在金融市场研究中，股票市场的预测一直是投资者和研究者关注的重点。然而，由于股票市场的复杂性和非线性特征，预测工作极具挑战性。
本研究创新性地提出了一种预测思路，选取中长期股票最大涨跌幅作为关键预测指标，以追求更优的投资策略。首先，本文构建了针对涨跌幅预测问题的多维特征的指标体系，以全面捕捉股票市场的不同时间频度特征。在此基础上，综合运用支持向量回归、随机森林和XGBoost算法，构建高效的集成模型，实现对股票市场中长期最大涨跌幅的精准预测。通过对比实验验证，发现该模型在预测中有较好的表现。此外，本研究还结合季节最大涨跌幅和季节平均涨跌幅的预测指标，提出创新的选股策略，为投资者提供科学决策依据。
综上所述，本研究成功实现对股票市场中长期最大涨跌幅的精准预测，并提出创新的选股策略，为投资者提供有力支持，丰富了股票预测理论体系。

In financial market research, stock market forecasting has been a primary focus for investors and researchers. However, due to the complexity and nonlinear nature of the stock market, forecasting remains a challenging task. 
This study introduces a novel forecasting approach by selecting the maximum stock rise and fall in the medium and long term as a crucial forecasting indicator. This approach aims to facilitate a more robust investment strategy. Firstly, this paper establishes a comprehensive indicator system with multi-dimensional features tailored for the forecasting of stock fluctuations. This is done to capture the diverse time-frequency characteristics of the stock market comprehensively. Subsequently, we integrate Support Vector Regression, Random Forest, and XGBoost algorithms to develop an efficient integrated model. This model aims to accurately predict the maximum upward and downward movements of the stock market in the medium and long term. The model underwent comparative experimental validation, revealing its superiority in prediction accuracy compared to existing approaches. Furthermore, the study incorporates seasonal maximum and seasonal average prediction indicators to propose innovative stock selection strategies, thereby providing investors with a scientific basis for decision-making.
In conclusion, the study successfully achieves accurate prediction of the maximum rise and fall of the stock market in the medium and long term, while introducing innovative stock selection strategies. This provides investors with valuable support and enhances the theoretical framework of stock prediction.