کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7378869 | 1480131 | 2016 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Compositional segmentation and complexity measurement in stock indices
ترجمه فارسی عنوان
تجزیه و تحلیل ترکیب و اندازه گیری پیچیدگی در شاخص های سهام
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کلمات کلیدی
پیچیدگی ترکیبی توالی، تقسیم بندی انترپنی، سیستم های مالی، همبستگی بلند مدت،
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
فیزیک ریاضی
چکیده انگلیسی
In this paper, we introduce a complexity measure based on the entropic segmentation called sequence compositional complexity (SCC) into the analysis of financial time series. SCC was first used to deal directly with the complex heterogeneity in nonstationary DNA sequences. We already know that SCC was found to be higher in sequences with long-range correlation than those with low long-range correlation, especially in the DNA sequences. Now, we introduce this method into financial index data, subsequently, we find that the values of SCC of some mature stock indices, such as S&P500 (simplified with S&P in the following) and HSI, are likely to be lower than the SCC value of Chinese index data (such as SSE). What is more, we find that, if we classify the indices with the method of SCC, the financial market of Hong Kong has more similarities with mature foreign markets than Chinese ones. So we believe that a good correspondence is found between the SCC of the index sequence and the complexity of the market involved.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 442, 15 January 2016, Pages 67-73
Journal: Physica A: Statistical Mechanics and its Applications - Volume 442, 15 January 2016, Pages 67-73
نویسندگان
Haifeng Wang, Pengjian Shang, Jianan Xia,