Assets Forecasting with Feature Engineering and Transformation Methods for LightGBM
面向 LightGBM 的资产预测特征工程与变换方法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确使用 LightGBM、技术特征和收益率等目标变换进行股票市场预测。
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面向 LightGBM 的资产预测特征工程与变换方法
- 研究问题
- 如何通过特征工程与变量变换改善 LightGBM 的股票预测，同时维持普通个人计算机能够承担的训练成本？
- 方法与思路
- 作者构造指标与价格斜率比等特征，比较收益率、对数收益率、EMA 比率及标准化等七种特征和目标变换方法。
- 材料陈述的贡献
- 摘要称对数收益率、收益率和 EMA 差分比率作为目标表现较好，新特征具有较高重要性，而部分标准化目标训练更慢。
- 阅读关注点
- 应核对预测目标与时距、时间顺序划分、特征计算是否使用未来信息，以及方向准确率和训练成本的比较口径。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明资产样本、样本外设计及性能数值，特征重要性不等于因果作用。
原文摘要
Fluctuations in the stock market rapidly shape the economic world and consumer markets, impacting millions of individuals. Hence, accurately forecasting it is essential for mitigating risks, including those associated with inactivity. Although research shows that hybrid models of Deep Learning (DL) and Machine Learning (ML) yield promising results, their computational requirements often exceed the capabilities of average personal computers, rendering them inaccessible to many. In order to address this challenge in this paper we optimize LightGBM (an efficient implementation of gradient-boosted decision trees (GBDT)) for maximum performance, while maintaining low computational requirements. We introduce novel feature engineering techniques including indicator-price slope ratios and differences of close and open prices divided by the corresponding 14-period Exponential Moving Average (EMA), designed to capture market dynamics and enhance predictive accuracy. Additionally, we test seven different feature and target variable transformation methods, including returns, logarithmic returns, EMA ratios and their standardized counterparts as well as EMA difference ratios, so as to identify the most effective ones weighing in both efficiency and accuracy. The results demonstrate Log Returns, Returns and EMA Difference Ratio constitute the best target variable transformation methods, with EMA ratios having a lower percentage of correct directional forecasts, and standardized versions of target variable transformations requiring significantly more training time. Moreover, the introduced features demonstrate high feature importance in predictive performance across all target variable transformation methods. This study highlights an accessible, computationally efficient approach to stock market forecasting using LightGBM, making advanced forecasting techniques more widely attainable.