Spearman’s Rank Correlation is a non-parametric measure of the statistical dependence between the rankings of two variables. Unlike Pearson’s correlation, which assesses linear relationships between raw data, Spearman’s coefficient evaluates monotonic relationships, whether linear or non-linear, by correlating the ranks of data points. In crypto investing, it is particularly useful for analyzing the relationship between digital assets or market indicators when their distributions are not normal or when outliers significantly influence Pearson’s coefficient.
Mechanism
The mechanism involves converting the raw data values for each variable into their respective ranks. The smallest value receives a rank of 1, the next smallest 2, and so on. If ties exist, the average rank is assigned. Once ranked, the Pearson correlation coefficient is then calculated on these ranks. This process yields a value between -1 and +1, where +1 indicates a perfect monotonic relationship, -1 indicates a perfect inverse monotonic relationship, and 0 indicates no monotonic relationship.
Methodology
The methodology for applying Spearman’s Rank Correlation involves collecting paired data for two variables, such as the daily returns of two cryptocurrencies or a cryptocurrency’s price and a sentiment indicator. The data is then ranked independently for each variable, and the rank differences are used in the correlation formula. Strategic application in crypto analysis includes assessing the comovement of assets in diversified portfolios, identifying leading or lagging indicators for smart trading algorithms, or evaluating the robustness of relationships where extreme price movements might distort standard linear correlation measures.
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