利用XGBoost机器学习模型辅助长期投资决策
Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model
摘要：
识别股票市场趋势的能力对投资者来说有着明显的优势。在上升趋势时买入股票（以及在下跌趋势时卖出）会带来利润。因此，趋势的起点和终点是进入和离开市场的最佳点。该研究集中于识别股票市场的长期上升和下降趋势。关键的结果是使用梯度增强算法，特别是XGBoost。原始数据由基本股票市场报价的时间序列表示，专家将时间段标记为趋势或平稳。然后通过各种数据转换获得特征，旨在捕捉导致股票方向变化的隐含因素。建模分为两个阶段：第一阶段旨在检测趋势的端点（即滑动窗口），第二阶段识别窗口内的趋势本身。该研究解决了诸如不平衡的数据集和自相矛盾的标签等问题，以及需要特定的质量指标来跟上实际适用性。该模型可用于设计投资策略，但需要进一步的特征工程研究和精细校准。本文是第20届人工智能和软计算Web系统国际会议（ICAISC 2021）上发表的研究全文。
英文摘要：
The ability to identify stock market trends has obvious advantages for investors. Buying stock on an upward trend (as well as selling it in case of downward movement) results in profit. Accordingly, the start and end-points of the trend are the optimal points for entering and leaving the market. The research concentrates on recognizing stock market long-term upward and downward trends. The key results are obtained with the use of gradient boosting algorithms, XGBoost in particular. The raw data is represented by time series with basic stock market quotes with periods labelled by experts as Trend or Flat. The features are then obtained via various data transformations, aiming to catch implicit factors resulting in a change of stock direction. Modelling is done in two stages: stage one aims to detect endpoints of tendencies (i.e. sliding windows), stage two recognizes the tendency itself inside the window. The research addresses such issues as imbalanced datasets and contradicting labels, as well as the need for specific quality metrics to keep up with practical applicability. The model can be used to design an investment strategy though further research in feature engineering and fine calibration is required.This paper is the full text of the research, presented at the 20th International Conference on Artificial Intelligence and Soft Computing Web System (ICAISC 2021)
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
