基于模糊变换的趋势周期估计及其在市场牛熊阶段识别中的应用

Q1 Economics, Econometrics and Finance

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-06-11 DOI:10.1002/isaf.1473

Linh Nguyen, Vilém Novák, Soheyla Mirshahi

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引用次数: 1

摘要

本文主要研究金融时间序列分析中的一个基本问题;即对历史牛市和熊市阶段的识别。首先证明了利用高阶模糊变换技术可以很好地估计趋势周期。然后，我们提出了牛和熊阶段的数学定义，并提供了一种新的技术来识别它们。因此，转折点(即市场改变其阶段的点)被检测到。我们用几个例子来说明我们的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Trend-cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets

This paper is focused on one of the fundamental problems in financial time-series analysis; namely, the identification of the historical bull and bear phases. We start with the proof that the trend-cycle can be well estimated using the technique of a higher degree fuzzy transform. Then, we suggest a mathematical definition of the bull and bear phases and provide a novel technique for their identification. As a consequence, the turning points (i.e. the points where the market changes its phase) are detected. We illustrate our methodology on several examples.

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来源期刊

Intelligent Systems in Accounting, Finance and Management Economics, Econometrics and Finance-Finance

CiteScore

6.00

自引率

0.00%

发文量

期刊介绍： Intelligent Systems in Accounting, Finance and Management is a quarterly international journal which publishes original, high quality material dealing with all aspects of intelligent systems as they relate to the fields of accounting, economics, finance, marketing and management. In addition, the journal also is concerned with related emerging technologies, including big data, business intelligence, social media and other technologies. It encourages the development of novel technologies, and the embedding of new and existing technologies into applications of real, practical value. Therefore, implementation issues are of as much concern as development issues. The journal is designed to appeal to academics in the intelligent systems, emerging technologies and business fields, as well as to advanced practitioners who wish to improve the effectiveness, efficiency, or economy of their working practices. A special feature of the journal is the use of two groups of reviewers, those who specialize in intelligent systems work, and also those who specialize in applications areas. Reviewers are asked to address issues of originality and actual or potential impact on research, teaching, or practice in the accounting, finance, or management fields. Authors working on conceptual developments or on laboratory-based explorations of data sets therefore need to address the issue of potential impact at some level in submissions to the journal.