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<p>Turning points are important for analyzing financial data, marking the end of existing date trends and the beginning of new ones. We present a stochastic optimization-based model designed specifically for identifying turning points in high-frequency markets. The model effectively mitigates market noise, allowing for rapid detection of turning points with minimal error rates. The identified turning points can inform future data trends. Empirical analyses demonstrate that our method significantly reduces error rates and outperforms benchmark trend-following invest strategies.</p>

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Detecting turning points in high-frequency financial data analysis

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Yaosong Zhan,
  • Zhenya Liu

摘要

Turning points are important for analyzing financial data, marking the end of existing date trends and the beginning of new ones. We present a stochastic optimization-based model designed specifically for identifying turning points in high-frequency markets. The model effectively mitigates market noise, allowing for rapid detection of turning points with minimal error rates. The identified turning points can inform future data trends. Empirical analyses demonstrate that our method significantly reduces error rates and outperforms benchmark trend-following invest strategies.