Study of the periodicity in Euro-US Dollar exchange rates using local alignment and random matrices

IF 0.3 Q4 BUSINESS, FINANCE Algorithmic Finance Pub Date : 2017-01-01 DOI:10.3233/AF-170182
E. Korotkov, M. Korotkova
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引用次数: 2

Abstract

The purpose of this study was to detect latent periodicity in the presence of deletions or insertions in the analyzed data, when the points of deletions or insertions are unknown. A mathematical method was developed to search for periodicity in the numerical series, using dynamic programming and random matrices. The developed method was applied to search for periodicity in the Euro/Dollar (Eu/$) exchange rate. The presence of periodicity within the period length equal to 24 hours and 25 hours, in the analyzed financial series, was shown. Periodicity can be detected only with insertions and deletions. The results of this study show that periodicity phase shifts, depend on the observation time. A period of 24 hours is a common phenomenon for foreign exchange rates, indices and stocks of different companies. We show it for the Bank of America and Microsoft stocks, S&P500 and NASDAG indexes and for the gold and silver prices as examples. The reasons for the existence of the periodicity in the financial ranks are discussed. The results can find application in computer systems, for the purpose of forecasting exchange rates.
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用局部对齐和随机矩阵研究欧元-美元汇率的周期性
本研究的目的是在缺失或插入点未知的情况下,检测分析数据中缺失或插入存在的潜在周期性。提出了一种利用动态规划和随机矩阵寻找数值序列周期性的数学方法。该方法被用于搜索欧元/美元(Eu/$)汇率的周期性。经分析的财务序列显示,在等于24小时和25小时的期间长度内存在周期性。周期性只能通过插入和删除来检测。研究结果表明,周期性相移与观测时间有关。汇率、指数、不同公司的股票,24小时的周期是普遍现象。我们以美国银行和微软股票、标准普尔500指数和纳斯达克指数以及黄金和白银价格为例。讨论了财务职级存在周期性的原因。所得结果可应用于计算机系统,用于预测汇率。
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来源期刊
Algorithmic Finance
Algorithmic Finance BUSINESS, FINANCE-
CiteScore
0.40
自引率
0.00%
发文量
6
期刊介绍: Algorithmic Finance is both a nascent field of study and a new high-quality academic research journal that seeks to bridge computer science and finance. It covers such applications as: High frequency and algorithmic trading Statistical arbitrage strategies Momentum and other algorithmic portfolio management Machine learning and computational financial intelligence Agent-based finance Complexity and market efficiency Algorithmic analysis of derivatives valuation Behavioral finance and investor heuristics and algorithms Applications of quantum computation to finance News analytics and automated textual analysis.
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