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“hong Kong Forex Clubs: Networking For Financial Prosperity”

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Application received: 11 February 2021 / Revised: 3 March 2021 / Accepted: 11 March 2021 / Published: 15 March 2021

Examining the complexities of globalization during the financial crisis The research is based on Forex exchange rates. The Standardized Classification Scheme (PLCS) is used in the analysis. Studies show that during a crisis Cross connection increases As a result, the group increased significantly. and the quality of nodes in fused time series networks. This suggests that the crisis exposed the process of globalization. This can be confirmed by the presented analysis.

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Economics is a human activity in which relationships are of great importance. Mutual influence occurs through the exchange of goods, services, and cooperation. but also through competition Overtaking companies Industrial espionage, etc. As a result, we see a division into various subjects. in the form of cooperation, branching, shared interests or competition in the market These effects have been the subject of many research areas, such as portfolio analysis [1, 2, 3, 4], market structure analysis [5, 6], globalization research [7, 8, 9], and so on.

Cross-functional relationship analysis is an important tool for investigating the nature of interdependencies between different entities (companies, subsidiaries, departments, countries, etc.). In fact, the term embraces a large number of methods. We mention only the most commonly used ones: classical analysis of variance and Pearson’s correlation coefficient [10, 11, 12, 13, 14, 15], correlation analysis [16, 17, 18. , 19] multifractal analysis [20, 21, 22, 23], random matrix theory [24, 25, 26, 27], classification schemes [28, 29, 30] or methods. Using entropy [31, 32]

The spectrum of problems examined by cross-correlation analysis is very broad. Starting with sociology, economics, economic physics [20, 23, 33, 34], transportation [35, 36], genetic analysis, biology, dietary fibers, biochemical fibers, and science – cooperation networks [37], for sports [38] and others.

In the framework of this study Globalization is analyzed through the power law classification scheme (PLCS), unlike other cross-correlation methods such as trend fluctuation analysis (DFA) [39, 40, 41, 42, 43, 44, 45] or methods that It uses Pearson coefficients [12, 15, 46, 47] focusing on noise interactions. PLCS focuses on trends. In the case of globalization Trends reflecting convergent evolution appear to be more important than interdependence and sensitivity to external stimuli. In addition, PLCS analysis makes it possible to observe relationships between traits and stages. On the other hand, this method It is sensitive to long-term determined interactions related to “baseline” effects [48]. The research analyzes time series of exchange rates as a measure of the relationships and interactions between economies. The exchange rate is one of the most important variables of economic conditions. There are many platforms where currencies are traded. One of the most popular and important from a global perspective is the Forex market, which is focused on the institutional market. There are also many exchange platforms aimed at individuals, such as exchange offices, banks, and internet exchanges. This study focuses on the time series of the foreign exchange market. This is because the main goal is to analyze the globalization of the economy. Especially the formation of groups during the stock market crisis.

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The Power Law Classification Scheme (PLCS) focuses on trend correlation [28]. The algorithm is briefly described. Here for clarity of presentation and convenience of readers.

Assume that two time series are recorded simultaneously with the same length N. In the first step, subsequences are taken from k starting points and the Manhattan distance is calculated. This process is repeated for each item.

– At this point, the continuous Manhattan distance will be obtained. Each point in this set corresponds to a different “k”. Finally, a power law function was fitted to the overall Manhattan distance series. The power of the fitted function determines the strength of the subtraction relationship.

The final step is fitting the power law function. A more popular method is to fit a linear function to the log transformed data, e.g.

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– Of course, the quality of the fit also depends on the length of the series. In the case of the analyzed function

The fitted exponent for the first 100 data points is 1.922, but for 1000 data points it is 1.982 and asymptotically approaches 2. The observed uncertainty results from the numerical limitations of computer memory when calculating logarithms. To get the strength of the relationship It is necessary to reduce the exponent of the adjusted function by one and finally get 1. This result, of course, corresponds to the linear relationship between the considered functions. Other examples and a detailed analysis can be found in [28, 29, 30].

When the strength of the relationship is less than zero The distance between time series decreases and the time series converge.


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