Rescaled Range Analysis

Rescaled Range Analysis

A method of analysis of financial information over time to see what patterns, if any, arise. Theoretically, R/S analysis can be useful to determine potential future price movements for a stock or future performance for a company, but critics contend that its accuracy is limited.
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In this study, we estimate the fractal dimension of price returns and test the Efficient Market Hypothesis (EMH), employing rescaled range analysis in order to use fewer assumptions about the underlying system.
It is also recommended that investors, financial practitioners and academicians apply the rescaled range analysis, and soft computing to financial time series.
The work presented in this paper was conducted to compare the suitability of statistical, mutual information function, spectral and Hurst's Rescaled Range analysis for discrimination of flow regime transitions in a semi-cylindrical gas-solid spouted bed.
Several analysis methods were utilized: statistical, spectral analysis, mutual information, Hurst rescaled range analysis, and the P statistic, in order to evaluate their usefulness for characterization of flow regimes.
Lim, "Characterization of Gas Spouted Beds Using the Rescaled Range Analysis," Can.
The two tools employed for further evaluation are the rescaled range analysis and the three moments test.
Table 7 reports the results of the rescaled range analysis for each examined foreign exchange rate movement series.
The rescaled range analysis suggested the existence of some form of nonlinear dynamics in the exchange rate movements of the Australian dollar, the German mark, the Japanese yen, the Spanish peseta, the Swiss franc, and the British pound.
The Rescaled Range analysis is based on the simple hypothesis that any IID data would show an increase in standardized ranges which are proportional to increase in sample sizes as samples of increasing subperiod lengths are considered.
The rescaled range analysis provides no strong indication of long-term dependency in the returns of any of the examined indexes.
To briefly restate the methodology, the tools used in this paper are autoregressive filters to remove linear influences, the BDS statistics to check for the violation of the IID assumption, the rescaled range analysis to attain an idea of long-term temporal persistence, Hsieh's three moments test to obtain an indication of chaotic determinism, and the Grassberger and Procaccia correlation dimension analysis performed in search of conclusive evidence of chaotic determinsm.