Forecasting Cryptocurrency Markets Through the Use of Time Series Models
This paper analyses the efficiency of cryptocurrency markets by applying econometric models to different short-term investment horizons. A number of experiments are carried out to demonstrate that small training sets can still be used to build efficient and useful forecasts, which in turn can be transformed into straight-forward investment strategies. It also compares the application of selected models on cryptocurrency and mature stock markets. The forecasting accuracy of the models is explored using different error metrics and different horizons. The results suggest that the variation of the error estimates doesn't appear to be tightly related to the maturity of the markets, but rather depends on the intrinsic characteristics of the analyzed time series. (original abstract)
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