Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation
This addresses the problem of improving prediction accuracy for cryptocurrency traders, though it is incremental as it builds on traditional machine learning methods.
The paper tackled intraday price movement prediction for cryptocurrencies by applying a novel linear law-based transformation (LLT) to 1-minute price data, resulting in greatly increased accuracy for Bitcoin, Ethereum, Binance Coin, and Ripple.
The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.