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This paper investigates experimentally the feasibility of Fuzzy Rough Sets in building trend prediction models for financial time series, as related research is scarce. Aside of the standard classification accuracy measures, financial profit and loss backtesting using a sample market timing strategy was performed, and profit related quality of the tested methods compared against that of buy&hold strategy applied to the used market indices. The experiments show that Fuzzy Rough Sets models present a viable basis for forecasting market movement direction and thus can support profitable market timing strategies.