Forecasting Stock Prices using Tweets

DOI

10.2991/978-94-6463-268-2_35How to use a DOI?

Keywords

CNN-LSTM; LSTM; FinBERT; linear regression; twitter; smart user; TF-IDF; sentiment analysis

Abstract

Stock market price prediction is a challenging problem since the market is an immensely complex, stochastic and dynamic environment. There are many studies from various areas aiming to improve the performance of prediction and analysis of public emotion has been the focus of one of them. We use information shared over Kaggle, an online community of data scientists and machine learning practitioners, to better understand and predict stock prices of Tesla. This article studies the methods to preprocess tweets and to tune models so that neural network models and linear regression can adapt to the preprocessed tweets. According to previous authors, one way to preprocess tweets is to keep the tweets of smart user, and output can be the next-day close prices or the next-day return. For that goal, prediction models (CNN-LSTM, LSTM and linear regression) were built and modified step by step and their results were analyzed by Mean Square Error and Mean Absolute Error. Finally, the LSTM model, with close value and weighted labels as input features and return as output feature, wins the prediction of stock price of Tesla among other candidate models with different input and output features.

Copyright

© 2024 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.

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TY  - CONF
AU  - Jiacheng Wang
PY  - 2023
DA  - 2023/10/10
TI  - Forecasting Stock Prices using Tweets
BT  - Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023)
PB  - Atlantis Press
SP  - 309
EP  - 330
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-268-2_35
DO  - 10.2991/978-94-6463-268-2_35
ID  - Wang2023
ER  -