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. 56 , 01 December 2023
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Stock Price Forecasts Based on KNN and LSTM
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Abstract
Every stock trader wants to successfully predict the price or trend of a stock in order to make a profit because stock price forecasts provide investors, traders, and financial professionals with signals about potential price movements, which can help them make more informed decisions about buying, selling, or holding stocks. This article selects the four largest stocks in the U.S. stock market by market capitalization: Google, Apple, Microsoft, and Amazon, and predicts their closing prices from 2013 to 2023. First, K-Nearest Neighbors (KNN) model is established for the closing price sequence after the first-order difference. Then a two-layer LSTM model is constructed to visualize the prediction results of the two models, and RMSE is calculated respectively. Comparing the prediction results of the two models, LSTM has a better prediction effect on the data set used in this paper. This paper finds that the LSTM model can capture the crucial time dependencies and relationships in financial time series data, which are essential for stock price prediction. Therefore, the LSTM model can often be used when predicting stocks in the future.
Keywords
stock market, KNN, LSTM, time series
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)
- 978-1-83558-159-9
- ISBN (Online)
- 978-1-83558-160-5
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/56/20231064
- 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