Trend-Based K-Nearest Neighbor Algorithm in Stock Price Prediction

DOI

10.2991/978-94-6463-304-7_78How to use a DOI?

Keywords

stock price; predict; machine learning; KNN Introduction

Abstract

This paper introduces a trend-based stock price prediction method that employs the K-nearest neighbors (KNN) algorithm for trend forecasting. Experiments were conducted using a historical stock price dataset, and the prediction performance was evaluated. Experimental evidence suggests that, in relation to accuracy in stock price prediction, the trend-based KNN algorithm exhibits superior performance over conventional machine learning approaches. In addition, the impact of prediction time span on model performance was investigated. The findings suggest that the trend-based KNN algorithm exhibits clear advantages when dealing with predictions over larger time spans.

Copyright

© 2023 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  - Shengtao Gao
PY  - 2023
DA  - 2023/12/04
TI  - Trend-Based K-Nearest Neighbor Algorithm in Stock Price Prediction
BT  - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023)
PB  - Atlantis Press
SP  - 746
EP  - 756
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-304-7_78
DO  - 10.2991/978-94-6463-304-7_78
ID  - Gao2023
ER  -