| Summary: | The study of cycles in the context of economic time series has been active for many decades, if not centuries; however, it was only in recent decades that more formal approaches for identifying cycles have been developed. Litvine and Bismans (2015) proposed a new approach for dating cycles in financial time series, for purposes of optimising buysell strategies. In this approach, cycle dating is presented as an optimisation problem. They also introduced a method for optimising this problem, known as the hierarchical method (using full evaluation 2, or HR-FE2). However, this method may be impractical for large data sets as it may require unacceptably long computation time. In this study, new procedures that date cycles using the approach proposed by Litvine and Bismans (2015), were introduced, and were speciffically developed to be feasible for large time series data sets. These procedures are the stochastic generation and adaptation (SGA), buy-sell adapted Extrema importance identity sequence retrieval (BSA-EIISR) and buysell adapted bottom-up (BSA-BU) methods. An existing optimisation technique, known as particle swarm optimisation (PSO), was also employed. A statistical comparison was then made between these methods, including HR-FE2. This involved evaluating, on simulated data, the performance of the algorithms in terms of objective function value and computation time on different time series lengths, Hurst exponent, and number of buy-sell points. The SRace methodology (T. Zhang, Georgiopoulos, and Anagnostopoulos 2013) was then applied to these results in order to determine the most effcient methods. It was determined that, statistically, SGA, BSA-EIISR and BSA-BU are the most effcient methods. Number of buysell points was found to have the largest effect on relative performance of these methods. In some cases, the Hurst exponent also has a small effect on relative performance.
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