用内幕交易数据预测特斯拉股价，对比多种机器学习模型表现。
A Comparative Study of Machine Learning Algorithms for Stock Price Prediction Using Insider Trading Data
- 基于内幕交易数据，用多种算法建模股价趋势。
- RBF核SVM准确率最高，但计算耗时最长。
- 适合金融分析师优化投资策略参考。
本研究实证分析了多种机器学习算法在利用内幕交易信息预测股票价格方面的表现。内幕交易能反映市场情绪，预示股价变动。研究使用2020年4月至2023年3月的特斯拉股票交易数据，评估决策树、随机森林、支持向量机（SVM）不同核函数及K-Means聚类的效果。通过递归特征消除（RFE）与特征重要性分析优化特征集，提升预测精度。结果显示，采用径向基函数（RBF）核的SVM模型准确率最优，但计算时间显著更长。研究揭示了模型精度与效率之间的权衡，并建议融合多源数据以进一步提升预测性能。结果可为金融分析师和投资者选择高效算法提供参考。
原文摘要 · Abstract (English)
The research paper empirically investigates several machine learning algorithms to forecast stock prices depending on insider trading information. Insider trading offers special insights into market sentiment, pointing to upcoming changes in stock prices. This study examines the effectiveness of algorithms like decision trees, random forests, support vector machines (SVM) with different kernels, and K-Means Clustering using a dataset of Tesla stock transactions. Examining past data from April 2020 to March 2023, this study focuses on how well these algorithms identify trends and forecast stock price fluctuations. The paper uses Recursive Feature Elimination (RFE) and feature importance analysis to optimize the feature set and, hence, increase prediction accuracy. While it requires substantially greater processing time than other models, SVM with the Radial Basis Function (RBF) kernel displays the best accuracy. This paper highlights the trade-offs between accuracy and efficiency in machine learning models and proposes the possibility of pooling multiple data sources to raise prediction performance. The results of this paper aim to help financial analysts and investors in choosing strong algorithms to optimize investment strategies.
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