英文标题：
《Identification of short-term and long-term time scales in stock markets
  and effect of structural break》
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作者：
Ajit Mahata, Debi Prasad Bal and Md Nurujjaman
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最新提交年份：
2019
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英文摘要：
  The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time series before structural break (TSB) and time series after structural break (TSA). The empirical mode decomposition based Hurst exponent and variance techniques have been applied to the TSO, TSB and TSA to identify the time scales in short-term and long-term from the decomposed intrinsic mode functions. We found that for TSO, TSB and TSA the short-term time scales and long-term time scales are within the range of few days to 3 months and greater than 5 months respectively, which indicates that the short-term and long-term time scales are present in the stock market. The Hurst exponent is $\\sim 0.5$ and $\\geq 0.75$ for TSO, TSB and TSA in short-term and long-term respectively, which indicates that the market is random in short-term and strongly correlated in long-term. The identification of time scales at short-term and long-term investment horizon will be useful for investors to design investment and trading strategies.
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中文摘要：
本文对股票数据有无结构性突破的短期和长期时间尺度下的股票市场性质进行了比较研究。通过应用Zivot和Andrews结构趋势突变模型将原始时间序列（TSO）分解为结构突变前的时间序列（TSB）和结构突变后的时间序列（TSA），确定了结构突变点。基于Hurst指数和方差的经验模态分解技术已应用于TSO、TSB和TSA，以从分解的固有模态函数中识别短期和长期的时间尺度。我们发现，对于TSO、TSB和TSA，短期时间尺度和长期时间尺度分别在几天到3个月和大于5个月的范围内，这表明股票市场存在短期和长期时间尺度。短期和长期内，TSO、TSB和TSA的赫斯特指数分别为0.5美元和0.75美元，这表明市场在短期内是随机的，在长期内是强相关的。确定短期和长期投资期限的时间尺度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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