Stock Price Prediction of the TMT Industry Based on Machine-Learning
Published in Highlights in Science Engineering and Technology, 2023
This article delves into using machine learning models—specifically, multi-linear regression and random forest—to predict TMT stock market movements, utilizing data from AAPL, NFLX, and TSLA. The models are tested with an 80-20 data split, and their accuracy is assessed using R-squared and mean absolute percentage error. Notably, the random forest model performs well, especially with larger datasets, though at the cost of increased computational resources. The study highlights the potential of machine learning for stock predictions and suggests the use of advanced models for improved accuracy.
Recommended citation: Hu, T. (2023). Stock price prediction of the TMT industry based on Machine-Learning. Highlights in Science Engineering and Technology, 49, 250–255. https://doi.org/10.54097/hset.v49i.8514 http://tyrionhuu.github.io/files/Stock Price Prediction of the TMT Industry Based on Machine-Learning.pdf