine learning regressors for stock market prediction. (arXiv preprint arXiv:2104.07469).
3. Song, D. Baek, A. M. C. Kim, N. 2021, Forecasting stock market indices using padding-based fourier transform denoising and time series deep learning models. (IEEE Access, vol. 9), pp. 83786-83796.
4. Singh, R. Srivastava, S. 2017, Stock prediction using deep learning. (Multimedia Tools and Applications, vol. 76), pp. 18569-18584.
5. Lazonick, W. Mazzucato, M. Tulum, Ö. 2013, Apple’s changing business model: What should the world’s richest company do with all those profits. (In Accounting Forum, vol. 37), no. 4, pp. 249-267.
6. Tripathy, N. Jaipuria, S. 2020, Forecasting stock market using discrete wavelet transforms and artificial neural networks model. (The Empirical Economics Letters, vol. 19), no. 11, pp. 1263-1277.
7. Long, W. Lu, Z. Cui, L. 2019, Deep learning-based feature engineering for stock price movement prediction. (Knowledge-Based Systems, vol. 164), pp. 163-173.
8. Gondaliya, C. Patel, A. Shah, T. 2021, Sentiment analysis and prediction of Indian stock market amid Covid-19 pandemic. (In IOP Conference Series: Materials Science and Engineering, vol. 1020), no. 1, pp. 012023.
9. Bhardwaj, A. Narayan, Y. Dutta, M. 2015, Sentiment analysis for Indian stock market prediction using Sensex and Nifty. (Procedia computer science, vol. 70), pp. 85-91.
10. Abraham, R. Samad, M. E. Bakhach, A. M. El-Chaarani, H. Sardouk, A. Nemar, S. E. Jaber, D. 2022, Forecasting a stock trend using genetic algorithm and random forest. (Journal of Risk and Financial Management, vol. 15), no. 5, pp. 188.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-283-1
- ISBN (Online)
- 978-1-83558-284-8
- Published Date
- 24 January 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/30/20241077
- Copyright
- 24 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited