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利用波动率聚类和格兰杰因果关系预测股票趋势

文章 arXiv papers · 作者: Ivan Letteri
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总结

该系统利用大盘股之间的波动率关系生成趋势信号。研究首先通过相关性和自相关分析、技术指标、假设检验、统计模型和变量选择探索价格数据。随后，研究使用 k-means++ 对来自NYSE和 NasdaqGS 的九只大盘股的平均波动率进行分组，并重点分析由此形成的中等波动率聚类。

研究在该聚类内使用格兰杰因果检验，识别其波动率可能有助于预测另一只股票的个股。预测关系较强的股票可作为目标股票买入、卖出或持有决策的趋势指标。文中报告进行了大量回测和表现评估，并声称存在盈利机会且结果稳健，但所提供文本没有给出数值结果、基准比较或测试设计细节。格兰杰因果关系可以表明某个数据集内存在预测价值；仅凭该描述无法证明这些关系会持续存在，或能在扣除成本后支持实盘交易。

核心观点

  • 该系统结合统计分析、机器学习和技术指标研究股票趋势。
  • k-means++根据平均波动率对股票分组，分析重点为中等波动率聚类。
  • 格兰杰因果检验用于识别该聚类中可能基于波动率预测其他股票的候选股票。
  • 预测股票为目标股票提供买入、卖出或持有的趋势信号。
  • 作者报告进行了回测，但所提供文本没有给出定量结果或验证细节。

标签

股票波动率机器学习统计学技术指标回测
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# VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning


# VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning









Volatility-based trading strategies have attracted a lot of attention in financial markets due to their ability to capture opportunities for profit from market dynamics. In this article, we propose a new volatility-based trading strategy that combines statistical analysis with machine learning techniques to forecast stock markets trend. The method consists of several steps including, data exploration, correlation and autocorrelation analysis, technical indicator use, application of hypothesis tests and statistical models, and use of variable selection algorithms. In particular, we use the k-means++ clustering algorithm to group the mean volatility of the nine largest stocks in the NYSE and NasdaqGS markets. The resulting clusters are the basis for identifying relationships between stocks based on their volatility behaviour. Next, we use the Granger Causality Test on the clustered dataset with mid-volatility to determine the predictive power of a stock over another stock. By identifying stocks with strong predictive relationships, we establish a trading strategy in which the stock acting as a reliable predictor becomes a trend indicator to determine the buy, sell, and hold of target stock trades. Through extensive backtesting and performance evaluation, we find the reliability and robustness of our volatility-based trading strategy. The results suggest that our approach effectively captures profitable trading opportunities by leveraging the predictive power of volatility clusters, and Granger causality relationships between stocks. The proposed strategy offers valuable insights and practical implications to investors and market participants who seek to improve their trading decisions and capitalize on market trends. It provides valuable insights and practical implications for market participants looking to.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。