Dimensionality reduction for prediction: Application to Bitcoin and Ethereum
This work addresses the need for improved forecasting models in cryptocurrency trading, but it is incremental as it applies standard statistical methods to a new dataset.
The paper tackled the problem of linking Bitcoin and Ethereum by applying dimensionality reduction techniques to forecast Ethereum returns using Bitcoin features, finding that canonical correlation analysis and principal component analysis provided measurable performance improvements in prediction accuracy.
The objective of this paper is to assess the performances of dimensionality reduction techniques to establish a link between cryptocurrencies. We have focused our analysis on the two most traded cryptocurrencies: Bitcoin and Ethereum. To perform our analysis, we took log returns and added some covariates to build our data set. We first introduced the pearson correlation coefficient in order to have a preliminary assessment of the link between Bitcoin and Ethereum. We then reduced the dimension of our data set using canonical correlation analysis and principal component analysis. After performing an analysis of the links between Bitcoin and Ethereum with both statistical techniques, we measured their performance on forecasting Ethereum returns with Bitcoin s features.