英文标题：
《Time scales in stock markets》
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作者：
Ajit Mahata and Md Nurujjaman
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最新提交年份：
2019
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英文摘要：
Different investment strategies are adopted in short-term and long-term depending on the time scales, even though time scales are adhoc in nature. Empirical mode decomposition based Hurst exponent analysis and variance technique have been applied to identify the time scales for short-term and long-term investment from the decomposed intrinsic mode functions(IMF). Hurst exponent ($H$) is around 0.5 for the IMFs with time scales from few days to 3 months, and $H\\geq0.75$ for the IMFs with the time scales $\\geq5$ months. Short term time series [$X_{ST}(t)$] with time scales from few days to 3 months and $H~0.5$ and long term time series [$X_{LT}(t)$] with time scales $\\geq5$ and $H\\geq0.75$, which represent the dynamics of the market, are constructed from the IMFs. The $X_{ST}(t)$ and $X_{LT}(t)$ show that the market is random in short-term and correlated in long term. The study also show that the $X_{LT}(t)$ is correlated with fundamentals of the company. The analysis will be useful for investors to design the investment and trading strategy.
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中文摘要：
根据时间尺度的不同，短期和长期采用不同的投资策略，即使时间尺度是临时性的。基于经验模态分解的Hurst指数分析和方差技术，从分解的内禀模态函数（IMF）中识别短期和长期投资的时间尺度。对于时间尺度从几天到3个月的IMF，赫斯特指数（H$）约为0.5，对于时间尺度为5$个月的IMF，赫斯特指数为0.75$。短期时间序列[$X{ST}（t）$]，时间尺度从几天到3个月，H ~ 0.5$，长期时间序列[$X{LT}（t）$]，时间尺度为$\\ geq5$和$H\\geq0.75$，代表市场动态，由IMF构建。美元X{ST}（t）$和美元X{LT}（t）$表明市场在短期内是随机的，在长期内是相关的。这项研究还表明，X{LT}（t）$与公司的基本面相关。该分析将有助于投资者设计投资和交易策略。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Physics 物理学
二级分类：Chaotic Dynamics 混沌动力学
分类描述：Dynamical systems, chaos, quantum chaos, topological dynamics, cycle expansions, turbulence, propagation
动力系统，混沌，量子混沌，拓扑动力学，循环展开，湍流，传播
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