Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 46 , 01 December 2023
Open
Access | Article
Machine Learning and Event Study to Explore the Influence of ChatGPT on Microsoft Stock
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Abstract
In light of the increasingly vital role of Artificial Intelligence (AI) has played in this era, it is imperative to conduct a comprehensive examination of the exact impact of specific event associated with AI on advanced corporations, economies and even countries. This study focuses on analyzing the recent event involving the connection of ChatGPT to Microsoft. To be more specific, this paper employed 3 machine learning models and one Stata model “event study” to respectively identify the best fitted model and its precise impact. In this work, 3 machine learning have been used, namely Support Vector Regression (SVR), K-Nearest Neighbor categorization algorithm (KNN) and Random Forest, to spot the model that fits the Microsoft stock the best. Initially, data was collected from Yahoo Finance, which is set and indexed in advance. Subsequently, data is individually put to train the 3 models. Ultimately, Mean Square Error (MSE), Root Mean Squared Error (RMSE) and R squared score are calculated with care and compared to obtain the results. Additionally, after collecting the data, a sensible window of event study has been set. Experimental results demonstrated that Random Forest performs the best among the 3 models and the specific event of ChatGPT connecting to Microsoft has a limited effect on the firm’s stock price.
Keywords
machine learning, event study, ChatGPT, Microsoft, stock prediction
References
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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 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-139-1
- ISBN (Online)
- 978-1-83558-140-7
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/46/20230308
- Copyright
- 01 December 2023
- 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